OpenRaiser/Intern-Atlas huggingface dataset:
Resilient Task Allocation in Heterogeneous Multi-Robot Systems
Venue: arXiv cs.MA 2021
Year: 2021

For a multi-robot system equipped with heterogeneous capabilities, this paper presents a mechanism to allocate robots to tasks in a resilient manner when anomalous environmental conditions such as weather events or adversarial attacks affect the performance of robots within the tasks. Our primary objective is to ensure that each task is assigned the requisite level of resources, measured as the aggregated capabilities of the robots allocated to the task. By keeping track of task performance deviations under external perturbations, our framework quantifies the extent to which robot capabilities (e.g., visual sensing or aerial mobility) are affected by environmental conditions. This enables an optimization-based framework to flexibly reallocate robots to tasks based on the most degraded capabilities within each task. In the face of resource limitations and adverse environmental conditions, our algorithm minimally relaxes the resource constraints corresponding to some tasks, thus exhibiting a graceful degradation of performance. Simulated experiments in a multi-robot coverage and target tracking scenario demonstrate the efficacy of the proposed approach.
Rayleigh Fading Modeling and Channel Hardening for Reconfigurable Intelligent Surfaces
Venue: arXiv cs.IT 2021
Year: 2021

A realistic performance assessment of any wireless technology requires the use of a channel model that reflects its main characteristics. The independent and identically distributed Rayleigh fading channel model has been (and still is) the basis of most theoretical research on multiple antenna technologies in scattering environments. This letter shows that such a model is not physically appearing when using a reconfigurable intelligent surface (RIS) with rectangular geometry and provides an alternative physically feasible Rayleigh fading model that can be used as a baseline when evaluating RIS-aided communications. The model is used to revisit the basic RIS properties, e.g., the rank of spatial correlation matrices and channel hardening.
Theory Exploration Powered By Deductive Synthesis
Venue: arXiv cs.PL 2021
Year: 2021

Recent years have seen tremendous growth in the amount of verified software. Proofs for complex properties can now be achieved using higher-order theories and calculi. Complex properties lead to an ever-growing number of definitions and associated lemmas, which constitute an integral part of proof construction. Following this -- whether automatic or semi-automatic -- methods for computer-aided lemma discovery have emerged. In this work, we introduce a new symbolic technique for bottom-up lemma discovery, that is, the generation of a library of lemmas from a base set of inductive data types and recursive definitions. This is known as the theory exploration problem, and so far, solutions have been proposed based either on counter-example generation or the more prevalent random testing combined with first-order solvers. Our new approach, being purely deductive, eliminates the need for random testing as a filtering phase and for SMT solvers. Therefore it is amenable compositional reasoning and for the treatment of user-defined higher-order functions. Our implementation has shown to find more lemmas than prior art, while avoiding redundancy.
What am I allowed to do here?: Online Learning of Context-Specific Norms by Pepper
Venue: arXiv cs.CV 2021
Year: 2021

Social norms support coordination and cooperation in society. With social robots becoming increasingly involved in our society, they also need to follow the social norms of the society. This paper presents a computational framework for learning contexts and the social norms present in a context in an online manner on a robot. The paper utilizes a recent state-of-the-art approach for incremental learning and adapts it for online learning of scenes (contexts). The paper further utilizes Dempster-Schafer theory to model context-specific norms. After learning the scenes (contexts), we use active learning to learn related norms. We test our approach on the Pepper robot by taking it through different scene locations. Our results show that Pepper can learn different scenes and related norms simply by communicating with a human partner in an online manner.
Sentinel: An Onboard Lane Change Advisory System for Intelligent Vehicles to Reduce Traffic Delay during Freeway Incidents
Venue: arXiv cs.RO 2021
Year: 2021

