pwc-archive/papers-with-abstracts huggingface dataset:
Off-the-grid prediction and testing for linear combination of translated features
Authors: Cristina Butucea, Jean-François Delmas, Anne Dutfoy, Clément Hardy
Date: 2022-12-02 00:00:00
Tasks: compressed sensing

We consider a model where a signal (discrete or continuous) is observed with an additive Gaussian noise process. The signal is issued from a linear combination of a finite but increasing number of translated features. The features are continuously parameterized by their location and depend on some scale parameter. First, we extend previous prediction results for off-the-grid estimators by taking into account here that the scale parameter may vary. The prediction bounds are analogous, but we improve the minimal distance between two consecutive features locations in order to achieve these bounds. Next, we propose a goodness-of-fit test for the model and give non-asymptotic upper bounds of the testing risk and of the minimax separation rate between two distinguishable signals. In particular, our test encompasses the signal detection framework. We deduce upper bounds on the minimal energy,expressed as the $\ell_2$-norm of the linear coefficients, to successfully detect a signal in presence of noise. The general model considered in this paper is a non-linear extension of the classical high-dimensional regression model. It turns out that,in this framework, our upper bound on the minimax separation rate matches (up to a logarithmic factor) the lower bound on the minimax separation rate for signal detection in the high-dimensional linear model associated to a fixed dictionary of features. We also propose a procedure to test whether the features of the observed signal belong to a given finite collection under the assumption that the linear coefficients may vary, but have prescribed signs under the null hypothesis. A non-asymptotic upper bound on the testing risk is given.We illustrate our results on the spikes deconvolution model with Gaussian features on the real line and with the Dirichlet kernel, frequently used in the compressed sensing literature, on the torus.
Information Retrieval from the Digitized Books
Authors: Riya Gupta, C. V. Jawahar
Date: 2022-12-02 00:00:00
Tasks: Image Retrieval, Information Retrieval, Optical Character Recognition, Optical Character Recognition (OCR), Retrieval

Extracting the relevant information out of a large number of documents is a challenging and tedious task. The quality of results generated by the traditionally available full-text search engine and text-based image retrieval systems is not optimal. Information retrieval (IR) tasks become more challenging with the nontraditional language scripts, as in the case of Indic scripts. The authors have developed OCR (Optical Character Recognition) Search Engine to make an Information Retrieval & Extraction (IRE) system that replicates the current state-of-the-art methods using the IRE and Natural Language Processing (NLP) techniques. Here we have presented the study of the methods used for performing search and retrieval tasks. The details of this system, along with the statistics of the dataset (source: National Digital Library of India or NDLI), is also presented. Additionally, the ideas to further explore and add value to research in IRE are also discussed.
Changepoint Detection in Noisy Data Using a Novel Residuals Permutation-Based Method (RESPERM): Benchmarking and Application to Single Trial ERPs
Authors: Werner Sommer, Katarzyna Stapor, Grzegorz Kończak, Krzysztof Kotowski, Piotr Fabian, Jeremi Ochab, Anna Bereś, Grażyna Ślusarczyk
Date: 2022-04-21 00:00:00
Proceeding: Brain sciences 2022 4
Tasks: Benchmarking, Change Point Detection, Electroencephalogram (EEG), ERP, Time Series, Time Series Analysis, Time Series Regression

An important problem in many fields dealing with noisy time series, such as psychophysiological single trial data during learning or monitoring treatment effects over time, is detecting a change in the model underlying a time series. Here, we present a new method for detecting a single changepoint in a linear time series regression model, termed residuals permutation-based method (RESPERM). The optimal changepoint in RESPERM maximizes Cohen’s effect size with the parameters estimated by the permutation of residuals in a linear model. RESPERM was compared with the SEGMENTED method, a well-established and recommended method for detecting changepoints, using extensive simulated data sets, varying the amount and distribution characteristics of noise and the location of the change point. In time series with medium to large amounts of noise, the variance of the detected changepoint was consistently smaller for RESPERM than SEGMENTED. Finally, both methods were applied to a sample dataset of single trial amplitudes of the N250 ERP component during face learning. In conclusion, RESPERM appears to be well suited for changepoint detection especially in noisy data, making it the method of choice in neuroscience, medicine and many other fields.
CAVE: Correcting Attribute Values in E-commerce Profiles
Authors: Kassem Sabeh, Mouna Kacimi, Johann Gamper
Date: 2022-10-17 00:00:00
Proceeding: ACM International Conference on Information & Knowledge Management 2022 10
Tasks: Attribute, Attribute Value Extraction, Question Answering, Retrieval

