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During the last decade, Machine Learning (ML) has increasingly become a hot
topic in the field of Computer Networks and is expected to be gradually adopted
for a plethora of control, monitoring and management tasks in real-world
deployments. This poses the need to count on new generations of students,
researchers and practitioners with a solid background in ML applied to
networks. During 2020, the International Telecommunication Union (ITU) has
organized the "ITU AI/ML in 5G challenge'', an open global competition that has
introduced to a broad audience some of the current main challenges in ML for
networks. This large-scale initiative has gathered 23 different challenges
proposed by network operators, equipment manufacturers and academia, and has
attracted a total of 1300+ participants from 60+ countries. This paper narrates
our experience organizing one of the proposed challenges: the "Graph Neural
Networking Challenge 2020''. We describe the problem presented to participants,
the tools and resources provided, some organization aspects and participation
statistics, an outline of the top-3 awarded solutions, and a summary with some
lessons learned during all this journey. As a result, this challenge leaves a
curated set of educational resources openly available to anyone interested in
the topic. | [
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] |
Brain functional connectivity (FC) reveals biomarkers for identification of
various neuropsychiatric disorders. Recent application of deep neural networks
(DNNs) to connectome-based classification mostly relies on traditional
convolutional neural networks using input connectivity matrices on a regular
Euclidean grid. We propose a graph deep learning framework to incorporate the
non-Euclidean information about graph structure for classifying functional
magnetic resonance imaging (fMRI)-derived brain networks in major depressive
disorder (MDD). We design a novel graph autoencoder (GAE) architecture based on
the graph convolutional networks (GCNs) to embed the topological structure and
node content of large-sized fMRI networks into low-dimensional latent
representations. In network construction, we employ the Ledoit-Wolf (LDW)
shrinkage method to estimate the high-dimensional FC metrics efficiently from
fMRI data. We consider both supervised and unsupervised approaches for the
graph embedding learning. The learned embeddings are then used as feature
inputs for a deep fully-connected neural network (FCNN) to discriminate MDD
from healthy controls. Evaluated on two resting-state fMRI (rs-fMRI) MDD
datasets, results show that the proposed GAE-FCNN model significantly
outperforms several state-of-the-art methods for brain connectome
classification, achieving the best accuracy using the LDW-FC edges as node
features. The graph embeddings of fMRI FC networks learned by the GAE also
reveal apparent group differences between MDD and HC. Our new framework
demonstrates feasibility of learning graph embeddings on brain networks to
provide discriminative information for diagnosis of brain disorders. | [
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] |
E-commerce sites strive to provide users the most timely relevant information
in order to reduce shopping frictions and increase customer satisfaction. Multi
armed bandit models (MAB) as a type of adaptive optimization algorithms provide
possible approaches for such purposes. In this paper, we analyze using three
classic MAB algorithms, epsilon-greedy, Thompson sampling (TS), and upper
confidence bound 1 (UCB1) for dynamic content recommendations, and walk through
the process of developing these algorithms internally to solve a real world
e-commerce use case. First, we analyze the three MAB algorithms using simulated
purchasing datasets with non-stationary reward distributions to simulate the
possible time-varying customer preferences, where the traffic allocation
dynamics and the accumulative rewards of different algorithms are studied.
Second, we compare the accumulative rewards of the three MAB algorithms with
more than 1,000 trials using actual historical A/B test datasets. We find that
the larger difference between the success rates of competing recommendations
the more accumulative rewards the MAB algorithms can achieve. In addition, we
find that TS shows the highest average accumulative rewards under different
testing scenarios. Third, we develop a batch-updated MAB algorithm to overcome
the delayed reward issue in e-commerce and enable an online content
optimization on our App homepage. For a state-of-the-art comparison, a real A/B
test among our batch-updated MAB algorithm, a third-party MAB solution, and the
default business logic are conducted. The result shows that our batch-updated
MAB algorithm outperforms the counterparts and achieves 6.13% relative
click-through rate (CTR) increase and 16.1% relative conversion rate (CVR)
increase compared to the default experience, and 2.9% relative CTR increase and
1.4% relative CVR increase compared to the external MAB service. | [
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Artificial intelligence (AI) and Machine learning (ML) workloads are an
increasingly larger share of the compute workloads in traditional
High-Performance Computing (HPC) centers and commercial cloud systems. This has
led to changes in deployment approaches of HPC clusters and the commercial
cloud, as well as a new focus on approaches to optimized resource usage,
allocations and deployment of new AI frame- works, and capabilities such as
Jupyter notebooks to enable rapid prototyping and deployment. With these
changes, there is a need to better understand cluster/datacenter operations
with the goal of developing improved scheduling policies, identifying
inefficiencies in resource utilization, energy/power consumption, failure
prediction, and identifying policy violations. In this paper we introduce the
MIT Supercloud Dataset which aims to foster innovative AI/ML approaches to the
analysis of large scale HPC and datacenter/cloud operations. We provide
detailed monitoring logs from the MIT Supercloud system, which include CPU and
GPU usage by jobs, memory usage, file system logs, and physical monitoring
data. This paper discusses the details of the dataset, collection methodology,
data availability, and discusses potential challenge problems being developed
using this data. Datasets and future challenge announcements will be available
via https://dcc.mit.edu. | [
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] |
Understanding a controller's performance in different scenarios is crucial
for robots that are going to be deployed in safety-critical tasks. If we do not
have a model of the dynamics of the world, which is often the case in complex
domains, we may need to approximate a performance function of the robot based
on its interaction with the environment. Such a performance function gives us
insights into the behaviour of the robot, allowing us to fine-tune the
controller with manual interventions. In high-dimensionality systems, where the
actionstate space is large, fine-tuning a controller is non-trivial. To
overcome this problem, we propose a performance function whose domain is
defined by external features and parameters of the controller. Attainment
regions are defined over such a domain defined by feature-parameter pairs, and
serve the purpose of enabling prediction of successful execution of the task.