This paper introduces Sentinel, an onboard system for intelligent vehicles that guides their lane changing behavior during a freeway incident with the goal of reducing traffic congestion, capacity drop, and delay. When an incident blocking the lanes ahead is detected, Sentinel calculates the probability of leaving the blocked lane(s) before reaching the incident point at each time step. It advises the vehicle to leave the blocked lane(s) when that probability drops below a certain threshold, as the vehicle nears the congestion boundary. By doing this, Sentinel reduces the number of late-stage lane changes of vehicles in the blocked lane(s) trying to move to other lanes, and distributes those maneuvers upstream of the incident point. A simulation case study is conducted in which one lane of a four-lane section of the I-66 interstate highway in the U.S. is temporarily blocked due to an incident, to understand how Sentinel impacts traffic flow and how different parameters - traffic flow, system penetration rate, and incident duration - affect Sentinel's performance. The results show that Sentinel has a positive impact on traffic flow, reducing average delay by up to 37%, particularly when it has a considerable penetration rate. Working alongside Traffic Incident Management Systems (TIMS), Sentinel can be a valuable asset for reducing traffic delay and potentially saving billions of dollars annually in costs associated with congestion caused by freeway incidents.
ARM: A Confidence-Based Adversarial Reweighting Module for Coarse Semantic Segmentation
Venue: arXiv cs.CV 2021
Year: 2021

Coarsely-labeled semantic segmentation annotations are easy to obtain, but therefore bear the risk of losing edge details and introducing background pixels. Impeded by the inherent noise, existing coarse annotations are only taken as a bonus for model pre-training. In this paper, we try to exploit their potentials with a confidence-based reweighting strategy. To expand, loss-based reweighting strategies usually take the high loss value to identify two completely different types of pixels, namely, valuable pixels in noise-free annotations and mislabeled pixels in noisy annotations. This makes it impossible to perform two tasks of mining valuable pixels and suppressing mislabeled pixels at the same time. However, with the help of the prediction confidence, we successfully solve this dilemma and simultaneously perform two subtasks with a single reweighting strategy. Furthermore, we generalize this strategy into an Adversarial Reweighting Module (ARM) and prove its convergence strictly. Experiments on standard datasets shows our ARM can bring consistent improvements for both coarse annotations and fine annotations. Specifically, built on top of DeepLabv3+, ARM improves the mIoU on the coarsely-labeled Cityscapes by a considerable margin and increases the mIoU on the ADE20K dataset to 47.50.
End-to-end Learning for OFDM: From Neural Receivers to Pilotless Communication
Venue: arXiv cs.IT 2021
Year: 2021

Previous studies have demonstrated that end-to-end learning enables significant shaping gains over additive white Gaussian noise (AWGN) channels. However, its benefits have not yet been quantified over realistic wireless channel models. This work aims to fill this gap by exploring the gains of end-to-end learning over a frequency- and time-selective fading channel using orthogonal frequency division multiplexing (OFDM). With imperfect channel knowledge at the receiver, the shaping gains observed on AWGN channels vanish. Nonetheless, we identify two other sources of performance improvements. The first comes from a neural network (NN)-based receiver operating over a large number of subcarriers and OFDM symbols which allows to significantly reduce the number of orthogonal pilots without loss of bit error rate (BER). The second comes from entirely eliminating orthognal pilots by jointly learning a neural receiver together with either superimposed pilots (SIPs), linearly combined with conventional quadrature amplitude modulation (QAM), or an optimized constellation geometry. The learned geometry works for a wide range of signal-to-noise ratios (SNRs), Doppler and delay spreads, has zero mean and does hence not contain any form of superimposed pilots. Both schemes achieve the same BER as the pilot-based baseline with around 7% higher throughput. Thus, we believe that a jointly learned transmitter and receiver are a very interesting component for beyond-5G communication systems which could remove the need and associated control overhead for demodulation reference signals (DMRSs).
AFP-SRC: Identification of Antifreeze Proteins Using Sparse Representation Classifier
Venue: arXiv cs.CV 2021
Year: 2021

