pwc-archive/papers-with-abstracts huggingface dataset:
Beyond KNN: Deep Neighborhood Learning for WiFi-based Indoor Positioning Systems
Authors: Yinhuan Dong, Francisco Zampella, Firas Alsehly
Date: 2023-02-02 00:00:00
Tasks: Graph Learning

K-Neares Neighbors (KNN) and its variant weighted KNN (WKNN) have been explored for years in both academy and industry to provide stable and reliable performance in WiFi-based indoor positioning systems. Such algorithms estimate the location of a given point based on the locality information from the selected nearest WiFi neighbors according to some distance metrics calculated from the combination of WiFi received signal strength (RSS). However, such a process does not consider the relational information among the given point, WiFi neighbors, and the WiFi access points (WAPs). Therefore, this study proposes a novel Deep Neighborhood Learning (DNL). The proposed DNL approach converts the WiFi neighborhood to heterogeneous graphs, and utilizes deep graph learning to extract better representation of the WiFi neighborhood to improve the positioning accuracy. Experiments on 3 real industrial datasets collected from 3 mega shopping malls on 26 floors have shown that the proposed approach can reduce the mean absolute positioning error by 10% to 50% in most of the cases. Specially, the proposed approach sharply reduces the root mean squared positioning error and 95\% percentile positioning error, being more robust to the outliers than conventional KNN and WKNN.
Multi-Carrier Wideband OCDM-Based THz Automotive Radar
Authors: Sangeeta Bhattacharjee, Kumar Vijay Mishra, Ramesh Annavajjala, Chandra R. Murthy
Date: 2023-02-01 00:00:00

Automotive radars at the Terahertz (THz) frequency band have the potential to be compact and lightweight while providing high (nearly-optical) angular resolution. In this paper, we propose a bistatic THz automotive radar that employs the recently proposed orthogonal chirp division multiplexing (OCDM) multi-carrier waveform. As a stand-alone communications waveform, OCDM has been investigated for robustness against interference in time-frequency selective channels. The THz-band path loss, and, hence, radar signal bandwidth, are range-dependent. We address this unique feature through a multi-carrier wideband OCDM sensing transceiver that exploits the coherence bandwidth of the THz channel. We develop an optimal scheme to combine the returns at different range/bandwidths by assigning weights based on the Cramer-Rao lower bound on the range and velocity estimates. Numerical experiments demonstrate improved target estimates using our proposed combined estimation from multiple varied-attenuation THz frequencies.
Noncommutative $C^*$-algebra Net: Learning Neural Networks with Powerful Product Structure in $C^*$-algebra
Authors: Ryuichiro Hataya, Yuka Hashimoto
Date: 2023-01-26 00:00:00

We propose a new generalization of neural network parameter spaces with noncommutative $C^*$-algebra, which possesses a rich noncommutative structure of products. We show that this noncommutative structure induces powerful effects in learning neural networks. Our framework has a wide range of applications, such as learning multiple related neural networks simultaneously with interactions and learning equivariant features with respect to group actions. Numerical experiments illustrate the validity of our framework and its potential power.
A time-dependent Markovian model of a limit order book
Authors: Jonathan A. Chávez-Casillas
Date: 2023-02-02 00:00:00
Tasks: Point Processes

This paper considers a Markovian model of a limit order book where time-dependent rates are allowed. With the objective of understanding the mechanisms through which a microscopic model of an orderbook can converge to more general diffusion than a Brownian motion with constant coefficient, a simple time-dependent model is proposed. The model considered here starts by describing the processes that govern the arrival of the different orders such as limit orders, market orders and cancellations. In this sense, this is a microscopic model rather than a ``mesoscopic'' model where the starting point is usually the point processes describing the times at which the price changes occur and aggregate in these all the information pertaining to the arrival of individual orders. Furthermore, several empirical studies are performed to shed some light into the validity of the modeling assumptions and to verify whether certain stocks satisfy the conditions for their price process to converge to a more complex diffusion.
Kainate receptor modulation by NETO2
Authors: Lingli He, Jiahui Sun, Yiwei Gao, Bin Li, Yuhang Wang, Yanli Dong, Weidong An, Hang Li, Bei Yang, Yuhan Ge, Xuejun Cai Zhang, Yun Stone Shi, Yan Zhao
Date: 2023-02-02 00:00:00