The use of the feature-parameter space -in contrast to the action-state space-
allows us to adapt, explain and finetune the controller over a simpler (i.e.,
lower dimensional space). When the robot successfully executes the task, we use
the attainment regions to gain insights into the limits of the controller, and
its robustness. When the robot fails to execute the task, we use the regions to
debug the controller and find adaptive and counterfactual changes to the
solutions. Another advantage of this approach is that we can generalise through
the use of Gaussian processes regression of the performance function in the
high-dimensional space. To test our approach, we demonstrate learning an
approximation to the performance function in simulation, with a mobile robot
traversing different terrain conditions. Then, with a sample-efficient method,
we propagate the attainment regions to a physical robot in a similar
environment. | [
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] |
Realizing today's cloud-level artificial intelligence functionalities
directly on devices distributed at the edge of the internet calls for edge
hardware capable of processing multiple modalities of sensory data (e.g. video,
audio) at unprecedented energy-efficiency. AI hardware architectures today
cannot meet the demand due to a fundamental "memory wall": data movement
between separate compute and memory units consumes large energy and incurs long
latency. Resistive random-access memory (RRAM) based compute-in-memory (CIM)
architectures promise to bring orders of magnitude energy-efficiency
improvement by performing computation directly within memory. However,
conventional approaches to CIM hardware design limit its functional flexibility
necessary for processing diverse AI workloads, and must overcome hardware
imperfections that degrade inference accuracy. Such trade-offs between
efficiency, versatility and accuracy cannot be addressed by isolated
improvements on any single level of the design. By co-optimizing across all
hierarchies of the design from algorithms and architecture to circuits and
devices, we present NeuRRAM - the first multimodal edge AI chip using RRAM CIM
to simultaneously deliver a high degree of versatility for diverse model
architectures, record energy-efficiency $5\times$ - $8\times$ better than prior
art across various computational bit-precisions, and inference accuracy
comparable to software models with 4-bit weights on all measured standard AI
benchmarks including accuracy of 99.0% on MNIST and 85.7% on CIFAR-10 image
classification, 84.7% accuracy on Google speech command recognition, and a 70%
reduction in image reconstruction error on a Bayesian image recovery task. This
work paves a way towards building highly efficient and reconfigurable edge AI
hardware platforms for the more demanding and heterogeneous AI applications of
the future. | [
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Currently, there is a burgeoning demand for deploying deep learning (DL)
models on ubiquitous edge Internet of Things (IoT) devices attributed to their
low latency and high privacy preservation. However, DL models are often large
in size and require large-scale computation, which prevents them from being
placed directly onto IoT devices, where resources are constrained and 32-bit
floating-point (float-32) operations are unavailable. Commercial framework
(i.e., a set of toolkits) empowered model quantization is a pragmatic solution
that enables DL deployment on mobile devices and embedded systems by
effortlessly post-quantizing a large high-precision model (e.g., float-32) into
a small low-precision model (e.g., int-8) while retaining the model inference
accuracy. However, their usability might be threatened by security
vulnerabilities.
