pwc-archive/papers-with-abstracts huggingface dataset:
Inhibitory feedback from the motor circuit gates mechanosensory processing in C. elegans
Authors: Sandeep Kumar, Anuj K Sharma, Andrew Tran, Andrew M Leifer
Date: 2023-01-06 00:00:00

Animals must integrate sensory cues with their current behavioral context to generate a suitable response. How this integration occurs is poorly understood. Previously we developed high throughput methods to probe neural activity in populations of Caenorhabditis elegans and discovered that the animal's mechanosensory processing is rapidly modulated by the animal's locomotion. Specifically we found that when the worm turns it suppresses its mechanosensory-evoked reversal response. Here we report that C. elegans use inhibitory feedback from turning-associated neurons to provide this rapid modulation of mechanosensory processing. By performing high-throughput optogenetic perturbations triggered on behavior, we show that turning associated neurons SAA, RIV and/or SMB suppress mechanosensory-evoked reversals during turns. We find that activation of the gentle-touch mechanosensory neurons or of any of the interneurons AIZ, RIM, AIB and AVE during a turn is less likely to evoke a reversal than activation during forward movement. Inhibiting neurons SAA, RIV and SMB during a turn restores the likelihood with which mechanosensory activation evokes reversals. Separately, activation of premotor interneuron AVA evokes reversals regardless of whether the animal is turning or moving forward. We therefore propose that inhibitory signals from SAA, RIV and/or SMB gate mechanosensory signals upstream of neuron AVA. We conclude that C. elegans rely on inhibitory feedback from the motor circuit to modulate its response to sensory stimuli on fast timescales. This need for motor signals in sensory processing may explain the ubiquity in many organisms of motor-related neural activity patterns seen across the brain, including in sensory processing areas.
Bayesian modelling of visual discrimination learning in mice
Authors: Pouya Baniasadi
Date: 2022-11-15 00:00:00
Tasks: Decision Making, Descriptive

The brain constantly turns large flows of sensory information into selective representations of the environment. It, therefore, needs to learn to process those sensory inputs that are most relevant for behaviour. It is not well understood how learning changes neural circuits in visual and decision-making brain areas to adjust and improve its visually guided decision-making. To address this question, head-fixed mice were trained to move through virtual reality environments and learn visual discrimination while neural activity was recorded with two-photon calcium imaging. Previously, descriptive models of neuronal activity were fitted to the data, which was used to compare the activity of excitatory and different inhibitory cell types. However, the previous models did not take the internal representations and learning dynamics into account. Here, I present a framework to infer a model of internal representations that are used to generate the behaviour during the task. We model the learning process from untrained mice to trained mice within the normative framework of the ideal Bayesian observer and provide a Markov model for generating the movement and licking. The framework provides a space of models where a range of hypotheses about the internal representations could be compared for a given data set.
Cell Population Growth Kinetics in the Presence of Stochastic Heterogeneity of Cell Phenotype
Authors: Yue Wang, Joseph X. Zhou, Edoardo Pedrini, Irit Rubin, May Khalil, Roberto Taramelli, Hong Qian, Sui Huang
Date: 2023-01-10 00:00:00

Recent studies at individual cell resolution have revealed phenotypic heterogeneity in nominally clonal tumor cell populations. The heterogeneity affects cell growth behaviors, which can result in departure from the idealized uniform exponential growth of the cell population. Here we measured the stochastic time courses of growth of an ensemble of populations of HL60 leukemia cells in cultures, starting with distinct initial cell numbers to capture a departure from the {uniform exponential growth model for the initial growth (``take-off'')}. Despite being derived from the same cell clone, we observed significant variations in the early growth patterns of individual cultures with statistically significant differences in growth dynamics, which could be explained by the presence of inter-converting subpopulations with different growth rates, and which could last for many generations. Based on the hypothesis of existence of multiple subpopulations, we developed a branching process model that was consistent with the experimental observations.
Runaway signals: Exaggerated displays of commitment may result from second-order signaling
Authors: Julien Lie-Panis, Jean-Louis Dessalles
Date: 2023-01-09 00:00:00

