pwc-archive/papers-with-abstracts huggingface dataset:
A Multi-level Acoustic Feature Extraction Framework for Transformer Based End-to-End Speech Recognition
Authors: Jin Li, Rongfeng Su, Xurong Xie, Nan Yan, Lan Wang
Date: 2021-08-18 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Decoder, Diversity, Feature Correlation, speech-recognition, Speech Recognition

Transformer based end-to-end modelling approaches with multiple stream inputs have been achieved great success in various automatic speech recognition (ASR) tasks. An important issue associated with such approaches is that the intermediate features derived from each stream might have similar representations and thus it is lacking of feature diversity, such as the descriptions related to speaker characteristics. To address this issue, this paper proposed a novel multi-level acoustic feature extraction framework that can be easily combined with Transformer based ASR models. The framework consists of two input streams: a shallow stream with high-resolution spectrograms and a deep stream with low-resolution spectrograms. The shallow stream is used to acquire traditional shallow features that is beneficial for the classification of phones or words while the deep stream is used to obtain utterance-level speaker-invariant deep features for improving the feature diversity. A feature correlation based fusion strategy is used to aggregate both features across the frequency and time domains and then fed into the Transformer encoder-decoder module. By using the proposed multi-level acoustic feature extraction framework, state-of-the-art word error rate of 21.7% and 2.5% were obtained on the HKUST Mandarin telephone and Librispeech speech recognition tasks respectively.
FDN: Finite Difference Network with Hierarchical Convolutional Features for Text-independent Speaker Verification
Authors: Jin Li, Nan Yan, Lan Wang
Date: 2021-08-18 00:00:00
Tasks: Rhythm, Speaker Verification, Text-Independent Speaker Verification

In recent years, using raw waveforms as input for deep networks has been widely explored for the speaker verification system. For example, RawNet and RawNet2 extracted speaker's feature embeddings from waveforms automatically for recognizing their voice, which can vastly reduce the front-end computation and obtain state-of-the-art performance. However, these models do not consider the speaker's high-level behavioral features, such as intonation, indicating each speaker's universal style, rhythm, \textit{etc}. This paper presents a novel network that can handle the intonation information by computing the finite difference of different speakers' utterance variations. Furthermore, a hierarchical way is also designed to enhance the intonation property from coarse to fine to improve the system accuracy. The high-level intonation features are then fused with the low-level embedding features. Experimental results on official VoxCeleb1 test data, VoxCeleb1-E, and VoxCeleb-H protocols show our method outperforms and robustness existing state-of-the-art systems. To facilitate further research, code is available at https://github.com/happyjin/FDN
Combined neural network-based intra prediction and transform selection
Authors: Thierry Dumas, Franck Galpin, Philippe Bordes
Date: 2021-08-18 00:00:00
Tasks: Prediction

The interactions between different tools added successively to a block-based video codec are critical to its rate-distortion efficiency. In particular, when deep neural network-based intra prediction modes are inserted into a block-based video codec, as the neural network-based prediction function cannot be easily characterized, the adaptation of the transform selection process to the new modes can hardly be performed manually. That is why this paper presents a combined neural network-based intra prediction and transform selection for a block-based video codec. When putting a single neural network-based intra prediction mode and the learned prediction of the selected LFNST pair index into VTM-8.0, -3.71%, -3.17%, and -3.37% of mean BD-rate reduction in all-intra is obtained.
Modular Sparse Conical Multi-beam Phased Array Design for Air Traffic Control Radar
Authors: Paolo Rocca, Nicola Anselmi, Mohammad Abdul Hannan, Andrea Massa
Date: 2021-08-18 00:00:00
Tasks: Compressive Sensing

