OpenRaiser/Intern-Atlas huggingface dataset:
Prefetcher-based DRAM Architecture
Venue: arXiv cs.AR 2021
Year: 2021

Advancement in Processor technology has made it easy to handle data-intensive workloads, but limiting main memory advances has created performance bottlenecks. In DRAM, there have been improvements in DRAM access latency as well as reduction in cost-per-bit with the increase in cell density. But still DRAM data transfer rate lags behind the processing speed of the current generation processors. As Memory advancements based on hardware have been progressing at a slower pace, to cope up with High-end Processors, Architectural level advancements such as Prediction techniques, Replacement policies, etc are the major subject. In the recent field of research, Data prediction is a sought out topic as correct prediction can boost performance by decreasing the amount of excess memory access by predicting data beforehand using data access trends and behaviors. Though prediction techniques have been implemented at most of the Computer Architecture, We propose implementing data prediction in DRAM level architectures like TL-DRAM and CROW. Both of these method distributes the DRAM into different parts which contain a smaller section which is faster and larger section which contains the bulk of data but is comparatively slower. We wish to use data prediction in between these sections of memory to have predicted data transferred to the faster sections to improve the overall performance by reducing the memory access time.
Verification of Multi-Layered Assignment Problems
Venue: arXiv cs.DS 2021
Year: 2021

The class of assignment problems is a fundamental and well-studied class in the intersection of Social Choice, Computational Economics and Discrete Allocation. In a general assignment problem, a group of agents expresses preferences over a set of items, and the task is to allocate items to agents in an "optimal" way. A verification variant of this problem includes an allocation as part of the input, and the question becomes whether this allocation is "optimal". In this paper, we generalize the verification variant to the setting where each agent is equipped with multiple incomplete preference lists: Each list (called a layer) is a ranking of items in a possibly different way according to a different criterion. In particular, we introduce three multi-layer verification problems, each corresponds to an optimality notion that weakens the notion of global optimality (that is, pareto optimality in multiple layers) in a different way. Informally, the first notion requires that, for each group of agents whose size is exactly some input parameter k, the agents in the group will not be able to trade their assigned items among themselves and benefit in at least alpha layers; the second notion is similar, but it concerns all groups of size at most k rather than exactly k; the third notion strengthens these notions by requiring that groups of k agents will not be part of possibly larger groups that benefit in at least alpha layers. We study the three problems from the perspective of parameterized complexity under several natural parameterizations such as the number of layers, the number of agents, the number of items, the number of allocated items, the maximum length of a preference list, and more. We present an almost comprehensive picture of the parameterized complexity of the problems with respect to these parameters.
Driving-Signal Aware Full-Body Avatars
Venue: arXiv cs.CV 2021
Year: 2021

We present a learning-based method for building driving-signal aware full-body avatars. Our model is a conditional variational autoencoder that can be animated with incomplete driving signals, such as human pose and facial keypoints, and produces a high-quality representation of human geometry and view-dependent appearance. The core intuition behind our method is that better drivability and generalization can be achieved by disentangling the driving signals and remaining generative factors, which are not available during animation. To this end, we explicitly account for information deficiency in the driving signal by introducing a latent space that exclusively captures the remaining information, thus enabling the imputation of the missing factors required during full-body animation, while remaining faithful to the driving signal. We also propose a learnable localized compression for the driving signal which promotes better generalization, and helps minimize the influence of global chance-correlations often found in real datasets. For a given driving signal, the resulting variational model produces a compact space of uncertainty for missing factors that allows for an imputation strategy best suited to a particular application. We demonstrate the efficacy of our approach on the challenging problem of full-body animation for virtual telepresence with driving signals acquired from minimal sensors placed in the environment and mounted on a VR-headset.
Setting Out a Software Stack Capable of Hosting a Virtual ROS-based Competition
Venue: arXiv cs.SE 2021
Year: 2021