Attribute value extraction from product profiles is essential for many applications such as product retrieval, comparison, and recommendation. While existing techniques focus mainly on the extraction task, none of them deals with the problem of correcting wrong attribute values. In this paper we propose CAVE, a novel system for attribute correction and enrichment using the Question Answering (QA) paradigm. CAVE learns information from both titles and attribute tables, using encoder and language models to correct attribute values. It also has the capability to enrich existing product descriptions with new attribute values extracted from titles. To the best of our knowledge, CAVE is the first system that allows users to experiment with a number of powerful QA models and compare their performances on attribute values correction using real-word datasets.
Compressed Sensing Off the Grid
Authors: Gongguo Tang
Date: 2013-08-07 00:00:00
Proceeding: IEEE Transactions on Information Theory ( Volume: 59, Issue: 11, November 2013) 2013 8
Tasks: compressed sensing

This paper investigates the problem of estimating the frequency components of a mixture of complex sinusoids from
a random subset of regularly spaced samples. Unlike previous work in compressed sensing, the frequencies are not assumed to lie on a grid,but can assume any values in the normalized frequency domain. An atomic norm minimization approach is proposed to exactly recover the unobserved samples and identify the unknown frequencies, which is then reformulated as an exact semidefinite program. Even with this continuous dictionary, it is shown that random samples are sufficient to guarantee exact frequency localization with high probability, provided the frequencies are well separated. Extensive numerical experiments are performed to illustrate the effectiveness of the proposed method.
Martinize2 and Vermouth: Unified Framework for Topology Generation
Authors: Peter C. Kroon, Fabian Grünewald, Jonathan Barnoud, Marco van Tilburg, Chris Brasnett, Paulo C. T. Souza, Tsjerk A. Wassenaar, Siewert-Jan Marrink
Date: 2022-11-29 00:00:00

Ongoing advances in force field and computer hardware development enable the use of molecular dynamics (MD) to simulate increasingly complex systems with the ultimate goal of reaching cellular complexity. At the same time, rational design by high-throughput (HT) simulations is another forefront of MD. In these areas, the Martini coarse-grained force field, especially the latest version (i.e. v3), is being actively explored because it offers an enhanced spatial-temporal resolution. However, the automation tools for preparing simulations with the Martini force field, accompanying the previous version, were not designed for HT simulations or studies of complex cellular systems. Therefore, they become a major limiting factor. To address these shortcomings, we present the open-source Vermouth python library. Vermouth is designed to become the unified framework for developing programs, which prepare, run, and analyze Martini simulations of complex systems. To demonstrate the power of the Vermouth library, the Martinize2 program is showcased as a generalization of the martinize script, originally aimed to set up simulations of proteins. In contrast to the previous version, Martinize2 automatically handles protonation states in proteins and post-translation modifications, offers more options to fine-tune structural biases such as the elastic network (EN), and can convert non-protein molecules such as ligands. Finally, Martinize2 is used in two high-complexity benchmarks. The entire I-TASSER protein template database as well as a subset of 200,000 structures from the AlphaFold Protein Structure Database are converted to CG resolution and we illustrate how the checks on input structure quality can safeguard high-throughput applications.
OOG- Optuna Optimized GAN Sampling Technique for Tabular Imbalanced Malware Data
Authors: S. M Towhidul Islam Tonmoy, S. M Mehedi Zaman
Date: 2022-11-25 00:00:00
Tasks: Generative Adversarial Network