This work reveals that the standard quantization toolkits can be abused to
activate a backdoor. We demonstrate that a full-precision backdoored model
which does not have any backdoor effect in the presence of a trigger -- as the
backdoor is dormant -- can be activated by the default i) TensorFlow-Lite
(TFLite) quantization, the only product-ready quantization framework to date,
and ii) the beta released PyTorch Mobile framework. When each of the float-32
models is converted into an int-8 format model through the standard TFLite or
Pytorch Mobile framework's post-training quantization, the backdoor is
activated in the quantized model, which shows a stable attack success rate
close to 100% upon inputs with the trigger, while it behaves normally upon
non-trigger inputs. This work highlights that a stealthy security threat occurs
when an end user utilizes the on-device post-training model quantization
frameworks, informing security researchers of cross-platform overhaul of DL
models post quantization even if these models pass front-end backdoor
inspections. | [
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] |
Real world tournaments are almost always intransitive. Recent works have
noted that parametric models which assume $d$ dimensional node representations
can effectively model intransitive tournaments. However, nothing is known about
the structure of the class of tournaments that arise out of any fixed $d$
dimensional representations. In this work, we develop a novel theory for
understanding parametric tournament representations. Our first contribution is
to structurally characterize the class of tournaments that arise out of $d$
dimensional representations. We do this by showing that these tournament
classes have forbidden configurations which must necessarily be union of flip
classes, a novel way to partition the set of all tournaments. We further
characterise rank $2$ tournaments completely by showing that the associated
forbidden flip class contains just $2$ tournaments. Specifically, we show that
the rank $2$ tournaments are equivalent to locally-transitive tournaments. This
insight allows us to show that the minimum feedback arc set problem on this
tournament class can be solved using the standard Quicksort procedure. For a
general rank $d$ tournament class, we show that the flip class associated with
a coned-doubly regular tournament of size $\mathcal{O}(\sqrt{d})$ must be a
forbidden configuration. To answer a dual question, using a celebrated result
of \cite{forster}, we show a lower bound of $\mathcal{O}(\sqrt{n})$ on the
minimum dimension needed to represent all tournaments on $n$ nodes. For any
given tournament, we show a novel upper bound on the smallest representation
dimension that depends on the least size of the number of unique nodes in any
feedback arc set of the flip class associated with a tournament. We show how
our results also shed light on upper bound of sign-rank of matrices. | [
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Extensive research has been performed on continuous, non-invasive, cuffless
blood pressure (BP) measurement using artificial intelligence algorithms. This
approach involves extracting certain features from physiological signals like
ECG, PPG, ICG, BCG, etc. as independent variables and extracting features from
Arterial Blood Pressure (ABP) signals as dependent variables, and then using
machine learning algorithms to develop a blood pressure estimation model based
on these data. The greatest challenge of this field is the insufficient
accuracy of estimation models. This paper proposes a novel blood pressure
estimation method with a clustering step for accuracy improvement. The proposed
method involves extracting Pulse Transit Time (PTT), PPG Intensity Ratio (PIR),
and Heart Rate (HR) features from Electrocardiogram (ECG) and
Photoplethysmogram (PPG) signals as the inputs of clustering and regression,
extracting Systolic Blood Pressure (SBP) and Diastolic Blood Pressure (DBP)
features from ABP signals as dependent variables, and finally developing
regression models by applying Gradient Boosting Regression (GBR), Random Forest
Regression (RFR), and Multilayer Perceptron Regression (MLP) on each cluster.
The method was implemented using the MIMICII dataset with the silhouette
criterion used to determine the optimal number of clusters. The results showed
that because of the inconsistency, high dispersion, and multi-trend behavior of
the extracted features vectors, the accuracy can be significantly improved by
running a clustering algorithm and then developing a regression model on each
cluster, and finally weighted averaging of the results based on the error of
each cluster. When implemented with 5 clusters and GBR, this approach yielded
an MAE of 2.56 for SBP estimates and 2.23 for DBP estimates, which were
significantly better than the best results without clustering (DBP: 6.27, SBP:
6.36). | [
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Adversarial attacks against commercial black-box speech platforms, including
cloud speech APIs and voice control devices, have received little attention
until recent years. The current "black-box" attacks all heavily rely on the
knowledge of prediction/confidence scores to craft effective adversarial
examples, which can be intuitively defended by service providers without
returning these messages. In this paper, we propose two novel adversarial
attacks in more practical and rigorous scenarios. For commercial cloud speech
APIs, we propose Occam, a decision-only black-box adversarial attack, where
only final decisions are available to the adversary. In Occam, we formulate the
decision-only AE generation as a discontinuous large-scale global optimization
problem, and solve it by adaptively decomposing this complicated problem into a
set of sub-problems and cooperatively optimizing each one. Our Occam is a
one-size-fits-all approach, which achieves 100% success rates of attacks with
an average SNR of 14.23dB, on a wide range of popular speech and speaker
recognition APIs, including Google, Alibaba, Microsoft, Tencent, iFlytek, and
Jingdong, outperforming the state-of-the-art black-box attacks. For commercial
voice control devices, we propose NI-Occam, the first non-interactive physical
adversarial attack, where the adversary does not need to query the oracle and
has no access to its internal information and training data. We combine
adversarial attacks with model inversion attacks, and thus generate the
physically-effective audio AEs with high transferability without any
interaction with target devices. Our experimental results show that NI-Occam
can successfully fool Apple Siri, Microsoft Cortana, Google Assistant, iFlytek
and Amazon Echo with an average SRoA of 52% and SNR of 9.65dB, shedding light
on non-interactive physical attacks against voice control devices. | [
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] |
A general-purpose robot should be able to master a wide range of tasks and
quickly learn a novel one by leveraging past experiences. One-shot imitation
learning (OSIL) approaches this goal by training an agent with (pairs of)
expert demonstrations, such that at test time, it can directly execute a new
task from just one demonstration. However, so far this framework has been
