pwc-archive/papers-with-abstracts huggingface dataset:
A Better Test of Choice Overload
Authors: Mark Dean, Dilip Ravindran, Jörg Stoye
Date: 2022-12-07 00:00:00

Choice overload - in which larger choice sets are detrimental to a chooser's well-being - is potentially of great importance in the design of economic policy. Yet the current evidence on its prevalence is inconclusive. We argue that existing tests are likely to be underpowered and hence that choice overload may occur more often than the literature suggests. We propose more powerful tests based on richer data and characterization theorems for the Random Utility Model. These new approaches come with significant econometric challenges, which we show how to address. We apply our tests to new experimental data and find strong evidence of choice overload that would likely be missed using current approaches.
Utility-Based Communication Requirements for Stable Matching in Large Markets
Authors: Naveen Durvasula
Date: 2022-12-08 00:00:00

Results from the communication complexity literature have demonstrated that stable matching requires communication: one cannot find or verify a stable match without having access to essentially all of the ordinal preference information held privately by the agents in the market. Stated differently, these results show that stable matching mechanisms are not robust to even a small number of labeled inaccuracies in the input preferences. In practice, these results indicate that agents must go through the time-intensive process of accurately ranking each and every potential match candidate if they wish for the resulting match to be guaranteedly stable. Thus, in large markets, communication requirements for stable matching may be impractically high. A natural question to ask, given this result, is whether some higher-order structure in the market can indicate which large markets have steeper communication requirements. In this paper, we perform such an analysis in a regime where agents have a utility-based notion of preference. We consider a dynamic model where agents only have access to an approximation of their utility that satisfies a universal multiplicative error bound. We apply guarantees from the theoretical computer science literature on low-distortion embeddings of finite metric spaces to understand the communication requirements of stable matching in large markets in terms of their structural properties. Our results show that for a broad family of markets, the error bound may not grow faster than $n^2\log(n)$ while maintaining a deterministic guarantee on the behavior of stable matching mechanisms in the limit. We also show that a stronger probabilistic guarantee may be made so long as the bound grows at most logarithmically in the underlying topological complexity of the market.
DRED: Deep REDundancy Coding of Speech Using a Rate-Distortion-Optimized Variational Autoencoder
Authors: Jean-Marc Valin, Jan Büthe, Ahmed Mustafa, Michael Klingbeil
Date: 2022-12-08 00:00:00
Tasks: Packet Loss Concealment

Despite recent advancements in packet loss concealment (PLC) using deep learning techniques, packet loss remains a significant challenge in real-time speech communication. Redundancy has been used in the past to recover the missing information during losses. However, conventional redundancy techniques are limited in the maximum loss duration they can cover and are often unsuitable for burst packet loss. We propose a new approach based on a rate-distortion-optimized variational autoencoder (RDO-VAE), allowing us to optimize a deep speech compression algorithm for the task of encoding large amounts of redundancy at very low bitrate. The proposed Deep REDundancy (DRED) algorithm can transmit up to 50x redundancy using less than 32 kb/s. Results show that DRED outperforms the existing Opus codec redundancy. We also demonstrate its benefits when operating in the context of WebRTC.
Improving block-based compensated wavelet lifting by reconstructing unconnected pixels
Authors: Wolfgang Schnurrer, Jürgen Seiler, André Kaup
Date: 2022-12-08 00:00:00
Tasks: Motion Compensation

This paper presents a new approach for improving the visual quality of the lowpass band of a compensated wavelet transform. A high quality of the lowpass band is very important as it can then be used as a downscaled version of the original signal. To adapt the transform to the signal, compensation methods can be implemented directly into the transform. We propose an improved inversion of the block-based motion compensation by processing unconnected pixels by a reconstruction method. We obtain a better subjective visual quality while furthermore saving up to 2.6% of bits for lossless coding.
3-D mesh compensated wavelet lifting for 3-D+t medical CT data
Authors: Wolfgang Schnurrer, Thomas Richter, Jürgen Seiler, Christian Herglotz, André Kaup
Date: 2022-12-08 00:00:00

