pwc-archive/papers-with-abstracts huggingface dataset:
Beam-Space MIMO Radar for Joint Communication and Sensing with OTFS Modulation
Authors: Saeid K. Dehkordi, Lorenzo Gaudio, Mari Kobayashi, Giuseppe Caire, Giulio Colavolpe
Date: 2022-07-12 00:00:00
Tasks: Dimensionality Reduction, parameter estimation

Motivated by automotive applications, we consider joint radar sensing and data communication for a system operating at millimeter wave (mmWave) frequency bands, where a Base Station (BS) is equipped with a co-located radar receiver and sends data using the Orthogonal Time Frequency Space (OTFS) modulation format. We consider two distinct modes of operation. In Discovery mode, a single common data stream is broadcast over a wide angular sector. The radar receiver must detect the presence of not yet acquired targets and perform coarse estimation of their parameters (angle of arrival, range, and velocity). In Tracking mode, the BS transmits multiple individual data streams to already acquired users via beamforming, while the radar receiver performs accurate estimation of the aforementioned parameters. Due to hardware complexity and power consumption constraints, we consider a hybrid digital-analog architecture where the number of RF chains and A/D converters is significantly smaller than the number of antenna array elements. In this case, a direct application of the conventional MIMO radar approach is not possible. Consequently, we advocate a beam-space approach where the vector observation at the radar receiver is obtained through a RF-domain beamforming matrix operating the dimensionality reduction from antennas to RF chains. Under this setup, we propose a likelihood function-based scheme to perform joint target detection and parameter estimation in Discovery, and high-resolution parameter estimation in Tracking mode, respectively. Our numerical results demonstrate that the proposed approach is able to reliably detect multiple targets while closely approaching the Cramer-Rao Lower Bound (CRLB) of the corresponding parameter estimation problem.
Multi-Camera View Based Proactive BS Selection and Beam Switching for V2X
Authors: Bo Lin, Feifei Gao, Yong Zhang, Chengkang Pan, Guangyi Liu
Date: 2022-07-12 00:00:00
Tasks: Multi-Task Learning

Due to the short wavelength and large attenuation of millimeter-wave (mmWave), mmWave BSs are densely distributed and require beamforming with high directivity. When the user moves out of the coverage of the current BS or is severely blocked, the mmWave BS must be switched to ensure the communication quality. In this paper, we proposed a multi-camera view based proactive BS selection and beam switching that can predict the optimal BS of the user in the future frame and switch the corresponding beam pair. Specifically, we extract the features of multi-camera view images and a small part of channel state information (CSI) in historical frames, and dynamically adjust the weight of each modality feature. Then we design a multi-task learning module to guide the network to better understand the main task, thereby enhancing the accuracy and the robustness of BS selection and beam switching. Using the outputs of all tasks, a prior knowledge based fine tuning network is designed to further increase the BS switching accuracy. After the optimal BS is obtained, a beam pair switching network is proposed to directly predict the optimal beam pair of the corresponding BS. Simulation results in an outdoor intersection environment show the superior performance of our proposed solution under several metrics such as predicting accuracy, achievable rate, harmonic mean of precision and recall.
Action potential solitons and waves in axons
Authors: Gaspar Cano, Rui Dilão
Date: 2022-07-12 00:00:00

We show that the action potential signals generated inside axons are reaction-diffusion solitons or reaction-diffusion waves, refuting the Hodgkin and Huxley hypothesis that action potentials propagate along axons with an elastic wave mechanism. Reaction-diffusion action potential wavefronts and solitons annihilate at collision and boundaries of axons, in contrast with elastic waves, where amplitudes add up and reflect at boundaries. We numerically calculate the values of the speed of the action potential spikes and the dispersion relations. These findings suggest several experiments as validating and falsifying tests for the Hodgkin and Huxley model.
Entropy production of Multivariate Ornstein-Uhlenbeck processes correlates with consciousness levels in the human brain
Authors: Matthieu Gilson, Enzo Tagliazucchi, Rodrigo Cofre
Date: 2022-07-11 00:00:00

