pwc-archive/papers-with-abstracts huggingface dataset:
Detection by Sampling: Massive MIMO Detector based on Langevin Dynamics
Authors: Nicolas Zilberstein, Chris Dick, Rahman Doost-Mohammady, Ashutosh Sabharwal, Santiago Segarra
Date: 2022-02-24 00:00:00

Optimal symbol detection in multiple-input multiple-output (MIMO) systems is known to be an NP-hard problem. Hence, the objective of any detector of practical relevance is to get reasonably close to the optimal solution while keeping the computational complexity in check. In this work, we propose a MIMO detector based on an annealed version of Langevin (stochastic) dynamics. More precisely, we define a stochastic dynamical process whose stationary distribution coincides with the posterior distribution of the symbols given our observations. In essence, this allows us to approximate the maximum a posteriori estimator of the transmitted symbols by sampling from the proposed Langevin dynamic. Furthermore, we carefully craft this stochastic dynamic by gradually adding a sequence of noise with decreasing variance to the trajectories, which ensures that the estimated symbols belong to a pre-specified discrete constellation. Through numerical experiments, we show that our proposed detector yields state-of-the-art symbol error rate performance.
Deep learning-based UAV detection in the low altitude clutter background
Authors: Zeyang Wu, Wenbo Wang, Yuexing Peng
Date: 2022-02-24 00:00:00
Tasks: Contrastive Learning

Unmanned aerial vehicles (UAVs) are widely used due to their low cost and versatility, but they also pose security and privacy threats. Therefore, reliable detection for low-altitude UAVs is an important issue. The strong ground clutter makes the radar echoes from small UAVs submerged in noise, resulting in low radar detection reliability. A low-altitude UAV detection method based on deep contrastive learning is proposed to address the above problems: Concretely, a low-altitude UAV radar echo model under low-altitude clutter interference is first established. Based on the echo components and the UAV Doppler domain identifiable mechanism, a time-frequency transformation method combining ZAM transform and morphological operations is used to suppress the ambiguity problem under clutter. Then feature extraction and fusion method introducing contrast learning is utilized to suppress non-target ground clutter interference. Finally, a detector relying on semantic features is designed for the reliable identification of low-altitude UAVs. The experiments carried out on both real and simulated data confirm that the proposed method can effectively suppress ground clutter and reliably extract recognizable semantic features of UAVs. The proposed method achieves lower false and missing alarms compared with recent state-of-art solutions and improves the detection accuracy by more than 5% for the same signal-to-noise ratio, which effectively improves the detection reliability.
Supervised Learning based Sparse Channel Estimation for RIS aided Communications
Authors: Dilin Dampahalage, K. B. Shashika Manosha, Nandana Rajatheva, Matti Latva-aho
Date: 2022-02-24 00:00:00

An reconfigurable intelligent surface (RIS) can be used to establish line-of-sight (LoS) communication when the direct path is compromised, which is a common occurrence in a millimeter wave (mmWave) network. In this paper, we focus on the uplink channel estimation of a such network. We formulate this as a sparse signal recovery problem, by discretizing the angle of arrivals (AoAs) at the base station (BS). On-grid and off-grid AoAs are considered separately. In the on-grid case, we propose an algorithm to estimate the direct and RIS channels. Neural networks trained based on supervised learning is used to estimate the residual angles in the off-grid case, and the AoAs in both cases. Numerical results show the performance gains of the proposed algorithms in both cases.
Noncoherent Massive MIMO with Embedded One-Way Function Physical Layer Security
Authors: Yuma Katsuki, Giuseppe Thadeu Freitas de Abreu, Koji Ishibashi, Naoki Ishikawa
Date: 2022-02-24 00:00:00
Tasks: Combinatorial Optimization