For scalable coding, a high quality of the lowpass band of a wavelet transform is crucial when it is used as a downscaled version of the original signal. However, blur and motion can lead to disturbing artifacts. By incorporating feasible compensation methods directly into the wavelet transform, the quality of the lowpass band can be improved. The displacement in dynamic medical 3-D+t volumes from Computed Tomography is mainly given by expansion and compression of tissue over time and can be modeled well by mesh-based methods. We extend a 2-D mesh-based compensation method to three dimensions to obtain a volume compensation method that can additionally compensate deforming displacements in the third dimension. We show that a 3-D mesh can obtain a higher quality of the lowpass band by 0.28 dB with less than 40% of the model parameters of a comparable 2-D mesh. Results from lossless coding with JPEG 2000 3D and SPECK3D show that the compensated subbands using a 3-D mesh need about 6% less data compared to using a 2-D mesh.
DONEX: Real-time occupancy grid based dynamic echo classification for 3D point cloud
Authors: Niklas Stralau, Chengxuan Fu
Date: 2022-12-08 00:00:00
Tasks: Autonomous Driving

For driving assistance and autonomous driving systems, it is important to differentiate between dynamic objects such as moving vehicles and static objects such as guard rails. Among all the sensor modalities, RADAR and FMCW LiDAR can provide information regarding the motion state of the raw measurement data. On the other hand, perception pipelines using measurement data from ToF LiDAR typically can only differentiate between dynamic and static states on the object level. In this work, a new algorithm called DONEX was developed to classify the motion state of 3D LiDAR point cloud echoes using an occupancy grid approach. Through algorithmic improvements, e.g. 2D grid approach, it was possible to reduce the runtime. Scenarios, in which the measuring sensor is located in a moving vehicle, were also considered.
SISO-OFDM and MISO-OFDM Counterparts for "Wideband Waveforming for Integrated Data and Energy Transfer: Creating Extra Gain Beyond Multiple Antennas and Multiple Carriers"
Authors: Zhonglun Wang, Jie Hu, Kun Yang
Date: 2022-12-08 00:00:00

In this article, we proposethe SISO-OFDM and MISO-OFDM based IDET systems, which are the counterparts of our optimal wideband waveforming strategy in [1]. The fair-throughput and the sum-throughput optimisation problems are formulated. By exploiting the FP based alternating algorithm, we obtain the optimal fair-throughput and sum-throughput for both the SISO-OFDM and MISO-OFDM.
D2D-aided LoRaWAN LR-FHSS in Direct-to-Satellite IoT Networks
Authors: Alireza Maleki, Ha H. Nguyen, Ebrahim Bedeer, Robert Barton
Date: 2022-12-08 00:00:00
Tasks: Scheduling

In this paper, we present a device-to-device (D2D) transmission scheme for aiding long-range frequency hopping spread spectrum (LR-FHSS) LoRaWAN protocol with application in direct-to-satellite IoT networks. We consider a practical ground-to-satellite fading model, i.e. shadowed-Rice channel, and derive the outage performance of the LR-FHSS network. With the help of network coding, D2D-aided LR-FHSS transmission scheme is proposed to improve the network capacity for which a closed-form outage probability expression is also derived. The obtained analytical expressions for both LR-FHSS and D2D-aided LR-FHSS outage probabilities are validated by computer simulations for different parts of the analysis capturing the effects of noise, fading, unslotted ALOHA-based time scheduling, the receiver's capture effect, IoT device distributions, and distance from node to satellite. The total outage probability for the D2D-aided LR-FHSS shows a considerable increase of 249.9% and 150.1% in network capacity at a typical outage of 10^-2 for DR6 and DR5, respectively, when compared to LR-FHSS. This is obtained at the cost of minimum of one and maximum of two additional transmissions per each IoT end device imposed by the D2D scheme in each time-slot.
Adaptive Radar Detection and Bearing Estimation in the Presence of Unknown Mutual Coupling
Authors: Augusto Aubry, Antonio De Maio, Lan Lan, Massimo Rosamilia
Date: 2022-12-08 00:00:00

