pwc-archive/papers-with-abstracts huggingface dataset:
Addressing Issues of Cross-Linguality in Open-Retrieval Question Answering Systems For Emergent Domains
Authors: Alon Albalak, Sharon Levy, William Yang Wang
Date: 2022-01-26 00:00:00
Tasks: Articles, Question Answering, Retrieval, Translation

Open-retrieval question answering systems are generally trained and tested on large datasets in well-established domains. However, low-resource settings such as new and emerging domains would especially benefit from reliable question answering systems. Furthermore, multilingual and cross-lingual resources in emergent domains are scarce, leading to few or no such systems. In this paper, we demonstrate a cross-lingual open-retrieval question answering system for the emergent domain of COVID-19. Our system adopts a corpus of scientific articles to ensure that retrieved documents are reliable. To address the scarcity of cross-lingual training data in emergent domains, we present a method utilizing automatic translation, alignment, and filtering to produce English-to-all datasets. We show that a deep semantic retriever greatly benefits from training on our English-to-all data and significantly outperforms a BM25 baseline in the cross-lingual setting. We illustrate the capabilities of our system with examples and release all code necessary to train and deploy such a system.
OntoProtein: Protein Pretraining With Gene Ontology Embedding
Authors: Ningyu Zhang, Zhen Bi, Xiaozhuan Liang, Siyuan Cheng, Haosen Hong, Shumin Deng, Jiazhang Lian, Qiang Zhang, Huajun Chen
Date: 2022-01-23 00:00:00
Proceeding: ICLR 2022 4
Tasks: Contrastive Learning, Knowledge Graphs, Ontology Embedding, Protein Function Prediction

Self-supervised protein language models have proved their effectiveness in learning the proteins representations. With the increasing computational power, current protein language models pre-trained with millions of diverse sequences can advance the parameter scale from million-level to billion-level and achieve remarkable improvement. However, those prevailing approaches rarely consider incorporating knowledge graphs (KGs), which can provide rich structured knowledge facts for better protein representations. We argue that informative biology knowledge in KGs can enhance protein representation with external knowledge. In this work, we propose OntoProtein, the first general framework that makes use of structure in GO (Gene Ontology) into protein pre-training models. We construct a novel large-scale knowledge graph that consists of GO and its related proteins, and gene annotation texts or protein sequences describe all nodes in the graph. We propose novel contrastive learning with knowledge-aware negative sampling to jointly optimize the knowledge graph and protein embedding during pre-training. Experimental results show that OntoProtein can surpass state-of-the-art methods with pre-trained protein language models in TAPE benchmark and yield better performance compared with baselines in protein-protein interaction and protein function prediction. Code and datasets are available in https://github.com/zjunlp/OntoProtein.
GUDN: A novel guide network with label reinforcement strategy for extreme multi-label text classification
Authors: Qing Wang, Jia Zhu, Hongji Shu, Kwame Omono Asamoah, Jianyang Shi, Cong Zhou
Date: 2022-01-10 00:00:00
Tasks: 4k, Multi Label Text Classification, Multi-Label Text Classification, text-classification, Text Classification

In natural language processing, extreme multi-label text classification is an emerging but essential task. The problem of extreme multi-label text classification (XMTC) is to recall some of the most relevant labels for a text from an extremely large label set. Large-scale pre-trained models have brought a new trend to this problem. Though the large-scale pre-trained models have made significant achievements on this problem, the valuable fine-tuned methods have yet to be studied. Though label semantics have been introduced in XMTC, the vast semantic gap between texts and labels has yet to gain enough attention. This paper builds a new guide network (GUDN) to help fine-tune the pre-trained model to instruct classification later. Furthermore, GUDN uses raw label semantics combined with a helpful label reinforcement strategy to effectively explore the latent space between texts and labels, narrowing the semantic gap, which can further improve predicted accuracy. Experimental results demonstrate that GUDN outperforms state-of-the-art methods on Eurlex-4k and has competitive results on other popular datasets. In an additional experiment, we investigated the input lengths' influence on the Transformer-based model's accuracy. Our source code is released at https://t.hk.uy/aFSH.
Error-driven Input Modulation: Solving the Credit Assignment Problem without a Backward Pass
Authors: Giorgia Dellaferrera, Gabriel Kreiman
Date: 2022-01-27 00:00:00