Cyberspace occupies a large portion of people's life in the age of modern technology, and while there are those who utilize it for good, there are also those who do not. Malware is an application whose construction was not motivated by a benign goal and it can harm, steal, or even alter personal information and secure applications and software. Thus, there are numerous techniques to avoid malware, one of which is to develop samples of malware so that the system can be updated with the growing number of malwares, allowing it to recognize when malwares attempt to enter. The Generative Adversarial Network (GAN) sampling technique has been used in this study to generate new malware samples. GANs have multiple variants, and in order to determine which variant is optimal for a given dataset sample, their parameters must be modified. This study employs Optuna, an autonomous hyperparameter tuning algorithm, to determine the optimal settings for the dataset under consideration. In this study, the architecture of the Optuna Optimized GAN (OOG) method is shown, along with scores of 98.06%, 99.00%, 97.23%, and 98.04% for accuracy, precision, recall and f1 score respectively. After tweaking the hyperparameters of five supervised boosting algorithms, XGBoost, LightGBM, CatBoost, Extra Trees Classifier, and Gradient Boosting Classifier, the methodology of this paper additionally employs the weighted ensemble technique to acquire this result. In addition to comparing existing efforts in this domain, the study demonstrates how promising GAN is in comparison to other sampling techniques such as SMOTE.
Deep-Learning-based Vulnerability Detection in Binary Executables
Authors: Andreas Schaad, Dominik Binder
Date: 2022-11-25 00:00:00
Tasks: Binary Classification, Deep Learning, Vulnerability Detection

The identification of vulnerabilities is an important element in the software development life cycle to ensure the security of software. While vulnerability identification based on the source code is a well studied field, the identification of vulnerabilities on basis of a binary executable without the corresponding source code is more challenging. Recent research [1] has shown, how such detection can be achieved by deep learning methods. However, that particular approach is limited to the identification of only 4 types of vulnerabilities. Subsequently, we analyze to what extent we could cover the identification of a larger variety of vulnerabilities. Therefore, a supervised deep learning approach using recurrent neural networks for the application of vulnerability detection based on binary executables is used. The underlying basis is a dataset with 50,651 samples of vulnerable code in the form of a standardized LLVM Intermediate Representation. The vectorised features of a Word2Vec model are used to train different variations of three basic architectures of recurrent neural networks (GRU, LSTM, SRNN). A binary classification was established for detecting the presence of an arbitrary vulnerability, and a multi-class model was trained for the identification of the exact vulnerability, which achieved an out-of-sample accuracy of 88% and 77%, respectively. Differences in the detection of different vulnerabilities were also observed, with non-vulnerable samples being detected with a particularly high precision of over 98%. Thus, the methodology presented allows an accurate detection of 23 (compared to 4 [1]) vulnerabilities.
Holographic MIMO Communications: Theoretical Foundations, Enabling Technologies, and Future Directions
Authors: Tierui Gong, Panagiotis Gavriilidis, Ran Ji, Chongwen Huang, George C. Alexandropoulos, Li Wei, Zhaoyang Zhang, Mérouane Debbah, H. Vincent Poor, Chau Yuen
Date: 2022-12-02 00:00:00

Future wireless systems are envisioned to create an endogenously holography-capable, intelligent, and programmable radio propagation environment, that will offer unprecedented capabilities for high spectral and energy efficiency, low latency, and massive connectivity. A potential and promising technology for supporting the expected extreme requirements of the sixth-generation (6G) communication systems is the concept of the holographic multiple-input multiple-output (HMIMO), which will actualize holographic radios with reasonable power consumption and fabrication cost. The HMIMO is facilitated by ultra-thin, extremely large, and nearly continuous surfaces that incorporate reconfigurable and sub-wavelength-spaced antennas and/or metamaterials. Such surfaces comprising dense electromagnetic (EM) excited elements are capable of recording and manipulating impinging fields with utmost flexibility and precision, as well as with reduced cost and power consumption, thereby shaping arbitrary-intended EM waves with high energy efficiency. The powerful EM processing capability of HMIMO opens up the possibility of wireless communications of holographic imaging level, paving the way for signal processing techniques realized in the EM-domain, possibly in conjunction with their digital-domain counterparts. However, in spite of the significant potential, the studies on HMIMO communications are still at an initial stage, its fundamental limits remain to be unveiled, and a certain number of critical technical challenges need to be addressed. In this survey, we present a comprehensive overview of the latest advances in the HMIMO communications paradigm, with a special focus on their physical aspects, their theoretical foundations, as well as the enabling technologies for HMIMO systems. We also compare the HMIMO with existing multi-antenna technologies, especially the massive MIMO, present various...
Dynamic Independent Component Extraction with Blending Mixing Vector: Lower Bound on Mean Interference-to-Signal Ratio
Authors: Jaroslav Čmejla, Zbyněk Koldovský, Václav Kautský, Tülay Adalı
Date: 2022-12-02 00:00:00
Tasks: Position