We propose a novel physical layer security scheme that exploits an optimization method as a one-way function. The proposed scheme builds on nonsquare differential multiple-input multiple-output (MIMO), which is capable of noncoherent detection even in massive MIMO scenarios and thus resilient against risky pilot insertion and pilot contamination attacks. In contrast to conventional nonsquare differential MIMO schemes, which require space-time projection matrices designed via highly complex, discrete, and combinatorial optimization, the proposed scheme utilizes projection matrices constructed via low-complexity continuous optimization designed to maximize the coding gain of the system. Furthermore, using a secret key generated from the true randomness nature of the wireless channel as an initial value, the proposed continuous optimization-based projection matrix construction method becomes a one-way function, making the proposed scheme a physical layer secure differential MIMO system. An attack algorithm to challenge the proposed scheme is also devised, which demonstrates that the security level achieved improves as the number of transmit antennas increases, even in an environment where the eavesdropper can perfectly estimate channel coefficients and experience asymptotically large signal-to-noise ratios.
Analysis of Coronavirus Envelope Protein with Cellular Automata (CA) Model
Authors: Raju Hazari, P Pal Chaudhuri
Date: 2022-01-15 00:00:00

The reason of significantly higher transmissibility of SARS Covid (2019 CoV-2) compared to SARS Covid (2003 CoV) and MERS Covid (2012 MERS) can be attributed to mutations reported in structural proteins, and the role played by non-structural proteins (nsps) and accessory proteins (ORFs) for viral replication, assembly, and shedding. Envelope protein E is one of the four structural proteins of minimum length. Recent studies have confirmed critical role played by the envelope protein in the viral life cycle including assembly of virion exported from infected cell for its transmission. However, the determinants of the highly complex viral - host interactions of envelope protein, particularly with host Golgi complex, have not been adequately characterized. CoV-2 and CoV Envelope proteins of length 75 and 76 amino acids differ in four amino acid locations. The additional amino acid Gly (G) at location 70 makes CoV length 76. The amino acid pair EG at location 69-70 of CoV in place of amino acid R in location 69 of CoV-2, has been identified as a major determining factor in the current investigation. This paper concentrates on the design of computational model to compare the structure/function of wild and mutants of CoV-2 with wild and mutants of CoV in the functionally important region of the protein chain pair. We hypothesize that differences of CAML model parameter of CoV-2 and CoV characterize the deviation in structure and function of envelope proteins in respect of interaction of virus with host Golgi complex; and this difference gets reflected in the difference of their transmissibility. The hypothesis has been validated from single point mutational study on- (i) human HBB beta-globin hemoglobin protein associated with sickle cell anemia, (ii) mutants of envelope protein of Covid-2 infected patients reported in recent publications.
Average abundancy of cooperation in multi-player games with random payoffs
Authors: Dhaker Kroumi, Sabin Lessard
Date: 2022-02-24 00:00:00

We consider interactions between players in groups of size $d\geq2$ with payoffs that not only depend on the strategies used in the group but also fluctuate at random over time. An individual can adopt either cooperation or defection as strategy and the population is updated from one-time step to the next by a birth-death event according to a Moran model. Assuming recurrent symmetric mutation and payoffs with expected values, variances, and covariances of the same small order, we derive a first-order approximation of the average abundance of cooperation in the selection-mutation equilibrium. We show that increasing the variance of any payoff for defection or decreasing the variance of any payoff for cooperation increases the average abundance of cooperation. As for the effect of the covariance between any payoff for cooperation and any payoff for defection, we show that it depends on the number of cooperators in the group associated with these payoffs. We study in particular the public goods game, the stag hunt game, and the snowdrift game, all social dilemmas based on random benefit $b$ and cost $c$ for cooperation. We show that a decrease in the scaled variance of $b$ or $c$, or an increase in their scaled covariance, makes it easier for weak selection to favor the abundance of cooperation in the stag hunt game and the snowdrift game. The same conclusion holds for the public goods game except that the covariance of $b$ has no effect on the average abundance of $C$. On the other hand, increasing the scaled mutation rate or the group size can enhance or lessen the condition for weak selection to favor the abundance of $C$.
Hyperacute pathophysiology of traumatic and vascular brain injury captured by ultrasound, photoacoustic, and magnetic resonance imaging
Authors: Ali Kamali, Laurel Dieckhaus, Emily C. Peters, Collin A. Preszler, Russel S. Witte, Paulo W. Pires, Elizabeth B. Hutchinson, Kaveh Laksari
Date: 2022-02-24 00:00:00

