pwc-archive/papers-with-abstracts huggingface dataset:
Spatial and Wavelength Division Joint Multiplexing System Design for Visible Light Communications
Authors: Cheng Chen, Shenjie Huang, Iman Tavakkolnia, Majid Safari, Harald Haas
Date: 2022-09-20 00:00:00

The low-pass characteristics of front-end elements including light-emitting diodes (LEDs) and photodiodes (PDs) limit the transmission data rate of visible light communication (VLC) and Light Fidelity (LiFi) systems. Using multiplexing transmission techniques, such as spatial multiplexing (SMX) and wavelength division multiplexing (WDM), is a solution to overcome bandwidth limitation. However, spatial correlation in optical wireless channels and optical filter bandpass shifts typically limit the achievable multiplexing gain in SMX and WDM systems, respectively. In this paper, we consider a multiple-input multiple output (MIMO) joint multiplexing VLC system that exploits available degrees-offreedom (DoFs) across space, wavelength and frequency dimensions simultaneously. Instead of providing a new precoder/post-detector design, we investigate the considered joint multiplexing system from a system configuration perspective by tuning system parameters in both spatial and wavelength domains, such as LED positions and optical filter passband. We propose a novel spatial clustering with wavelength division (SCWD) strategy which enhances the MIMO channel condition. We propose to use a state-of-the-art black-box optimization tool: Bayesian adaptive direct search (BADS) to determine the desired system parameters, which can significantly improve the achievable rate. The extensive numerical results demonstrate the superiority of the proposed method over conventional SMX and WDM VLC systems.
Proportionate Adaptive Graph Signal Recovery
Authors: Razieh Torkamani, Hadi Zayyani, Mehdi Korki
Date: 2022-09-20 00:00:00
Tasks: Vocal Bursts Type Prediction

This paper generalizes the proportionate-type adaptive algorithm to the graph signal processing and proposes two proportionate-type adaptive graph signal recovery algorithms. The gain matrix of the proportionate algorithm leads to faster convergence than least mean squares (LMS) algorithm. In this paper, the gain matrix is obtained in a closed-form by minimizing the gradient of the mean-square deviation (GMSD). The first algorithm is the Proportionate-type Graph LMS (Pt-GLMS) algorithm which simply uses a gain matrix in the recursion process of the LMS algorithm and accelerates the convergence of the Pt-GLMS algorithm compared to the LMS algorithm. The second algorithm is the Proportionate-type Graph Extended LMS (Pt-GELMS) algorithm, which uses the previous signal vectors alongside the signal of the current iteration. The Pt-GELMS algorithm utilizes two gain matrices to control the effect of the signal of the previous iterations. The stability analyses of the algorithms are also provided. Simulation results demonstrate the efficacy of the two proposed proportionate-type LMS algorithms.
Estimation of Soiling Losses in Unlabeled PV Data
Authors: Bennet Meyers
Date: 2022-09-20 00:00:00

We provide a methodology for estimating the losses due to soiling for photovoltaic (PV) systems. We focus this work on estimating the losses from historical power production data that are unlabeled, i.e. power measurements with time stamps, but no other information such as site configuration or meteorological data. We present a validation of this approach on a small fleet of typical rooftop PV systems. The proposed method differs from prior work in that the construction of a performance index is not required to analyze soiling loss. This approach is appropriate for analyzing the soiling losses in field production data from fleets of distributed rooftop systems and is highly automatic, allowing for scaling to large fleets of heterogeneous PV systems.
Estimation of Shade Losses in Unlabeled PV Data
Authors: Bennet Meyers, David Jose Florez Rodriguez
Date: 2022-09-20 00:00:00
Tasks: Time Series, Time Series Analysis

We provide a methodology for estimating the losses due to shade in power generation data sets produced by real-world photovoltaic (PV) systems. We focus this work on estimating shade loss from data that are unlabeled, i.e. power measurements with time stamps but no other information such as site configuration or meteorological data. This approach enables, for the first time, the analysis of data generated by small scale, distributed PV systems, which do not have the data quality or richness of large, utility-scale PV systems or research-grade installations. This work is an application of the newly published signal decomposition (SD) framework, which provides an extensible approach for estimating hidden components in time-series data.
Stochastic Models for Replication Origin Spacings in Eukaryotic DNA Replication
Authors: Huw Day, N C Snaith
Date: 2022-09-20 00:00:00