This paper deals with dynamic Blind Source Extraction (BSE) from where the mixing parameters characterizing the position of a source of interest (SOI) are allowed to vary over time. We present a new source extraction model called CvxCSV which is a parameter-reduced modification of the recent Constant Separation Vector (CSV) mixing model. In CvxCSV, the mixing vector evolves as a convex combination of its initial and final values. We derive a lower bound on the achievable mean interference-to-signal ratio (ISR) based on the Cram\'er-Rao theory. The bound reveals advantageous properties of CvxCSV compared with CSV and compared with a sequential BSE based on independent component extraction (ICE). In particular, the achievable ISR by CvxCSV is lower than that by the previous approaches. Moreover, the model requires significantly weaker conditions for identifiability, even when the SOI is Gaussian.
Relative Acoustic Features for Distance Estimation in Smart-Homes
Authors: Francesco Nespoli, Daniel Barreda, Patrick A. Naylor
Date: 2022-12-02 00:00:00
Tasks: Room Impulse Response (RIR)

Any audio recording encapsulates the unique fingerprint of the associated acoustic environment, namely the background noise and reverberation. Considering the scenario of a room equipped with a fixed smart speaker device with one or more microphones and a wearable smart device (watch, glasses or smartphone), we employed the improved proportionate normalized least mean square adaptive filter to estimate the relative room impulse response mapping the audio recordings of the two devices. We performed inter-device distance estimation by exploiting a new set of features obtained extending the definition of some acoustic attributes of the room impulse response to its relative version. In combination with the sparseness measure of the estimated relative room impulse response, the relative features allow precise inter-device distance estimation which can be exploited for tasks such as best microphone selection or acoustic scene analysis. Experimental results from simulated rooms of different dimensions and reverberation times demonstrate the effectiveness of this computationally lightweight approach for smart home acoustic ranging applications
Injecting Spatial Information for Monaural Speech Enhancement via Knowledge Distillation
Authors: Xinmeng Xu, Weiping tu, Yuhong Yang
Date: 2022-12-02 00:00:00
Tasks: Knowledge Distillation, Speech Enhancement

Monaural speech enhancement (SE) provides a versatile and cost-effective approach to SE tasks by utilizing recordings from a single microphone. However, the monaural SE lags performance behind multi-channel SE as the monaural SE methods are unable to extract spatial information from one-channel recordings, which greatly limits their application scenarios. To address this issue, we inject spatial information into the monaural SE model and propose a knowledge distillation strategy to enable the monaural SE model to learn binaural speech features from the binaural SE model, which makes monaural SE model possible to reconstruct higher intelligibility and quality enhanced speeches under low signal-to-noise ratio (SNR) conditions. Extensive experiments show that our proposed monaural SE model by injecting spatial information via knowledge distillation achieves favorable performance against other monaural SE models with fewer parameters.
Shaping Nonlinearity in Reset Controllers for Precision Motion Systems
Authors: Nima Karbasizadeh, S. Hassan HosseinNia
Date: 2022-12-02 00:00:00

The precision motion industry has an ever-increasing demand for faster, more precise and more robust controllers. From the perspective of frequency domain and loopshaping technique, this demand has pushed the linear controllers to their inherent limits, namely, the waterbed effect and Bode's phase-gain relationship. Mathematically, complex-order transfer functions are not bound by Bode's phase-gain relationship. However, implementing them in practice is a challenge to be solved. This extended abstract will show review the previous work of the authors in shaping nonlinearities in reset controllers and contribute and propose an overall architecture for reset control systems to approximate complex order controllers.
Hybrid Tunable Magnet Actuator: Modeling and Design
Authors: W. B. Hoekwater, J. D. Wiersema1, S. H. HosseinNia
Date: 2022-12-02 00:00:00
Tasks: Position