Cerebrovascular dynamics and pathomechanisms that evolve in the minutes and hours following traumatic vascular injury in the brain remain largely unknown. We investigated the pathophysiology evolution within the first three hours after closed-head traumatic brain injury (TBI) and subarachnoid hemorrhage (SAH), two common traumatic vascular injuries, in mice. We took a multi-modal imaging approach using photoacoustic, color Doppler, and magnetic resonance imaging (MRI) in mice. Brain oxygenation (%sO2) and velocity-weighted volume of blood flow (VVF) values significantly decreased from baseline to fifteen minutes after both TBI and SAH. TBI resulted in 19.2% and 41.0% ipsilateral %sO2 and VVF reductions 15 minutes post injury while SAH resulted in 43.9% %sO2 and 85.0% VVF reduction ipsilaterally (p<0.001). We found partial recovery of %sO2 from 15 minutes to 3-hours after injury for TBI but not SAH. Hemorrhage, edema, reduced perfusion, and altered diffusivity were evident from MRI scans acquired 90-150 minutes after injury in both models although the spatial distribution was mostly focal for TBI and diffuse for SAH. The results reveal that the cerebral %sO2 deficits immediately following injuries are reversible for TBI and irreversible for SAH. Our findings can inform future studies on mitigating these early responses to improve long-term recovery.
Analysis of Genotype-Phenotype Association using Genomic Informational Field Theory (GIFT)
Authors: Jonathan Wattis, Sian Bray, Panagiota Kyratzi, Cyril Rauch
Date: 2022-02-24 00:00:00

We show how field- and information theory can be used to quantify the relationship between genotype and phenotype in cases where phenotype is a continuous variable. Given a sample population of phenotype measurements, from various known genotypes, we show how the ordering of phenotype data can lead to quantification of the effect of genotype. This method does not assume that the data has a Gaussian distribution, it is particularly effective at extracting weak and unusual dependencies of genotype on phenotype. However, in cases where data has a special form, (eg Gaussian), we observe that the effective phenotype field has a special form. We use asymptotic analysis to solve both the forward and reverse formulations of the problem. We show how $p$-values can be calculated so that the significance of correlation between phenotype and genotype can be quantified. This provides a significant generalisation of the traditional methods used in genome-wide association studies GWAS. We derive a field-strength which can be used to deduce how the correlations between genotype and phenotype, and their impact on the distribution of phenotypes.
From Zero-Intelligence to Queue-Reactive: Limit Order Book modeling for high-frequency volatility estimation and optimal execution
Authors: Tommaso Mariotti, Fabrizio Lillo, Giacomo Toscano
Date: 2022-02-24 00:00:00

The estimation of the volatility with high-frequency data is plagued by the presence of microstructure noise, which leads to biased measures. Alternative estimators have been developed and tested either on specific structures of the noise or by the speed of convergence to their asymptotic distributions. Gatheral and Oomen (2010) proposed to use the Zero-Intelligence model of the limit order book to test the finite-sample performance of several estimators of the integrated variance. Building on this approach, in this paper we introduce three main innovations: (i) we use as data-generating process the Queue-Reactive model of the limit order book (Huang et al. (2015)), which - compared to the Zero-Intelligence model - generates more realistic microstructure dynamics, as shown here by using an Hausman test; (ii) we consider not only estimators of the integrated volatility but also of the spot volatility; (iii) we show the relevance of the estimator in the prediction of the variance of the cost of a simulated VWAP execution. Overall we find that, for the integrated volatility, the pre-averaging estimator optimizes the estimation bias, while the unified and the alternation estimator lead to optimal mean squared error values. Instead, in the case of the spot volatility, the Fourier estimator yields the optimal accuracy, both in terms of bias and mean squared error. The latter estimator leads also to the optimal prediction of the cost variance of a VWAP execution.
An Efficient, Probabilistically Sound Algorithm for Segmentation and Word Discovery
Authors: Michael R. Brent
Date: 1999-05-12 00:00:00
Tasks: Language Acquisition