Species living in the extreme cold environment fight against the harsh conditions using antifreeze proteins (AFPs), that manipulates the freezing mechanism of water in more than one way. This amazing nature of AFP turns out to be extremely useful in several industrial and medical applications. The lack of similarity in their structure and sequence makes their prediction an arduous task and identifying them experimentally in the wet-lab is time-consuming and expensive. In this research, we propose a computational framework for the prediction of AFPs which is essentially based on a sample-specific classification method using the sparse reconstruction. A linear model and an over-complete dictionary matrix of known AFPs are used to predict a sparse class-label vector that provides a sample-association score. Delta-rule is applied for the reconstruction of two pseudo-samples using lower and upper parts of the sample-association vector and based on the minimum recovery score, class labels are assigned. We compare our approach with contemporary methods on a standard dataset and the proposed method is found to outperform in terms of Balanced accuracy and Youden's index. The MATLAB implementation of the proposed method is available at the author's GitHub page (\{https://github.com/Shujaat123/AFP-SRC}{https://github.com/Shujaat123/AFP-SRC}).
Semantic Relations and Deep Learning
Venue: arXiv cs.CL 2021
Year: 2021

The second edition of "Semantic Relations Between Nominals" by Vivi Nastase, Stan Szpakowicz, Preslav Nakov and Diarmuid Ó Séaghdha has been published in April 2021 by Morgan & Claypool (www.morganclaypoolpublishers.com/catalog_Orig/product_info.php?products_id=1627). A new Chapter 5 of the book, by Vivi Nastase and Stan Szpakowicz, discusses relation classification/extraction in the deep-learning paradigm which arose after the first edition appeared. This is Chapter 5, made public by the kind permission of Morgan & Claypool.
A black-box adversarial attack for poisoning clustering
Venue: arXiv cs.LG 2021
Year: 2021

Clustering algorithms play a fundamental role as tools in decision-making and sensible automation processes. Due to the widespread use of these applications, a robustness analysis of this family of algorithms against adversarial noise has become imperative. To the best of our knowledge, however, only a few works have currently addressed this problem. In an attempt to fill this gap, in this work, we propose a black-box adversarial attack for crafting adversarial samples to test the robustness of clustering algorithms. We formulate the problem as a constrained minimization program, general in its structure and customizable by the attacker according to her capability constraints. We do not assume any information about the internal structure of the victim clustering algorithm, and we allow the attacker to query it as a service only. In the absence of any derivative information, we perform the optimization with a custom approach inspired by the Abstract Genetic Algorithm (AGA). In the experimental part, we demonstrate the sensibility of different single and ensemble clustering algorithms against our crafted adversarial samples on different scenarios. Furthermore, we perform a comparison of our algorithm with a state-of-the-art approach showing that we are able to reach or even outperform its performance. Finally, to highlight the general nature of the generated noise, we show that our attacks are transferable even against supervised algorithms such as SVMs, random forests, and neural networks.
Key Superposition Simultaneously Achieves Security and Privacy in Cache-Aided Linear Function Retrieval
Venue: arXiv cs.IT 2021
Year: 2021

This work investigates the problem of cache-aided content Secure and demand Private Linear Function Retrieval (SP-LFR), where three constraints are imposed on the system:(a) each user is interested in retrieving an arbitrary linear combination of the files in the server's library;(b) the content of the library must be kept secure from a wiretapper who obtains the signal sent by the server; and (c) no colluding subset of users together obtain information about the demands of the remaining users. A procedure is proposed to derive an SP-LFR scheme from a given Placement Delivery Array (PDA), which is known to give coded caching schemes with low subpacketization for systems with neither security nor privacy constraints. This procedure uses the superposition of security keys and privacy keys in both the cache placement and transmitted signal to guarantee content security and demand privacy, respectively. In particular, among all PDA-based SP-LFR schemes, the memory-load pairs achieved by the PDA describing the Maddah-Ali and Niesen's scheme are Pareto-optimal and have the lowest subpacketization. Moreover, the achieved load-memory tradeoff is optimal to within a constant multiplicative gap except for the small memory regime (i.e., when the cache size is between 1 and 2) and the number of files is smaller than the number of users. Remarkably, the memory-load tradeoff does not increase compared to the best known schemes that guarantee either only content security in all regimes or only demand privacy in regime mentioned above.
Circuit Design for $k$-coloring Problem and Its Implementation on Near-term Quantum Devices
Venue: arXiv cs.ET 2021
Year: 2021