Traditional academic competitions that foster collaboration from student communities all around the globe have either been postponed indefinitely or cancelled due to restrictions posed upon in-person gathering. Owing to this, virtual competitions are gaining importance as they provide the student community with an outlet for exposure and academic growth whilst having the convenience of being online and safe. Mars Society South Asia (MSSA) has developed a software stack capable of replicating on-site competition tasks via ROS-based simulations. The software enables users to perform necessary simulations without them having to interact with ROS or Gazebo. Currently, the software has been set out to be used for the Virtual Mars Rover Challenge (VMRC), a competition that focuses on simulating the next generation of Mars rovers and thereby providing the student community with a substitute to their on-site competitions. The objective of this white paper is to explain the software architecture, installation, usage, maintenance and ease of use which is the singular most important factor in software adoption.
Addressing the Multiplicity of Solutions in Optical Lens Design as a Niching Evolutionary Algorithms Computational Challenge
Venue: arXiv cs.CE 2021
Year: 2021

Optimal Lens Design constitutes a fundamental, long-standing real-world optimization challenge. Potentially large number of optima, rich variety of critical points, as well as solid understanding of certain optimal designs per simple problem instances, provide altogether the motivation to address it as a niching challenge. This study applies established Niching-CMA-ES heuristic to tackle this design problem (6-dimensional Cooke triplet) in a simulation-based fashion. The outcome of employing Niching-CMA-ES `out-of-the-box' proves successful, and yet it performs best when assisted by a local searcher which accurately drives the search into optima. The obtained search-points are corroborated based upon concrete knowledge of this problem-instance, accompanied by gradient and Hessian calculations for validation. We extensively report on this computational campaign, which overall resulted in (i) the location of 19 out of 21 known minima within a single run, (ii) the discovery of 540 new optima. These are new minima similar in shape to 21 theoretical solutions, but some of them have better merit function value (unknown heretofore), (iii) the identification of numerous infeasibility pockets throughout the domain (also unknown heretofore). We conclude that niching mechanism is well-suited to address this problem domain, and hypothesize on the apparent multidimensional structures formed by the attained new solutions.
Bounds on Optimal Revisit Times in Persistent Monitoring Missions with a Distinct \& Remote Service Station
Venue: arXiv cs.RO 2021
Year: 2021

Persistent monitoring missions require an up-to-date knowledge of the changing state of the underlying environment. UAVs can be gainfully employed to continually visit a set of targets representing tasks (and locations) in the environment and collect data therein for long time periods. The enduring nature of these missions requires the UAV to be regularly recharged at a service station. In this paper, we consider the case in which the service station is not co-located with any of the targets. An efficient monitoring requires the revisit time, defined as the maximum of the time elapsed between successive revisits to targets, to be minimized. Here, we consider the problem of determining UAV routes that lead to the minimum revisit time. The problem is NP-hard, and its computational difficulty increases with the fuel capacity of the UAV. We develop an algorithm to construct near-optimal solutions to the problem quickly, when the fuel capacity exceeds a threshold. We also develop lower bounds to the optimal revisit time and use these bounds to demonstrate (through numerical simulations) that the constructed solutions are, on an average, at most 0.01% away from the optimum.
CEREC: A Corpus for Entity Resolution in Email Conversations
Venue: arXiv cs.CL 2021
Year: 2021

We present the first large scale corpus for entity resolution in email conversations (CEREC). The corpus consists of 6001 email threads from the Enron Email Corpus containing 36,448 email messages and 60,383 entity coreference chains. The annotation is carried out as a two-step process with minimal manual effort. Experiments are carried out for evaluating different features and performance of four baselines on the created corpus. For the task of mention identification and coreference resolution, a best performance of 59.2 F1 is reported, highlighting the room for improvement. An in-depth qualitative and quantitative error analysis is presented to understand the limitations of the baselines considered.
Time-Gated Photon Counting Receivers for Optical Wireless Communication
Venue: arXiv cs.IT 2021
Year: 2021