Supervised learning in artificial neural networks typically relies on backpropagation, where the weights are updated based on the error-function gradients and sequentially propagated from the output layer to the input layer. Although this approach has proven effective in a wide domain of applications, it lacks biological plausibility in many regards, including the weight symmetry problem, the dependence of learning on non-local signals, the freezing of neural activity during error propagation, and the update locking problem. Alternative training schemes have been introduced, including sign symmetry, feedback alignment, and direct feedback alignment, but they invariably rely on a backward pass that hinders the possibility of solving all the issues simultaneously. Here, we propose to replace the backward pass with a second forward pass in which the input signal is modulated based on the error of the network. We show that this novel learning rule comprehensively addresses all the above-mentioned issues and can be applied to both fully connected and convolutional models. We test this learning rule on MNIST, CIFAR-10, and CIFAR-100. These results help incorporate biological principles into machine learning.
On the Mitigation of Read Disturbances in Neuromorphic Inference Hardware
Authors: Ankita Paul, Shihao Song, Twisha Titirsha, Anup Das
Date: 2022-01-27 00:00:00

Non-Volatile Memory (NVM) cells are used in neuromorphic hardware to store model parameters, which are programmed as resistance states. NVMs suffer from the read disturb issue, where the programmed resistance state drifts upon repeated access of a cell during inference. Resistance drifts can lower the inference accuracy. To address this, it is necessary to periodically reprogram model parameters (a high overhead operation). We study read disturb failures of an NVM cell. Our analysis show both a strong dependency on model characteristics such as synaptic activation and criticality, and on the voltage used to read resistance states during inference. We propose a system software framework to incorporate such dependencies in programming model parameters on NVM cells of a neuromorphic hardware. Our framework consists of a convex optimization formulation which aims to implement synaptic weights that have more activations and are critical, i.e., those that have high impact on accuracy on NVM cells that are exposed to lower voltages during inference. In this way, we increase the time interval between two consecutive reprogramming of model parameters. We evaluate our system software with many emerging inference models on a neuromorphic hardware simulator and show a significant reduction in the system overhead.
Change Detection of Markov Kernels with Unknown Pre and Post Change Kernel
Authors: Hao Chen, Jiacheng Tang, Abhishek Gupta
Date: 2022-01-27 00:00:00
Tasks: Change Detection

In this paper, we develop a new change detection algorithm for detecting a change in the Markov kernel over a metric space in which the post-change kernel is unknown. Under the assumption that the pre- and post-change Markov kernel is uniformly ergodic, we derive an upper bound on the mean delay and a lower bound on the mean time between false alarms. A numerical simulation is provided to demonstrate the effectiveness of our method.
The Role of Reusable and Single-Use Side Information in Private Information Retrieval
Authors: Anoosheh Heidarzadeh, Alex Sprintson
Date: 2022-01-27 00:00:00
Tasks: Information Retrieval, Retrieval

