pwc-archive/papers-with-abstracts huggingface dataset:
A Co-design view of Compute in-Memory with Non-Volatile Elements for Neural Networks
Authors: Wilfried Haensch, Anand Raghunathan, Kaushik Roy, Bhaswar Chakrabart, Charudatta M. Phatak, Cheng Wang, Supratik Guha
Date: 2022-06-03 00:00:00

Deep Learning neural networks are pervasive, but traditional computer architectures are reaching the limits of being able to efficiently execute them for the large workloads of today. They are limited by the von Neumann bottleneck: the high cost in energy and latency incurred in moving data between memory and the compute engine. Today, special CMOS designs address this bottleneck. The next generation of computing hardware will need to eliminate or dramatically mitigate this bottleneck. We discuss how compute-in-memory can play an important part in this development. Here, a non-volatile memory based cross-bar architecture forms the heart of an engine that uses an analog process to parallelize the matrix vector multiplication operation, repeatedly used in all neural network workloads. The cross-bar architecture, at times referred to as a neuromorphic approach, can be a key hardware element in future computing machines. In the first part of this review we take a co-design view of the design constraints and the demands it places on the new materials and memory devices that anchor the cross-bar architecture. In the second part, we review what is knows about the different new non-volatile memory materials and devices suited for compute in-memory, and discuss the outlook and challenges.
k-Sliced Mutual Information: A Quantitative Study of Scalability with Dimension
Authors: Ziv Goldfeld, Kristjan Greenewald, Theshani Nuradha, Galen Reeves
Date: 2022-06-17 00:00:00

Sliced mutual information (SMI) is defined as an average of mutual information (MI) terms between one-dimensional random projections of the random variables. It serves as a surrogate measure of dependence to classic MI that preserves many of its properties but is more scalable to high dimensions. However, a quantitative characterization of how SMI itself and estimation rates thereof depend on the ambient dimension, which is crucial to the understanding of scalability, remain obscure. This work provides a multifaceted account of the dependence of SMI on dimension, under a broader framework termed $k$-SMI, which considers projections to $k$-dimensional subspaces. Using a new result on the continuity of differential entropy in the 2-Wasserstein metric, we derive sharp bounds on the error of Monte Carlo (MC)-based estimates of $k$-SMI, with explicit dependence on $k$ and the ambient dimension, revealing their interplay with the number of samples. We then combine the MC integrator with the neural estimation framework to provide an end-to-end $k$-SMI estimator, for which optimal convergence rates are established. We also explore asymptotics of the population $k$-SMI as dimension grows, providing Gaussian approximation results with a residual that decays under appropriate moment bounds. All our results trivially apply to SMI by setting $k=1$. Our theory is validated with numerical experiments and is applied to sliced InfoGAN, which altogether provide a comprehensive quantitative account of the scalability question of $k$-SMI, including SMI as a special case when $k=1$.
Generalised Bayesian Inference for Discrete Intractable Likelihood
Authors: Takuo Matsubara, Jeremias Knoblauch, François-Xavier Briol, Chris. J. Oates
Date: 2022-06-16 00:00:00
Tasks: Bayesian Inference

Discrete state spaces represent a major computational challenge to statistical inference, since the computation of normalisation constants requires summation over large or possibly infinite sets, which can be impractical. This paper addresses this computational challenge through the development of a novel generalised Bayesian inference procedure suitable for discrete intractable likelihood. Inspired by recent methodological advances for continuous data, the main idea is to update beliefs about model parameters using a discrete Fisher divergence, in lieu of the problematic intractable likelihood. The result is a generalised posterior that can be sampled from using standard computational tools, such as Markov chain Monte Carlo, circumventing the intractable normalising constant. The statistical properties of the generalised posterior are analysed, with sufficient conditions for posterior consistency and asymptotic normality established. In addition, a novel and general approach to calibration of generalised posteriors is proposed. Applications are presented on lattice models for discrete spatial data and on multivariate models for count data, where in each case the methodology facilitates generalised Bayesian inference at low computational cost.
CLEAR: A Fully User-side Image Search System
Authors: Ryoma Sato
Date: 2022-06-17 00:00:00
Tasks: Image Retrieval, Privacy Preserving