Consciousness is supported by complex patterns of brain activity which are indicative of irreversible non-equilibrium dynamics. While the framework of stochastic thermodynamics has facilitated the understanding of physical systems of this kind, its application to infer the level of consciousness from empirical data remains elusive. We faced this challenge by calculating entropy production in a multivariate Ornstein-Uhlenbeck process fitted to fMRI brain activity recordings. To test this approach, we focused on the transition from wakefulness to deep sleep, revealing a monotonous relationship between entropy production and the level of consciousness. Our results constitute robust signatures of consciousness while also advancing our understanding of the link between consciousness and complexity from the fundamental perspective of statistical physics.
Inferring and Conveying Intentionality: Beyond Numerical Rewards to Logical Intentions
Authors: Susmit Jha, John Rushby
Date: 2022-07-06 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Shared intentionality is a critical component in developing conscious AI agents capable of collaboration, self-reflection, deliberation, and reasoning. We formulate inference of shared intentionality as an inverse reinforcement learning problem with logical reward specifications. We show how the approach can infer task descriptions from demonstrations. We also extend our approach to actively convey intentionality. We demonstrate the approach on a simple grid-world example.
Verifying Attention Robustness of Deep Neural Networks against Semantic Perturbations
Authors: Satoshi Munakata, Caterina Urban, Haruki Yokoyama, Koji Yamamoto, Kazuki Munakata
Date: 2022-07-13 00:00:00
Tasks: valid

It is known that deep neural networks (DNNs) classify an input image by paying particular attention to certain specific pixels; a graphical representation of the magnitude of attention to each pixel is called a saliency-map. Saliency-maps are used to check the validity of the classification decision basis, e.g., it is not a valid basis for classification if a DNN pays more attention to the background rather than the subject of an image. Semantic perturbations can significantly change the saliency-map. In this work, we propose the first verification method for attention robustness, i.e., the local robustness of the changes in the saliency-map against combinations of semantic perturbations. Specifically, our method determines the range of the perturbation parameters (e.g., the brightness change) that maintains the difference between the actual saliency-map change and the expected saliency-map change below a given threshold value. Our method is based on activation region traversals, focusing on the outermost robust boundary for scalability on larger DNNs. Experimental results demonstrate that our method can show the extent to which DNNs can classify with the same basis regardless of semantic perturbations and report on performance and performance factors of activation region traversals.
dpart: Differentially Private Autoregressive Tabular, a General Framework for Synthetic Data Generation
Authors: Sofiane Mahiou, Kai Xu, Georgi Ganev
Date: 2022-07-12 00:00:00
Tasks: Synthetic Data Generation

We propose a general, flexible, and scalable framework dpart, an open source Python library for differentially private synthetic data generation. Central to the approach is autoregressive modelling -- breaking the joint data distribution to a sequence of lower-dimensional conditional distributions, captured by various methods such as machine learning models (logistic/linear regression, decision trees, etc.), simple histogram counts, or custom techniques. The library has been created with a view to serve as a quick and accessible baseline as well as to accommodate a wide audience of users, from those making their first steps in synthetic data generation, to more experienced ones with domain expertise who can configure different aspects of the modelling and contribute new methods/mechanisms. Specific instances of dpart include Independent, an optimized version of PrivBayes, and a newly proposed model, dp-synthpop. Code: https://github.com/hazy/dpart
Pyramid Transformer for Traffic Sign Detection
Authors: Omid Nejati Manzari, Amin Boudesh, Shahriar B. Shokouhi
Date: 2022-07-13 00:00:00
Tasks: Inductive Bias, Self-Driving Cars, Traffic Sign Detection

