pwc-archive/papers-with-abstracts huggingface dataset:
OFDM-guided Deep Joint Source Channel Coding for Wireless Multipath Fading Channels
Authors: Mingyu Yang, Chenghong Bian, Hun-Seok Kim
Date: 2021-09-11 00:00:00
Tasks: Decoder

We investigate joint source channel coding (JSCC) for wireless image transmission over multipath fading channels. Inspired by recent works on deep learning based JSCC and model-based learning methods, we combine an autoencoder with orthogonal frequency division multiplexing (OFDM) to cope with multipath fading. The proposed encoder and decoder use convolutional neural networks (CNNs) and directly map the source images to complex-valued baseband samples for OFDM transmission. The multipath channel and OFDM are represented by non-trainable (deterministic) but differentiable layers so that the system can be trained end-to-end. Furthermore, our JSCC decoder further incorporates explicit channel estimation, equalization, and additional subnets to enhance the performance. The proposed method exhibits 2.5 -- 4 dB SNR gain for the equivalent image quality compared to conventional schemes that employ state-of-the-art but separate source and channel coding such as BPG and LDPC. The performance further improves when the system incorporates the channel state information (CSI) feedback. The proposed scheme is robust against OFDM signal clipping and parameter mismatch for the channel model used in training and evaluation.
Feasibility of Modeling Orthogonal Frequency-Division Multiplexing Communication Signals with Unsupervised Generative Adversarial Networks
Authors: Jack Sklar, Adam Wunderlich
Date: 2021-09-10 00:00:00

High-quality recordings of radio frequency (RF) emissions from commercial communication hardware in realistic environments are often needed to develop and assess spectrum-sharing technologies and practices, e.g., for training and testing spectrum sensing algorithms and for interference testing. Unfortunately, the time-consuming, expensive nature of such data collections together with data-sharing restrictions pose significant challenges that limit data set availability. Furthermore, developing accurate models of real-world RF emissions from first principles is often very difficult because system parameters and implementation details are at best only partially known, and complex system dynamics are difficult to characterize. Hence, there is a need for flexible, data-driven methods that can leverage existing data sets to synthesize additional similar waveforms. One promising machine-learning approach is unsupervised deep generative modeling with generative adversarial networks (GANs). To date, GANs for RF communication signals have not been studied thoroughly. In this paper, we present the first in-depth investigation of generated signal fidelity for GANs trained with baseband orthogonal frequency-division multiplexing (OFDM) signals, where each subcarrier is digitally modulated with quadrature amplitude modulation (QAM). Building on prior GAN methods, we developed two novel GAN models and evaluated their performance using simulated data sets with known ground truth. Specifically, we investigated model performance with respect to increasing data set complexity over a range of OFDM parameters and conditions, including fading channels. The findings presented here inform the feasibility of use cases and provide a foundation for further investigations into deep generative models for RF communication signals.
Minimizing AoI in Resource-Constrained Multi-Source Relaying Systems with Stochastic Arrivals
Authors: Abolfazl Zakeri, Mohammad Moltafet, Markus Leinonen, Marian Codreanu
Date: 2021-09-10 00:00:00
Tasks: Stochastic Optimization

We consider a multi-source relaying system where the sources independently and randomly generate status update packets which are sent to the destination with the aid of a bufferaided relay through unreliable links. We formulate a stochastic optimization problem aiming to minimize the sum average age of information (AAoI) of sources under per-slot transmission capacity constraints and a long-run average resource constraint. To solve the problem, we recast it as a constrained Markov decision process (CMDP) problem and adopt the Lagrangian method. We analyze the structure of an optimal policy for the resulting MDP problem that possesses a switching-type structure. We propose an algorithm that obtains a stationary deterministic near-optimal policy, establishing a benchmark for the system. Simulation results show the effectiveness of our algorithm compared to benchmark algorithms.
Rate of coalescence of pairs of lineages in the spatial λ-Fleming-Viot process
Authors: Johannes Wirtz, Stéphane Guindon
Date: 2021-09-13 00:00:00