We use many search engines on the Internet in our daily lives. However, they are not perfect. Their scoring function may not model our intent or they may accept only text queries even though we want to carry out a similar image search. In such cases, we need to make a compromise: We continue to use the unsatisfactory service or leave the service. Recently, a new solution, user-side search systems, has been proposed. In this framework, each user builds their own search system that meets their preference with a user-defined scoring function and user-defined interface. Although the concept is appealing, it is still not clear if this approach is feasible in practice. In this demonstration, we show the first fully user-side image search system, CLEAR, which realizes a similar-image search engine for Flickr. The challenge is that Flickr does not provide an official similar image search engine or corresponding API. Nevertheless, CLEAR realizes it fully on a user-side. CLEAR does not use a backend server at all nor store any images or build search indices. It is in contrast to traditional search algorithms that require preparing a backend server and building a search index. Therefore, each user can easily deploy their own CLEAR engine, and the resulting service is custom-made and privacy-preserving. The online demo is available at https://clear.joisino.net. The source code is available at https://github.com/joisino/clear.
Model-based selection of most informative diagnostic tests and test parameters
Authors: Sven Herrmann, Mathias Dietz
Date: 2021-12-10 00:00:00
Proceeding: Acta Acustica 2021 12
Tasks: Diagnostic

Given the complexity of most brain and body processes, it is often not possible to relate experimental data from an individual to the underlying subject-specific physiology or pathology. Computer simulations of these processes have been suggested to assist in establishing such a relation. However, the aforementioned complexity and required simulation accuracy impose considerable challenges. To date, the best-case scenario is varying the model parameters to fit previously recorded experimental data. Confidence intervals can be given in the units of the data, but usually not for the model parameters that are the ultimate interest of the diagnosis. We propose a likelihood-based fitting procedure, operating in the model-parameter space and providing confidence intervals for the parameters under diagnosis. The procedure is capable of running parallel to the measurement, and can adaptively set test parameters to the values that are expected to provide the most diagnostic information. Using the pre-defined acceptable confidence interval, the experiment continues until the goal is reached. As an example, the approach was tested with a simplistic three-parameter auditory model and a psychoacoustic binaural tone in a noise-detection experiment. For a given number of trials, the model-based measurement steering provided 80% more information.
Is decentralized finance actually decentralized? A social network analysis of the Aave protocol on the Ethereum blockchain
Authors: Ziqiao Ao, Lin William Cong, Gergely Horvath, Luyao Zhang
Date: 2022-06-16 00:00:00

Decentralized finance (DeFi) has the potential to disrupt centralized finance by validating peer-to-peer transactions through tamper-proof smart contracts, thus significantly lowering the transaction cost charged by financial intermediaries. However, the actual realization of peer-to-peer transactions and the levels and effects of decentralization are largely unknown. Our research pioneers a blockchain network study that applies social network analysis to measure the level, dynamics, and impacts of decentralization in DeFi token transactions on the Ethereum blockchain. First, we find a significant core-periphery structure in the AAVE token transaction network where the cores include the two largest centralized crypto exchanges. Second, we provide evidence that multiple network features consistently characterize decentralization dynamics. Finally, we document that a more decentralized network significantly predicts a higher return and lower volatility of the decentralized market of AAVE tokens on the Ethereum blockchain. We point out that our approach is seminal for inspiring future extensions related to the facets of application scenarios, research questions, and methodologies on the mechanics of blockchain decentralization.
Entity Resolution with Hierarchical Graph Attention Networks
Authors: Dezhong Yao, Yuhong Gu, Gao Cong, Hai Jin, Xinqiao Lv
Date: 2022-06-01 00:00:00
Proceeding: SIGMOD/PODS 2022 6
Tasks: Attribute, Entity Resolution, Graph Attention, Word Embeddings