Traffic sign detection is a vital task in the visual system of self-driving cars and the automated driving system. Recently, novel Transformer-based models have achieved encouraging results for various computer vision tasks. We still observed that vanilla ViT could not yield satisfactory results in traffic sign detection because the overall size of the datasets is very small and the class distribution of traffic signs is extremely unbalanced. To overcome this problem, a novel Pyramid Transformer with locality mechanisms is proposed in this paper. Specifically, Pyramid Transformer has several spatial pyramid reduction layers to shrink and embed the input image into tokens with rich multi-scale context by using atrous convolutions. Moreover, it inherits an intrinsic scale invariance inductive bias and is able to learn local feature representation for objects at various scales, thereby enhancing the network robustness against the size discrepancy of traffic signs. The experiments are conducted on the German Traffic Sign Detection Benchmark (GTSDB). The results demonstrate the superiority of the proposed model in the traffic sign detection tasks. More specifically, Pyramid Transformer achieves 77.8% mAP on GTSDB when applied to the Cascade RCNN as the backbone, which surpasses most well-known and widely-used state-of-the-art models.
Reward-Sharing Relational Networks in Multi-Agent Reinforcement Learning as a Framework for Emergent Behavior
Authors: Hossein Haeri, Reza Ahmadzadeh, Kshitij Jerath
Date: 2022-07-12 00:00:00
Tasks: Multi-agent Reinforcement Learning, Sociology

In this work, we integrate `social' interactions into the MARL setup through a user-defined relational network and examine the effects of agent-agent relations on the rise of emergent behaviors. Leveraging insights from sociology and neuroscience, our proposed framework models agent relationships using the notion of Reward-Sharing Relational Networks (RSRN), where network edge weights act as a measure of how much one agent is invested in the success of (or `cares about') another. We construct relational rewards as a function of the RSRN interaction weights to collectively train the multi-agent system via a multi-agent reinforcement learning algorithm. The performance of the system is tested for a 3-agent scenario with different relational network structures (e.g., self-interested, communitarian, and authoritarian networks). Our results indicate that reward-sharing relational networks can significantly influence learned behaviors. We posit that RSRN can act as a framework where different relational networks produce distinct emergent behaviors, often analogous to the intuited sociological understanding of such networks.
Using Hashtags to Analyze Purpose and Technology Application of Open-Source Project Related to COVID-19
Authors: Liang Tian, Chengzhi Zhang
Date: 2022-07-03 00:00:00

COVID-19 has had a profound impact on the lives of all human beings. Emerging technologies have made significant contributions to the fight against the pandemic. An extensive review of the application of technology will help facilitate future research and technology development to provide better solutions for future pandemics. In contrast to the extensive surveys of academic communities that have already been conducted, this study explores the IT community of practice. Using GitHub as the study target, we analyzed the main functionalities of the projects submitted during the pandemic. This study examines trends in projects with different functionalities and the relationship between functionalities and technologies. The study results show an imbalance in the number of projects with varying functionalities in the GitHub community, i.e., applications account for more than half of the projects. In contrast, other data analysis and AI projects account for a smaller share. This differs significantly from the survey of the academic community, where the findings focus more on cutting-edge technologies while projects in the community of practice use more mature technologies. The spontaneous behavior of developers may lack organization and make it challenging to target needs.
Image warp preserving content intensity
Authors: Enrico Segre
Date: 2022-07-13 00:00:00

An accurate method for warping images is presented. Differently from most commonly used techniques, this method guarantees the conservation of the intensity of the transformed image, evaluated as the sum of its pixel values over the whole image or over corresponding transformed subregions of it. Such property is mandatory for quantitative analysis, as, for instance, when deformed images are used to assess radiances, to measure optical fluxes from light sources, or to characterize material optical densities. The proposed method enforces area resampling by decomposing each rectangular pixel in two triangles, and projecting the pixel intensity onto half pixels of the transformed image, with weights proportional to the area of overlap of the triangular half-pixels. The result is quantitatively exact, as long as the original pixel value is assumed to represent a constant image density within the pixel area, and as long as the coordinate transformation is diffeomorphic. Implementation details and possible variations of the method are discussed.
Masked Autoencoders that Listen
Authors: Po-Yao Huang, Hu Xu, Juncheng Li, Alexei Baevski, Michael Auli, Wojciech Galuba, Florian Metze, Christoph Feichtenhofer
Date: 2022-07-13 00:00:00
Tasks: Audio Classification, Decoder, Representation Learning, Speaker Identification