To demonstrate their commitment, for instance during wartime, members of a group will sometimes all engage in the same ruinous display. Such uniform, high-cost signals are hard to reconcile with standard models of signaling. For signals to be stable, they should honestly inform their audience; yet, uniform signals are trivially uninformative. To explain this phenomenon, we design a simple model, which we call the signal runaway game. In this game, senders can express outrage at non-senders. Outrage functions as a second-order signal. By expressing outrage at non-senders, senders draw attention to their own signal, and benefit from its increased visibility. Using our model and a simulation, we show that outrage can stabilize uniform signals, and can lead signal costs to run away. Second-order signaling may explain why groups sometimes demand displays of commitment from all their members, and why these displays can entail extreme costs.
Protein Co-Enrichment Analysis of Extracellular Vesicles
Authors: Molly L. Shen, Zijie Jin, Rosalie Martel, Andreas Wallucks, Lucile Alexandre, Philippe DeCorwin-Martin, Lorenna Oliveira Fernandes de Araujo, Andy Ng, David Juncker
Date: 2023-01-10 00:00:00

Extracellular Vesicles (EVs) carry cell-derived proteins that confer functionality and selective cell uptake. However, whether proteins are packaged stochastically or co-enriched within individual EVs, and whether co-enrichment fluctuates under homeostasis and disease, has not been measured. EV abundance and protein global relative expression have been qualified by bulk analysis. Meanwhile, co-enrichment is not directly accessible via bulk measurement and has not been reported for single EV analysis. Here, we introduce the normalized index of co-enrichment (NICE) to measure protein co-enrichment. NICE was derived by (i) capturing EVs based on the expression of a membrane-bound protein, (ii) probing for the co-expression of a second protein at the population level - EV integrity underwrites the detection of single EV co-expression without the need to resolve single EVs - and (iii) normalizing measured values using two universal normalization probes. Axiomatically, NICE = 1 for stochastic inclusion or no overall co-enrichment, while for positive and negative co-enrichment NICE > 1 or < 1, respectively. We quantified the NICE of tetraspanins, growth factor receptors and integrins in EVs of eight breast cancer cell lines of varying metastatic potential and organotropism, combinatorially mapping up to 104 protein pairs. Our analysis revealed protein enrichment and co-expression patterns consistent with previous findings. For the organotropic cell lines, most protein pairs were co-enriched on EVs, with the majority of NICE values between 0.2 to 11.5, and extending from 0.037 to 80.4. Median NICE were either negative, neutral or positive depending on the cells. NICE analysis is easily multiplexed and is compatible with microarrays, bead-based and single EV assays. Additional studies are needed to deepen our understanding of the potential and significance of NICE for research and clinical uses.
Decreased serum vitamin D level as a prognostic marker in patients with COVID-19
Authors: Ruyi Qu, Qiuji Yang, Yingying Bi, Jiajing Cheng, Mengna He, Xin Wei, Yiqi Yuan, Yuxin Yang, Jinlong Qin
Date: 2022-12-25 00:00:00
Tasks: Prognosis

Background: The corona virus disease 2019 (COVID-19) pandemic, which is caused by severe acute respiratory syndrome coronavirus 2, is still localized outbreak and has resulted in a high rate of infection and severe disease in older patients with comorbidities. The vitamin D status of the population has been found to be an important factor that could influence outcome of COVID-19. However, whether vitamin D can lessen the symptoms or severity of COVID-19 still remains controversial. Methods: A total of 719 patients with confirmed COVID-19 were enrolled retrospectively in this study from April 13 to June 6, 2022 at Shanghai Forth People's Hospital. The circulating levels of 25(OH)D3, inflammatory factors, and clinical parameters were assayed. Time to viral RNA clearance (TVRC), classification and prognosis of COVID-19 were used to evaluate the severity of COVID-19 infection. Results: The median age was 76 years (interquartile range, IQR, 64.5-84.6), 44.1% of patients were male, and the TVRC was 11 days (IQR, 7-16) in this population. The median level of 25(OH)D3 was 27.15 (IQR, 19.31-38.89) nmol/L. Patients with lower serum 25(OH)D3 had prolonged time to viral clearance, more obvious inflammatory response, more severe respiratory symptoms and higher risks of impaired hepatic and renal function. Multiple regression analyses revealed that serum 25(OH)D3 level was negatively associated with TVRC independently. ROC curve showed the serum vitamin D level could predict the severity classification and prognosis of COVID-19 significantly.Conclusions: Serum 25(OH)D3 level is independently associated with the severity of COVID-19 in elderly, and it could be used as a predictor of the severity of COVID-19. In addition, supplementation with vitamin D might provide beneficial effects in old patients with COVID-19.
Simulation schemes for the Heston model with Poisson conditioning
Authors: Jaehyuk Choi, Yue Kuen Kwok
Date: 2023-01-07 00:00:00