We revisit the spatial ${\lambda}$-Fleming-Viot process introduced in [1]. Particularly, we are interested in the time $T_0$ to the most recent common ancestor for two lineages. We distinguish between the case where the process acts on the entire two-dimensional plane, and on a finite rectangle. Utilizing a differential equation linking $T_0$ with the physical distance between the lineages, we arrive at simple and reasonably accurate approximation schemes for both cases. Furthermore, our analysis enables us to address the question of whether the genealogical process of the model "comes down from infinity", which has been partly answered before in [2].
Restricted Hidden Cardinality Constraints in Causal Models
Authors: Beata Zjawin, Elie Wolfe, Robert W. Spekkens
Date: 2021-09-13 00:00:00

Causal models with unobserved variables impose nontrivial constraints on the distributions over the observed variables. When a common cause of two variables is unobserved, it is impossible to uncover the causal relation between them without making additional assumptions about the model. In this work, we consider causal models with a promise that unobserved variables have known cardinalities. We derive inequality constraints implied by d-separation in such models. Moreover, we explore the possibility of leveraging this result to study causal influence in models that involve quantum systems.
An interdisciplinary approach to high school curriculum development: Swarming Powered by Neuroscience
Authors: Elise Buckley, Joseph D. Monaco, Kevin M. Schultz, Robert Chalmers, Armin Hadzic, Kechen Zhang, Grace M. Hwang, M. Dwight Carr
Date: 2021-09-12 00:00:00

This article discusses how to create an interactive virtual training program at the intersection of neuroscience, robotics, and computer science for high school students. A four-day microseminar, titled Swarming Powered by Neuroscience (SPN), was conducted virtually through a combination of presentations and interactive computer game simulations, delivered by subject matter experts in neuroscience, mathematics, multi-agent swarm robotics, and education. The objective of this research was to determine if taking an interdisciplinary approach to high school education would enhance the students learning experiences in fields such as neuroscience, robotics, or computer science. This study found an improvement in student engagement for neuroscience by 16.6%, while interest in robotics and computer science improved respectively by 2.7% and 1.8%. The curriculum materials, developed for the SPN microseminar, can be used by high school teachers to further evaluate interdisciplinary instructions across life and physical sciences and computer science.
Online Optimization of Stimulation Speed in an Auditory Brain-Computer Interface under Time Constraints
Authors: Jan Sosulski, David Hübner, Aaron Klein, Michael Tangermann
Date: 2021-08-26 00:00:00
Tasks: Bayesian Optimization, Brain Computer Interface

The decoding of brain signals recorded via, e.g., an electroencephalogram, using machine learning is key to brain-computer interfaces (BCIs). Stimulation parameters or other experimental settings of the BCI protocol typically are chosen according to the literature. The decoding performance directly depends on the choice of parameters, as they influence the elicited brain signals and optimal parameters are subject-dependent. Thus a fast and automated selection procedure for experimental parameters could greatly improve the usability of BCIs. We evaluate a standalone random search and a combined Bayesian optimization with random search in a closed-loop auditory event-related potential protocol. We aimed at finding the individually best stimulation speed -- also known as stimulus onset asynchrony (SOA) -- that maximizes the classification performance of a regularized linear discriminant analysis. To make the Bayesian optimization feasible under noise and the time pressure posed by an online BCI experiment, we first used offline simulations to initialize and constrain the internal optimization model. Then we evaluated our approach online with 13 healthy subjects. We could show that for 8 out of 13 subjects, the proposed approach using Bayesian optimization succeeded to select the individually optimal SOA out of multiple evaluated SOA values. Our data suggests, however, that subjects were influenced to very different degrees by the SOA parameter. This makes the automatic parameter selection infeasible for subjects where the influence is limited. Our work proposes an approach to exploit the benefits of individualized experimental protocols and evaluated it in an auditory BCI. When applied to other experimental parameters our approach could enhance the usability of BCI for different target groups -- specifically if an individual disease progress may prevent the use of standard parameters.
Analytical Investigation of Long-time Diffusion Dynamics in a Synaptic Channel with Glial Cells
Authors: Enes Oncu, Halil U. Ozdemir, Halil I. Orhan, Bayram Cevdet Akdeniz, Asaf Toprakci, Anil Aslihak, H. Birkan Yilmaz, Ali E. Pusane, Tuna Tugcu, Fatih Dinc
Date: 2021-08-21 00:00:00