This paper studies a simple extension of image-based Masked Autoencoders (MAE) to self-supervised representation learning from audio spectrograms. Following the Transformer encoder-decoder design in MAE, our Audio-MAE first encodes audio spectrogram patches with a high masking ratio, feeding only the non-masked tokens through encoder layers. The decoder then re-orders and decodes the encoded context padded with mask tokens, in order to reconstruct the input spectrogram. We find it beneficial to incorporate local window attention in the decoder, as audio spectrograms are highly correlated in local time and frequency bands. We then fine-tune the encoder with a lower masking ratio on target datasets. Empirically, Audio-MAE sets new state-of-the-art performance on six audio and speech classification tasks, outperforming other recent models that use external supervised pre-training. The code and models will be at https://github.com/facebookresearch/AudioMAE.
3D Concept Grounding on Neural Fields
Authors: Yining Hong, Yilun Du, Chunru Lin, Joshua B. Tenenbaum, Chuang Gan
Date: 2022-07-13 00:00:00
Tasks: Instance Segmentation, Question Answering, Segmentation, Semantic Segmentation, Visual Reasoning

In this paper, we address the challenging problem of 3D concept grounding (i.e. segmenting and learning visual concepts) by looking at RGBD images and reasoning about paired questions and answers. Existing visual reasoning approaches typically utilize supervised methods to extract 2D segmentation masks on which concepts are grounded. In contrast, humans are capable of grounding concepts on the underlying 3D representation of images. However, traditionally inferred 3D representations (e.g., point clouds, voxelgrids, and meshes) cannot capture continuous 3D features flexibly, thus making it challenging to ground concepts to 3D regions based on the language description of the object being referred to. To address both issues, we propose to leverage the continuous, differentiable nature of neural fields to segment and learn concepts. Specifically, each 3D coordinate in a scene is represented as a high-dimensional descriptor. Concept grounding can then be performed by computing the similarity between the descriptor vector of a 3D coordinate and the vector embedding of a language concept, which enables segmentations and concept learning to be jointly learned on neural fields in a differentiable fashion. As a result, both 3D semantic and instance segmentations can emerge directly from question answering supervision using a set of defined neural operators on top of neural fields (e.g., filtering and counting). Experimental results show that our proposed framework outperforms unsupervised/language-mediated segmentation models on semantic and instance segmentation tasks, as well as outperforms existing models on the challenging 3D aware visual reasoning tasks. Furthermore, our framework can generalize well to unseen shape categories and real scans.
Re2G: Retrieve, Rerank, Generate
Authors: Michael Glass, Gaetano Rossiello, Md Faisal Mahbub Chowdhury, Ankita Rajaram Naik, Pengshan Cai, Alfio Gliozzo
Date: 2022-07-13 00:00:00
Proceeding: NAACL 2022 7
Tasks: Fact Checking, Fact Verification, GPU, Knowledge Distillation, Open-Domain Dialog, Open-Domain Question Answering, Question Answering, RAG, Reranking, Retrieval, Slot Filling, Zero-shot Slot Filling