This paper introduces the problem of Private Information Retrieval with Reusable and Single-use Side Information (PIR-RSSI). In this problem, one or more remote servers store identical copies of a set of $K$ messages, and there is a user that initially knows $M$ of these messages, and wants to privately retrieve one other message from the set of $K$ messages. The objective is to design a retrieval scheme in which the user downloads the minimum amount of information from the server(s) while the identity of the message wanted by the user and the identities of an $M_1$-subset of the $M$ messages known by the user (referred to as reusable side information) are protected, but the identities of the remaining $M_2=M-M_1$ messages known by the user (referred to as single-use side information) do not need to be protected. The PIR-RSSI problem reduces to the classical Private Information Retrieval (PIR) problem when ${M_1=M_2=0}$, and reduces to the problem of PIR with Private Side Information or PIR with Side Information when ${M_1\geq 1,M_2=0}$ or ${M_1=0,M_2\geq 1}$, respectively. In this work, we focus on the single-server setting of the PIR-RSSI problem. We characterize the capacity of this setting for the cases of ${M_1=1,M_2\geq 1}$ and ${M_1\geq 1,M_2=1}$, where the capacity is defined as the maximum achievable download rate over all PIR-RSSI schemes. Our results show that for sufficiently small values of $K$, the single-use side information messages can help in reducing the download cost only if they are kept private; and for larger values of $K$, the reusable side information messages cannot help in reducing the download cost.
Phase Retrieval for Radar Waveform Design
Authors: Samuel Pinilla, Kumar Vijay Mishra, Brian M. Sadler, Henry Arguello
Date: 2022-01-27 00:00:00
Tasks: Radar waveform design, Retrieval

The ability of a radar to discriminate in both range and Doppler velocity is completely characterized by the ambiguity function (AF) of its transmit waveform. Mathematically, it is obtained by correlating the waveform with its Doppler-shifted and delayed replicas. We consider the inverse problem of designing a radar transmit waveform that satisfies the specified AF magnitude. This process may be viewed as a signal reconstruction with some variation of phase retrieval methods. We provide a trust-region algorithm that minimizes a smoothed non-convex least-squares objective function to iteratively recover the underlying signal-of-interest for either time- or band-limited support. The method first approximates the signal using an iterative spectral algorithm and then refines the attained initialization based on a sequence of gradient iterations. Our theoretical analysis shows that unique signal reconstruction is possible using signal samples no more than thrice the number of signal frequencies or time samples. Numerical experiments demonstrate that our method recovers both time- and band-limited signals from sparsely and randomly sampled, noisy, and noiseless AFs.
Searching, Learning, and Subtopic Ordering: A Simulation-based Analysis
Authors: Arthur Câmara, David Maxwell, Claudia Hauff
Date: 2022-01-26 00:00:00

Complex search tasks - such as those from the Search as Learning (SAL) domain - often result in users developing an information need composed of several aspects. However, current models of searcher behaviour assume that individuals have an atomic need, regardless of the task. While these models generally work well for simpler informational needs, we argue that searcher models need to be developed further to allow for the decomposition of a complex search task into multiple aspects. As no searcher model yet exists that considers both aspects and the SAL domain, we propose, by augmenting the Complex Searcher Model (CSM), the Subtopic Aware Complex Searcher Model (SACSM) - modelling aspects as subtopics to the user's need. We then instantiate several agents (i.e., simulated users), with different subtopic selection strategies, which can be considered as different prototypical learning strategies (e.g., should I deeply examine one subtopic at a time, or shallowly cover several subtopics?). Finally, we report on the first large-scale simulated analysis of user behaviours in the SAL domain. Results demonstrate that the SACSM, under certain conditions, simulates user behaviours accurately.
Rapid solution for searching similar audio items
Authors: Kastriot Kadriu
Date: 2022-01-26 00:00:00
Tasks: Clustering

A naive approach for finding similar audio items would be to compare each entry from the feature vector of the test example with each feature vector of the candidates in a k-nearest neighbors fashion. There are already two problems with this approach: audio signals are represented by high dimensional vectors and the number of candidates can be very large - think thousands. The search process would have a high complexity. Our paper will treat this problem through hashing methodologies more specifically the Locality Sensitive Hashing. This project will be in the spirit of classification and clustering problems. The computer sound production principles will be used to determine which features that describe an audio signal are the most useful. That will down-sample the size of the feature vectors and speed up the process subsequently.
Analysis and Optimization of the Latency Budget in Wireless Systems with Mobile Edge Computing
Authors: Suraj Suman, Cedomir Stefanovic, Strahinja Došen, Petar Popovski
Date: 2022-01-27 00:00:00
Tasks: Data Compression, Edge-computing