This paper presents a model-based, unsupervised algorithm for recovering word boundaries in a natural-language text from which they have been deleted. The algorithm is derived from a probability model of the source that generated the text. The fundamental structure of the model is specified abstractly so that the detailed component models of phonology, word-order, and word frequency can be replaced in a modular fashion. The model yields a language-independent, prior probability distribution on all possible sequences of all possible words over a given alphabet, based on the assumption that the input was generated by concatenating words from a fixed but unknown lexicon. The model is unusual in that it treats the generation of a complete corpus, regardless of length, as a single event in the probability space. Accordingly, the algorithm does not estimate a probability distribution on words; instead, it attempts to calculate the prior probabilities of various word sequences that could underlie the observed text. Experiments on phonemic transcripts of spontaneous speech by parents to young children suggest that this algorithm is more effective than other proposed algorithms, at least when utterance boundaries are given and the text includes a substantial number of short utterances. Keywords: Bayesian grammar induction, probability models, minimum description length (MDL), unsupervised learning, cognitive modeling, language acquisition, segmentation
Deeper Convolutional Neural Networks and Broad Augmentation Policies Improve Performance in Musical Key Estimation
Authors: Stefan Andreas Baumann
Date: 2021-11-07 00:00:00
Proceeding: Proceedings of the International Society for Music Information Retrieval Conference (ISMIR) 2021 11
Tasks: image-classification, Information Retrieval, Key Detection, Music Information Retrieval, Retrieval

In recent years, complex convolutional neural network architectures such as the Inception architecture have been shown to offer significant improvements over previous architectures in image classification. So far, little work has been done applying these architectures to music information retrieval tasks, with most models still relying on sequential neural network architectures. In this paper, we adapt the Inception architecture to the specific needs of harmonic music analysis and use it to create a model (InceptionKeyNet) for the task of key estimation. We then show that the resulting model can significantly outperform state-of-the-art single-task models when trained on the same datasets. Additionally, we evaluate a broad range of augmentation methods and find that extending augmentation policies to include a more diverse set of methods further improves accuracy. Finally, we train both the proposed and state-of-the-art single-task models on differently sized training datasets and different augmentation policies and compare the differences in generalization performance.
The IWSLT 2018 Evaluation Campaign
Authors: Jan Niehues, Rolando Cattoni, Sebastian Stüker, Mauro Cettolo, Marco Turchi, Marcello Federico
Date: 2018-10-01 00:00:00
Proceeding: IWSLT (EMNLP) 2018 10
Tasks: Machine Translation, Translation

The International Workshop of Spoken Language Translation (IWSLT) 2018 Evaluation Campaign featured two tasks: low-resource machine translation and speech translation. In the first task, manually transcribed speech had to be translated from Basque to English. Since this translation direction is a under-resourced language pair, participants were encouraged to use additional parallel data from related languages. In the second task, participants had to translate English audio into German text with a full speech-translation system. In the baseline condition, participants were free to use composite architectures, while in the end-to-end condition they were restricted to use a single model for the task. This year, eight research groups took part in the low-resource machine translation task and nine in the speech translation task.
Unsupervised Parallel Sentence Extraction from Comparable Corpora
Authors: Viktor Hangya, Fabienne Braune, Yuliya Kalasouskaya, Alexander Fraser
Date: 2018-10-01 00:00:00
Proceeding: IWSLT (EMNLP) 2018 10
Tasks: Sentence, Word Embeddings