limited to training on many variations of one task, and testing on other unseen
but similar variations of the same task. In this work, we push for a higher
level of generalization ability by investigating a more ambitious multi-task
setup. We introduce a diverse suite of vision-based robot manipulation tasks,
consisting of 7 tasks, a total of 61 variations, and a continuum of instances
within each variation. For consistency and comparison purposes, we first train
and evaluate single-task agents (as done in prior few-shot imitation work). We
then study the multi-task setting, where multi-task training is followed by (i)
one-shot imitation on variations within the training tasks, (ii) one-shot
imitation on new tasks, and (iii) fine-tuning on new tasks. Prior
state-of-the-art, while performing well within some single tasks, struggles in
these harder multi-task settings. To address these limitations, we propose
MOSAIC (Multi-task One-Shot Imitation with self-Attention and Contrastive
learning), which integrates a self-attention model architecture and a temporal
contrastive module to enable better task disambiguation and more robust
representation learning. Our experiments show that MOSAIC outperforms prior
state of the art in learning efficiency, final performance, and learns a
multi-task policy with promising generalization ability via fine-tuning on
novel tasks. | [
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] |
Deep learning frameworks such as TensorFlow and PyTorch provide a productive
interface for expressing and training a deep neural network (DNN) model on a
single device or using data parallelism. Still, they may not be flexible or
efficient enough in training emerging large models on distributed devices,
which require more sophisticated parallelism beyond data parallelism. Plugins
or wrappers have been developed to strengthen these frameworks for model or
pipeline parallelism, but they complicate the usage and implementation of
distributed deep learning. Aiming at a simple, neat redesign of distributed
deep learning frameworks for various parallelism paradigms, we present OneFlow,
a novel distributed training framework based on an SBP (split, broadcast and
partial-value) abstraction and the actor model. SBP enables much easier
programming of data parallelism and model parallelism than existing frameworks,
and the actor model provides a succinct runtime mechanism to manage the complex
dependencies imposed by resource constraints, data movement and computation in
distributed deep learning. We demonstrate the general applicability and
efficiency of OneFlow for training various large DNN models with case studies
and extensive experiments. The results show that OneFlow outperforms many
well-known customized libraries built on top of the state-of-the-art
frameworks. The code of OneFlow is available at:
https://github.com/Oneflow-Inc/oneflow. | [
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A practical approach to learning robot skills, often termed sim2real, is to
train control policies in simulation and then deploy them on a real robot.
Popular techniques to improve the sim2real transfer build on domain
randomization (DR) -- training the policy on a diverse set of randomly
generated domains with the hope of better generalization to the real world. Due
to the large number of hyper-parameters in both the policy learning and DR
algorithms, one often ends up with a large number of trained policies, where
choosing the best policy among them demands costly evaluation on the real
robot. In this work we ask - can we rank the policies without running them in
the real world? Our main idea is that a predefined set of real world data can
be used to evaluate all policies, using out-of-distribution detection (OOD)
techniques. In a sense, this approach can be seen as a `unit test' to evaluate
policies before any real world execution. However, we find that by itself, the
OOD score can be inaccurate and very sensitive to the particular OOD method.
Our main contribution is a simple-yet-effective policy score that combines OOD
with an evaluation in simulation. We show that our score - VSDR - can
significantly improve the accuracy of policy ranking without requiring
additional real world data. We evaluate the effectiveness of VSDR on sim2real
transfer in a robotic grasping task with image inputs. We extensively evaluate
different DR parameters and OOD methods, and show that VSDR improves policy
selection across the board. More importantly, our method achieves significantly
better ranking, and uses significantly less data compared to baselines. Project
website is available at https://sites.google.com/view/vsdr/home. | [
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] |
Ethics statements have been proposed as a mechanism to increase transparency
and promote reflection on the societal impacts of published research. In 2020,
the machine learning (ML) conference NeurIPS broke new ground by requiring that
all papers include a broader impact statement. This requirement was removed in
2021, in favour of a checklist approach. The 2020 statements therefore provide
a unique opportunity to learn from the broader impact experiment: to
investigate the benefits and challenges of this and similar governance
mechanisms, as well as providing an insight into how ML researchers think about
the societal impacts of their own work. Such learning is needed as NeurIPS and
other venues continue to question and adapt their policies. To enable this, we
have created a dataset containing the impact statements from all NeurIPS 2020
papers, along with additional information such as affiliation type, location
and subject area, and a simple visualisation tool for exploration. We also
provide an initial quantitative analysis of the dataset, covering
representation, engagement, common themes, and willingness to discuss potential
harms alongside benefits. We investigate how these vary by geography,
affiliation type and subject area. Drawing on these findings, we discuss the
potential benefits and negative outcomes of ethics statement requirements, and
their possible causes and associated challenges. These lead us to several
lessons to be learnt from the 2020 requirement: (i) the importance of creating
the right incentives, (ii) the need for clear expectations and guidance, and
(iii) the importance of transparency and constructive deliberation. We
encourage other researchers to use our dataset to provide additional analysis,
to further our understanding of how researchers responded to this requirement,
and to investigate the benefits and challenges of this and related mechanisms. | [
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] |
There were fierce debates on whether the non-linear embedding propagation of
GCNs is appropriate to GCN-based recommender systems. It was recently found
that the linear embedding propagation shows better accuracy than the non-linear
embedding propagation. Since this phenomenon was discovered especially in
recommender systems, it is required that we carefully analyze the linearity and
non-linearity issue. In this work, therefore, we revisit the issues of i) which
of the linear or non-linear propagation is better and ii) which factors of