We consider eukaryotic DNA replication and in particular the role of replication origins in this process. We focus on origins which are `active' - that is, trigger themselves in the process before being read by the replication forks of other origins. We initially consider the spacings of these active replication origins in comparison to certain probability distributions of spacings taken from random matrix theory. We see how the spacings between neighbouring eigenvalues from certain collections of random matrices has some potential for modelling the spacing between active origins. This suitability can be further augmented with the use of uniform thinning which acts as a continuous deformation between correlated eigenvalue spacings and exponential (Poissonian) spacings. We model the process as a modified 2D Poisson process with an added exclusion rule to identify active points based on their position on the chromosome and trigger time relative to other origins. We see how this can be reduced to a stochastic geometry problem and show analytically that two active origins are unlikely to be close together, regardless of how many non-active points are between them. In particular, we see how these active origins repel linearly. We then see how data from various DNA datasets match with simulations from our model. We see that whilst there is variety in the DNA data, comparing the data with the model provides insight into the replication origin distribution of various organisms.
Computing Phylo-k-mers
Authors: Nikolai Romashchenko, Benjamin Linard, Fabio Pardi, Eric Rivals
Date: 2022-09-19 00:00:00

Phylogenetically informed k-mers, or phylo-k-mers for short, are k-mers that are predicted to appear within a given genomic region at predefined locations of a fixed phylogeny. Given a reference alignment for this genomic region and assuming a phylogenetic model of sequence evolution, we can compute a probability score for any given k-mer at any given tree node. The k-mers with sufficiently high probabilities can later be used to perform alignment-free phylogenetic classification of new sequences-a procedure recently proposed for the phylogenetic placement of metabarcoding reads and the detection of novel virus recombinants. While computing phylo-k-mers, we need to consider large numbers of k-mers at each tree node, which warrants the development of efficient enumeration algorithms. We consider a formal definition of the problem of phylo-k-mer computation: How to efficiently find all k-mers whose probability lies above a user-defined threshold for a given tree node? We describe and analyze algorithms for this problem, relying on branch-and-bound and divideand-conquer techniques. We exploit the redundancy of adjacent windows of the alignment and the structure of the probability matrix to save on computation. Besides computational complexity analyses, we provide an empirical evaluation of the relative performance of their implementations on real-world and simulated data. The divide-and-conquer algorithms, which to the best of our knowledge are novel, are found to be clear improvements over the branch-and-bound approach, especially when a large number of phylo-k-mers are found.
Unresolved excess accumulation of myelin-derived cholesterol contributes to scar formation after spinal cord injury
Authors: Bolin Zheng, Yijing He, Qing Zhao, Xu Zhu, Shuai Yin, Huiyi Yang, Zhaojie Wang, Liming Cheng
Date: 2022-09-20 00:00:00

Background: Spinal cord injury triggers complex pathological cascades, resulting in destructive tissue damage and incomplete tissue repair. Scar formation is generally considered as a barrier for regeneration in central nervous system (CNS), while the intrinsic mechanism of scar-forming after spinal cord injury has not been completed deciphered. Methods: We assessed cholesterol hemostasis in spinal cord lesions and injured peripheral nerves using confocal reflection microscopy and real-time PCR analyses. The involvement of the proteins, which were predicted to promote cholesterol efflux in spinal cord lesions, were assessed with Liver X receptor (LXR) agonist and Apolipoprotein E (APOE) deficiency. The role of reverse cholesterol transport (RCT) in cholesterol clearance was examined in APOE KO mice injured sciatic nerves and myelin-overloaded macrophages in vitro. Finally, we determined the consequence of excess cholesterol accumulation in CNS by transplantation of myelin into neonatal spinal cord lesions. Results: We found that excess cholesterol accumulates in phagocytes and is inefficiently removed in spinal cord lesions in young-adult mice. Interestingly, we observed that excessive cholesterol also accumulates in injured peripheral nerves, but is subsequently removed by RCT. Meanwhile, preventing RCT led to macrophage accumulation and fibrosis in injured peripheral nerves. Furthermore, the neonatal mouse spinal cord lesions are devoid of myelin-derived lipids, and able to heal without excess cholesterol accumulation. We found that transplantation of myelin into neonatal lesions disrupts healing with excessive cholesterol accumulation, persistent macrophage activation and fibrosis, indicating myelin-derived cholesterol plays a critical role in impaired wound healing.
Valuation of Music Catalogs
Authors: SASHA STOIKOV, Ivan Kosyuk
Date: 2022-09-20 00:00:00

