EagerMOT 3D Multi Object Tracking via Sensor Fusion

Multi-object tracking (MOT) enables mobile robots to perform well-informedmotion planning and navigation by localizing surrounding objects in 3D space and time . Existing methods rely on depth sensors (e.g., LiDAR) to detect andtrack targets in 3d space, but only up to a limited sensing range due to the sparsely-sensing range .…

On the Role of Incentives in Evolutionary Approaches to Organizational Design

This paper introduces a model of a stylized organization that is comprised of departments that autonomously allocate tasks . The organization guides the departments’ behavior by either an individualistic, a balanced, or an altruistic linear incentive scheme . Short-sighted decisions appear favorable since they do not only increase performance in the short run but also result in significantly higher performances in the long run .…

Temporal Stream Logic modulo Theories

Temporal Stream Logic (TSL) is a temporal logic that extends LTL with updatesand predicates over arbitrary function terms . This allows for specifying data-intensive systems for which LTL is not expressive enough . In TSL,functions and predicates are uninterpreted . We present an algorithm for checking the satisfiability of a TSL formula .…

A numerical exploration of signal detector arrangement in a spin wave reservoir computing device

This paper studies numerically how the signal detector arrangement influencesthe performance of reservoir computing using spin waves excited in aferrimagnetic garnet film . This investigation is essentially important sincethe input information is not only conveyed but also transformed by the spinwaves into high-dimensional information space when the waves propagate in the film in a spatially distributed manner .…

Formalizing the Face Lattice of Polyhedra

Faces play a central role in the combinatorial and computational aspects of polyhedra . We present the first formalization of faces ofpolyhedra in the proof assistant Coq . We also prove a theorem due to Balinski on the$d$-connectedness of the adjacency graph of polytopes of dimension $d$ .…

Quantum Foundations of Classical Reversible Computing

The reversible computation paradigm aims to provide a new foundation for forgeneral classical digital computing that is capable of circumventing thethermodynamic limits to the energy efficiency of the conventional,non-reversible paradigm . We use the framework ofGorini-Kossakowski-Sudarshan-Lindblad dynamics (a.k.a. Lindbladians) with multiple asymptotic states, incorporating recent results from resource theory, full counting statistics, and stochastic thermodynamics .…

Verification of Distributed Quantum Programs

Distributed quantum systems and especially the Quantum Internet have the potential to fully demonstrate the power of quantumcomputing . Developing a general-purpose quantum computer is much more difficult than connecting many small quantum devices . One major challenge of implementing distributed quantum systems is programming them and verifying their correctness .…

Multi Structural Games and Number of Quantifiers

We study multi-structural games, played on two sets of structures . These games generalize Ehrenfeucht-Fra\”{i}ss\'{e} games . We use these games to give a completecharacterization of the number of quantifiers required to distinguish linearorders of different sizes . We also develop machinery for analyzing structures beyond linear orders .…

Analyzing Semantics of Aggregate Answer Set Programming Using Approximation Fixpoint Theory

Aggregates provide a concise way to express complex knowledge . Formalizing aggregates for answer set programming has proven to be challenging . We show that ternary satisfactionrelations bridge the gap between the standard Gelfond-Lifschitz reduct, and stable semantics as defined in the framework of ApproximationFixpoint Theory (AFT) We also show how different methods for handling aggregates taken from the literature can be described in terms of the framework and we study the corresponding Ternary Satisfaction Relations .…

On the Computation of PSNR for a Set of Images or Video

When comparing learned image/video restoration and compression methods, it iscommon to report peak-signal to noise ratio (PSNR) results . However, there doesnot exist a generally agreed upon practice to compute PSNR for sets of imagesor video . Some authors report average of individual image/frame PSNR, which is equivalent to computing PSNR from geometric mean of individualimage/frame mean-square error (MSE) Others compute a single PSNR of Y-channel only, while others compute MSE/PSNR for RGB channels .…

Memory Reduction using a Ring Abstraction over GPU RDMA for Distributed Quantum Monte Carlo Solver

The authors studied one such US Department of Energy mission-critical condensed matter physics application, Dynamical ClusterApproximation (DCA++) This paper discusses how device memory-bound challenges were successfully reduced by proposing an effective “all-to-all” communication method — a ring communication algorithm . The ring algorithm was optimized with sub-ring communicatorsand multi-threaded support to further reduce communication overhead and exposemore concurrency, respectively .…

Speeding up Python based Lagrangian Fluid Flow Particle Simulations via Dynamic Collection Data Structures