users/items decide the linearity/non-linearity of the embedding propagation. We
propose a novel Hybrid Method of Linear and non-linEar collaborative filTering
method (HMLET, pronounced as Hamlet). In our design, there exist both linear
and non-linear propagation steps, when processing each user or item node, and
our gating module chooses one of them, which results in a hybrid model of the
linear and non-linear GCN-based collaborative filtering (CF). The proposed
model yields the best accuracy in three public benchmark datasets. Moreover, we
classify users/items into the following three classes depending on our gating
modules' selections: Full-Non-Linearity (FNL), Partial-Non-Linearity (PNL), and
Full-Linearity (FL). We found that there exist strong correlations between
nodes' centrality and their class membership, i.e., important user/item nodes
exhibit more preferences towards the non-linearity during the propagation
steps. To our knowledge, we are the first who design a hybrid method and report
the correlation between the graph centrality and the linearity/non-linearity of
nodes. All HMLET codes and datasets are available at:
https://github.com/qbxlvnf11/HMLET. | [
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] |
A backdoor deep learning (DL) model behaves normally upon clean inputs but
misbehaves upon trigger inputs as the backdoor attacker desires, posing severe
consequences to DL model deployments. State-of-the-art defenses are either
limited to specific backdoor attacks (source-agnostic attacks) or
non-user-friendly in that machine learning (ML) expertise or expensive
computing resources are required. This work observes that all existing backdoor
attacks have an inevitable intrinsic weakness, non-transferability, that is, a
trigger input hijacks a backdoored model but cannot be effective to another
model that has not been implanted with the same backdoor. With this key
observation, we propose non-transferability enabled backdoor detection (NTD) to
identify trigger inputs for a model-under-test (MUT) during
run-time.Specifically, NTD allows a potentially backdoored MUT to predict a
class for an input. In the meantime, NTD leverages a feature extractor (FE) to
extract feature vectors for the input and a group of samples randomly picked
from its predicted class, and then compares similarity between the input and
the samples in the FE's latent space. If the similarity is low, the input is an
adversarial trigger input; otherwise, benign. The FE is a free pre-trained
model privately reserved from open platforms. As the FE and MUT are from
different sources, the attacker is very unlikely to insert the same backdoor
into both of them. Because of non-transferability, a trigger effect that does
work on the MUT cannot be transferred to the FE, making NTD effective against
different types of backdoor attacks. We evaluate NTD on three popular
customized tasks such as face recognition, traffic sign recognition and general
animal classification, results of which affirm that NDT has high effectiveness
(low false acceptance rate) and usability (low false rejection rate) with low
detection latency. | [
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] |
The Observation--Hypothesis--Prediction--Experimentation loop paradigm for
scientific research has been practiced by researchers for years towards
scientific discoveries. However, with data explosion in both mega-scale and
milli-scale scientific research, it has been sometimes very difficult to
manually analyze the data and propose new hypotheses to drive the cycle for
scientific discovery. In this paper, we discuss the role of Explainable AI in
scientific discovery process by demonstrating an Explainable AI-based paradigm
for science discovery. The key is to use Explainable AI to help derive data or
model interpretations, hypotheses, as well as scientific discoveries or
insights. We show how computational and data-intensive methodology -- together
with experimental and theoretical methodology -- can be seamlessly integrated
for scientific research. To demonstrate the AI-based science discovery process,
and to pay our respect to some of the greatest minds in human history, we show
how Kepler's laws of planetary motion and Newton's law of universal gravitation
can be rediscovered by (Explainable) AI based on Tycho Brahe's astronomical
observation data, whose works were leading the scientific revolution in the
16-17th century. This work also highlights the important role of Explainable AI
(as compared to Blackbox AI) in science discovery to help humans prevent or
better prepare for the possible technological singularity that may happen in
the future, since science is not only about the know how, but also the know
why. Presentation of the work is available at
https://slideslive.com/38986142/from-kepler-to-newton-explainable-ai-for-science-discovery. | [
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] |
Highly dynamic mobile ad-hoc networks (MANETs) remain as one of the most
challenging environments to develop and deploy robust, efficient, and scalable
routing protocols. In this paper, we present DeepCQ+ routing protocol which, in
a novel manner integrates emerging multi-agent deep reinforcement learning
(MADRL) techniques into existing Q-learning-based routing protocols and their
variants and achieves persistently higher performance across a wide range of
topology and mobility configurations. While keeping the overall protocol
structure of the Q-learning-based routing protocols, DeepCQ+ replaces
statically configured parameterized thresholds and hand-written rules with
carefully designed MADRL agents such that no configuration of such parameters
is required a priori. Extensive simulation shows that DeepCQ+ yields
significantly increased end-to-end throughput with lower overhead and no
apparent degradation of end-to-end delays (hop counts) compared to its
Q-learning based counterparts. Qualitatively, and perhaps more significantly,
DeepCQ+ maintains remarkably similar performance gains under many scenarios
that it was not trained for in terms of network sizes, mobility conditions, and
traffic dynamics. To the best of our knowledge, this is the first successful
application of the MADRL framework for the MANET routing problem that
demonstrates a high degree of scalability and robustness even under
environments that are outside the trained range of scenarios. This implies that
our MARL-based DeepCQ+ design solution significantly improves the performance
of Q-learning based CQ+ baseline approach for comparison and increases its
practicality and explainability because the real-world MANET environment will
likely vary outside the trained range of MANET scenarios. Additional techniques
to further increase the gains in performance and scalability are discussed. | [
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The increasing use of Machine Learning (ML) components embedded in autonomous
systems -- so-called Learning-Enabled Systems (LESs) -- has resulted in the
pressing need to assure their functional safety. As for traditional functional
safety, the emerging consensus within both, industry and academia, is to use
assurance cases for this purpose. Typically assurance cases support claims of
reliability in support of safety, and can be viewed as a structured way of
