pwc-archive/papers-with-abstracts huggingface dataset:
Measurement-based Characterization of Physical Layer Security for RIS-assisted Wireless Systems
Authors: Samed Keşir, Sefa Kayraklık, İbrahim Hökelek, Ali Emre Pusane, Ertugrul Basar, Ali Görçin
Date: 2022-12-14 00:00:00

There have been recently many studies demonstrating that the performance of wireless communication systems can be significantly improved by a reconfigurable intelligent surface (RIS), which is an attractive technology due to its low power requirement and low complexity. This paper presents a measurement-based characterization of RISs for providing physical layer security, where the transmitter (Alice), the intended user (Bob), and the eavesdropper (Eve) are deployed in an indoor environment. Each user is equipped with a software-defined radio connected to a horn antenna. The phase shifts of reflecting elements are software controlled to collaboratively determine the amount of received signal power at the locations of Bob and Eve in such a way that the secrecy capacity is aimed to be maximized. An iterative method is utilized to configure a Greenerwave RIS prototype consisting of 76 passive reflecting elements. Computer simulation and measurement results demonstrate that an RIS can be an effective tool to significantly increase the secrecy capacity between Bob and Eve.
RIS-Aided Monostatic Sensing and Object Detection with Single and Double Bounce Multipath
Authors: Hyowon Kim, Alessio Fascista, Hui Chen, Yu Ge, George C. Alexandropoulos, Gonzalo Seco-Granados, Henk Wymeersch
Date: 2022-12-14 00:00:00
Tasks: object-detection, Object Detection

We propose a framework for monostatic sensing by a user equipment (UE), aided by a reconfigurable intelligent surface (RIS) in environments with single- and double-bounce signal propagation. We design appropriate UE-side precoding and combining, to facilitate signal separation. We derive the adaptive detection probabilities of the resolvable signals, based on the geometric channel parameters of the links. Then, we estimate the passive objects using both the double-bounce signals via passive RIS (i.e., RIS-sensing) and the single-bounce multipath direct to the objects (i.e., non-RIS-sensing), based on a mapping filter. Finally, we provide numerical results to demonstrate that effective sensing can be achieved through the proposed framework.
RIS-Enabled and Access-Point-Free Simultaneous Radio Localization and Mapping
Authors: Hyowon Kim, Hui Chen, Musa Furkan Keskin, Yu Ge, Kamran Keykhosravi, George C. Alexandropoulos, Sunwoo Kim, Henk Wymeersch
Date: 2022-12-14 00:00:00

In the upcoming sixth generation (6G) of wireless communication systems, reconfigurable intelligent surfaces~(RISs) are regarded as one of the promising technological enablers, which can provide programmable signal propagation. Therefore, simultaneous radio localization and mapping(SLAM) with RISs appears as an emerging research direction within the 6G ecosystem. In this paper, we propose a novel framework of RIS-enabled radio SLAM for wireless operation without the intervention of access points (APs). We first design the RIS phase profiles leveraging prior information for the user equipment~(UE), such that they uniformly illuminate the angular sector where the UE is probabilistically located. Second, we modify the marginal Poisson multi-Bernoulli SLAM filter and estimate the UE state and landmarks, which enables efficient mapping of the radio propagation environment. Third, we derive the theoretical Cram\'er-Rao lower bounds on the estimators for the channel parameters and the UE state. We finally evaluate the performance of the proposed method under scenarios with a limited number of transmissions, taking into account the channel coherence time. Our results demonstrate that the RIS enables solving the radio SLAM problem with zero APs, and that the consideration of the Doppler shift contributes to improving the UE speed estimates.
A Fast Successive QP Algorithm for General Mean-Variance Portfolio Optimization
Authors: Shengjie Xiu, Xiwen Wang, Daniel P. Palomar
Date: 2022-12-14 00:00:00
Tasks: Portfolio Optimization