We present a framework to analyse the latency budget in wireless systems with Mobile Edge Computing (MEC). Our focus is on teleoperation and telerobotics, as use cases that are representative of mission-critical uplink-intensive IoT systems with requirements on low latency and high reliability. The study is motivated by a general question: What is the optimal compression strategy in reliability and latency constrained systems? We address this question by studying the latency of an uplink connection from a multi-sensor IoT device to the base station. This is a critical link tasked with a timely and reliable transfer of potentially significant amount of data from the multitude of sensors. We introduce a comprehensive model for the latency budget, incorporating data compression and data transmission. The uplink latency is a random variable whose distribution depends on the computational capabilities of the device and on the properties of the wireless link. We formulate two optimization problems corresponding to two transmission strategies: (1) Outage-constrained, and (2) Latency-constrained. We derive the optimal system parameters under a reliability criterion. We show that the obtained results are superior compared to the ones based on the optimization of the expected latency.
Design of Battery management system for Residential applications
Authors: Poushali Pal, Devabalaji K. R, S. Priyadarshini
Date: 2022-01-27 00:00:00
Tasks: Management

Battery management system plays an important role for modern battery-powered application such as Electric vehicles, portable electronic equipment and storage for renewable energy sources. It also increases the life-cycle of the battery, battery state and efficiency. Monitoring the state of charge of the battery is a crucial factor for battery management system. This paper deals with monitoring the state of charge of the battery along with temperature, current for Solar panel fitted with battery for residential application. Microcontroller is used for controlling purpose, analog sensors are used for sensing the parameters of voltage, current. The information of the battery is given with tabular form and shown in photograph. Battery parameters are displayed with the LCD screen.
Implementation of Advanced Wind Turbine Controllers for Scaled Turbine Testing in a Wind Tunnel
Authors: Michael Sinner, Vlaho Petrović, Lucy Y. Pao
Date: 2022-01-26 00:00:00
Tasks: Model Predictive Control

Based on a series of two experimental campaigns testing advanced controllers on a scaled wind turbine operating in a wind tunnel, this contribution describes the overall experimental method, challenges faced, lessons learned, and opportunities for future work. The two campaigns, run in Fall 2018 and Fall 2019, tested unconstrained and constrained optimal blade pitch controllers, respectively, using preview disturbance measurements of the oncoming wind. Specifically, the first study considered an extension to the linear-quadratic regulator to include feedforward action, while the second deployed model predictive control to incorporate actuator constraints into the optimal control problem. The results of the campaigns have already been published in technical conference and journal papers on control systems; however, detail on how the controllers were implemented was not included in those works. We aim to fill that gap with this contribution, which is targeted at the wind energy community. We describe several aspects of the experimental setup, in particular providing details of the software and hardware used for the controller; share insight on several aspects of the procedure that were difficult and how we overcame those challenges; and summarize the key differences between simulation-based studies and physical testing. By doing so, we hope to share what we learned during our experimental campaigns and provide a point of reference for others looking to carry out experiments on scaled wind turbines operating in wind tunnel facilities.
Capture Agent Free Biosensing using Porous Silicon Arrays and Machine Learning
Authors: Simon J. Ward, Tengfei Cao, Xiang Zhou, Catie Chang, Sharon M. Weiss
Date: 2022-01-22 00:00:00
Tasks: BIG-bench Machine Learning, Dimensionality Reduction