The design of a conical phased array antenna for air traffic control (ATC) radar systems is addressed in this work. The array, characterized by a fully digital beam-forming (DBF) architecture, is composed of equal vertical modules consisting of linear sparse arrays able to generate on receive multiple instantaneous beams pointing along different elevation directions. The synthesis problem is cast in the Compressive Sensing (CS) framework to achieve the best trade-off between the antenna complexity (i.e., minimum number of array elements and/or radio frequency components) and radiation performance (i.e., matching of a set of reference patterns). Towards this aim, the positions of the array elements and the set of complex element excitations of each beam are jointly defined through a customized CS-based optimization tool. Representative numerical results, concerned with ideal as well as real antenna models, are reported and discussed to validate the proposed design strategy and point out the features of the deigned modular sparse arrays also in comparison with those obtained from conventional arrays with uniformly spaced elements.
Multi-Target Localization Using Polarization Sensitive Arrays
Authors: William W. Howard, R. Michael Buehrer
Date: 2021-08-18 00:00:00

In this work we develop clustering techniques for the Bearing Only Target Localization (BOTL) problem. Our scenario has a receiver move along some path, generating bearing estimates on some interval. Multiple emitting targets exist in the environment, and may be physically close with respect to the receiver distance. The first method is iterative and uses only a series of observed bearings to multiple targets to establish clusters. In each iteration, target positions are estimated using a nonlinear least squares solution. The second technique uses additional polarization information, and is shown to be more effective while requiring more information. In addition the second technique is non-iterative and requires far less computation. In this work we presume knowledge of the number of targets. We conclude by providing simulations of our method and show that the proposed approach outperforms previously proposed methods.
Adaptive Radar Detection in Heterogeneous Clutter-dominated Environments
Authors: Angelo Coluccia, Danilo Orlando, Giuseppe Ricci
Date: 2021-08-18 00:00:00

In this paper, we propose a new solution for the detection problem of a coherent target in heterogeneous environments. Specifically, we first assume that clutter returns from different range bins share the same covariance structure but different power levels. This model meets the experimental evidence related to non-Gaussian and non-homogeneous scenarios. Then, unlike existing solutions that are based upon estimate and plug methods, we propose an approximation of the generalized likelihood ratio test where the maximizers of the likelihoods are obtained through an alternating estimation procedure. Remarkably, we also prove that such estimation procedure leads to an architecture possessing the constant false alarm rate (CFAR) when a specific initialization is used. The performance analysis, carried out on simulated as well as measured data and in comparison with suitable well-known competitors, highlights that the proposed architecture can overcome the CFAR competitors and exhibits a limited loss with respect to the other non-CFAR detectors.
Experimental Study of Outdoor UAV Localization and Tracking using Passive RF Sensing
Authors: Udita Bhattacherjee, Ender Ozturk, Ozgur Ozdemir, Ismail Guvenc, Mihail L. Sichitiu, Huaiyu Dai
Date: 2021-08-17 00:00:00

Extensive use of unmanned aerial vehicles (UAVs) is expected to raise privacy and security concerns among individuals and communities. In this context, the detection and localization of UAVs will be critical for maintaining safe and secure airspace in the future. In this work, Keysight N6854A radio frequency (RF) sensors are used to detect and locate a UAV by passively monitoring the signals emitted from the UAV. First, the Keysight sensor detects the UAV by comparing the received RF signature with various other UAVs' RF signatures in the Keysight database using an envelope detection algorithm. Afterward, time difference of arrival (TDoA) based localization is performed by a central controller using the sensor data, and the drone is localized with some error. To mitigate the localization error, implementation of an extended Kalman filter~(EKF) is proposed in this study. The performance of the proposed approach is evaluated on a realistic experimental dataset. EKF requires basic assumptions on the type of motion throughout the trajectory, i.e., the movement of the object is assumed to fit some motion model~(MM) such as constant velocity (CV), constant acceleration (CA), and constant turn (CT). In the experiments, an arbitrary trajectory is followed, therefore it is not feasible to fit the whole trajectory into a single MM. Consequently, the trajectory is segmented into sub-parts and a different MM is assumed in each segment while building the EKF model. Simulation results demonstrate an improvement in error statistics when EKF is used if the MM assumption aligns with the real motion.
New gene formation in hybrid Drosophila
Authors: Rebekah L. Rogers, Cathy C. Moore, Nicholas B. Stewart
Date: 2021-08-17 00:00:00