We propose a risk neutral approach to forecast the cashflows of music catalogs, based on historical revenue data. We use a discounted cashflows formula to produce reasonable ranges of multipliers for these assets, based on the age of the catalog, the last-twelve-months revenue and the duration of the contract. We compare the multipliers implied by the cashflows of top, median and bottom performing songs on the Royalty Exchange platform. We find that ask prices are close to the multipliers justified by median song cashflows. The best bids are near the multipliers justified by the bottom decile of song cashflows.
Nonparametric estimates of option prices via Hermite basis functions
Authors: Carlo Marinelli, Stefano d'Addona
Date: 2022-09-20 00:00:00

We consider approximate pricing formulas for European options based on approximating the logarithmic return's density of the underlying by a linear combination of rescaled Hermite polynomials. The resulting models, that can be seen as perturbations of the classical Black-Scholes one, are nonpararametric in the sense that the distribution of logarithmic returns at fixed times to maturity is only assumed to have a square-integrable density. We extensively investigate the empirical performance, defined in terms of out-of-sample relative pricing error, of this class of approximating models, depending on their order (that is, roughly speaking, the degree of the polynomial expansion) as well as on several ways to calibrate them to observed data. Empirical results suggest that such approximate pricing formulas, when compared with simple nonparametric estimates based on interpolation and extrapolation on the implied volatility curve, perform reasonably well only for options with strike price not too far apart from the strike prices of the observed sample.
Calibrating Segmentation Networks with Margin-based Label Smoothing
Authors: Balamurali Murugesan, Bingyuan Liu, Adrian Galdran, Ismail Ben Ayed, Jose Dolz
Date: 2022-09-09 00:00:00
Tasks: Image Segmentation, Medical Image Segmentation, Semantic Segmentation

Despite the undeniable progress in visual recognition tasks fueled by deep neural networks, there exists recent evidence showing that these models are poorly calibrated, resulting in over-confident predictions. The standard practices of minimizing the cross entropy loss during training promote the predicted softmax probabilities to match the one-hot label assignments. Nevertheless, this yields a pre-softmax activation of the correct class that is significantly larger than the remaining activations, which exacerbates the miscalibration problem. Recent observations from the classification literature suggest that loss functions that embed implicit or explicit maximization of the entropy of predictions yield state-of-the-art calibration performances. Despite these findings, the impact of these losses in the relevant task of calibrating medical image segmentation networks remains unexplored. In this work, we provide a unifying constrained-optimization perspective of current state-of-the-art calibration losses. Specifically, these losses could be viewed as approximations of a linear penalty (or a Lagrangian term) imposing equality constraints on logit distances. This points to an important limitation of such underlying equality constraints, whose ensuing gradients constantly push towards a non-informative solution, which might prevent from reaching the best compromise between the discriminative performance and calibration of the model during gradient-based optimization. Following our observations, we propose a simple and flexible generalization based on inequality constraints, which imposes a controllable margin on logit distances. Comprehensive experiments on a variety of public medical image segmentation benchmarks demonstrate that our method sets novel state-of-the-art results on these tasks in terms of network calibration, whereas the discriminative performance is also improved.
Activity report analysis with automatic single or multispan answer extraction
Authors: Ravi Choudhary, Arvind Krishna Sridhar, Erik Visser
Date: 2022-09-09 00:00:00
Tasks: Anomaly Detection, Question Answering

In the era of loT (Internet of Things) we are surrounded by a plethora of Al enabled devices that can transcribe images, video, audio, and sensors signals into text descriptions. When such transcriptions are captured in activity reports for monitoring, life logging and anomaly detection applications, a user would typically request a summary or ask targeted questions about certain sections of the report they are interested in. Depending on the context and the type of question asked, a question answering (QA) system would need to automatically determine whether the answer covers single-span or multi-span text components. Currently available QA datasets primarily focus on single span responses only (such as SQuAD[4]) or contain a low proportion of examples with multiple span answers (such as DROP[3]). To investigate automatic selection of single/multi-span answers in the use case described, we created a new smart home environment dataset comprised of questions paired with single-span or multi-span answers depending on the question and context queried. In addition, we propose a RoBERTa[6]-based multiple span extraction question answering (MSEQA) model returning the appropriate answer span for a given question. Our experiments show that the proposed model outperforms state-of-the-art QA models on our dataset while providing comparable performance on published individual single/multi-span task datasets.
Personal Attribute Prediction from Conversations
Authors: Yinan Liu, Hu Chen, Wei Shen
Date: 2022-08-29 00:00:00
Tasks: Attribute, Language Modeling, Language Modelling, Prediction