Photon counting detectors such as single-photon avalanche diode (SPAD) arrays are commonly considered for reliable optical wireless communication at power limited regimes. However, SPAD-based receivers suffer from significant dead time induced intersymbol interference (ISI) especially when the incident photon rate is relatively high and the dead time is comparable or even larger than the symbol duration, i.e., sub-dead-time regime. In this work, we propose a novel time-gated SPAD receiver to mitigate such ISI effects and improve the communication performance. When operated in the gated mode, the SPAD can be activated and deactivated in well-defined time intervals. We investigate the statistics of the detected photon count for the proposed time-gated SPAD receiver. It is demonstrated that the gate-ON time interval can be optimized to achieve the best bit error rate (BER) performance. Our extensive performance analysis illustrates the superiority of the time-gated SPAD receiver over the traditional free-running receiver in terms of the BER performance and the tolerance to background light.
COVID-19 Detection Using Recorded Coughs in the 2021 DiCOVA Challenge
Venue: arXiv cs.SD 2021
Year: 2021

COVID-19 has resulted in over 100 million infections and caused worldwide lock downs due to its high transmission rate and limited testing options. Current diagnostic tests can be expensive, limited in availability, time-intensive and require risky in-person appointments. It has been established that symptomatic COVID-19 seriously impairs normal functioning of the respiratory system, thus affecting the coughing acoustics. The 2021 DiCOVA Challenge @ INTERSPEECH was designed to find scientific and engineering insights to the question by enabling participants to analyze an acoustic dataset gathered from COVID-19 positive and non-COVID-19 individuals. In this report we describe our participation in the Challenge (Track 1). We achieved 82.37% AUC ROC on the blind test outperforming the Challenge's baseline of 69.85%.
An algorithm to verify local threshold testability of deterministic finite automata
Venue: arXiv cs.FL 2021
Year: 2021

A locally threshold testable language L is a language with the property that for some non negative integers k and l, whether or not a word u is in the language L depends on (1) the prefix and suffix of the word u of length k > 1 and (2) the set of intermediate substrings of length k of the word u where the sets of substrings occurring at least j times are the same, for j <= L. For given k and L the language is called l-threshold k-testable. A finite deterministic automaton is called l-threshold k-testable if the automaton accepts a l-threshold k-testable language. In this paper, the necessary and sufficient conditions for an automaton to be locally threshold testable are found. We introduce the first polynomial time algorithm to verify local threshold testability of the automaton based on this characterization. New version of polynomial time algorithm to verify the local testability will be presented too.
Protocol Stack Perspective For Low Latency and Massive Connectivity in Future Cellular Networks
Venue: arXiv cs.NI 2021
Year: 2021

With the emergence of Internet-of-Things (IoT) and ever-increasing demand for the newly connected devices, there is a need for more effective storage and processing paradigms to cope with the data generated from these devices. In this study, we have discussed different paradigms for data processing and storage including Cloud, Fog, and Edge computing models and their suitability in integrating with the IoT. Moreover, a detailed discussion on low latency and massive connectivity requirements of future cellular networks in accordance with machine-type communication (MTC) is also presented. Furthermore, the need to bring IoT devices to Internet connectivity and a standardized protocol stack to regulate the data transmission between these devices is also addressed while keeping in view the resource constraint nature of IoT devices.
Photonic neural field on a silicon chip: large-scale, high-speed neuro-inspired computing and sensing
Venue: arXiv cs.NE 2021
Year: 2021