The origin of new genes is among the most fundamental processes underlying genetic innovation. The substrate of new genetic material available defines the outcomes of evolutionary processes in nature. Historically, the field of genetic novelty has commonly invoked new mutations at the DNA level to explain the ways that new genes might originate. In this work, we explore a fundamentally different source of epistatic interactions that can create new gene sequences in hybrids. We observe "bursts" of new gene creation in F1 hybrids of D. yakuba and D. santomea, a species complex known to hybridize in nature. The number of new genes is higher in the gonads than soma. We observe asymmetry in new gene creation based on the direction of the cross. Greater numbers of new transcripts form in the testes of F1 male offspring in D. santomea female x D. yakuba male crosses and greater numbers of new transcripts appear in ovaries of F1 female offspring of D. yakuba female x D. santomea male. These loci represent wholly new transcripts expressed in hybrids, but not in either parental reference strain of the cross. We further observe allelic activation, where transcripts silenced in one lineage are activated by the transcriptional machinery of the other genome. These results point to a fundamentally new model of new gene creation that does not rely on the formation of new mutations in the DNA. These results suggest that bursts of genetic novelty can appear in response to hybridization or introgression in a single generation. Ultimately these processes are expected to contribute to the substrate of genetic novelty available in nature, with broad impacts on our understanding new gene formation and on hybrid phenotypes in nature.
Memory capacity of neural network models
Authors: Stefano Fusi
Date: 2021-08-17 00:00:00
Tasks: Retrieval

Memory is a complex phenomenon that involves several distinct mechanisms. These mechanisms operate at different spatial and temporal levels. This chapter focuses on the theoretical framework and the mathematical models that have been developed to understand how these mechanisms are orchestrated to store, preserve and retrieve a large number of memories. In particular, this chapter reviews the theoretical studies on memory capacity, in which the investigators estimated how the number of storable memories scales with the number of neurons and synapses in the neural circuitry. The memory capacity depends on the complexity of the synapses, the sparseness of the representations, the spatial and temporal correlations between memories and the specific way memories are retrieved. Complexity is important when the synapses can only be modified with a limited precision, as in the case of biological synapses, and sparseness can greatly increase memory capacity and be particularly beneficial when memories are structured (correlated to each other). The theoretical tools discussed by this chapter can be harnessed to identify the important computational principles that underlie memory storage, preservation and retrieval and provide guidance in designing and interpreting memory experiments.
Recommendations on the use and reporting of race, ethnicity, and ancestry in genetic research: experiences from the NHLBI Trans-Omics for Precision Medicine (TOPMed) program
Authors: Alyna T. Khan, Stephanie M. Gogarten, Caitlin P. McHugh, Adrienne M. Stilp, Tamar Sofer, Michael Bowers, Quenna Wong, L. Adrienne Cupples, Bertha Hidalgo, Andrew D. Johnson, Merry-Lynn McDonald, Stephen T. McGarvey, Matthew R. G. Taylor, Stephanie M. Fullerton, Matthew P. Conomos, Sarah C. Nelson
Date: 2021-08-17 00:00:00
Tasks: ARC

The ways in which race, ethnicity, and ancestry are used and reported in human genomics research has wide-ranging implications for how research is translated into clinical care, incorporated into public understanding, and implemented in public policy. Genetics researchers play an essential role in proactively dismantling genetic conceptions of race and in recognizing the social and structural factors that drive health disparities. Here, we offer commentary and concrete recommendations on the use and reporting of race, ethnicity, and ancestry across the arc of genetic research, including terminology, data harmonization, analysis, and reporting. While informed by our experiences as researchers in the NHLBI Trans-Omics for Precision Medicine (TOPMed) program, the recommendations are broadly applicable to basic and translational genomic research in diverse populations. To fully realize the benefit of diversifying genetics research beyond primarily European ancestry populations, we as genetics researchers need to make structural changes to the research process and within the research community. Considerable collaborative effort and ongoing reflection will be required to root out elements of racism from the field and generate scientific knowledge that yields broad and equitable benefit.
Combined Trojan Y Chromosome Strategy and Sterile Insect Technique to Eliminate Mosquitoes: Modelling and Analysis
Authors: Jingjing Lyu, Musong Gu, Sheng Wang
Date: 2021-08-17 00:00:00