Entity Resolution (ER) links entities that refer to the same real-world entity from different sources. Existing work usually takes pairs of entities as input and judges those pairs independently. However, there is often interdependence between different pairs of ER decisions, e.g., the entities from the same data source are usually semantically related to each other. Furthermore, current ER approaches are mainly based on attribute similarity comparison, but ignore interdependence between attributes. To address the limits of existing methods, we propose HierGAT, a new method for ER based on a Hierarchical Graph Attention Transformer Network, which can model and exploit the interdependence between different ER decisions. The benefit of our method comes from: 1) The graph attention network model for joint ER decisions; 2) The graph-attention capability to identify the discriminative words from attributes and find the most discriminative attributes. Furthermore, we propose to learn contextual embeddings to enrich word embeddings for better performance. The experimental results on publicly available benchmark datasets show that HierGAT outperforms DeepMatcher by up to 32.5% of F1 score and up to 8.7% of F1 score compared with Ditto.
Domain Adaptation for Deep Entity Resolution: A Design Space Exploration
Authors: Jianhong Tu, Ju Fan, Nan Tang, Peng Wang, Chengliang Chai, Guoliang Li, Ruixue Fan, Xiaoyong Du
Date: 2022-06-01 00:00:00
Proceeding: SIGMOD/PODS 2022 6
Tasks: Data Integration, Domain Adaptation, Entity Resolution

Entity resolution (ER) is a core problem of data integration. The state-of-the-art (SOTA) results on ER are achieved by deep learning (DL) based methods, trained with a lot of labeled matching/non-matching entity pairs. This may not be a problem when using well-prepared benchmark datasets. Nevertheless, for many real-world ER applications, the situation changes dramatically, with a painful issue to collect large-scale labeled datasets. In this paper, we seek to answer: If we have a well-labeled source ER dataset, can we train a DL-based ER model for a target dataset, without any labels or with a few labels? This is known as domain adaptation (DA), which has achieved great successes in computer vision and natural language processing, but is not systematically studied for ER. Our goal is to systematically explore the benefits and limitations of a wide range of DA methods for ER. To this purpose, we develop a DADER (Domain Adaptation for Deep Entity Resolution) framework that significantly advances ER in applying DA. We define a space of design solutions for the three modules of DADER, namely Feature Extractor, Matcher, and Feature Aligner. We conduct so far the most comprehensive experimental study to explore the design space and compare different choices of DA for ER. We provide guidance for selecting appropriate design solutions based on extensive experiments.
Image Classification With Small Datasets: Overview and Benchmark
Authors: Lorenzo Brigato, Björn Barz, Luca Iocchi, Joachim Denzler
Date: 2022-05-05 00:00:00
Proceeding: IEEE Access 2022 5
Tasks: Classification, image-classification, Image Classification, Small Data Image Classification

Image classification with small datasets has been an active research area in the recent past.
However, as research in this scope is still in its infancy, two key ingredients are missing for ensuring
reliable and truthful progress: a systematic and extensive overview of the state of the art, and a common
benchmark to allow for objective comparisons between published methods. This article addresses both
issues. First, we systematically organize and connect past studies to consolidate a community that is currently
fragmented and scattered. Second, we propose a common benchmark that allows for an objective comparison
of approaches. It consists of five datasets spanning various domains (e.g., natural images, medical imagery,
satellite data) and data types (RGB, grayscale, multispectral). We use this benchmark to re-evaluate the
standard cross-entropy baseline and ten existing methods published between 2017 and 2021 at renowned
venues. Surprisingly, we find that thorough hyper-parameter tuning on held-out validation data results in a
highly competitive baseline and highlights a stunted growth of performance over the years. Indeed, only a
single specialized method dating back to 2019 clearly wins our benchmark and outperforms the baseline
classifier.
Machine Learning-Driven Process of Alumina Ceramics Laser Machining
Authors: Razyeh Behbahani, Hamidreza Yazdani Sarvestani, Erfan Fatehi, Elham Kiyani, Behnam Ashrafi, Mikko Karttunen, Meysam Rahmat
Date: 2022-06-13 00:00:00
Tasks: BIG-bench Machine Learning