Reluctance actuators are preferred for high-precision applications. Due to resistive losses in the coils, the accuracy of this type of actuator will reduce in quasi-static operation mode within a vacuum environment. By using soft permanent magnets whose magnetization states are in-situ tuned, no constant power is needed to create a force and thereby the resistive losses will reduce. In this paper, a new tuning method is investigated in order to reduce the resistive losses. By using a history-dependent and non-linear hysteresis model, a more efficient tuning algorithm is designed. Besides this, the position accuracy and control simplicity of a variable reluctance tunable magnet actuator are improved by linearizing the non-linear force-flux relationship. This is achieved by using bias fluxes generated by hard permanent magnets.
Linear Data-Driven Economic MPC with Generalized Terminal Constraint
Authors: Yifan Xie, Julian Berberich, Frank Allgöwer
Date: 2022-12-02 00:00:00
Tasks: LEMMA, Model Predictive Control

In this paper, we propose a data-driven economic model predictive control (EMPC) scheme with generalized terminal constraint to control an unknown linear time-invariant system. Our scheme is based on the Fundamental Lemma to predict future system trajectories using a persistently exciting input-output trajectory. The control objective is to minimize an economic cost objective. By employing a generalized terminal constraint with artificial equilibrium, the scheme does not require prior knowledge of the optimal equilibrium. We prove that the asymptotic average performance of the closed-loop system can be made arbitrarily close to that of the optimal equilibrium. Moreover, we extend our results to the case of an unknown linear stage cost function, where the Fundamental lemma is used to predict the stage cost directly. The effectiveness of the proposed scheme is shown by a numerical example.
On the evolutionary emergence of predation
Authors: Yaroslav Ispolatov, Carlos Doebeli, Michael Doebeli
Date: 2022-12-01 00:00:00

In models for the evolution of predation from initially purely competitive species interactions, the propensity of predation is most often assumed to be a direct consequence of the relative morphological and physiological traits of interacting species. Here we explore a model in which predation ability is an independently evolving phenotypic feature, so that even when the relative morphological or physiological traits allow for predation, predation only occurs if the predation ability of individuals has independently evolved to high enough values. In addition to delineating the conditions for the evolutionary emergence of predation, the model reproduces stationary and non-stationary multilevel food webs with the top predators not necessarily having size superiority.
Shifting to Telework and Firms' Location: Does Telework Make Our Society Efficient?
Authors: Kazufumi Tsuboi
Date: 2022-12-02 00:00:00

Although it has been suggested that the shift from on-site work to telework will change the city structure, the mechanism of this change is not clear. This study clarifies how the location of firms changes when the cost of teleworking decreases and how this affects the urban economy. The two main results obtained are as follows. (i) The expansion of teleworking causes firms to be located closer to urban centers or closer to urban fringes. (ii) Teleworking makes urban production more efficient and cities more compact. This is the first paper to show that two empirical studies can be represented in a unified theoretical model and that existing studies obtained by simulation can be explained analytically.
Who Controls the Agenda Controls the Polity
Authors: S. Nageeb Ali, B. Douglas Bernheim, Alexander W. Bloedel, Silvia Console Battilana
Date: 2022-12-02 00:00:00
Tasks: Decision Making

This paper models legislative decision-making with an agenda setter who can propose policies sequentially, tailoring each proposal to the status quo that prevails after prior votes. Voters are sophisticated and the agenda setter cannot commit to her future proposals. Nevertheless, the agenda setter obtains her favorite outcome in every equilibrium regardless of the initial default policy. Central to our results is a new condition on preferences, manipulability, that holds in rich policy spaces, including spatial settings and distribution problems. Our results overturn the conventional wisdom that voter sophistication alone constrains an agenda setter's power.