Sterile insect technique has been successfully applied in the control of agricultural pests, however, it has a limited ability to control mosquitoes. A promising alternative approach is Trojan Y Chromosome strategy, which works by manipulating the sex ratio of a population through the introduction of feminized Y Y supermales that guarantee male offspring. A combined Trojan Y chromosome strategy and sterile insect technique (TYC-SIT) strategy is modeled with ordinary differential equations that allow the kinetics of the female population decline of mosquitoes to be evaluated under identical modeling conditions. The dynamical analysis leads to results on both local and global stabilities of this combined model. Optimal control analysis is also implemented to investigate the optimal mechanisms for extinction of mosquitoes. In particular, the numerical results affirm that the combined TYC-SIT enables near elimination of mosquitoes. The conclusion has great significance for pest controls.
The Generalized Gamma distribution as a useful RND under Heston's stochastic volatility model
Authors: Ben Boukai
Date: 2021-08-18 00:00:00

Following Boukai (2021) we present the Generalized Gamma (GG) distribution as a possible RND for modeling European options prices under Heston's (1993) stochastic volatility (SV) model. This distribution is seen as especially useful in situations in which the spot's price follows a negatively skewed distribution and hence, Black-Scholes based (i.e. the log-normal distribution) modeling is largely inapt. We apply the GG distribution as RND to modeling current market option data on three large market-index ETFs, namely the SPY, IWM and QQQ as well as on the TLT (an ETF that tracks an index of long term US Treasury bonds). The current option chain of each of the three market-index ETFs shows of a pronounced skew of their volatility `smile' which indicates a likely distortion in the Black-Scholes modeling of such option data. Reflective of entirely different market expectations, this distortion appears not to exist in the TLT option data. We provide a thorough modeling of the available option data we have on each ETF (with the October 15, 2021 expiration) based on the GG distribution and compared it to the option pricing and RND modeling obtained directly from a well-calibrated Heston's (1993) SV model (both theoretically and empirically, using Monte-Carlo simulations of the spot's price). All three market-index ETFs exhibit negatively skewed distributions which are well-matched with those derived under the GG distribution as RND. The inadequacy of the Black-Scholes modeling in such instances which involve negatively skewed distribution is further illustrated by its impact on the hedging factor, delta, and the immediate implications to the retail trader. In contrast, for the TLT ETF, which exhibits no such distortion to the volatility `smile', the three pricing models (i.e. Heston's, Black-Scholes and Generalized Gamma) appear to yield similar results.
Three-Dimensional Bin Packing and Mixed-Case Palletization
Authors: Samir Elhedhli, Fatma Gzara, Burak Yildiz
Date: 2019-07-08 00:00:00
Proceeding: INFORMS 2019 7
Tasks: 3D Bin Packing

Despite its wide range of applications, the three-dimensional bin-packing problem is still one of the most difficult optimization problems to solve. Currently, medium- to large-size instances are only solved heuristically and remain out of reach of exact methods. This is particularly true for its practical variant, the mixed-case palletization problem, where item support is needed. This and the lack of a realistic benchmark data set are identified as major research gaps by a recent survey. In this work, we propose a novel formulation and a column-generation solution approach, where the pricing subproblem is a two-dimensional layer-generation problem. Layers are highly desirable in practical packings as they are easily packable and can accommodate important practical constraints such as item support, family groupings, isle friendliness, and load bearing. Being key to the success of the column-generation approach, the pricing subproblem is solved optimally as well as heuristically and is enhanced by using item grouping, item replacement, layer reorganization, and layer spacing. We conduct extensive computational experiments and compare against existing approaches. We also use industrial data to train and propose a realistic data set. The proposed approach outperforms the best-performing algorithm in the literature on most instances and succeeds to solve practical size instances in very reasonable computational times.
Perturb, Predict & Paraphrase: Semi-Supervised Learning using Noisy Student for Image Captioning
Authors: Arjit Jain, Pranay Reddy Samala, Preethi Jyothi, Deepak Mittal, Maneesh Singh
Date: 2021-08-19 00:00:00
Proceeding: IJCAI 2021 8
Tasks: Data Augmentation, Image Augmentation, Image Captioning, image-classification, Semi Supervised Learning for Image Captioning