Array-like collection data structures are widely established in Python’s scientific computing-ecosystem for high-performance computations . High performance is,however, only guaranteed for static computations with a fixed computational domain . We show that for dynamic computations within an actively changing domain, the array-like collections provided by NumPy and itsderivatives are a bottleneck for large computations.…

Ranking the information content of distance measures

Real-world data typically contain a large number of features that are often heterogeneous in nature, relevance, and units of measure . When assessing the similarity between data points, one can build various distance measures using subsets of these features . Using the fewest features but still retainingsufficient information about the system is crucial in many statistical learningapproaches, particularly when data are sparse .…

A Path to Smart Radio Environments An Industrial Viewpoint on Reconfigurable Intelligent Surfaces

Reconfigurable intelligent surfaces (RIS) have been identified to be potential components of the future wireless networks . They can reconfigure thepropagation environment for wireless signals with low-cost passive devices . RIS has not only become anattractive research area but also triggered a couple of projects to develop solutions to enable the set-up of hardware demonstrations and prototypes .…

Open Source Memory Compiler for Automatic RRAM Generation and Verification

This is the first open-source memory compilers that has been developed specifically to automate ResistiveRandom Access Memory (RRAM) generation . RRAM holds the promise of achieving high speed, high density and non-volatility . A novel RRAM architecture,additionally is proposed, and a number of generated RRAM arrays are evaluated to identify their worst case control line parasitics and worst case settlingtime across the memristors of their cells .…

Parallel implementation of a compatible high order meshless method for the Stokes equations

A parallel implementation of a compatible discretization scheme forsteady-state Stokes problems is presented in this work . The scheme usesgeneralized moving least squares to generate differential operators and applyboundary conditions . This meshless scheme allows a high-order convergence forboth the velocity and pressure, while also incorporates finite-difference-likesparse discretizing .…

GPU Acceleration of 3D Agent Based Biological Simulations

BioDynaMo is an open-source agent-based simulation platform that aims to alleviate researchers from the intricacies that go into the development of high-performance computing . Researchers in biology are faced with the tough challenge of developinghigh-performance computer simulations of their increasingly complex agents-basedmodels .…

Graph Aware Evolutionary Algorithms for Influence Maximization

Social networks represent nowadays in many contexts the main source of information transmission and the way opinions and actions are influenced . Modern marketing often involves paying some targeted users, or influencers, for advertising products or ideas . Find the set of nodes in a social network that lead to the highestinformation spread — the so-called Influence Maximization (IM) problem — is a pressing question and as such it has recently attracted a greatresearch interest .…

Black box adversarial attacks using Evolution Strategies

In the last decade, deep neural networks have proven to be very powerful incomputer vision tasks . However, they are not robust toperturbations of the input data . Several methods able to generateadversarial samples make use of gradients, which usually are not available to an attacker in real-world scenarios .…

Non Deterministic Functions as Non Deterministic Processes Extended Version

We study encodings of the lambdafail into the pi-calculus in the case of calculi with non-determinism and failures . Ourencoding precisely explains the interplay of non-Deterministic and fail-proneevaluation in lambfafail via typed processes in spi . In particular, it showshow failures in sequential evaluation (absence/excess of resources) can beneatly codified as interaction protocols .…

SoCRATES System on Chip Resource Adaptive Scheduling using Deep Reinforcement Learning

Deep Reinforcement Learning (DRL) is being increasingly applied to the problem of resource allocation for emerging System-on-Chip applications . The majority of SoC resource management approaches have been targeting makespanminimization with fixed number of jobs in the system . In contrast, SoCRATESaims at minimizing average latency in a steady-state condition while assigning tasks in the ready queue to heterogeneous resources .…

DriveGAN Towards a Controllable High Quality Neural Simulation

Realistic simulators are critical for training and verifying roboticssystems . DriveGAN is a novel high-quality neural simulator called DriveGAN thatachieves controllability by disentangling different components withoutsupervision . It also includes controls for sampling features of a scene, such as the weather as well as the location of non-player objects .…

SoCRATES System on Chip Resource Adaptive Scheduling using Deep Reinforcement Learning

Deep Reinforcement Learning (DRL) is being increasingly applied to the problem of resource allocation for emerging System-on-Chip applications . In this paper, we introduce SoCRATES (SoCResource AdapTivE Scheduler), an extremely efficient DRL-based SoC scheduler . The resulting model is also compact insize and has very favorable energy consumption behaviors, making it highlypractical for deployment in future SoC systems with built-in neuralaccelerator.…

Memory Optimality for Non Blocking Containers

A bounded container maintains a collection of elements that can be inserted and extracted as long as the number of stored elements does not exceed the capacity . We consider the concurrent implementations of a bounded container more or less memory-friendly depending on how much memory they use in addition to storing the elements .…