In this letter, we first derive the analytical channel impulse response for a cylindrical synaptic channel surrounded by glial cells and validate it with particle-based simulations. Afterwards, we provide an accurate analytical approximation for the long-time decay rate of the channel impulse response by employing Taylor expansion to the characteristic equations that determine the decay rates of the system. We validate our approximation by comparing it with the numerical decay rate obtained from the characteristic equation. Overall, we provide a fully analytical description for the long-time behavior of synaptic diffusion, e.g., the clean-up processes inside the channel after communication has long concluded.
Options Pricing under Bayesian MS-VAR Process
Authors: Battulga Gankhuu
Date: 2021-09-13 00:00:00

In this paper, we have studied option pricing methods that are based on a Bayesian Markov-Switching Vector Autoregressive (MS-BVAR) process using a risk-neutral valuation approach. A BVAR process, which is a special case of the Bayesian MS-VAR process is widely used to model interdependencies of economic variables and forecast economic variables. Here we assumed that a regime-switching process is generated by a homogeneous Markov process and a residual process follows a conditional heteroscedastic model. With a direct calculation and change of probability measure, for some frequently used options, we derived pricing formulas. An advantage of our model is it depends on economic variables and is easy to use compared to previous option pricing papers, which depend on regime-switching.
A note on closed-form spread option valuation under log-normal models
Authors: Nuerxiati Abudurexiti, Kai He, Dongdong Hu, Hasanjan Sayit
Date: 2021-09-12 00:00:00
Tasks: Form

In the papers Carmona and Durrleman [7] and Bjerksund and Stensland [1], closed form approximations for spread call option prices were studied under the log normal models. In this paper, we give an alternative closed form formula for the price of spread call options under the log-normal models also. Our formula can be seen as a generalization of the closed-form formula presented in Bjerksund and Stensland [1] as their formula can be obtained by selecting special parameter values to our formula. Numerical tests show that our formula performs better for certain range of model parameters than the closed-form formula presented in Bjerksund and Stensland [1].
On certain representations of pricing functionals
Authors: Carlo Marinelli
Date: 2021-09-12 00:00:00

We revisit two classical problems: the determination of the law of the underlying with respect to a risk-neutral measure on the basis of option prices, and the pricing of options with convex payoffs in terms of prices of call options with the same maturity (all options are European). The formulation of both problems is expressed in a language loosely inspired by the theory of inverse problems, and several proofs of the corresponding solutions are provided that do not rely on any special assumptions on the law of the underlying and that may, in some cases, extend results currently available in the literature.
Financial Trading with Feature Preprocessing and Recurrent Reinforcement Learning
Authors: Lin Li
Date: 2021-09-11 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Financial trading aims to build profitable strategies to make wise investment decisions in the financial market. It has attracted interests in the machine learning community for a long time. This paper proposes to trade financial assets automatically using feature preprocessing skills and Recurrent Reinforcement Learning (RRL) algorithm. The strategy starts from technical indicators extracted from assets' market information. Then these technical indicators are preprocessed by Principal Component Analysis (PCA) and Discrete Wavelet Transform (DWT) and eventually inputted to the RRL algorithm to do the trading. The extensive empirical evidence shows that the proposed strategy is not only effective and robust in its performance, but also can mitigate the drawbacks underlying the initial trading using RRL.
CoviHawkes: Temporal Point Process and Deep Learning based Covid-19 forecasting for India
Authors: Ambedkar Dukkipati, Tony Gracious, Shubham Gupta
Date: 2021-09-08 00:00:00
Tasks: Point Processes