Photonic neural networks have significant potential for high-speed neural processing with low latency and ultralow energy consumption. However, the on-chip implementation of a large-scale neural network is still challenging owing to its low scalability. Herein, we propose the concept of a photonic neural field and implement it experimentally on a silicon chip to realize highly scalable neuro-inspired computing. In contrast to existing photonic neural networks, the photonic neural field is a spatially continuous field that nonlinearly responds to optical inputs, and its high spatial degrees of freedom allow for large-scale and high-density neural processing on a millimeter-scale chip. In this study, we use the on-chip photonic neural field as a reservoir of information and demonstrate a high-speed chaotic time-series prediction with low errors using a training approach similar to reservoir computing. We discuss that the photonic neural field is potentially capable of executing more than one peta multiply-accumulate operations per second for a single input wavelength on a footprint as small as a few square millimeters. In addition to processing, the photonic neural field can be used for rapidly sensing the temporal variation of an optical phase, facilitated by its high sensitivity to optical inputs. The merging of optical processing with optical sensing paves the way for an end-to-end data-driven optical sensing scheme.
27.5-29.5 GHz Switched Array Sounder for Dynamic Channel Characterization: Design, Implementation and Measurements
Venue: arXiv cs.AR 2021
Year: 2021

A pre-requisite for the design of wireless systems is the understanding of the propagation channel. While a wealth of propagation knowledge exists for bands below 6 GHz, the same can not be said for bands approaching millimeter-wave frequencies. In this paper, we present the design, implementation and measurement-based verification of a re-configurable 27.5-29.5 GHz channel sounder for measuring dynamic directional channels. Based on the switched array principle, our design is capable of characterizing 128$\times$256 dual-polarized channels with snapshot times of around 600 ms. This is in sharp contrast to measurement times on the order of tens-of-minutes with rotating horn antenna sounders. Our design lends itself to high angular resolution at both link ends with calibrated antenna arrays sampled at 2$^\circ$ and 5$^\circ$ intervals in the azimuth and elevation domains. This is complemented with a bandwidth of up to 2 GHz, enabling nanosecond-level delay resolution. The short measurement times and stable radio frequency design facilitates real-time processing and averaging of the received wavefronts to gain measurement signal-to-noise ratio and dynamic range. After disclosing the sounder design and implementation, we demonstrate its capabilities by presenting dynamic and static measurements at 28 GHz over a 1 GHz bandwidth in an office corridor environment.
From Finite to Countable-Armed Bandits
Venue: arXiv cs.LG 2021
Year: 2021

We consider a stochastic bandit problem with countably many arms that belong to a finite set of types, each characterized by a unique mean reward. In addition, there is a fixed distribution over types which sets the proportion of each type in the population of arms. The decision maker is oblivious to the type of any arm and to the aforementioned distribution over types, but perfectly knows the total number of types occurring in the population of arms. We propose a fully adaptive online learning algorithm that achieves O(log n) distribution-dependent expected cumulative regret after any number of plays n, and show that this order of regret is best possible. The analysis of our algorithm relies on newly discovered concentration and convergence properties of optimism-based policies like UCB in finite-armed bandit problems with "zero gap," which may be of independent interest.
Trade-off Energy and Spectral Efficiency in 5G Massive MIMO System
Venue: arXiv cs.IT 2021
Year: 2021

A massive multiple input multiple-output system is very important to optimize the trade-off energy efficiency and spectral efficiency in fifth-generation cellular networks. The challenges for the next generation depend on increasing the high data traffic in the wireless communication system for both EE and SE. In this paper, the trade off energy efficiency and spectral efficiency based on the first derivative of transmit antennas and transmit power in a downlink massive MIMO system has been investigated. The trade off EE-SE by using a multiobjective optimization problem to decrease transmit power has been analyzed. The EE and SE based on constraint maximum transmit power allocation and a number of antennas by computing the first derivative of transmit power to maximize the trade-off energy efficiency and spectral efficiency has been improved. From the simulation results, the optimum trade-off between EE and SE can be obtained based on the first derivative by selecting the optimal antennas with a low cost of transmit power. Therefore, based on an optimal optimization problem is flexible to make trade-offs between EE-SE for distinct preferences
Finite Blocklength Secrecy Analysis of Polar and Reed-Muller Codes in BEC Semi-Deterministic Wiretap Channels
Venue: arXiv cs.IT 2021
Year: 2021