This paper deals with joint adaptive radar detection and target bearing estimation in the presence of mutual coupling among the array elements. First of all, a suitable model of the signal received by the multichannel radar is developed via a linearization procedure of the Uniform Linear Array (ULA) manifold around the nominal array looking direction together with the use of symmetric Toeplitz structured matrices to represent the mutual coupling effects. Hence, the Generalized Likelihood Ratio Test (GLRT) detector is evaluated under the assumption of homogeneous radar environment. Its computation leverages a specific Minorization-Maximization (MM) framework, with proven convergence properties, to optimize the concentrated likelihood function under the target presence hypothesis. Besides, when the number of active mutual coupling coefficients is unknown, a Multifamily Likelihood Ratio Test (MFLRT) approach is invoked. During the analysis phase, the performance of the new detectors is compared with benchmarks as well as with counterparts available in the open literature which neglect the mutual coupling phenomenon. The results indicate that it is necessary to consider judiciously the coupling effect since the design phase, to guarantee performance levels close to the benchmark.
Joint RIS Calibration and Multi-User Positioning
Authors: Yi Lu, Hui Chen, Jukka Talvitie, Henk Wymeersch, Mikko Valkama
Date: 2022-12-08 00:00:00
Tasks: State Estimation

Reconfigurable intelligent surfaces (RISs) are expected to be a key component enabling the mobile network evolution towards a flexible and intelligent 6G wireless platform. In most of the research works so far, RIS has been treated as a passive base station (BS) with a known state, in terms of its location and orientation, to boost the communication and/or terminal positioning performance. However, such performance gains cannot be guaranteed anymore when the RIS state is not perfectly known. In this paper, by taking the RIS state uncertainty into account, we formulate and study the performance of a joint RIS calibration and user positioning (JrCUP) scheme. From the Fisher information perspective, we formulate the JrCUP problem in a network-centric single-input multiple-output (SIMO) scenario with a single BS, and derive the analytical lower bound for the states of both user and RIS. We also demonstrate the geometric impact of different user locations on the JrCUP performance while also characterizing the performance under different RIS sizes. Finally, the study is extended to a multi-user scenario, shown to further improve the state estimation performance.
Spatial Deep Deconvolution U-Net for Traffic Analyses with Distributed Acoustic Sensing
Authors: Siyuan Yuan, Martijn van den Ende, Jingxiao Liu, Hae Young Noh, Robert Clapp, Cédric Richard, Biondo Biondi
Date: 2022-12-07 00:00:00

Distributed Acoustic Sensing (DAS) that transforms city-wide fiber-optic cables into a large-scale strain sensing array has shown the potential to revolutionize urban traffic monitoring by providing a fine-grained, scalable, and low-maintenance monitoring solution. However, the real-world application of DAS is hindered by challenges such as noise contamination and interference among closely traveling cars. In response, we introduce a self-supervised U-Net model that can suppress background noise and compress car-induced DAS signals into high-resolution pulses through spatial deconvolution. Our work extends recent research by introducing three key advancements. Firstly, we perform a comprehensive resolution analysis of DAS-recorded traffic signals, laying a theoretical foundation for our approach. Secondly, we incorporate space-domain vehicle wavelets into our U-Net model, enabling consistent high-resolution outputs regardless of vehicle speed variations. Finally, we employ L-2 norm regularization in the loss function, enhancing our model's sensitivity to weaker signals from vehicles in remote traffic lanes. We evaluate the effectiveness and robustness of our method through field recordings under different traffic conditions and various driving speeds. Our results show that our method can enhance the spatial-temporal resolution and better resolve closely traveling cars. The spatial deconvolution U-Net model also enables the characterization of large-size vehicles to identify axle numbers and estimate the vehicle length. Monitoring large-size vehicles also benefits imaging deep earth by leveraging the surface waves induced by the dynamic vehicle-road interaction.
A Paradigm Shift in Neuroscience Driven by Big Data: State of art, Challenges, and Proof of Concept
Authors: Zi-Xuan Zhou, Xi-Nian Zuo
Date: 2022-12-08 00:00:00