Lockdowns are one of the most effective measures for containing the spread of a pandemic. Unfortunately, they involve a heavy financial and emotional toll on the population that often outlasts the lockdown itself. This article argues in favor of ``local'' lockdowns, which are lockdowns focused on regions currently experiencing an outbreak. We propose a machine learning tool called CoviHawkes based on temporal point processes, called CoviHawkes that predicts the daily case counts for Covid-19 in India at the national, state, and district levels. Our short-term predictions ($<30$ days) may be helpful for policymakers in identifying regions where a local lockdown must be proactively imposed to arrest the spread of the virus. Our long-term predictions (up to a few months) simulate the progression of the pandemic under various lockdown conditions, thereby providing a noisy indicator for a potential third wave of cases in India. Extensive experimental results validate the performance of our tool at all levels.
Unsupervised Person Re-Identification: A Systematic Survey of Challenges and Solutions
Authors: Xiangtan Lin, Pengzhen Ren, Chung-Hsing Yeh, Lina Yao, Andy Song, Xiaojun Chang
Date: 2021-09-01 00:00:00
Tasks: Person Re-Identification, Unsupervised Person Re-Identification

Person re-identification (Re-ID) has been a significant research topic in the past decade due to its real-world applications and research significance. While supervised person Re-ID methods achieve superior performance over unsupervised counterparts, they can not scale to large unlabelled datasets and new domains due to the prohibitive labelling cost. Therefore, unsupervised person Re-ID has drawn increasing attention for its potential to address the scalability issue in person Re-ID. Unsupervised person Re-ID is challenging primarily due to lacking identity labels to supervise person feature learning. The corresponding solutions are diverse and complex, with various merits and limitations. Therefore, comprehensive surveys on this topic are essential to summarise challenges and solutions to foster future research. Existing person Re-ID surveys have focused on supervised methods from classifications and applications but lack detailed discussion on how the person Re-ID solutions address the underlying challenges. This survey review recent works on unsupervised person Re-ID from the perspective of challenges and solutions. Specifically, we provide an in-depth analysis of highly influential methods considering the four significant challenges in unsupervised person Re-ID: 1) lacking ground-truth identity labels to supervise person feature learning; 2) learning discriminative person features with pseudo-supervision; 3) learning cross-camera invariant person feature, and 4) the domain shift between datasets. We summarise and analyse evaluation results and provide insights on the effectiveness of the solutions. Finally, we discuss open issues and suggest some promising future research directions.
Semantics-Guided Contrastive Network for Zero-Shot Object detection
Authors: Caixia Yan, Xiaojun Chang, Minnan Luo, Huan Liu, Xiaoqin Zhang, Qinghua Zheng
Date: 2021-09-04 00:00:00
Tasks: Contrastive Learning, Generalized Zero-Shot Object Detection, Object, object-detection, Object Detection, Zero-Shot Object Detection

Zero-shot object detection (ZSD), the task that extends conventional detection models to detecting objects from unseen categories, has emerged as a new challenge in computer vision. Most existing approaches tackle the ZSD task with a strict mapping-transfer strategy, which may lead to suboptimal ZSD results: 1) the learning process of those models ignores the available unseen class information, and thus can be easily biased towards the seen categories; 2) the original visual feature space is not well-structured and lack of discriminative information. To address these issues, we develop a novel Semantics-Guided Contrastive Network for ZSD, named ContrastZSD, a detection framework that first brings contrastive learning mechanism into the realm of zero-shot detection. Particularly, ContrastZSD incorporates two semantics-guided contrastive learning subnets that contrast between region-category and region-region pairs respectively. The pairwise contrastive tasks take advantage of additional supervision signals derived from both ground truth label and pre-defined class similarity distribution. Under the guidance of those explicit semantic supervision, the model can learn more knowledge about unseen categories to avoid the bias problem to seen concepts, while optimizing the data structure of visual features to be more discriminative for better visual-semantic alignment. Extensive experiments are conducted on two popular benchmarks for ZSD, i.e., PASCAL VOC and MS COCO. Results show that our method outperforms the previous state-of-the-art on both ZSD and generalized ZSD tasks.
Multi-Document Summarization withDeterminantal Point Process Attention
Authors: Laura Perez-Beltrachini, Mirella Lapata
Date: 2021-07-13 00:00:00
Proceeding: Journal of Artificial Intelligence Research 2021 7
Tasks: Document Summarization, Multi-Document Summarization