Glutamate-gated kainate receptors (KARs) are ubiquitous in the central nervous system of vertebrates, mediate synaptic transmission on post-synapse, and modulate transmitter release on pre-synapse. In the brain, the trafficking, gating kinetics, and pharmacology of KARs are tightly regulated by Neuropilin and tolloid-like proteins (Netos). Here we report cryo-EM structures of homo-tetrameric GluK2 in complex with Neto2 at inhibited and desensitized states, illustrating variable stoichiometry of GluK2-Neto2 complexes, with one or two Neto2 subunits associate with the GluK2. We find that Neto2 accesses only two broad faces of KARs, intermolecularly crosslinking the lower-lobe of ATDA/C, upper-lobe of LBDB/D, and lower-lobe of LBDA/C, illustrating how Neto2 regulates receptor-gating kinetics. The transmembrane helix of Neto2 is positioned proximal to the selectivity filter and competes with the amphiphilic H1-helix after M4 for interacting with an ICD formed by the M1-M2 linkers of the receptor, revealing how rectification is regulated by Neto2.
The big challenge for livestock genomics is to make sequence data pay
Authors: M. Johnsson
Date: 2023-02-02 00:00:00

This paper will argue that one of the biggest challenges for livestock genomics is to make whole-genome sequencing and functional genomics applicable to breeding practice. It discusses potential explanations for why it is so difficult to consistently improve the accuracy of genomic prediction by means of whole-genome sequence data, and three potential attacks on the problem.
A Semi-Parametric Model for Decision Making in High-Dimensional Sensory Discrimination Tasks
Authors: Stephen Keeley, Benjamin Letham, Chase Tymms, Craig Sanders, Michael Shvartsman
Date: 2023-02-02 00:00:00
Tasks: Active Learning, Decision Making

Psychometric functions typically characterize binary sensory decisions along a single stimulus dimension. However, real-life sensory tasks vary along a greater variety of dimensions (e.g. color, contrast and luminance for visual stimuli). Approaches to characterizing high-dimensional sensory spaces either require strong parametric assumptions about these additional contextual dimensions, or fail to leverage known properties of classical psychometric curves. We overcome both limitations by introducing a semi-parametric model of sensory discrimination that applies traditional psychophysical models along a stimulus intensity dimension, but puts Gaussian process (GP) priors on the parameters of these models with respect to the remaining dimensions. By combining the flexibility of the GP with the deep literature on parametric psychophysics, our semi-parametric models achieve good performance with much less data than baselines on both synthetic and real-world high-dimensional psychophysics datasets. We additionally show strong performance in a Bayesian active learning setting, and present a novel active learning paradigm for the semi-parametric model.
Duration of Transients in Outbreaks: When can Infectiousness be Estimated?
Authors: Adam Mielke, Lasse Engbo Christiansen
Date: 2023-02-02 00:00:00

We investigate sub-leading orders of the classic SEIR-model using contact matrices from modeling of the Omicron and Delta variants of COVID-19 in Denmark. The goal of this is to illustrate when the growth rate, and by extension the infection transmission potential (basic or initial reproduction number), can be estimated in a new outbreak, e.g. after introduction of a new variant of a virus. In particular, we look at the time scale on which this happens in a realistic outbreak to guide future data collection. We find that as long as susceptible depletion is a minor effect, the transients are gone within around 3 weeks corresponding to about 4-5 times the incubation time. We also argue that this result generalizes to other airborne diseases in a fully mixed population.
Quantifying optimal resource allocation strategies for controlling epidemics
Authors: Biplab Maity, Swarnendu Banerjee, Abhishek Senapati, Joydev Chattopadhyay
Date: 2023-02-02 00:00:00
Tasks: Management