A recent editorial in Nature noted that cognitive neuroscience is at a crossroads where it is a thorny issue to reliably reveal brain-behavior associations. This commentary sketches a big data science way out for cognitive neuroscience, namely population neuroscience. In terms of design, analysis, and interpretations, population neuroscience research takes the design control to an unprecedented level, greatly expands the dimensions of the data analysis space, and paves a paradigm shift for exploring mechanisms on brain-behavior associations.
Explicit Caplet Implied Volatilities for Quadratic Term-Structure Models
Authors: Matthew Lorig, Natchanon Suaysom
Date: 2022-12-08 00:00:00

We derive an explicit asymptotic approximation for implied volatilities of caplets under the assumption that the short-rate is described by a generic quadratic term-structure model. In addition to providing an asymptotic accuracy result, we perform experiments in order to gauge the numerical accuracy of our approximation.
Optimal investment under partial information and robust VaR-type constraint
Authors: Nicole Bäuerle, An Chen
Date: 2022-12-08 00:00:00
Tasks: Vocal Bursts Type Prediction

This paper extends the utility maximization literature by combining partial information and (robust) regulatory constraints. Partial information is characterized by the fact that the stock price itself is observable by the optimizing financial institution, but the outcome of the market price of the risk $\theta$ is unknown to the institution. The regulator develops either a congruent or distinct perception of the market price of risk in comparison to the financial institution when imposing the Value-at-Risk (VaR) constraint. We also discuss a robust VaR constraint in which the regulator uses a worst-case measure. The solution to our optimization problem takes the same form as in the full information case: optimal wealth can be expressed as a decreasing function of state price density. The optimal wealth is equal to the minimum regulatory financing requirement in the intermediate economic states. The key distinction lies in the fact that the price density in the final state depends on the overall evolution of the estimated market price of risk, denoted as $\hat{\theta}(s)$ or that the upper boundary of the intermediate region exhibits stochastic behavior.
Designing with Non-Finite Output Dimension via Fourier Coefficients of Neural Waveforms
Authors: Jonathan S. Kent
Date: 2022-08-17 00:00:00

Ordinary Deep Learning models require having the dimension of their outputs determined by a human practitioner prior to training and operation. For design tasks, this places a hard limit on the maximum complexity of any designs produced by a neural network, which is disadvantageous if a greater allowance for complexity would result in better designs. In this paper, we introduce a methodology for taking outputs of non-finite dimension from neural networks, by learning a "neural waveform," and then taking as outputs the coefficients of its Fourier series representation. We then present experimental evidence that neural networks can learn in this setting on a toy problem.
Can Offline Reinforcement Learning Help Natural Language Understanding?
Authors: Ziqi Zhang, Yile Wang, Yue Zhang, Donglin Wang
Date: 2022-09-15 00:00:00
Tasks: Language Modeling, Language Modelling, Natural Language Understanding, Offline RL, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Pre-training has been a useful method for learning implicit transferable knowledge and it shows the benefit of offering complementary features across different modalities. Recent work mainly focuses on the modalities such as image and text, for example, studies show that visual features learned from images can help visual-grounded language understanding. In this paper, we consider investigating the potential connection between offline reinforcement learning (RL) and language modeling (LM). Intuitively, RL and LM are similar in predicting the next states based on the current and previous states, which rely on both local and long-range dependency across states. To validate such an assumption, we pre-trained different offline RL tasks using Transformer and then evaluate these models on various language-related tasks. Experimental results show that our RL pre-trained models can give close performance compared with the models using the LM training objective, showing that there exist common useful features across these two modalities. To further explore the potential relationship, we investigate some factors such as Markov property and the sequential nature of RL trajectory.
Montague semantics and modifier consistency measurement in neural language models
Authors: Danilo S. Carvalho, Edoardo Manino, Julia Rozanova, Lucas Cordeiro, André Freitas
Date: 2022-10-10 00:00:00
Tasks: Fairness