Recent semi-supervised learning (SSL) methods are predominantly focused on multi-class classification tasks. Classification tasks allow for easy mixing of class labels during augmentation which does not trivially extend to structured outputs such as word sequences that appear in tasks like image captioning. Noisy Student Training is a recent SSL paradigm proposed for image classification that is an extension of self-training and teacher-student learning. In this work, we provide an in-depth analysis of the noisy student SSL framework for the task of image captioning and derive state-of-the-art results. The original algorithm relies on computationally expensive data augmentation steps that involve perturbing the raw images and computing features for each perturbed image. We show that, even in the absence of raw image augmentation, the use of simple model and feature perturbations to the input images for the student model are beneficial to SSL training. We also show how a paraphrase generator could be effectively used for label augmentation to improve the quality of pseudo labels and significantly improve performance. Our final results in the limited labeled data setting (1% of the MS-COCO labeled data) outperform previous state-of-the-art approaches by 2.5 on BLEU4 and 11.5 on CIDEr scores.
Amplitude-Phase Recombination: Rethinking Robustness of Convolutional Neural Networks in Frequency Domain
Authors: Guangyao Chen, Peixi Peng, Li Ma, Jia Li, Lin Du, Yonghong Tian
Date: 2021-08-19 00:00:00
Proceeding: ICCV 2021 10
Tasks: Adversarial Attack, Data Augmentation, Domain Generalization, Out-of-Distribution Detection

Recently, the generalization behavior of Convolutional Neural Networks (CNN) is gradually transparent through explanation techniques with the frequency components decomposition. However, the importance of the phase spectrum of the image for a robust vision system is still ignored. In this paper, we notice that the CNN tends to converge at the local optimum which is closely related to the high-frequency components of the training images, while the amplitude spectrum is easily disturbed such as noises or common corruptions. In contrast, more empirical studies found that humans rely on more phase components to achieve robust recognition. This observation leads to more explanations of the CNN's generalization behaviors in both robustness to common perturbations and out-of-distribution detection, and motivates a new perspective on data augmentation designed by re-combing the phase spectrum of the current image and the amplitude spectrum of the distracter image. That is, the generated samples force the CNN to pay more attention to the structured information from phase components and keep robust to the variation of the amplitude. Experiments on several image datasets indicate that the proposed method achieves state-of-the-art performances on multiple generalizations and calibration tasks, including adaptability for common corruptions and surface variations, out-of-distribution detection, and adversarial attack.
Blind Identification of State-Space Models in Physical Coordinates
Authors: Runzhe Han, Christian Bohn, Georg Bauer
Date: 2021-08-19 00:00:00
Tasks: Audio Signal Processing, State Space Models, Structural Health Monitoring

Blind identification is popular for modeling a system without the input information, such as in the research areas of structural health monitoring and audio signal processing. Existing blind identification methods have both advantages and disadvantages, in this paper, we briefly outline current methods and propose a novel blind identification method for identifying state-space models in physical coordinates. The idea behind this proposed method is first to regard the collected input data of a state-space model as a part of a periodic signal sequence, and then transform the state-space model with input and output into a model without input by augmenting the state-space model with the input model (which is a periodic signal model), and afterwards use merely the output information to identify a state-space model up to a similarity transformation, and finally derive the state-space model in physical coordinates by using a unique similarity transformation. With the above idea, physical parameters and modal parameters of a state-space system can be obtained. Both numerical and practical examples were used to validate the proposed method. The result showed the effectiveness of the novel blind identification method.