Frequent emergence of communicable diseases has been a major concern worldwide. Lack of sufficient resources to mitigate the disease-burden makes the situation even more challenging for lower-income countries. Hence, strategy development towards disease eradication and optimal management of the social and economic burden has garnered a lot of attention in recent years. In this context, we quantify the optimal fraction of resources that can be allocated to two major intervention measures, namely reduction of disease transmission and improvement of healthcare infrastructure. Our results demonstrate that the effectiveness of each of the interventions has a significant impact on the optimal resource allocation in both long-term disease dynamics and outbreak scenarios. Often allocating resources to both strategies is optimal. For long-term dynamics, a non-monotonic behavior of optimal resource allocation with intervention effectiveness is observed which is different from the more intuitive strategy recommended in case of outbreaks. Further, our result indicates that the relationship between investment into interventions and the corresponding outcomes has a decisive role in determining optimal strategies. Intervention programs with decreasing returns promote the necessity for resource sharing. Our study provides a fundamental insight into determining the best response strategy in case of controlling epidemics under resource-constrained situations.
Existence of exact solution of the Susceptible-Exposed-Infectious-Recovered (SEIR) epidemic model
Authors: Norio Yoshida
Date: 2023-02-02 00:00:00
Tasks: Form

Exact solutions of the SEIR epidemic model are derived, and various properties of solutions are obtained directly from the exact solution. In this paper Abel differential equations play an important role in establishing the exact solution of SEIR differential system, in particular the number of infected individuals can be represented in a simple form by using a positive solution of an Abel differential equation. It is shown that the parametric form of the exact solution satisfies some linear differential system including a positive solution of an Abel differential equation.
PASSerRank: Prediction of Allosteric Sites with Learning to Rank
Authors: Hao Tian, Sian Xiao, Xi Jiang, Peng Tao
Date: 2023-02-02 00:00:00
Tasks: Drug Discovery, Learning-To-Rank, Prediction

Allostery plays a crucial role in regulating protein activity, making it a highly sought-after target in drug development. One of the major challenges in allosteric drug research is the identification of allosteric sites. In recent years, many computational models have been developed for accurate allosteric site prediction. Most of these models focus on designing a general rule that can be applied to pockets of proteins from various families. In this study, we present a new approach using the concept of Learning to Rank (LTR). The LTR model ranks pockets based on their relevance to allosteric sites, i.e., how well a pocket meets the characteristics of known allosteric sites. The model outperforms other common machine learning models with higher F1 score and Matthews correlation coefficient. After the training and validation on two datasets, the Allosteric Database (ASD) and CASBench, the LTR model was able to rank an allosteric pocket in the top 3 positions for 83.6% and 80.5% of test proteins, respectively. The trained model is available on the PASSer platform (https://passer.smu.edu) to aid in drug discovery research.
Meta-Analytic Operation of Threshold-independent Filtering (MOTiF) Reveals Sub-threshold Genomic Robustness in Trisomy
Authors: Roy Siegelmann, Hava Siegelmann
Date: 2023-02-01 00:00:00

Trisomy, a form of aneuploidy wherein the cell possesses an additional copy of a specific chromosome, exhibits a high correlation with cancer. Studies from across different hosts, cell-lines, and labs into the cellular effects induced by aneuploidy have conflicted, ranging from small, chaotic global changes to large instances of either overexpression or underexpression throughout the trisomic chromosome. We ascertained that conflicting findings may be correct but miss the overarching ground truth due to careless use of thresholds. To correct this deficiency, we introduce the Meta-analytic Operation of Threshold-independent Filtering (MOTiF) method, which begins by providing a panoramic view of all thresholds, transforms the data to eliminate the effects accounted for by known mechanisms, and then reconstructs an explanation of the mechanisms that underly the difference between the baseline and the uncharacterized effects observed. As a proof of concept, we applied MOTiF to human colonic epithelial cells, discovering a uniform decrease in gene expression levels throughout the genome, which while significant, is beneath most common thresholds. Using Hi-C data we identified the structural correlate, wherein the physical genomic architecture condenses, compactifying in a uniform, genome-wide manner, which we hypothesize is a robustness mechanism counteracting the addition of a chromosome. We were able to decompose the gene expression alterations into three overlapping mechanisms: the raw chromosome content, the genomic compartmentalization, and the global structural condensation. While further studies must be conducted to corroborate the hypothesized robustness mechanism, MOTiF presents a useful meta-analytic tool in the realm of gene expression and beyond.
Numerical Issues for a Non-autonomous Logistic Model
Authors: Marina Mancuso, Carrie Manore, Kaitlyn Martinez, Fabio Milner
Date: 2022-12-20 00:00:00
Tasks: model