organising arguments and evidence generated from safety analysis and
reliability modelling activities. While such assurance activities are
traditionally guided by consensus-based standards developed from vast
engineering experience, LESs pose new challenges in safety-critical application
due to the characteristics and design of ML models. In this article, we first
present an overall assurance framework for LESs with an emphasis on
quantitative aspects, e.g., breaking down system-level safety targets to
component-level requirements and supporting claims stated in reliability
metrics. We then introduce a novel model-agnostic Reliability Assessment Model
(RAM) for ML classifiers that utilises the operational profile and robustness
verification evidence. We discuss the model assumptions and the inherent
challenges of assessing ML reliability uncovered by our RAM and propose
solutions to practical use. Probabilistic safety argument templates at the
lower ML component-level are also developed based on the RAM. Finally, to
evaluate and demonstrate our methods, we not only conduct experiments on
synthetic/benchmark datasets but also scope our methods with case studies on
simulated Autonomous Underwater Vehicles and physical Unmanned Ground Vehicles. | [
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] |
Distributed training has become a pervasive and effective approach for
training a large neural network (NN) model with processing massive data.
However, it is very challenging to satisfy requirements from various NN models,
diverse computing resources, and their dynamic changes during a training job.
In this study, we design our distributed training framework in a systematic
end-to-end view to provide the built-in adaptive ability for different
scenarios, especially for industrial applications and production environments,
by fully considering resource allocation, model partition, task placement, and
distributed execution. Based on the unified distributed graph and the unified
cluster object, our adaptive framework is equipped with a global cost model and
a global planner, which can enable arbitrary parallelism, resource-aware
placement, multi-mode execution, fault-tolerant, and elastic distributed
training. The experiments demonstrate that our framework can satisfy various
requirements from the diversity of applications and the heterogeneity of
resources with highly competitive performance. The ERNIE language model with
260 billion parameters is efficiently trained on thousands of AI processors
with 91.7% weak scalability. The throughput of the model from the recommender
system by employing the heterogeneous pipeline asynchronous execution can be
increased up to 2.1 times and 3.3 times that of the GPU-only and CPU-only
training respectively. Moreover, the fault-tolerant and elastic distributed
training have been successfully applied to the online industrial applications,
which give a reduction of 34.49% in the number of failed long-term training
jobs and an increase of 33.91% for the global scheduling efficiency in the
production environment. | [
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] |
The original "Seven Motifs" set forth a roadmap of essential methods for the
field of scientific computing, where a motif is an algorithmic method that
captures a pattern of computation and data movement. We present the "Nine
Motifs of Simulation Intelligence", a roadmap for the development and
integration of the essential algorithms necessary for a merger of scientific
computing, scientific simulation, and artificial intelligence. We call this
merger simulation intelligence (SI), for short. We argue the motifs of
simulation intelligence are interconnected and interdependent, much like the
components within the layers of an operating system. Using this metaphor, we
explore the nature of each layer of the simulation intelligence operating
system stack (SI-stack) and the motifs therein: (1) Multi-physics and
multi-scale modeling; (2) Surrogate modeling and emulation; (3)
Simulation-based inference; (4) Causal modeling and inference; (5) Agent-based
modeling; (6) Probabilistic programming; (7) Differentiable programming; (8)
Open-ended optimization; (9) Machine programming. We believe coordinated
efforts between motifs offers immense opportunity to accelerate scientific
discovery, from solving inverse problems in synthetic biology and climate
science, to directing nuclear energy experiments and predicting emergent
behavior in socioeconomic settings. We elaborate on each layer of the SI-stack,
detailing the state-of-art methods, presenting examples to highlight challenges
and opportunities, and advocating for specific ways to advance the motifs and
the synergies from their combinations. Advancing and integrating these
technologies can enable a robust and efficient hypothesis-simulation-analysis
type of scientific method, which we introduce with several use-cases for
human-machine teaming and automated science. | [
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In this paper, we propose PATO-a producibility-aware topology optimization
(TO) framework to help efficiently explore the design space of components
fabricated using metal additive manufacturing (AM), while ensuring
manufacturability with respect to cracking. Specifically, parts fabricated
through Laser Powder Bed Fusion are prone to defects such as warpage or
cracking due to high residual stress values generated from the steep thermal
gradients produced during the build process. Maturing the design for such parts
and planning their fabrication can span months to years, often involving
multiple handoffs between design and manufacturing engineers. PATO is based on
the a priori discovery of crack-free designs, so that the optimized part can be
built defect-free at the outset. To ensure that the design is crack free during
optimization, producibility is explicitly encoded within the standard
formulation of TO, using a crack index. Multiple crack indices are explored and
using experimental validation, maximum shear strain index (MSSI) is shown to be
an accurate crack index. Simulating the build process is a coupled,
multi-physics computation and incorporating it in the TO loop can be
computationally prohibitive. We leverage the current advances in deep
convolutional neural networks and present a high-fidelity surrogate model based
on an Attention-based U-Net architecture to predict the MSSI values as a
spatially varying field over the part's domain. Further, we employ automatic
differentiation to directly compute the gradient of maximum MSSI with respect
to the input design variables and augment it with the performance-based
sensitivity field to optimize the design while considering the trade-off
between weight, manufacturability, and functionality. We demonstrate the
effectiveness of the proposed method through benchmark studies in 3D as well as
experimental validation. | [
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] |
The main objective of this paper is to outline a theoretical framework to
characterise humans' decision-making strategies under uncertainty, in
particular active learning in a black-box optimization task and trading-off
between information gathering (exploration) and reward seeking (exploitation).