Exact simulation schemes under the Heston stochastic volatility model (e.g., Broadie-Kaya and Glasserman-Kim) suffer from computationally expensive modified Bessel function evaluations. We propose a new exact simulation scheme without the modified Bessel function, based on the observation that the conditional integrated variance can be simplified when conditioned by the Poisson variate used for simulating the terminal variance. Our approach also enhances the low-bias and time discretization schemes, which are suitable for pricing derivatives with frequent monitoring. Extensive numerical tests reveal the good performance of the new simulation schemes in terms of accuracy, efficiency, and reliability when compared with existing methods.
Option pricing under the normal SABR model with Gaussian quadratures
Authors: Jaehyuk Choi, Byoung Ki Seo
Date: 2023-01-07 00:00:00

The stochastic-alpha-beta-rho (SABR) model has been widely adopted in options trading. In particular, the normal ($\beta=0$) SABR model is a popular model choice for interest rates because it allows negative asset values. The option price and delta under the SABR model are typically obtained via asymptotic implied volatility approximation, but these are often inaccurate and arbitrageable. Using a recently discovered price transition law, we propose a Gaussian quadrature integration scheme for price options under the normal SABR model. The compound Gaussian quadrature sum over only 49 points can calculate a very accurate price and delta that are arbitrage-free.
Quant 4.0: Engineering Quantitative Investment with Automated, Explainable and Knowledge-driven Artificial Intelligence
Authors: Jian Guo, Saizhuo Wang, Lionel M. Ni, Heung-Yeung Shum
Date: 2022-12-13 00:00:00
Tasks: Deep Learning, Philosophy

Quantitative investment (``quant'') is an interdisciplinary field combining financial engineering, computer science, mathematics, statistics, etc. Quant has become one of the mainstream investment methodologies over the past decades, and has experienced three generations: Quant 1.0, trading by mathematical modeling to discover mis-priced assets in markets; Quant 2.0, shifting quant research pipeline from small ``strategy workshops'' to large ``alpha factories''; Quant 3.0, applying deep learning techniques to discover complex nonlinear pricing rules. Despite its advantage in prediction, deep learning relies on extremely large data volume and labor-intensive tuning of ``black-box'' neural network models. To address these limitations, in this paper, we introduce Quant 4.0 and provide an engineering perspective for next-generation quant. Quant 4.0 has three key differentiating components. First, automated AI changes quant pipeline from traditional hand-craft modeling to the state-of-the-art automated modeling, practicing the philosophy of ``algorithm produces algorithm, model builds model, and eventually AI creates AI''. Second, explainable AI develops new techniques to better understand and interpret investment decisions made by machine learning black-boxes, and explains complicated and hidden risk exposures. Third, knowledge-driven AI is a supplement to data-driven AI such as deep learning and it incorporates prior knowledge into modeling to improve investment decision, in particular for quantitative value investing. Moreover, we discuss how to build a system that practices the Quant 4.0 concept. Finally, we propose ten challenging research problems for quant technology, and discuss potential solutions, research directions, and future trends.
Long-Term Returns Estimation of Leveraged Indexes and ETFs
Authors: Hayden Brown
Date: 2023-01-09 00:00:00