Laser machining is a highly flexible non-contact manufacturing technique that has been employed widely across academia and industry. Due to nonlinear interactions between light and matter, simulation methods are extremely crucial, as they help enhance the machining quality by offering comprehension of the inter-relationships between the laser processing parameters. On the other hand, experimental processing parameter optimization recommends a systematic, and consequently time-consuming, investigation over the available processing parameter space. An intelligent strategy is to employ machine learning (ML) techniques to capture the relationship between picosecond laser machining parameters for finding proper parameter combinations to create the desired cuts on industrial-grade alumina ceramic with deep, smooth and defect-free patterns. Laser parameters such as beam amplitude and frequency, scanner passing speed and the number of passes over the surface, as well as the vertical distance of the scanner from the sample surface, are used for predicting the depth, top width, and bottom width of the engraved channels using ML models. Owing to the complex correlation between laser parameters, it is shown that Neural Networks (NN) are the most efficient in predicting the outputs. Equipped with an ML model that captures the interconnection between laser parameters and the engraved channel dimensions, one can predict the required input parameters to achieve a target channel geometry. This strategy significantly reduces the cost and effort of experimental laser machining during the development phase, without compromising accuracy or performance. The developed techniques can be applied to a wide range of ceramic laser machining processes.
ReViSe: Remote Vital Signs Measurement Using Smartphone Camera
Authors: Donghao Qiao, Amtul Haq Ayesha, Farhana Zulkernine, Raihan Masroor, Nauman Jaffar
Date: 2022-06-13 00:00:00
Tasks: Heart Rate Variability

We propose an end-to-end framework to measure people's vital signs including Heart Rate (HR), Heart Rate Variability (HRV), Oxygen Saturation (SpO2) and Blood Pressure (BP) based on the rPPG methodology from the video of a user's face captured with a smartphone camera. We extract face landmarks with a deep learning-based neural network model in real-time. Multiple face patches also called Regions-of-Interest (RoIs) are extracted by using the predicted face landmarks. Several filters are applied to reduce the noise from the RoIs in the extracted cardiac signals called Blood Volume Pulse (BVP) signal. The measurements of HR, HRV and SpO2 are validated on two public rPPG datasets namely the TokyoTech rPPG and the Pulse Rate Detection (PURE) datasets, on which our models achieved the following Mean Absolute Errors (MAE): a) for HR, 1.73Beats-Per-Minute (bpm) and 3.95bpm respectively; b) for HRV, 18.55ms and 25.03ms respectively, and c) for SpO2, an MAE of 1.64% on the PURE dataset. We validated our end-to-end rPPG framework, ReViSe, in daily living environment, and thereby created the Video-HR dataset. Our HR estimation model achieved an MAE of 2.49bpm on this dataset. Since no publicly available rPPG datasets existed for BP measurement with face videos, we used a dataset with signals from fingertip sensor to train our deep learning-based BP estimation model and also created our own video dataset, Video-BP. On our Video-BP dataset, our BP estimation model achieved an MAE of 6.7mmHg for Systolic Blood Pressure (SBP), and an MAE of 9.6mmHg for Diastolic Blood Pressure (DBP). ReViSe framework has been validated on datasets with videos recorded in daily living environment as opposed to less noisy laboratory environment as reported by most state-of-the-art techniques.
Secure Wireless Transmission for Reconfigurable Intelligent Surface Aided Full Duplex Systems
Authors: Pengxin Guan, Yiru Wang, Yuping Zhao
Date: 2022-06-17 00:00:00