Nowadays in Quantum Computing, the implementation of quantum algorithm has created a stir since Noisy Intermediate-Scale Quantum (NISQ) devices are out in the market. Researchers are mostly interested in solving NP-complete problems with the help of quantum algorithms for its speed-up. As per the work on computational complexity by Karp \cite{karp}, if any of the NP-complete problem can be solved then any other NP-complete problem can be reduced to that problem in polynomial time. In this Paper, $k$-coloring problem (NP-complete problem) has been considered to solve using Grover's search. A comparator-based approach has been used to implement $k$-coloring problem which enables the reduction of the qubit cost compared to the state-of-the-art. An end-to-end automated framework has been proposed to implement the $k$-coloring problem for any unweighted and undirected graph on any available Noisy Intermediate-Scale Quantum (NISQ) devices, which helps in generalizing our approach.
Identity-Based Patterns in Deep Convolutional Networks: Generative Adversarial Phonology and Reduplication
Venue: arXiv cs.CL 2021
Year: 2021

This paper models unsupervised learning of an identity-based pattern (or copying) in speech called reduplication from raw continuous data with deep convolutional neural networks. We use the ciwGAN architecture Beguš (2021a; arXiv:2006.02951) in which learning of meaningful representations in speech emerges from a requirement that the CNNs generate informative data. We propose a technique to wug-test CNNs trained on speech and, based on four generative tests, argue that the network learns to represent an identity-based pattern in its latent space. By manipulating only two categorical variables in the latent space, we can actively turn an unreduplicated form into a reduplicated form with no other substantial changes to the output in the majority of cases. We also argue that the network extends the identity-based pattern to unobserved data. Exploration of how meaningful representations of identity-based patterns emerge in CNNs and how the latent space variables outside of the training range correlate with identity-based patterns in the output has general implications for neural network interpretability.
One-bit Supervision for Image Classification
Venue: arXiv cs.CV 2021
Year: 2021

This paper presents one-bit supervision, a novel setting of learning from incomplete annotations, in the scenario of image classification. Instead of training a model upon the accurate label of each sample, our setting requires the model to query with a predicted label of each sample and learn from the answer whether the guess is correct. This provides one bit (yes or no) of information, and more importantly, annotating each sample becomes much easier than finding the accurate label from many candidate classes. There are two keys to training a model upon one-bit supervision: improving the guess accuracy and making use of incorrect guesses. For these purposes, we propose a multi-stage training paradigm which incorporates negative label suppression into an off-the-shelf semi-supervised learning algorithm. In three popular image classification benchmarks, our approach claims higher efficiency in utilizing the limited amount of annotations.
SeqROCTM: A Matlab toolbox for the analysis of Sequence of Random Objects driven by Context Tree Models
Venue: arXiv cs.AI 2021
Year: 2021

In several research problems we deal with probabilistic sequences of inputs (e.g., sequence of stimuli) from which an agent generates a corresponding sequence of responses and it is of interest to model the relation between them. A new class of stochastic processes, namely \textit{sequences of random objects driven by context tree models}, has been introduced to model such relation in the context of auditory statistical learning. This paper introduces a freely available Matlab toolbox (SeqROCTM) that implements this new class of stochastic processes and three model selection procedures to make inference on it. Besides, due to the close relation of the new mathematical framework with context tree models, the toolbox also implements several existing model selection algorithms for context tree models.
Accelerating Graph Sampling for Graph Machine Learning using GPUs
Venue: arXiv cs.DC 2021
Year: 2021