The mean and variance of portfolio returns are the standard quantities to measure the expected return and risk of a portfolio. Efficient portfolios that provide optimal trade-offs between mean and variance warrant consideration. To express a preference among these efficient portfolios, investors have put forward many mean-variance portfolio (MVP) formulations which date back to the classical Markowitz portfolio. However, most existing algorithms are highly specialized to particular formulations and cannot be generalized for broader applications. Therefore, a fast and unified algorithm would be extremely beneficial. In this paper, we first introduce a general MVP problem formulation that can fit most existing cases by exploring their commonalities. Then, we propose a widely applicable and provably convergent successive quadratic programming algorithm (SCQP) for the general formulation. The proposed algorithm can be implemented based on only the QP solvers and thus is computationally efficient. In addition, a fast implementation is considered to accelerate the algorithm. The numerical results show that our proposed algorithm significantly outperforms the state-of-the-art ones in terms of convergence speed and scalability.
Reconstructing Volatility: Pricing of Index Options under Rough Volatility
Authors: Peter K. Friz, Thomas Wagenhofer
Date: 2022-12-15 00:00:00

In previous works Avellaneda et al. pioneered the pricing and hedging of index options - products highly sensitive to implied volatility and correlation assumptions - with large deviations methods, assuming local volatility dynamics for all components of the index. We here present an extension applicable to non-Markovian dynamics and in particular the case of rough volatility dynamics.
An estimator for the recombination rate from a continuously observed diffusion of haplotype frequencies
Authors: Robert C. Griffiths, Paul A. Jenkins
Date: 2022-12-15 00:00:00

Recombination is a fundamental evolutionary force, but it is difficult to quantify because the effect of a recombination event on patterns of variation in a sample of genetic data can be hard to discern. Estimators for the recombination rate, which are usually based on the idea of integrating over the unobserved possible evolutionary histories of a sample, can therefore be noisy. Here we consider a related question: how would an estimator behave if the evolutionary history actually was observed? This would offer an upper bound on the performance of estimators used in practice. In this paper we derive an expression for the maximum likelihood estimator for the recombination rate based on a continuously observed, multi-locus, Wright--Fisher diffusion of haplotype frequencies, complementing existing work for an estimator of selection. We show that, contrary to selection, the estimator has unusual properties because the observed information matrix can explode in finite time whereupon the recombination parameter is learned without error. We also show that the recombination estimator is robust to the presence of selection in the sense that incorporating selection into the model leaves the estimator unchanged. We study the properties of the estimator by simulation and show that its distribution can be quite sensitive to the underlying mutation rates.
Extending Universal Approximation Guarantees: A Theoretical Justification for the Continuity of Real-World Learning Tasks
Authors: Naveen Durvasula
Date: 2022-12-06 00:00:00

Universal Approximation Theorems establish the density of various classes of neural network function approximators in $C(K, \mathbb{R}^m)$, where $K \subset \mathbb{R}^n$ is compact. In this paper, we aim to extend these guarantees by establishing conditions on learning tasks that guarantee their continuity. We consider learning tasks given by conditional expectations $x \mapsto \mathrm{E}\left[Y \mid X = x\right]$, where the learning target $Y = f \circ L$ is a potentially pathological transformation of some underlying data-generating process $L$. Under a factorization $L = T \circ W$ for the data-generating process where $T$ is thought of as a deterministic map acting on some random input $W$, we establish conditions (that might be easily verified using knowledge of $T$ alone) that guarantee the continuity of practically \textit{any} derived learning task $x \mapsto \mathrm{E}\left[f \circ L \mid X = x\right]$. We motivate the realism of our conditions using the example of randomized stable matching, thus providing a theoretical justification for the continuity of real-world learning tasks.
Protein Structure Prediction until CASP15
Authors: Arne Elofsson
Date: 2022-12-15 00:00:00
Tasks: Prediction, Protein Design, Protein Structure Prediction

In Dec 2020, the results of AlphaFold2 were presented at CASP14, sparking a revolution in the field of protein structure predictions. For the first time, a purely computational method could challenge experimental accuracy for structure prediction of single protein domains. The code of AlphaFold2 was released in the summer of 2021, and since then, it has been shown that it can be used to accurately predict the structure of most (ordered) proteins and many protein-protein interactions. It has also sparked an explosion of development in the field, improving AI-based methods to predict protein complexes, disordered regions, and protein design. Here I will review some of the inventions sparked by the release of AlphaFold.
Sequential Labelling and DNABERT For Splice Site Prediction in Homo Sapiens DNA
Authors: Muhammad Anwari Leksono, Ayu Purwarianti
Date: 2022-12-15 00:00:00
Tasks: Prediction, Splice Site Prediction