This work proposes a novel methodology for measuring compositional behavior in contemporary language embedding models. Specifically, we focus on adjectival modifier phenomena in adjective-noun phrases. In recent years, distributional language representation models have demonstrated great practical success. At the same time, the need for interpretability has elicited questions on their intrinsic properties and capabilities. Crucially, distributional models are often inconsistent when dealing with compositional phenomena in natural language, which has significant implications for their safety and fairness. Despite this, most current research on compositionality is directed towards improving their performance on similarity tasks only. This work takes a different approach, introducing three novel tests of compositional behavior inspired by Montague semantics. Our experimental results indicate that current neural language models do not behave according to the expected linguistic theories. This indicates that current language models may lack the capability to capture the semantic properties we evaluated on limited context, or that linguistic theories from Montagovian tradition may not match the expected capabilities of distributional models.
PhraseTransformer: An Incorporation of Local Context Information into Sequence-to-sequence Semantic Parsing
Authors: Phuong Minh Nguyen, Tung Le, Huy Tien Nguyen, Vu Tran, Minh Le Nguyen
Date: 2022-11-29 00:00:00
Proceeding: Applied Intelligence 2022 11
Tasks: Machine Translation, NMT, Semantic Parsing, Sentence, Translation

Semantic parsing is a challenging task mapping a natural language utterance to machine-understandable information representation. Recently, approaches using neural machine translation (NMT) have achieved many promising results, especially the Transformer. However, the typical drawback of adapting the vanilla Transformer to semantic parsing is that it does not consider the phrase in expressing the information of sentences while phrases play an important role in constructing the sentence meaning. Therefore, we propose an architecture, PhraseTransformer, that is capable of a more detailed meaning representation by learning the phrase dependencies in the sentence. The main idea is to incorporate Long Short-Term Memory into the Self-Attention mechanism of the original Transformer to capture the local context of a word. Experimental results show that our proposed model performs better than the original Transformer in terms of understanding sentences structure as well as logical representation and raises the model local context-awareness without any support from external tree information. Besides, although the recurrent architecture is integrated, the number of sequential operations of the PhraseTransformer is still (1) similar to the original Transformer. Our proposed model achieves strong competitive performance on Geo and MSParS datasets, and leads to SOTA performance on the Atis dataset for methods using neural networks. In addition, to prove the generalization of our proposed model, we also conduct extensive experiments on three translation datasets IWLST14 German-English, IWSLT15 Vietnamese-English, WMT14 English-German, and show significant improvement. Our code is available at https://github.com/phuongnm94/PhraseTransformer.git.
Detection algorithm of defects on polyethylene gas pipe using image recognition
Authors: Cong Li a, Hui-Qing Lan a, *, Ya-Nan Sun a, Jun-Qiang Wang b, **
Date: 2021-01-01 00:00:00
Proceeding: International Journal of Pressure Vessels and Piping 2021 1
Tasks: Defect Detection, Edge Detection

Aiming at the defects in the polyethylene (PE) gas pipeline, a defect detection algorithm based on image 
recognition is proposed. Firstly, the collected image is preliminarily screened to obtain the image with defects. 
Gamma correction algorithm is used to enhance the image, and then dual filtering is used to eliminate the 
interference of noise on image recognition. Then the defect edge is extracted by the improved traditional Sobel 
edge detection algorithm. Image defects are segmented by adaptive threshold method, and defect contours are 
effectively extracted through an open operation. Finally, the defect feature parameters are extracted and inputted 
into an optimized support vector machine for defect recognition. The experimental results show that the 
detection algorithm in this paper can effectively improve the observability of the image. Through the trained 
support vector machine, it can effectively identify the open joint, crack, and deformation defects. The recognition 
rate of pipeline image defects reached 96.3%.