Representation learning algorithms automatically learn the features of data. Several representation learning algorithms for graph data, such as DeepWalk, node2vec, and GraphSAGE, sample the graph to produce mini-batches that are suitable for training a DNN. However, sampling time can be a significant fraction of training time, and existing systems do not efficiently parallelize sampling. Sampling is an embarrassingly parallel problem and may appear to lend itself to GPU acceleration, but the irregularity of graphs makes it hard to use GPU resources effectively. This paper presents NextDoor, a system designed to effectively perform graph sampling on GPUs. NextDoor employs a new approach to graph sampling that we call transit-parallelism, which allows load balancing and caching of edges. NextDoor provides end-users with a high-level abstraction for writing a variety of graph sampling algorithms. We implement several graph sampling applications, and show that NextDoor runs them orders of magnitude faster than existing systems.
Optimality of short-term synaptic plasticity in modelling certain dynamic environments
Venue: arXiv cs.NE 2021
Year: 2021

Biological neurons and their in-silico emulations for neuromorphic artificial intelligence (AI) use extraordinarily energy-efficient mechanisms, such as spike-based communication and local synaptic plasticity. It remains unclear whether these neuronal mechanisms only offer efficiency or also underlie the superiority of biological intelligence. Here, we prove rigorously that, indeed, the Bayes-optimal prediction and inference of randomly but continuously transforming environments, a common natural setting, relies on short-term spike-timing-dependent plasticity, a hallmark of biological synapses. Further, this dynamic Bayesian inference through plasticity enables circuits of the cerebral cortex in simulations to recognize previously unseen, highly distorted dynamic stimuli. Strikingly, this also introduces a biologically-modelled AI, the first to overcome multiple limitations of deep learning and outperform artificial neural networks in a visual task. The cortical-like network is spiking and event-based, trained only with unsupervised and local plasticity, on a small, narrow, and static training dataset, but achieves recognition of unseen, transformed, and dynamic data better than deep neural networks with continuous activations, trained with supervised backpropagation on the transforming data. These results link short-term plasticity to high-level cortical function, suggest optimality of natural intelligence for natural environments, and repurpose neuromorphic AI from mere efficiency to computational supremacy altogether.
Using Known Words to Learn More Words: A Distributional Analysis of Child Vocabulary Development
Venue: arXiv cs.CL 2021
Year: 2021

Why do children learn some words before others? Understanding individual variability across children and also variability across words, may be informative of the learning processes that underlie language learning. We investigated item-based variability in vocabulary development using lexical properties of distributional statistics derived from a large corpus of child-directed speech. Unlike previous analyses, we predicted word trajectories cross-sectionally, shedding light on trends in vocabulary development that may not have been evident at a single time point. We also show that whether one looks at a single age group or across ages as a whole, the best distributional predictor of whether a child knows a word is the number of other known words with which that word tends to co-occur. Keywords: age of acquisition; vocabulary development; lexical diversity; child-directed speech;
Demand Forecasting of Individual Probability Density Functions with Machine Learning
Venue: arXiv cs.LG 2021
Year: 2021

Demand forecasting is a central component of the replenishment process for retailers, as it provides crucial input for subsequent decision making like ordering processes. In contrast to point estimates, such as the conditional mean of the underlying probability distribution, or confidence intervals, forecasting complete probability density functions allows to investigate the impact on operational metrics, which are important to define the business strategy, over the full range of the expected demand. Whereas metrics evaluating point estimates are widely used, methods for assessing the accuracy of predicted distributions are rare, and this work proposes new techniques for both qualitative and quantitative evaluation methods. Using the supervised machine learning method "Cyclic Boosting", complete individual probability density functions can be predicted such that each prediction is fully explainable. This is of particular importance for practitioners, as it allows to avoid "black-box" models and understand the contributing factors for each individual prediction. Another crucial aspect in terms of both explainability and generalizability of demand forecasting methods is the limitation of the influence of temporal confounding, which is prevalent in most state of the art approaches.