The logistic equation has been extensively used to model biological phenomena across a variety of disciplines and has provided valuable insight into how our universe operates. Incorporating time-dependent parameters into the logistic equation allows the modeling of more complex behavior than its autonomous analog, such as a tumor's varying growth rate under treatment, or the expansion of bacterial colonies under varying resource conditions. Some of the most commonly used numerical solvers produce vastly different approximations for a non-autonomous logistic model with a periodically-varying growth rate changing signum. Incorrect, inconsistent, or even unstable approximate solutions for this non-autonomous problem can occur from some of the most frequently used numerical methods, including the lsoda, implicit backwards difference, and Runge-Kutta methods, all of which employ a black-box framework. Meanwhile, a simple, manually-programmed Runge-Kutta method is robust enough to accurately capture the analytical solution for biologically reasonable parameters and consistently produce reliable simulations. Consistency and reliability of numerical methods are fundamental for simulating non-autonomous differential equations and dynamical systems, particularly when applications are physically or biologically informed.
Data-driven Approach for Static Hedging of Exchange Traded Options
Authors: Vikranth Lokeshwar Dhandapani, Shashi Jain
Date: 2023-02-01 00:00:00
Tasks: Benchmarking, Interpretable Machine Learning, Position, Single Particle Analysis

This paper presents a data-driven interpretable machine learning algorithm for semi-static hedging of Exchange Traded options, considering transaction costs with efficient run-time. Further, we provide empirical evidence on the performance of hedging longer-term National Stock Exchange (NSE) Index options using a self-replicating portfolio of shorter-term options and cash position, achieved by the automated algorithm, under different modeling assumptions and market conditions, including Covid period. We also systematically assess the model's performance using the Superior Predictive Ability (SPA) test by benchmarking against the static hedge proposed by Peter Carr and Liuren Wu and industry-standard dynamic hedging. We finally perform a thorough Profit and Loss (PnL) attribution analysis on the target option and hedge portfolios (dynamic and static) to discern the factors explaining the superior performance of static hedging.
A mathematical framework for modelling order book dynamics
Authors: Rama Cont, Pierre Degond, Lifan Xuan
Date: 2023-02-02 00:00:00

We present a general framework for modelling the dynamics of limit order books, built on the combination of two modelling ingredients: the order flow, modelled as a general spatial point process, and market clearing, modelled via a deterministic mass transport operator acting on distributions of buy and sell orders. At the mathematical level, this corresponds to a natural decomposition of the infinitesimal generator describing the evolution of the limit order book into two operators: the generator of the order flow and the clearing operator. Our model provides a flexible framework for modelling and simulating order book dynamics and studying various scaling limits of discrete order book models. We show that our framework includes previous models as special cases and yields insights into the interplay between order flow and price dynamics.
Risk Budgeting Portfolios from Simulations
Authors: Bernardo Freitas Paulo da Costa, Silvana M. Pesenti, Rodrigo S. Targino
Date: 2023-02-02 00:00:00

Risk budgeting is a portfolio strategy where each asset contributes a prespecified amount to the aggregate risk of the portfolio. In this work, we propose an efficient numerical framework that uses only simulations of returns for estimating risk budgeting portfolios. Besides a general cutting planes algorithm for determining the weights of risk budgeting portfolios for arbitrary coherent distortion risk measures, we provide a specialised version for the Expected Shortfall, and a tailored Stochastic Gradient Descent (SGD) algorithm, also for the Expected Shortfall. We compare our algorithm to standard convex optimisation solvers and illustrate different risk budgeting portfolios, constructed using an especially designed Julia package, on real financial data and compare it to classical portfolio strategies.
Performance attribution with respect to interest rates, FX, carry, and residual market risks
Authors: Jan-Frederik Mai
Date: 2023-02-02 00:00:00
Tasks: Position