In this paper, we consider a semi-deterministic wiretap channel where the main channel is noiseless and the eavesdropper's channel is a binary erasure channel (BEC). We provide a lower bound for the achievable secrecy rates of polar and Reed Muller codes and compare it to the second order coding rate for the semi-deterministic wiretap channel. To the best of our knowledge, this is the first work which demonstrates the secrecy performance of polar and Reed-Muller codes in short blocklengths. The results show that under a total variation secrecy metric, Reed Muller codes can achieve secrecy rates very close to the second order approximation rate. On the other hand, we observe a significant gap between the lower bound for the achievable rates of polar codes and the the second order approximation rate for short blocklengths.
Effects of VR Gaming and Game Genre on Player Experience
Venue: arXiv cs.HC 2021
Year: 2021

With the increasing availability of modern virtual reality (VR) headsets, the use and applications of VR technology for gaming purposes have become more pervasive than ever. Despite the growing popularity of VR gaming, user studies into how it might affect the player experience (PX) during the gameplay are scarce. Accordingly, the current study investigated the effects of VR gaming and game genre on PX. We compared PX metrics for two game genres, strategy (more interactive) and racing (less interactive), across two gaming platforms, VR and traditional desktop gaming. Participants were randomly assigned to one of the gaming platforms, played both a strategy and racing game on their corresponding platform, and provided PX ratings. Results revealed that, regardless of the game genre, participants in the VR gaming condition experienced a greater level of sense of presence than did those in the desktop gaming condition. That said, results showed that the two gaming platforms did not significantly differ from one another in PX ratings. As for the effect of game genre, participants provided greater PX ratings for the strategy game than for the racing game, regardless of whether the game was played on a VR headset or desktop computer. Collectively, these results indicate that although VR gaming affords a greater sense of presence in the game environment, this increase in presence does not seem to translate into a more satisfactory PX when playing either a strategy or racing game.
AOT: Anonymization by Oblivious Transfer
Venue: arXiv cs.CR 2021
Year: 2021

We introduce AOT, an anonymous communication system based on mix network architecture that uses oblivious transfer (OT) to deliver messages. Using OT to deliver messages helps AOT resist blending ($n-1$) attacks and helps AOT preserve receiver anonymity, even if a covert adversary controls all nodes in AOT. AOT comprises three levels of nodes, where nodes at each level perform a different function and can scale horizontally. The sender encrypts their payload and a tag, derived from a secret shared between the sender and receiver, with the public key of a Level-2 node and sends them to a Level-1 node. On a public bulletin board, Level-3 nodes publish tags associated with messages ready to be retrieved. Each receiver checks the bulletin board, identifies tags, and receives the associated messages using OT. A receiver can receive their messages even if the receiver is offline when messages are ready. Through what we call a "handshake" process, communicants can use the AOT protocol to establish shared secrets anonymously. Users play an active role in contributing to the unlinkability of messages: periodically, users initiate requests to AOT to receive dummy messages, such that an adversary cannot distinguish real and dummy requests.
On the Complexity and Parallel Implementation of Hensel's Lemma and Weierstrass Preparation
Venue: arXiv cs.SC 2021
Year: 2021

Hensel's lemma, combined with repeated applications of Weierstrass preparation theorem, allows for the factorization of polynomials with multivariate power series coefficients. We present a complexity analysis for this method and leverage those results to guide the load-balancing of a parallel implementation to concurrently update all factors. In particular, the factorization creates a pipeline where the terms of degree k of the first factor are computed simultaneously with the terms of degree k-1 of the second factor, etc. An implementation challenge is the inherent irregularity of computational work between factors, as our complexity analysis reveals. Additional resource utilization and load-balancing is achieved through the parallelization of Weierstrass preparation. Experimental results show the efficacy of this mixed parallel scheme, achieving up to 9x parallel speedup on 12 cores.