As demonstrated by GPT-3 and T5, transformers grow in capability as parameter spaces become larger and larger. However, for tasks that require a large amount of knowledge, non-parametric memory allows models to grow dramatically with a sub-linear increase in computational cost and GPU memory requirements. Recent models such as RAG and REALM have introduced retrieval into conditional generation. These models incorporate neural initial retrieval from a corpus of passages. We build on this line of research, proposing Re2G, which combines both neural initial retrieval and reranking into a BART-based sequence-to-sequence generation. Our reranking approach also permits merging retrieval results from sources with incomparable scores, enabling an ensemble of BM25 and neural initial retrieval. To train our system end-to-end, we introduce a novel variation of knowledge distillation to train the initial retrieval, reranker, and generation using only ground truth on the target sequence output. We find large gains in four diverse tasks: zero-shot slot filling, question answering, fact-checking, and dialog, with relative gains of 9% to 34% over the previous state-of-the-art on the KILT leaderboard. We make our code available as open source at https://github.com/IBM/kgi-slot-filling/tree/re2g.
Reinforcement Learning Assisted Recursive QAOA
Authors: Yash J. Patel, Sofiene Jerbi, Thomas Bäck, Vedran Dunjko
Date: 2022-07-13 00:00:00
Tasks: Combinatorial Optimization, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Variational quantum algorithms such as the Quantum Approximation Optimization Algorithm (QAOA) in recent years have gained popularity as they provide the hope of using NISQ devices to tackle hard combinatorial optimization problems. It is, however, known that at low depth, certain locality constraints of QAOA limit its performance. To go beyond these limitations, a non-local variant of QAOA, namely recursive QAOA (RQAOA), was proposed to improve the quality of approximate solutions. The RQAOA has been studied comparatively less than QAOA, and it is less understood, for instance, for what family of instances it may fail to provide high quality solutions. However, as we are tackling $\mathsf{NP}$-hard problems (specifically, the Ising spin model), it is expected that RQAOA does fail, raising the question of designing even better quantum algorithms for combinatorial optimization. In this spirit, we identify and analyze cases where RQAOA fails and, based on this, propose a reinforcement learning enhanced RQAOA variant (RL-RQAOA) that improves upon RQAOA. We show that the performance of RL-RQAOA improves over RQAOA: RL-RQAOA is strictly better on these identified instances where RQAOA underperforms, and is similarly performing on instances where RQAOA is near-optimal. Our work exemplifies the potentially beneficial synergy between reinforcement learning and quantum (inspired) optimization in the design of new, even better heuristics for hard problems.
DiverGet: A Search-Based Software Testing Approach for Deep Neural Network Quantization Assessment
Authors: Ahmed Haj Yahmed, Houssem Ben Braiek, Foutse khomh, Sonia Bouzidi, Rania Zaatour
Date: 2022-07-13 00:00:00
Tasks: Astronomy, Quantization, software testing

Quantization is one of the most applied Deep Neural Network (DNN) compression strategies, when deploying a trained DNN model on an embedded system or a cell phone. This is owing to its simplicity and adaptability to a wide range of applications and circumstances, as opposed to specific Artificial Intelligence (AI) accelerators and compilers that are often designed only for certain specific hardware (e.g., Google Coral Edge TPU). With the growing demand for quantization, ensuring the reliability of this strategy is becoming a critical challenge. Traditional testing methods, which gather more and more genuine data for better assessment, are often not practical because of the large size of the input space and the high similarity between the original DNN and its quantized counterpart. As a result, advanced assessment strategies have become of paramount importance. In this paper, we present DiverGet, a search-based testing framework for quantization assessment. DiverGet defines a space of metamorphic relations that simulate naturally-occurring distortions on the inputs. Then, it optimally explores these relations to reveal the disagreements among DNNs of different arithmetic precision. We evaluate the performance of DiverGet on state-of-the-art DNNs applied to hyperspectral remote sensing images. We chose the remote sensing DNNs as they're being increasingly deployed at the edge (e.g., high-lift drones) in critical domains like climate change research and astronomy. Our results show that DiverGet successfully challenges the robustness of established quantization techniques against naturally-occurring shifted data, and outperforms its most recent concurrent, DiffChaser, with a success rate that is (on average) four times higher.
ACLNet: An Attention and Clustering-based Cloud Segmentation Network
Authors: Dhruv Makwana, Subhrajit Nag, Onkar Susladkar, Gayatri Deshmukh, Sai Chandra Teja R, Sparsh Mittal, C Krishna Mohan
Date: 2022-07-13 00:00:00
Tasks: Clustering, Segmentation, Semantic Segmentation

We propose a novel deep learning model named ACLNet, for cloud segmentation from ground images. ACLNet uses both deep neural network and machine learning (ML) algorithm to extract complementary features. Specifically, it uses EfficientNet-B0 as the backbone, "`a trous spatial pyramid pooling" (ASPP) to learn at multiple receptive fields, and "global attention module" (GAM) to extract finegrained details from the image. ACLNet also uses k-means clustering to extract cloud boundaries more precisely. ACLNet is effective for both daytime and nighttime images. It provides lower error rate, higher recall and higher F1-score than state-of-art cloud segmentation models. The source-code of ACLNet is available here: https://github.com/ckmvigil/ACLNet.