We develop a method to decompose the PnL of a portfolio of assets into four parts: (a) PnL due to FX rate changes, (b) PnL due to interest rate changes, (c) carry gain due to time passing, (d) PnL due to residual market risk changes (credit risk, liquidity risk, volatility risk etc.). We demonstrate the usefulness of our approach by decomposing the performance of an FX- and interest rate-hedged negative basis position in our fund XAIA Credit Basis II, and we apply the methodology to decompose the performance of our fund XAIA Credit Debt Capital in the first quarter of 2022 into PnL contributions of the single positions.
Artist Agent: A Reinforcement Learning Approach to Automatic Stroke Generation in Oriental Ink Painting
Authors: Ning Xie, Hirotaka Hachiya, Masashi Sugiyama
Date: 2012-06-18 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning (RL)

Oriental ink painting, called Sumi-e, is one of the most appealing painting styles that has attracted artists around the world. Major challenges in computer-based Sumi-e simulation are to abstract complex scene information and draw smooth and natural brush strokes. To automatically find such strokes, we propose to model the brush as a reinforcement learning agent, and learn desired brush-trajectories by maximizing the sum of rewards in the policy search framework. We also provide elaborate design of actions, states, and rewards tailored for a Sumi-e agent. The effectiveness of our proposed approach is demonstrated through simulated Sumi-e experiments.
Transmitter Linearization by Beamforming
Authors: ChuanKang Liang, Behzad Razavi
Date: 2011-09-01 00:00:00
Proceeding: IEEE JOURNAL OF SOLID-STATE CIRCUITS, VOL. 46, NO. 9 2011 9

Millimeter-wave transmitters designed for dense signal constellations must deal with severe linearity-efficiency trade-offs. This paper proposes a method of blending beamforming and linearization that reduces the number of power amplifiers and avoids the loss of on-chip transformers. Two constant-envelope beams are combined in space to deliver a variable-envelope signal, relaxing the linearity of transmitters. A dual-transmitter prototype fabricated in 65-nm CMOS technology and designed for the 60-GHz band produces a 16QAM output of +9.7 dBm with 11% efficiency.
Unsupervised Blur Kernel Estimation and Correction for Blind Super-Resolution
Authors: Youngsoo Kim, JEONGHYO HA, Yooshin Cho, Junmo Kim
Date: 2022-04-25 00:00:00
Proceeding: IEEE Access 2022 4
Tasks: Blind Super-Resolution, Super-Resolution

Blind super-resolution (blind-SR) is an important task in the field of computer vision and has
various applications in real-world. Blur kernel estimation is the main element of blind-SR along with the
adaptive SR networks and a more accurately estimated kernel guarantees a better performance. Recently,
generative adversarial networks (GANs), comparing recurrence patches across scales, have been the most
successful unsupervised kernel estimation methods. However, they still involve several problems. ① Their
sharpness discrimination ability has been noted as being too weak, causing them to focus more on pattern
shapes than sharpness. ② In some cases, kernel correction processes were omitted; however, these are
essential because the optimally generated kernel may be narrower than a point spread function (PSF)
except when the PSF is ideal low-pass filter. ③ Previous studies also did not consider that GANs are
affected by the thickness of edges as well as PSF. Thus, in this paper, 1) we propose a degradation and
ranking comparison process designed to induce GAN models to became sensitive to image sharpness,
and 2) propose a scale-free kernel correction technique using Gaussian kernel approximation including a
thickness parameter. To improve the kernel accuracy further, we 3) propose a combination model of the
proposed GAN and DIP(deep image prior) for more supervision, and designed a kernel correction network
to propagate gradients through developed correction method. Several experiments demonstrate that our
methods enhanced the l2 error and the shape of the kernel significantly. In addition, by combining with
ordinary blind-SR algorithms, the best reconstruction accuracy was achieved among unsupervised blur kernel
estimation methods.