Long Range Arena A Benchmark for Efficient Transformers

Transformers do not scale very well to long sequence lengths largely because of quadratic self-attention complexity . To this date, there is no well-established consensus on how to evaluate this class of models . This paper proposes a systematic and unifiedbenchmark, LRA, specifically focused on evaluating model quality underlong-context scenarios .…

The algebra of binary trees is affine complete

A function on an algebra is congruence preserving if it maps pairs of congruent elements onto pairs of congruent 7 elements . We show that on the algebra of binary trees whose leaves 8 are labeled by letters of an analphabet containing at least three letters, a 9 function is a polynomial .…

The Twelvefold Way of Non Sequential Lossless Compression

We consider an entireclassification of these invariances called the twelvefold way in enumerativecombinatorics . We develop a method to characterize lossless compression limits . Explicit computations for all twelve settings are carried out for i.i.d.uniform and Bernoulli distributions . Comparisons among settings provide quantitative insight .…

Detecting Emerging Symptoms of COVID 19 using Context based Twitter Embeddings

In this paper, we present an iterative graph-based approach for the detectionof symptoms of COVID-19 . The method can be applied to finding context-specific words andtexts (e.g. symptom mentions) in large imbalanced corpora . We find that the approach applied to Twitter data can detect symptommentions substantially before being reported by the Centers for Disease Control(CDC) We also test if the proposed approach generalizes to the problem of detecting Adverse Drug Reaction .…

Locally Adaptive Nearest Neighbors

When training automated systems, it has been shown to be beneficial to adapt the representation of data by learning a problem-specific metric . We extend this idea and, for the widely used family of k nearestneighbors algorithms, develop a method that allows learning locally adaptivemetrics .…

DyERNIE Dynamic Evolution of Riemannian Manifold Embeddings for Temporal Knowledge Graph Completion

Dy-ERNIE is a non-Euclidean embedding approach that learns evolving entityrepresentations in a product of Riemannian manifolds . This approach is better than existing embedding approaches for temporal KGs, such as hierarchical and cyclic structures . We let the entity representations evolve according to a velocity vector defined in the tangent space at each timestamp .…

Learning to Beamform in Heterogeneous Massive MIMO Networks

The problem of finding the optimal beamformers in massive multiple-input multiple- input multiple-output (MIMO) networks is challenging because of its non-convexity . This paper proposes a noveldeep learning based beamforming algorithm to address the above challenges . Numerical results based on both synthetic and ray-tracing channel models show that the proposed neural network can achieve high WSRs with significantly reduced runtime, while exhibiting favorable generalization capability with respect to the antenna number, BS number and the inter-BS distance .…

Unmasking Communication Partners A Low Cost AI Solution for Digitally Removing Head Mounted Displays in VR Based Telepresence

Face-to-face conversation in Virtual Reality (VR) is a challenge when participants wear head-mounted displays (HMD) A significant portion of aparticipant’s face is hidden and facial expressions are difficult to perceive . We propose one of the first low-cost systems for this task which uses only open source, free software and affordable hardware .…

Multimodal Trajectory Prediction via Topological Invariance for Navigation at Uncontrolled Intersections

MultipleTopologies Prediction (MTP) is a data-driven trajectory-prediction mechanism . MTP outperforms a state-of-the-art multimodaltrajectory prediction baseline (MFP) in terms of prediction accuracy by 78.24% on a challenging simulated dataset . We show that MTP enables ouroptimization-based planner, MTPnav, to achieve collision-free and time-efficient navigation across a variety of challenging intersectionscenarios on the CARLA simulator .…

The Cost of Privacy in Generalized Linear Models Algorithms and Minimax Lower Bounds

The trade-off between differential privacy and statistical accuracy in ingeneralized linear models (GLMs) is studied . We propose differentially private algorithms for parameter estimation in both low-dimensional and high-dimensional sparse GLMs and characterize their statistical performance . Weestablish privacy-constrained minimax lower bounds for GLMs, which imply that the proposed algorithms are rate-optimal up to logarithmic factors in samplesize .…

Reliable Off policy Evaluation for Reinforcement Learning

In a sequential decision-making problem, off-policy evaluation (OPE)estimates the expected cumulative reward of a target policy using loggedtransition data generated from a different behavior policy, without execution of the target policy . Reinforcement learning in high-stake environments, such as healthcare and education, is often limited to off-Policy settings due to safety or ethical concerns, or inability of exploration .…

Learning Neural Event Functions for Ordinary Differential Equations

The existing Neural ODE formulation relies on an explicit knowledge of the termination time . We extend neural ODEs to implicitly defined terminationcriteria modeled by neural event functions . We proposeimulation-based training of point processes with applications in discretecontrol. We testour approach in modeling hybrid discrete- and continuous- systems such asswitching dynamical systems and collision in multi-body systems, and we proposesimulation based training ofpoint processes .…

Open Area Path Finding to Improve Wheelchair Navigation

Navigation is one of the most widely used applications of the Location BasedServices (LBS) This paper proposes and implements a novel path finding algorithm for open areas, i.e.areas with no network of pathways such as grasslands and parks . The proposed algorithm creates a new graph in the open area, which can consider the obstacles and barriers and calculate the path based on the factors that are important for wheelchair users .…

Learning based 3D Occupancy Prediction for Autonomous Navigation in Occluded Environments

In autonomous navigation of mobile robots, sensors suffer from massiveocclusion in cluttered environments . Humans infer the exact shape of the obstacles from only partial observation andgenerate non-conservative trajectories that avoid possible collisions inoccluded space . Method leverages the performance of a kinodynamic planner by improving security with no reduction of speed in clustered environments, authors say .…

Adaptive Federated Dropout Improving Communication Efficiency and Generalization for Federated Learning