This letter considers the secure communication in a reconfigurable intelligent surface (RIS) aided full duplex (FD) system. An FD base station (BS) serves an uplink (UL) user and a downlink (DL) user simultaneously over the same time-frequency dimension assisted by a RIS in the presence of an eavesdropper. In addition, the artificial noise (AN) is also applied to interfere the eavesdropper's channel. We aim to maximize the sum secrecy rate of UL and DL users by jointly optimizing the transmit beamforming, receive beamforming and AN covariance matrix at the BS, and passive beamforming at the RIS. To handle the non-convex problem, we decompose it into tractable subproblems and propose an efficient algorithm based on alternating optimization framework. Specifically, the receive beamforming is derived as a closed-form solution while other variables are obtained by using semidefinite relaxation (SDR) method and successive convex approximation (SCA) algorithm. Simulation results demonstrate the superior performance of our proposed scheme compared to other baseline schemes.
Resolution Limits of Non-Adaptive 20 Questions Search for a Moving Target
Authors: Lin Zhou, Alfred Hero
Date: 2022-06-14 00:00:00

Using the 20 questions estimation framework with query-dependent noise, we study non-adaptive search strategies for a moving target over the unit cube with unknown initial location and velocities under a piecewise constant velocity model. In this search problem, there is an oracle who knows the instantaneous location of the target at any time. Our task is to query the oracle as few times as possible to accurately estimate the location of the target at any specified time. We first study the case where the oracle's answer to each query is corrupted by discrete noise and then generalize our results to the case of additive white Gaussian noise. In our formulation, the performance criterion is the resolution, which is defined as the maximal $L_\infty$ distance between the true locations and estimated locations. We characterize the minimal resolution of an optimal non-adaptive query procedure with a finite number of queries by deriving non-asymptotic and asymptotic bounds. Our bounds are tight in the first-order asymptotic sense when the number of queries satisfies a certain condition and our bounds are tight in the stronger second-order asymptotic sense when the target moves with a constant velocity. To prove our results, we relate the current problem to channel coding, borrow ideas from finite blocklength information theory and construct bounds on the number of possible quantized target trajectories.
Information Geometry of Risks and Returns
Authors: Andrei N. Soklakov
Date: 2022-06-09 00:00:00

We reveal a geometric structure underlying both hedging and investment products. The structure follows from a simple formula expressing investment risks in terms of returns. This informs optimal product designs. Optimal pure hedging (including cost-optimal products) and hybrid hedging (where a partial hedge is built into an optimal investment product) are considered. Duality between hedging and investment is demonstrated with applications to optimal risk recycling. A geometric interpretation of rationality is presented.
Consensus Formation Tracking for Multiple AUV Systems Using Distributed Bioinspired Sliding Mode Control
Authors: Tao Yan, Zhe Xu, Simon X. Yang
Date: 2022-06-16 00:00:00

Consensus formation tracking of multiple autonomous underwater vehicles (AUVs) subject to nonlinear and uncertain dynamics is a challenging problem in robotics. To tackle this challenge, a distributed bioinspired sliding mode controller is proposed in this paper. First, the conventional sliding mode controller (SMC) is presented, and the consensus problem is addressed on the basis of graph theory. Next, to tackle the high frequency chattering issue in SMC scheme and meanwhile improve the robustness to the noises, a bioinspired approach is introduced, in which a neural dynamic model is employed to replace the nonlinear sign or saturation function in the synthesis of conventional sliding mode controllers. Furthermore, the input-to-state stability of the resulting closed-loop system is proved in the presence of bounded lumped disturbance by the Lyapunov stability theory. Finally, simulation experiments are conducted to demonstrate the effectiveness of the proposed distributed formation control protocol.
Control Barrier Functionals: Safety-critical Control for Time Delay Systems
Authors: Adam K. Kiss, Tamas G. Molnar, Aaron D. Ames, Gabor Orosz
Date: 2022-06-16 00:00:00

This work presents a theoretical framework for the safety-critical control of time delay systems. The theory of control barrier functions, that provides formal safety guarantees for delay-free systems, is extended to systems with state delay. The notion of control barrier functionals is introduced to attain formal safety guarantees, by enforcing the forward invariance of safe sets defined in the infinite dimensional state space. The proposed framework is able to handle multiple delays and distributed delays both in the dynamics and in the safety condition, and provides an affine constraint on the control input that yields provable safety. This constraint can be incorporated into optimization problems to synthesize pointwise optimal and provable safe controllers. The applicability of the proposed method is demonstrated by numerical simulation examples.