Genome sequencing technology has improved significantly in few last years and resulted in abundance genetic data. Artificial intelligence has been employed to analyze genetic data in response to its sheer size and variability. Gene prediction on single DNA has been conducted using various deep learning architectures to discover splice sites and therefore discover intron and exon region. Recent predictions are carried out with models trained on sequence with fixed splice site location which eliminates possibility of multiple splice sites existence in single sequence. This paper proposes sequential labelling to predict splice sites regardless their position in sequence. Sequential labelling is carried out on DNA to determine intron and exon region and thus discover splice sites. Sequential labelling models used are based on pretrained DNABERT-3 which has been trained on human genome. Both fine-tuning and feature-based approach are tested. Proposed model is benchmarked against latest sequential labelling model designed for mutation type and location prediction. While achieving high F1 scores on validation data, both baseline and proposed model perform poorly on test data. Error and test results analysis reveal that model experience overfitting and therefore, model is deemed not suitable for splice site prediction.
Human body-sway steady-state responses to small amplitude tilts and translations of the support surface -- Effects of superposition of the two stimuli
Authors: Vittorio Lippi, Christoph Maurer, Thomas Mergner
Date: 2022-12-15 00:00:00
Tasks: Translation

Upright stance tested with a superposition of support surface tilt and translation. Steady state response is characterized by frequency response function. Interaction between two stimuli absent in most of the cases. Larger stimuli may create interaction. Simulations suggest that the observed effects can be due to joint stiffness modulation.
Mechanistic description of spontaneous loss of memory persistent activity based on neuronal synaptic strength
Authors: Hillel Sanhedrai, Shlomo Havlin, Hila Dvir
Date: 2022-12-14 00:00:00

Persistent neural activity associated with working memory (WM) lasts for a limited time duration. Current theories suggest that its termination is \textit{actively} obtained via inhibitory currents, and there is currently no theory regarding the possibility of a \textit{passive} memory-loss mechanism that terminates memory persistent activity. Here, we develop an analytical-framework, based on synaptic strength, and show via simulations and fitting to wet-lab experiments, that passive memory-loss might be a result of an ionic-current long-term plateau, i.e., very slow reduction of memory followed by abrupt loss. We describe analytically the plateau, when the memory state is just below criticality. These results, including the plateau, are supported by experiments performed on rats. Moreover, we show that even just above criticality, forgetfulness can occur due to neuronal noise with ionic-current fluctuations, yielding a plateau, representing memory with very slow decay, and eventually a fast memory decay. Our results could have implications for developing new medications, targeted against memory impairments, through modifying neuronal noise.
The role of vehicle movement in swine disease dissemination: novel method accounting for pathogen stability and vehicle cleaning effectiveness uncertainties
Authors: Jason A. Galvis, Gustavo Machado
Date: 2022-12-14 00:00:00

The transmission dynamics of infectious diseases in animal production are driven by several propagation routes. Contaminated vehicles traveling between farms have been associated with indirect disease transmission. In this study, we used transportation vehicle data to analyze the magnitude of farm visits by different vehicles and to propose a methodology to reconstruct vehicle contact networks considering pathogen stability and cleaning and disinfection effectiveness. Here, we collected information from 6,363 farms and Global Positioning System (GPS) records from 567 vehicles used to transport feed, animals, and people. We reconstructed vehicle contacts among the farms, conserving pathogen stability decay and different probabilities of cleaning and disinfection. Results showed that vehicle movement networks were densely connected, with up to 86% of farms connected by these movements. Movements of vehicle transporting feed and pig among farms showed the highest network connectivity. The cleaning effectiveness of was variable among the different vehicle types and highly influenced by the frequency of vehicles stopping at clean stations. A large number of between-farm contacts with a pathogen stability >0.8 were present in the vehicle network even with 100% cleaning effectiveness. Finally, we identified that vehicles contacted farms from different companies. Thus, our results suggest the vehicle network is a potential mechanism for spreading pathogens among farms. Moreover, even with scenarios with high effectiveness of cleaning and disinfection, the risk of vehicles spreading diseases was not completely eliminated.
Multiple Phase Transitions Shape Biodiversity of a Migrating Population
Authors: Casey Barkan, Shenshen Wang
Date: 2022-12-13 00:00:00
Tasks: Diversity