Biosensors are an essential tool for medical diagnostics, environmental monitoring and food safety. Typically, biosensors are designed to detect specific analytes through functionalization with the appropriate capture agents. However, the use of capture agents limits the number of analytes that can be simultaneously detected and reduces the robustness of the biosensor. In this work, we report a versatile, capture agent free biosensor platform based on an array of porous silicon (PSi) thin films, which has the potential to robustly detect a wide variety of analytes based on their physical and chemical properties in the nanoscale porous media. The ability of this system to reproducibly classify, quantify, and discriminate three proteins is demonstrated to concentrations down to at least 0.02g/L (between 300nM and 450nM) by utilizing PSi array elements with a unique combination of pore size and buffer pH, employing linear discriminant analysis for dimensionality reduction, and using support vector machines as a classifier. This approach represents a significant step towards a low cost, simple and robust biosensor platform that is able to detect a vast range of biomolecules.
Effect of receptor cooperativity on methylation dynamics in bacterial chemotaxis with weak and strong gradient
Authors: Shobhan Dev Mandal, Sakuntala Chatterjee
Date: 2022-01-27 00:00:00

We study methylation dynamics of the chemoreceptors as an {\sl E.coli} cell moves around in a spatially varying chemo-attractant environment. We consider attractant concentration with strong and weak spatial gradient. During the uphill and downhill motion of the cell along the gradient, we measure the temporal variation of average methylation level of the receptor clusters. Our numerical simulations show that the methylation dynamics depends sensitively on the size of the receptor clusters and also on the strength of the gradient. At short times after the beginning of a run, the methylation dynamics is mainly controlled by short runs which are generally associated with high receptor activity. This results in demethylation at short times. But for intermediate or large times, long runs play an important role and depending on receptor cooperativity or gradient strength, the qualitative variation of methylation can be completely different in this time regime. For weak gradient, both for uphill and downhill runs, after the initial demethylation, we find methylation level increases steadily with time for all cluster sizes. Similar qualitative behavior is observed for strong gradient during uphill runs as well. However, the methylation dynamics for downhill runs in strong gradient show highly non-trivial dependence on the receptor cluster size. We explain this behavior as a result of interplay between the sensing and adaptation modules of the signaling network.
A projection based approach for interactive fixed effects panel data models
Authors: Georg Keilbar, Juan M. Rodriguez-Poo, Alexandra Soberon, Weining Wang
Date: 2022-01-27 00:00:00
Tasks: valid

This paper introduces a straightforward sieve-based approach for estimating and conducting inference on regression parameters in panel data models with interactive fixed effects. The method's key assumption is that factor loadings can be decomposed into an unknown smooth function of individual characteristics plus an idiosyncratic error term. Our estimator offers advantages over existing approaches by taking a simple partial least squares form, eliminating the need for iterative procedures or preliminary factor estimation. In deriving the asymptotic properties, we discover that the limiting distribution exhibits a discontinuity that depends on how well our basis functions explain the factor loadings, as measured by the variance of the error factor loadings. This finding reveals that conventional ``plug-in'' methods using the estimated asymptotic covariance can produce excessively conservative coverage probabilities. We demonstrate that uniformly valid non-conservative inference can be achieved through the cross-sectional bootstrap method. Monte Carlo simulations confirm the estimator's strong performance in terms of mean squared error and good coverage results for the bootstrap procedure. We demonstrate the practical relevance of our methodology by analyzing growth rate determinants across OECD countries.
Standard errors for two-way clustering with serially correlated time effects
Authors: Harold D Chiang, Bruce E Hansen, Yuya Sasaki
Date: 2022-01-27 00:00:00
Tasks: Clustering, Vocal Bursts Valence Prediction

We propose improved standard errors and an asymptotic distribution theory for two-way clustered panels. Our proposed estimator and theory allow for arbitrary serial dependence in the common time effects, which is excluded by existing two-way methods, including the popular two-way cluster standard errors of Cameron, Gelbach, and Miller (2011) and the cluster bootstrap of Menzel (2021). Our asymptotic distribution theory is the first which allows for this level of inter-dependence among the observations. Under weak regularity conditions, we demonstrate that the least squares estimator is asymptotically normal, our proposed variance estimator is consistent, and t-ratios are asymptotically standard normal, permitting conventional inference. We present simulation evidence that confidence intervals constructed with our proposed standard errors obtain superior coverage performance relative to existing methods. We illustrate the relevance of the proposed method in an empirical application to a standard Fama-French three-factor regression.