Recalibration of Neural Networks for Point Cloud Analysis

Spatial and channel re-calibration have become powerful concepts in computervision . Their ability to capture long-range dependencies is especially useful for those networks that extract local features, such as CNNs . We propose a set ofre-Calibration blocks that extend Squeeze and Excitation blocks and that can beadded to any network for 3D point cloud analysis that builds a globaldescriptor by hierarchically combining features from multiple localneighborhoods .…

Open World Learning Without Labels

Open-world learning is a problem where an autonomous agent detects thingssthat it does not know and learns them over time from a non-stationary andnever-ending stream of data . In an open-world environment, the training data and objective criteria are never available at once .…

Transforming Data Flow Diagrams for Privacy Compliance Long Version

Privacy,like security, is a non-functional property, yet most software design tools are focused on functional aspects . Recent regulations, such as the European General Data Protection Regulation, put stringent constraints on the handling of personal data . In this paper, we provide an explicit algorithm and a proof-of-concept implementation to transform DFDs into PA-DFDs .…

Learning sparse structures for physics inspired compressed sensing

In underwater acoustics, shallow water environments act as modal dispersivewaveguides when considering low-frequency sources . In this context, propagatingsignals can be described as a sum of few modal components, each of them propagating according to its own wavenumber . Estimating these wavenumbers is of interest to understand the propagating environment as well as the emittingssource .…

Genome assembly a universal theoretical framework unifying and generalizing the safe and complete algorithms

Genome assembly is a fundamental problem in Bioinformatics, requiring toreconstructing a source genome from an assembly graph built from a set of reads . The goal is to find what is definitely present in all solutions, orwhat is safe . The long-standing open problem offinding all the safe parts of the solutions was recently solved by a majortheoretical result [RECOMB’16].…

COVID 19 and Social Distancing Disparities in Mobility Adaptation by Income

Current literature from the United Statesinfers that only workers from limited socioeconomic groups have the ability to topractice remote work . However, there has been little research on mobilitydisparity across income groups in US cities during the pandemic . A longitudinal study was performed onmobility as measured by the total travel distance, the radius of gyration, and the number of visited locations in April 2020 compared to the data in January 2020 .…

Deep Physics aware Inference of Cloth Deformation for Monocular Human Performance Capture

existing methods either do not estimate clothing at all or model clothdeformations with simple geometric priors . This leads to noticeable artifacts in their constructions, such as baked-in wrinkles and implausible deformations that defy gravity . We propose a person-specific, learning-based method thatintegrates a finite element-based simulation layer into the training process .…

Iterations for the Unitary Sign Decomposition and the Unitary Eigendecomposition

We construct fast, structure-preserving iterations for computing the signdecomposition of a unitary matrix $A$ with no eigenvalues equal to $\pm i$ We use our iterations to construct a stable spectraldivide-and-conquer algorithm . Our iterationsrely on a recently discovered formula for the best (in the minimax sense)unimodular rational approximant of the scalar function $S =\operatorname{sign}(A) = A(A(A^2)^{-1/2) times a matrix $N = (A#2) + (A/N) The iterations converge significantly faster than Pad\’e iterations.…

Contract Scheduling With Predictions

Contract scheduling is a general technique that allows to design a system with interruptible capabilities, given an algorithm that is not necessarilyinterruptible . Previous work on this topic has largely assumed that the interruption is a worst-case deadline that is unknown to the scheduler .…

Exploring the landscapes of computing digital neuromorphic unconventional and beyond

The acceleration race of digital computing technologies seems to be steeringtoward impasses — technological, economical and environmental — a condition that has spurred research efforts in alternative, “neuromorphic” (brain-like)computing technologies . The idea of exploiting nonlinear physical phenomena “directly” for non-digital computing has been explored under names like “unconventional computing”, “natural computing”,”physical computing”, or “in-materio computing” I stake out how a general concept of “computing” can bedeveloped which comprises digital, neuromorphic, unconventional and possible future paradigms .…

Towards the sampling Lovász Local Lemma

We show that for $k, q = O(1)$ there is a deterministic, polynomial time algorithm to approximately count thenumber of satisfying assignments . For the special case of properly $q$-coloring $k$-uniform hypergraphs, the term $Delta^{7$ improves the previously best known $\Delta^{60}$ for deterministic algorithms [Moitra,J.ACM,…

Zero Shot Visual Slot Filling as Question Answering

This paper presents a new approach to visual zero-shot slot filling . The approach extends previous approaches by reformulating the slot filling task asQuestion Answering . The multi-task approach facilitates the incorporation of a large number of refinements and transfer learning across similar tasks .…

Interpretable Models in ANNs

Artificial neural networks are often very complex and too deep for a human to understand . In this paper, we try to find a way to explain anetwork and extract a human-readable equation that describes the model . In some cases, laws of physics, for example, the pattern can be described by relatively simplemathematical expressions .…

Min Sum Clustering with Outliers

We give a constant factor polynomial time pseudo-approximation algorithm formin-sum clustering with or without outliers . The algorithm is allowed to exclude an arbitrarily small constant fraction of the points . Our results apply to instances of points in real space, as well as to points in a metricspace, where the number of clusters, and also the dimension if relevant, isarbitrary (part of the input, not an absolute constant) The approximation guarantee growswith $\frac{1-\eps) n’$ points .…

Hindsight Network Credit Assignment

Hindsight Network Credit Assignment (HNCA) works by assigning credit to eachneuron’s stochastic output based on how it influences the output of its immediate children in the network . We prove that HNCA provides unbiased gradient estimates while reducing variance compared to the REINFORCE estimator .…

A Fixed Time Stable Adaptation Law for Safety Critical Control under Parametric Uncertainty

We present a novel technique for solving the problem of safe control for ageneral class of nonlinear, control-affine systems subject to parametric modeluncertainty . Invoking Lyapunov analysis and the notion of fixed-time stability(FxTS) We introduce a parameter adaptation law which guarantees convergence of the estimates of unknown parameters in the system dynamics to their true values within a fixed time independent of the initial parameter estimation error .…

A Model Free Loop Shaping Method based on Iterative Learning Control

This paper aims to develop a model-free loop-shaping method in controldesign . The core idea is to convert the model matching problem to atrajectory tracking problem . The proposed method is validated through numerical simulation on athird order plant . This method does not require the model of the controlled plant; hence it provides better performance of loop-Shaping controllesign.…

Health Focused Optimal Power Flow

Health-Focused Optimal Power Flow (HF-OPF) proposed to take into account equipment health in operational and physical constraints . The paper addresses theneed for understanding the relationship between health condition index and theoperational constraints in OPF problems . The results show that health conditioninflicts high cost of generation and can lead to infeasibility even with lesscritical faults .…

Safely Learning Dynamical Systems from Short Trajectories

A fundamental challenge in learning to control an unknown dynamical system is to reduce model uncertainty by making measurements while maintaining safety . In our framework, the state of the system is required to stay within a given safety region under the (possibly repeated) action of all systems that are consistent with the information gathered so far .…