In a wide variety of natural systems, closely-related microbial strains coexist stably, resulting in high levels of fine-scale biodiversity. However, the mechanisms that stabilize this coexistence are not fully understood. Spatial heterogeneity is one common stabilizing mechanism, but the rate at which organisms disperse throughout the heterogeneous environment may strongly impact the stabilizing effect that heterogeneity can provide. An intriguing example is the gut microbiome, where active mechanisms exist to control the movement of microbes and potentially maintain diversity. We investigate how biodiversity is affected by migration rate using a simple evolutionary model with heterogeneous selection pressure. We find that the biodiversity-migration rate relationship is shaped by multiple phase transitions, including a reentrant phase transition to coexistence. At each transition, an ecotype goes extinct and dynamics exhibit critical slowing down (CSD). CSD is encoded in the statistics of fluctuations due to demographic noise -- this may provide an experimental means for detecting and altering impending extinction.
MineProt: modern application for custom protein curation
Authors: Yunchi Zhu, Chengda Tong, Zuohan Zhao, Zuhong Lu
Date: 2022-12-14 00:00:00

AI systems represented by AlphaFold are rapidly expanding the scale of protein structure modelling data, and the MineProt project provides an effective solution for custom curation of these novel high-throughput data. It enables researchers to build their own protein server in simple steps, run almost out-of-the-box scripts to annotate and curate their proteins, visualize, browse and search their data via a user-friendly online interface, and utilize plugins to extend the functionality of server. It is expected to support researcher productivity and facilitate data sharing in the new era of structural proteomics. MineProt is open-sourced at https://github.com/huiwenke/MineProt.
A Flexible Commodity Skew Model with Maturity Effects
Authors: Orcan Ogetbil, Bernhard Hientzsch
Date: 2022-12-15 00:00:00
Tasks: model

We propose a non-parametric extension with leverage functions to the Andersen commodity curve model. We calibrate this model to market data for WTI and NG including option skew at the standard maturities. While the model can be calibrated by an analytical formula for the deterministic rate case, the stochastic rate case demands estimation of an expectation for which we employ Monte Carlo simulation. We find that the market smile is captured for the deterministic rate case; and with relatively low number of paths, for the stochastic rate case. Since there is typically at most one standard maturity with liquid volatility data for each futures contract, there is flexibility on the shape of nonstandard maturity implied volatility and how the total implied variance accumulates. We equip the model with different total implied variance accumulators to demonstrate that flexibility.
Fundamentals of Perpetual Futures
Authors: Songrun He, Asaf Manela, Omri Ross, Victor von Wachter
Date: 2022-12-13 00:00:00
Tasks: Position

Perpetual futures are the most popular cryptocurrency derivatives. Perpetuals offer leveraged exposure to their underlying without rollover or direct ownership. Unlike fixed-maturity futures, perpetuals are not guaranteed to converge to the spot price. To minimize the gap between perpetual and spot prices, long investors periodically pay shorts a funding rate proportional to this difference. We derive no-arbitrage prices for perpetual futures in frictionless markets and bounds in markets with trading costs. Empirically, deviations from these prices in crypto are larger than in traditional currency markets, comove across currencies, and diminish over time. An implied arbitrage strategy yields high Sharpe ratios.
Naive Markowitz Policies
Authors: Lin Chen, Xun Yu Zhou
Date: 2022-12-14 00:00:00