Humans' decisions making according to these two objectives can be modelled in
terms of Pareto rationality. If a decision set contains a Pareto efficient
strategy, a rational decision maker should always select the dominant strategy
over its dominated alternatives. A distance from the Pareto frontier determines
whether a choice is Pareto rational. To collect data about humans' strategies
we have used a gaming application that shows the game field, with previous
decisions and observations, as well as the score obtained. The key element in
this paper is the representation of behavioural patterns of human learners as a
discrete probability distribution. This maps the problem of the
characterization of humans' behaviour into a space whose elements are
probability distributions structured by a distance between histograms, namely
the Wasserstein distance (WST). The distributional analysis gives new insights
about human search strategies and their deviations from Pareto rationality.
Since the uncertainty is one of the two objectives defining the Pareto
frontier, the analysis has been performed for three different uncertainty
quantification measures to identify which better explains the Pareto compliant
behavioural patterns. Beside the analysis of individual patterns WST has also
enabled a global analysis computing the barycenters and WST k-means clustering.
A further analysis has been performed by a decision tree to relate non-Paretian
behaviour, characterized by exasperated exploitation, to the dynamics of the
evolution of the reward seeking process. | [
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] |
Human activities within smart infrastructures generate a vast amount of IMU
data from the wearables worn by individuals. Many existing studies rely on such
sensory data for human activity recognition (HAR); however, one of the major
bottlenecks is their reliance on pre-annotated or labeled data. Manual
human-driven annotations are neither scalable nor efficient, whereas existing
auto-annotation techniques heavily depend on video signatures. Still,
video-based auto-annotation needs high computation resources and has privacy
concerns when the data from a personal space, like a smart-home, is transferred
to the cloud. This paper exploits the acoustic signatures generated from human
activities to label the wearables' IMU data at the edge, thus mitigating
resource requirement and data privacy concerns. We utilize acoustic-based
pre-trained HAR models for cross-modal labeling of the IMU data even when two
individuals perform simultaneous but different activities under the same
environmental context. We observe that non-overlapping acoustic gaps exist with
a high probability during the simultaneous activities performed by two
individuals in the environment's acoustic context, which helps us resolve the
overlapping activity signatures to label them individually. A principled
evaluation of the proposed approach on two real-life in-house datasets further
augmented to create a dual occupant setup, shows that the framework can
correctly annotate a significant volume of unlabeled IMU data from both
individuals with an accuracy of $\mathbf{82.59\%}$ ($\mathbf{\pm 17.94\%}$) and
$\mathbf{98.32\%}$ ($\mathbf{\pm 3.68\%}$), respectively, for a workshop and a
kitchen environment. | [
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A major impediment to successful drug development is the complexity, cost,
and scale of clinical trials. The detailed internal structure of clinical trial
data can make conventional optimization difficult to achieve. Recent advances
in machine learning, specifically graph-structured data analysis, have the
potential to enable significant progress in improving the clinical trial
design. TrialGraph seeks to apply these methodologies to produce a
proof-of-concept framework for developing models which can aid drug development
and benefit patients. In this work, we first introduce a curated clinical trial
data set compiled from the CT.gov, AACT and TrialTrove databases (n=1191
trials; representing one million patients) and describe the conversion of this
data to graph-structured formats. We then detail the mathematical basis and
implementation of a selection of graph machine learning algorithms, which
typically use standard machine classifiers on graph data embedded in a
low-dimensional feature space. We trained these models to predict side effect
information for a clinical trial given information on the disease, existing
medical conditions, and treatment. The MetaPath2Vec algorithm performed
exceptionally well, with standard Logistic Regression, Decision Tree, Random
Forest, Support Vector, and Neural Network classifiers exhibiting typical
ROC-AUC scores of 0.85, 0.68, 0.86, 0.80, and 0.77, respectively. Remarkably,
the best performing classifiers could only produce typical ROC-AUC scores of
0.70 when trained on equivalent array-structured data. Our work demonstrates
that graph modelling can significantly improve prediction accuracy on
appropriate datasets. Successive versions of the project that refine modelling
assumptions and incorporate more data types can produce excellent predictors
with real-world applications in drug development. | [
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] |
Deep Neural Networks (DNNs) have been extensively used in many areas
including image processing, medical diagnostics, and autonomous driving.