The  ability  to  convey  relevant  and  diverse  information  is  critical  in  multi-documentsummarization and yet remains elusive for neural seq-to-seq models whose outputs are of-ten redundant and fail to correctly cover important details.  In this work, we propose anattention mechanism which encourages greater focus onrelevanceanddiversity.  Attentionweights are computed based on (proportional) probabilities given by Determinantal PointProcesses (DPPs) defined on the set of content units to be summarized.  DPPs have beensuccessfully  used  in  extractive  summarisation,  here  we  use  them  to  select  relevant  anddiverse content for neural abstractive summarisation.  We integrate DPP-based attentionwith  various  seq-to-seq  architectures  ranging  from  CNNs  to  LSTMs,  and  Transformers.Experimental evaluation shows that our attention mechanism consistently improves sum-marization and delivers performance comparable with the state-of-the-art on the MultiNewsdataset.
LIVECell—A large-scale dataset for label-free live cell segmentation
Authors: Christoffer Edlund, Timothy R. Jackson, Nabeel Khalid, Nicola Bevan, Timothy Dale, Andreas Dengel, Sheraz Ahmed, Johan Trygg, Rickard Sjögren
Date: 2021-08-30 00:00:00
Proceeding: Nature Methods 2021 8
Tasks: Cell Segmentation, Cultural Vocal Bursts Intensity Prediction, Image Segmentation, Instance Segmentation, Segmentation, Semantic Segmentation

Light microscopy combined with well-established protocols of two-dimensional cell culture facilitates high-throughput quantitative imaging to study biological phenomena. Accurate segmentation of individual cells in images enables exploration of complex biological questions, but can require sophisticated imaging processing pipelines in cases of low contrast and high object density. Deep learning-based methods are considered state-of-the-art for image segmentation but typically require vast amounts of annotated data, for which there is no suitable resource available in the field of label-free cellular imaging. Here, we present LIVECell, a large, high-quality, manually annotated and expert-validated dataset of phase-contrast images, consisting of over 1.6 million cells from a diverse set of cell morphologies and culture densities. To further demonstrate its use, we train convolutional neural network-based models using LIVECell and evaluate model segmentation accuracy with a proposed a suite of benchmarks.
Deep multi-Wasserstein unsupervised domain adaptation
Authors: Tien-Nam Le, Amaury Habrard, Marc Sebban
Date: 2019-04-30 00:00:00
Proceeding: Pattern Recognition Letters 2019 4
Tasks: Domain Adaptation, Generalization Bounds, Unsupervised Domain Adaptation

In unsupervised domain adaptation (DA), 1 aims at learning from labeled source data and fully unlabeled target examples a model with a low error on the target domain. In this setting, standard generalization bounds prompt us to minimize the sum of three terms: (a) the source true risk, (b) the divergence be- tween the source and target domains, and (c) the combined error of the ideal joint hypothesis over the two domains. Many DA methods – especially those using deep neural networks – have focused on the first two terms by using different divergence measures to align the source and target distributions on a shared latent feature space, while ignoring the third term, assuming it is negligible to perform the adaptation. However, it has been shown that purely aligning the two distributions while minimizing the source error may lead to so-called negative transfer . In this paper, we address this issue with a new deep unsupervised DA method – called MCDA – minimizing the first two terms while controlling the third one. MCDA benefits from highly-confident target samples (using softmax predictions) to minimize class- wise Wasserstein distances and efficiently approximate the ideal joint hypothesis. Empirical results show that our approach outperforms state of the art methods.