We study a continuous-time Markowitz mean-variance portfolio selection model in which a naive agent, unaware of the underlying time-inconsistency, continuously reoptimizes over time. We define the resulting naive policies through the limit of discretely naive policies that are committed only in very small time intervals, and derive them analytically and explicitly. We compare naive policies with pre-committed optimal policies and with consistent planners' equilibrium policies in a Black-Scholes market, and find that the former are mean-variance inefficient starting from any given time and wealth, and always take riskier exposure than equilibrium policies.
Order routing and market quality: Who benefits from internalisation?
Authors: Umut Çetin, Alaina Danilova
Date: 2022-12-15 00:00:00

We analyse two models of liquidity provision to determine the retail traders' preference for marketable order routing. Order internalization is captured by a model of market makers competing for the retail order flow in a Bertrand fashion. On the other hand, the price-taking competitive liquidity providers characterize the open exchange model. We show that, when liquidity providers are risk averse, routing of the marketable orders to the wholesalers is preferred by all retail traders: informed, uninformed and noisy. The unwillingness of liquidity providers to bear risk causes the strategic trader (informed or not) to absorb large shocks in their inventories. This results in mean reverting inventories, price reversal, and lower market depth. The equilibria in both models coincide with Kyle (1985) when liquidity providers are risk neutral. We also identify a universal parameter that allows comparison of market liquidity, profit and value of information across different markets.
Hope Speech Detection on Social Media Platforms
Authors: Pranjal Aggarwal, Pasupuleti Chandana, Jagrut Nemade, Shubham Sharma, Sunil Saumya, Shankar Biradar
Date: 2022-11-14 00:00:00
Tasks: Hate Speech Detection, Hope Speech Detection, Sentence

Since personal computers became widely available in the consumer market, the amount of harmful content on the internet has significantly expanded. In simple terms, harmful content is anything online which causes a person distress or harm. It may include hate speech, violent content, threats, non-hope speech, etc. The online content must be positive, uplifting and supportive. Over the past few years, many studies have focused on solving this problem through hate speech detection, but very few focused on identifying hope speech. This paper discusses various machine learning approaches to identify a sentence as Hope Speech, Non-Hope Speech, or a Neutral sentence. The dataset used in the study contains English YouTube comments and is released as a part of the shared task "EACL-2021: Hope Speech Detection for Equality, Diversity, and Inclusion". Initially, the dataset obtained from the shared task had three classes: Hope Speech, non-Hope speech, and not in English; however, upon deeper inspection, we discovered that dataset relabeling is required. A group of undergraduates was hired to help perform the entire dataset's relabeling task. We experimented with conventional machine learning models (such as Na\"ive Bayes, logistic regression and support vector machine) and pre-trained models (such as BERT) on relabeled data. According to the experimental results, the relabeled data has achieved a better accuracy for Hope speech identification than the original data set.
Utilizing Background Knowledge for Robust Reasoning over Traffic Situations
Authors: Jiarui Zhang, Filip Ilievski, Aravinda Kollaa, Jonathan Francis, Kaixin Ma, Alessandro Oltramari
Date: 2022-12-04 00:00:00
Tasks: Knowledge Graphs, Multiple-choice, Natural Language Inference, Question Answering

Understanding novel situations in the traffic domain requires an intricate combination of domain-specific and causal commonsense knowledge. Prior work has provided sufficient perception-based modalities for traffic monitoring, in this paper, we focus on a complementary research aspect of Intelligent Transportation: traffic understanding. We scope our study to text-based methods and datasets given the abundant commonsense knowledge that can be extracted using language models from large corpus and knowledge graphs. We adopt three knowledge-driven approaches for zero-shot QA over traffic situations, based on prior natural language inference methods, commonsense models with knowledge graph self-supervision, and dense retriever-based models. We constructed two text-based multiple-choice question answering sets: BDD-QA for evaluating causal reasoning in the traffic domain and HDT-QA for measuring the possession of domain knowledge akin to human driving license tests. Among the methods, Unified-QA reaches the best performance on the BDD-QA dataset with the adaptation of multiple formats of question answers. Language models trained with inference information and commonsense knowledge are also good at predicting the cause and effect in the traffic domain but perform badly at answering human-driving QA sets. For such sets, DPR+Unified-QA performs the best due to its efficient knowledge extraction.