However, DNNs can exhibit erroneous behaviours that may lead to critical
errors, especially when used in safety-critical systems. Inspired by testing
techniques for traditional software systems, researchers have proposed neuron
coverage criteria, as an analogy to source code coverage, to guide the testing
of DNN models. Despite very active research on DNN coverage, several recent
studies have questioned the usefulness of such criteria in guiding DNN testing.
Further, from a practical standpoint, these criteria are white-box as they
require access to the internals or training data of DNN models, which is in
many contexts not feasible or convenient. In this paper, we investigate
black-box input diversity metrics as an alternative to white-box coverage
criteria. To this end, we first select and adapt three diversity metrics and
study, in a controlled manner, their capacity to measure actual diversity in
input sets. We then analyse their statistical association with fault detection
using four datasets and five DNN models. We further compare diversity with
state-of-the-art white-box coverage criteria. Our experiments show that relying
on the diversity of image features embedded in test input sets is a more
reliable indicator than coverage criteria to effectively guide the testing of
DNNs. Indeed, we found that one of our selected black-box diversity metrics far
outperforms existing coverage criteria in terms of fault-revealing capability
and computational time. Results also confirm the suspicions that
state-of-the-art coverage metrics are not adequate to guide the construction of
test input sets to detect as many faults as possible with natural inputs. | [
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] |
Explainable Artificial Intelligence (XAI) has been identified as a viable
method for determining the importance of features when making predictions using
Machine Learning (ML) models. In this study, we created models that take an
individual's health information (e.g. their drug history and comorbidities) as
inputs, and predict the probability that the individual will have an Acute
Coronary Syndrome (ACS) adverse outcome. Using XAI, we quantified the
contribution that specific drugs had on these ACS predictions, thus creating an
XAI-based technique for pharmacovigilance monitoring, using ACS as an example
of the adverse outcome to detect. Individuals aged over 65 who were supplied
Musculo-skeletal system (anatomical therapeutic chemical (ATC) class M) or
Cardiovascular system (ATC class C) drugs between 1993 and 2009 were
identified, and their drug histories, comorbidities, and other key features
were extracted from linked Western Australian datasets. Multiple ML models were
trained to predict if these individuals would have an ACS related adverse
outcome (i.e., death or hospitalisation with a discharge diagnosis of ACS), and
a variety of ML and XAI techniques were used to calculate which features --
specifically which drugs -- led to these predictions. The drug dispensing
features for rofecoxib and celecoxib were found to have a greater than zero
contribution to ACS related adverse outcome predictions (on average), and it
was found that ACS related adverse outcomes can be predicted with 72% accuracy.
Furthermore, the XAI libraries LIME and SHAP were found to successfully
identify both important and unimportant features, with SHAP slightly
outperforming LIME. ML models trained on linked administrative health datasets
in tandem with XAI algorithms can successfully quantify feature importance, and
with further development, could potentially be used as pharmacovigilance
monitoring techniques. | [
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One principal impediment in the successful deployment of AI-based
Computer-Aided Diagnosis (CAD) systems in clinical workflows is their lack of
transparent decision making. Although commonly used eXplainable AI methods
provide some insight into opaque algorithms, such explanations are usually
convoluted and not readily comprehensible except by highly trained experts. The
explanation of decisions regarding the malignancy of skin lesions from
dermoscopic images demands particular clarity, as the underlying medical
problem definition is itself ambiguous. This work presents ExAID (Explainable
AI for Dermatology), a novel framework for biomedical image analysis, providing
multi-modal concept-based explanations consisting of easy-to-understand textual
explanations supplemented by visual maps justifying the predictions. ExAID
relies on Concept Activation Vectors to map human concepts to those learnt by
arbitrary Deep Learning models in latent space, and Concept Localization Maps
to highlight concepts in the input space. This identification of relevant
concepts is then used to construct fine-grained textual explanations
supplemented by concept-wise location information to provide comprehensive and
coherent multi-modal explanations. All information is comprehensively presented
in a diagnostic interface for use in clinical routines. An educational mode
provides dataset-level explanation statistics and tools for data and model
exploration to aid medical research and education. Through rigorous
quantitative and qualitative evaluation of ExAID, we show the utility of
multi-modal explanations for CAD-assisted scenarios even in case of wrong
predictions. We believe that ExAID will provide dermatologists an effective
screening tool that they both understand and trust. Moreover, it will be the
basis for similar applications in other biomedical imaging fields. | [
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