Daily leveraged exchange traded funds amplify gains and losses of their underlying benchmark indexes on a daily basis. The result of going long in a daily leveraged ETF for more than one day is less clear. Here, bounds are given for the log-returns of a leveraged ETF when going long for more than just one day. The bounds are quadratic in the daily log-returns of the underlying benchmark index, and they are used to find sufficient conditions for outperformance and underperformance of a leveraged ETF in relation to its underlying benchmark index. Results show that if the underlying benchmark index drops 10+\% over the course of 63 consecutive trading days, and the standard deviation of the benchmark index's daily log-returns is no more than .015, then going long in a -3x leveraged ETF during that period gives a log-return of at least 1.5 times the log-return of a short position in the underlying benchmark index. Results also show promise for a 2x daily leveraged S&P 500 ETF. If the average annual log-return of the S&P 500 index continues to be at least .0658, as it has been in the past, and the standard deviation of daily S&P 500 log-returns is under .0125, then a 2x daily leveraged S&P 500 ETF will perform at least as well as the S&P 500 index in the long-run.
Minimum Cost Super-Hedging in a Discrete Time Incomplete Multi-Asset Binomial Market
Authors: Jarek Kędra, Assaf Libman, Victoria Steblovskaya
Date: 2023-01-07 00:00:00

We consider a multi-asset incomplete model of the financial market, where each of $m\geq 2$ risky assets follows the binomial dynamics, and no assumptions are made on the joint distribution of the risky asset price processes. We provide explicit formulas for the minimum cost super-hedging strategies for a wide class of European type multi-asset contingent claims. This class includes European basket call and put options, among others. Since a super-hedge is a non-self-financing arbitrage strategy, it produces non-negative local residuals, for which we also give explicit formulas. This paper completes the foundation started in previous work of the authors for the extension of our results to a more realistic market model.
Achieving a Given Financial Goal with Optimal Deferred Term Insurance Purchasing Policy
Authors: Yuqi Li, Lihua Zhang
Date: 2022-12-09 00:00:00

This paper researches the problem of purchasing deferred term insurance in the context of financial planning to maximize the probability of achieving a personal financial goal. Specifically, our study starts from the perspective of hedging death risk and longevity risk, and considers the purchase of deferred term life insurance and deferred term pure endowment to achieve a given financial goal for the first time in both deterministic and stochastic framework. In particular, we consider income, consumption and risky investment in the stochastic framework, extending previous results in \cite{Bayraktar2016}. The time cutoff m and n make the work more difficult. However, by establishing new controls,``\emph{quasi-ideal value}" and``\emph{ideal value}", we solve the corresponding ordinary differential equations or stochastic differential equations, and give the specific expressions for the maximum probability. Then we provide the optimal life insurance purchasing strategies and the optimal risk investment strategies. In general, when m \geqslant 0, n>0, deferred term insurance or term life insurance is a better choice for those who want to achieve their financial or bequest goals but are not financially sound. In particular, if m >0, n \rightarrow \infty, our viewpoint also sheds light on reaching a bequest goal by purchasing deferred whole life insurance. It is worth noting that when m=0, n \rightarrow \infty, our problem is equivalent to achieving the just mentioned bequest goal by purchasing whole life insurance, at which point the maximum probability and the life insurance purchasing strategies we provide are consistent with those in \cite{Bayraktar2014, Bayraktar2016}.
Diversification quotients based on VaR and ES
Authors: Xia Han, Liyuan Lin, Ruodu Wang
Date: 2023-01-09 00:00:00
Tasks: Portfolio Optimization

The diversification quotient (DQ) is recently introduced for quantifying the degree of diversification of a stochastic portfolio model. It has an axiomatic foundation and can be defined through a parametric class of risk measures. Since the Value-at-Risk (VaR) and the Expected Shortfall (ES) are the most prominent risk measures widely used in both banking and insurance, we investigate DQ constructed from VaR and ES in this paper. In particular, for the popular models of elliptical and multivariate regular varying (MRV) distributions, explicit formulas are available. The portfolio optimization problems for the elliptical and MRV models are also studied. Our results further reveal favourable features of DQ, both theoretically and practically, compared to traditional diversification indices based on a single risk measure.
BASPRO: a balanced script producer for speech corpus collection based on the genetic algorithm
Authors: Yu-Wen Chen, Hsin-Min Wang, Yu Tsao
Date: 2022-12-11 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Sentence, Speech Enhancement, speech-recognition, Speech Recognition, text-to-speech, Text to Speech