Federated Learning enables multiple clients to collaboratively learn a machinelearning model while keeping all their data on-device . Communication between the clients and the server is considered a main bottleneck in the convergence time of federated learning . Adaptive Federated Dropout (AFD) is a novel technique to reduce the communication costs associated with federatedlearning .…

Chatbots as conversational healthcare services

Chatbots are emerging as a promising platform for accessing and delivering healthcare services . Chatbots aim to take an active role in the provision of prevention, diagnosis, and treatment services . This article takes a closer look at how these emerging chatbots address design aspects relevant to healthcareservice provision, emphasizing the Human-AI interaction aspects .…

Pathwise Conditioning of Gaussian Processes

Monte Carlo methods act as a convenient bridge for connectingintractable mathematical expressions with actionable estimates via sampling . Instead of focusing on distributions, we articulate Gaussian conditionals at the level of randomvariables . We show how this interpretation of conditioning gives riseto a general family of approximations that lend themselves to fast sampling from Gaussian process posteriors.…

Echo Chambers in Collaborative Filtering Based Recommendation Systems

Recommendation systems underpin the serving of nearly all online content inthe modern age . From Youtube and Netflix recommendations, to Facebook feeds andGoogle searches, these systems are designed to filter content to the predictedpreferences of users . These systems have faced growing criticism withrespect to their impact on content diversity, social polarization, and the health of public discourse .…

Asymptotic Convergence of Thompson Sampling

Thompson sampling has been shown to be an effective policy across a variety of online learning tasks . We prove an asymptotic convergence result for Thompson sampling under the assumption of a sub-linear Bayesian regret, and show that the actions of a Thompson sampling agent provide a stronglyconsistent estimator of the optimal action .…

Bait and Switch Online Training Data Poisoning of Autonomous Driving Systems

We show that by controlling parts of a physical environment in which a deep neural network is being fine-tuned online, an adversary can launch subtle data poisoning attacks that degrade the performance of the system . While the attack can be applied in general to any perception task, weconsider a DNN based traffic light classifier for an autonomous car that has been trained in one city and is fine-tuneed online in another city .…

FairLens Auditing Black box Clinical Decision Support Systems

FairLens is a methodology for discovering and explaining biases in AI systems . It can be used to audit a fictional commercial black-box model acting as a clinical decision support system . FairLens allows experts to investigate whether to trust the model and to spotlight group-specific biases that might constitutepotential fairness issues .…

MLAS Metric Learning on Attributed Sequences

Distance metric learning has attracted much attention in recent years . Real-world applications often involveattributed sequence data (e.g., clickstreams), where each instance consists ofnot only a set of attributes but also a sequence of categorical items . We propose a deep learning framework, called MLAS (Metric Learningon Attributed Sequences), to learn a distance metric that effectively measures differences between attributed sequences .…

Enabling DER Participation in Frequency Regulation Markets

Distributed energy resources (DERs) are playing an increasing role inancillary services for the bulk grid, particularly in frequency regulation . The proposed framework is hierarchical, consisting of a toplayer and a bottom layer . The bottom layer consists of the DERs inside each microgrid whose power levels are adjusted so that the output of the corresponding aggregator in the top layer matches the output in the bottom layer.…

Multiscale Reduced Order Modeling of a Titanium Skin Panel Subjected to Thermo Mechanical Loading

This manuscript presents the formulation, implementation, calibration andapplication of a multiscale reduced-order model to simulate a titanium panelstructure subjected to thermo-mechanical loading associated with high-speedflight . The proposed approach was fully calibrated using a series ofuniaxial tension tests of Ti-6242S at a wide range of temperatures and twodifferent strain rates .…

Synthesis of Interval Observers for Nonlinear Discrete Time Systems

A systematic procedure to synthesize interval observers for nonlineardiscrete-time systems is proposed . The feedback gains and other matrices are found from the solutions to semidefinite feasibility programs . Two cases are considered: (1) the interval observer is in the same coordinate frame as the given system, and (2) the intervals observer uses a coordinate transformation .…

Optimal tiling of the Euclidean space using symmetric bodies

In theoretical computer science, the tiling problem is intimately to the study of parallel repetition theorems . Kindler et al.\ showed that for general bodies $B$ this is tight, i.e.\ that there is a tiling body of $\mathbb{R}^n$ whosesurface area is $O(n/\sqrt{n)$ The result suggests that while strong parallel repetition fails in general, there may be important special cases where it still applies .…

Online Multi Objective Model Independent Adaptive Tracking Mechanism for Dynamical Systems

The optimal tracking problem is addressed in robotics literature by using a variety of robust and adaptive control approaches . This scheme minimizes the tracking errors and optimizes the overall dynamical behavior using simultaneouslinear feedback control strategies . Reinforcement learning approaches based onvalue iteration processes are adopted to solve the underlying Bellmanoptimality equations .…

Sampling Constraint Satisfaction Solutions in the Local Lemma Regime

We give a Markov chain based algorithm for sampling almost uniform solutions of constraint satisfaction problems (CSPs) We give the current best almost-uniform samplers for hypergraphcolorings and for CNF solutions . Our main approach is a new technique called states compression, which generalizes the”mark/unmark” paradigm of Moitra (Moitra, JACM, 2019), and can give fastlocal-lemma-based sampling algorithms .…

Graphene based Wireless Agile Interconnects for Massive Heterogeneous Multi chip Processors

The main design principles in computer architecture have recently shifted from a monolithic scaling-driven approach to the development of heterogeneousarchitectures that tightly co-integrate multiple specialized processor andmemory chiplets . In such data-hungry multi-chip architectures, current networks-in-Package (NiPs) may not be enough to cater to their fast-changing communication demands .…