Mining parallel sentences from comparable corpora is of great interest for many downstream tasks. In the BUCC 2017 shared task, systems performed well by training on gold standard parallel sentences. However, we often want to mine parallel sentences without bilingual supervision. We present a simple approach relying on bilingual word embeddings trained in an unsupervised fashion. We incorporate orthographic similarity in order to handle words with similar surface forms. In addition, we propose a dynamic threshold method to decide if a candidate sentence-pair is parallel which eliminates the need to fine tune a static value for different datasets. Since we do not employ any language specific engineering our approach is highly generic. We show that our approach is effective, on three language-pairs, without the use of any bilingual signal which is important because parallel sentence mining is most useful in low resource scenarios.
Word Rewarding for Adequate Neural Machine Translation
Authors: Yuto Takebayashi, Chu Chenhui, Yuki Arase†, Masaaki Nagata
Date: 2018-10-01 00:00:00
Proceeding: IWSLT (EMNLP) 2018 10
Tasks: Decoder, Machine Translation, NMT, Prediction, Translation

To improve the translation adequacy in neural machine translation (NMT), we propose a rewarding model with target word prediction using bilingual dictionaries inspired by the success of decoder constraints in statistical machine translation. In particular, the model first predicts a set of target words promising for translation; then boosts the probabilities of the predicted words to give them better chances to be output. Our rewarding model minimally interacts with the decoder so that it can be easily applied to the decoder of an existing NMT system. Extensive evaluation under both resource-rich and resource-poor settings shows that (1) BLEU score improves more than 10 points with oracle prediction, (2) BLEU score improves about 1.0 point with target word prediction using bilingual dictionaries created either manually or automatically, (3) hyper-parameters of our model are relatively easy to optimize, and (4) undergeneration problem can be alleviated in exchange for increasing over-generated words.
Analyzing Knowledge Distillation in Neural Machine Translation
Authors: Dakun Zhang, Josep Crego, Jean Senellart
Date: 2018-10-01 00:00:00
Proceeding: IWSLT (EMNLP) 2018 10
Tasks: Knowledge Distillation, Machine Translation, Translation

Knowledge distillation has recently been successfully applied to neural machine translation. It allows for building shrunk networks while the resulting systems retain most of the quality of the original model. Despite the fact that many authors report on the benefits of knowledge distillation, few have discussed the actual reasons why it works, especially in the context of neural MT. In this paper, we conduct several experiments aimed at understanding why and how distillation impacts accuracy on an English-German translation task. We show that translation complexity is actually reduced when building a distilled/synthesised bi-text when compared to the reference bi-text. We further remove noisy data from synthesised translations and merge filtered synthesised data together with original reference, thus achieving additional gains in terms of accuracy.
A Machine Translation Approach for Modernizing Historical Documents Using Backtranslation
Authors: Miguel Domingo, Francisco Casacuberta
Date: 2018-10-01 00:00:00
Proceeding: IWSLT (EMNLP) 2018 10
Tasks: Machine Translation, Translation

Human language evolves with the passage of time. This makes historical documents to be hard to comprehend by contemporary people and, thus, limits their accessibility to scholars specialized in the time period in which a certain document was written. Modernization aims at breaking this language barrier and increase the accessibility of historical documents to a broader audience. To do so, it generates a new version of a historical document, written in the modern version of the document’s original language. In this work, we propose several machine translation approaches for modernizing historical documents. We tested these approaches in different scenarios, obtaining very encouraging results.
Machine Translation Human Evaluation: an investigation of evaluation based on Post-Editing and its relation with Direct Assessment
Authors: Luisa Bentivogli, Mauro Cettolo, Marcello Federico, Christian Federmann
Date: 2018-10-01 00:00:00
Proceeding: IWSLT (EMNLP) 2018 10
Tasks: Machine Translation

In this paper we present an analysis of the two most prominent methodologies used for the human evaluation of MT quality, namely evaluation based on Post-Editing (PE) and evaluation based on Direct Assessment (DA). To this purpose, we exploit a publicly available large dataset containing both types of evaluations. We first focus on PE and investigate how sensitive TER-based evaluation is to the type and number of references used. Then, we carry out a comparative analysis of PE and DA to investigate the extent to which the evaluation results obtained by methodologies addressing different human perspectives are similar. This comparison sheds light not only on PE but also on the so-called reference bias related to monolingual DA. Also, we analyze if and how the two methodologies can complement each other’s weaknesses.