The performance of speech-processing models is heavily influenced by the speech corpus that is used for training and evaluation. In this study, we propose BAlanced Script PROducer (BASPRO) system, which can automatically construct a phonetically balanced and rich set of Chinese sentences for collecting Mandarin Chinese speech data. First, we used pretrained natural language processing systems to extract ten-character candidate sentences from a large corpus of Chinese news texts. Then, we applied a genetic algorithm-based method to select 20 phonetically balanced sentence sets, each containing 20 sentences, from the candidate sentences. Using BASPRO, we obtained a recording script called TMNews, which contains 400 ten-character sentences. TMNews covers 84% of the syllables used in the real world. Moreover, the syllable distribution has 0.96 cosine similarity to the real-world syllable distribution. We converted the script into a speech corpus using two text-to-speech systems. Using the designed speech corpus, we tested the performances of speech enhancement (SE) and automatic speech recognition (ASR), which are one of the most important regression- and classification-based speech processing tasks, respectively. The experimental results show that the SE and ASR models trained on the designed speech corpus outperform their counterparts trained on a randomly composed speech corpus.
Temporal Weights
Authors: Adam Kohan, Ed Rietman, Hava Siegelmann
Date: 2022-12-13 00:00:00
Tasks: Time Series, Time Series Analysis

In artificial neural networks, weights are a static representation of synapses. However, synapses are not static, they have their own interacting dynamics over time. To instill weights with interacting dynamics, we use a model describing synchronization that is capable of capturing core mechanisms of a range of neural and general biological phenomena over time. An ideal fit for these Temporal Weights (TW) are Neural ODEs, with continuous dynamics and a dependency on time. The resulting recurrent neural networks efficiently model temporal dynamics by computing on the ordering of sequences, and the length and scale of time. By adding temporal weights to a model, we demonstrate better performance, smaller models, and data efficiency on sparse, irregularly sampled time series datasets.
White-box Inference Attacks against Centralized Machine Learning and Federated Learning
Authors: Jingyi Ge
Date: 2022-12-15 00:00:00
Tasks: Federated Learning, Inference Attack

With the development of information science and technology, various industries have generated massive amounts of data, and machine learning is widely used in the analysis of big data. However, if the privacy of machine learning applications' customers cannot be guaranteed, it will cause security threats and losses to users' personal privacy information and service providers. Therefore, the issue of privacy protection of machine learning has received wide attention. For centralized machine learning models, we evaluate the impact of different neural network layers, gradient, gradient norm, and fine-tuned models on member inference attack performance with prior knowledge; For the federated learning model, we discuss the location of the attacker in the target model and its attack mode. The results show that the centralized machine learning model shows more serious member information leakage in all aspects, and the accuracy of the attacker in the central parameter server is significantly higher than the local Inference attacks as participants.
Membership Inference Attacks Against Latent Factor Model
Authors: Dazhi Hu
Date: 2022-12-15 00:00:00
Tasks: model, Recommendation Systems

The advent of the information age has led to the problems of information overload and unclear demands. As an information filtering system, personalized recommendation systems predict users' behavior and preference for items and improves users' information acquisition efficiency. However, recommendation systems usually use highly sensitive user data for training. In this paper, we use the latent factor model as the recommender to get the list of recommended items, and we representing users from relevant items Compared with the traditional member inference against machine learning classifiers. We construct a multilayer perceptron model with two hidden layers as the attack model to complete the member inference. Moreover, a shadow recommender is established to derive the labeled training data for the attack model. The attack model is trained on the dataset generated by the shadow recommender and tested on the dataset generated by the target recommender. The experimental data show that the AUC index of our attack model can reach 0.857 on the real dataset MovieLens, which shows that the attack model has good performance.