Personal knowledge bases (PKBs) are critical to many applications, such as Web-based chatbots and personalized recommendation. Conversations containing rich personal knowledge can be regarded as a main source to populate the PKB. Given a user, a user attribute, and user utterances from a conversational system, we aim to predict the personal attribute value for the user, which is helpful for the enrichment of PKBs. However, there are three issues existing in previous studies: (1) manually labeled utterances are required for model training; (2) personal attribute knowledge embedded in both utterances and external resources is underutilized; (3) the performance on predicting some difficult personal attributes is unsatisfactory. In this paper, we propose a framework DSCGN based on the pre-trained language model with a noise-robust loss function to predict personal attributes from conversations without requiring any labeled utterances. We yield two categories of supervision, i.e., document-level supervision via a distant supervision strategy and contextualized word-level supervision via a label guessing method, by mining the personal attribute knowledge embedded in both unlabeled utterances and external resources to fine-tune the language model. Extensive experiments over two real-world data sets (i.e., a profession data set and a hobby data set) show our framework obtains the best performance compared with all the twelve baselines in terms of nDCG and MRR.
PainPoints: A Framework for Language-based Detection of Chronic Pain and Expert-Collaborative Text-Summarization
Authors: Shreyas Fadnavis, Amit Dhurandhar, Raquel Norel, Jenna M Reinen, Carla Agurto, Erica Secchettin, Vittorio Schweiger, Giovanni Perini, Guillermo Cecchi
Date: 2022-09-14 00:00:00
Tasks: Sentence, Text Summarization

Chronic pain is a pervasive disorder which is often very disabling and is associated with comorbidities such as depression and anxiety. Neuropathic Pain (NP) is a common sub-type which is often caused due to nerve damage and has a known pathophysiology. Another common sub-type is Fibromyalgia (FM) which is described as musculoskeletal, diffuse pain that is widespread through the body. The pathophysiology of FM is poorly understood, making it very hard to diagnose. Standard medications and treatments for FM and NP differ from one another and if misdiagnosed it can cause an increase in symptom severity. To overcome this difficulty, we propose a novel framework, PainPoints, which accurately detects the sub-type of pain and generates clinical notes via summarizing the patient interviews. Specifically, PainPoints makes use of large language models to perform sentence-level classification of the text obtained from interviews of FM and NP patients with a reliable AUC of 0.83. Using a sufficiency-based interpretability approach, we explain how the fine-tuned model accurately picks up on the nuances that patients use to describe their pain. Finally, we generate summaries of these interviews via expert interventions by introducing a novel facet-based approach. PainPoints thus enables practitioners to add/drop facets and generate a custom summary based on the notion of "facet-coverage" which is also introduced in this work.
emojiSpace: Spatial Representation of Emojis
Authors: Moeen Mostafavi, Mahsa Pahlavikhah Varnosfaderani, Fateme Nikseresht, Seyed Ahmad Mansouri
Date: 2022-09-12 00:00:00
Tasks: Sentiment Analysis

In the absence of nonverbal cues during messaging communication, users express part of their emotions using emojis. Thus, having emojis in the vocabulary of text messaging language models can significantly improve many natural language processing (NLP) applications such as online communication analysis. On the other hand, word embedding models are usually trained on a very large corpus of text such as Wikipedia or Google News datasets that include very few samples with emojis. In this study, we create emojiSpace, which is a combined word-emoji embedding using the word2vec model from the Genism library in Python. We trained emojiSpace on a corpus of more than 4 billion tweets and evaluated it by implementing sentiment analysis on a Twitter dataset containing more than 67 million tweets as an extrinsic task. For this task, we compared the performance of two different classifiers of random forest (RF) and linear support vector machine (SVM). For evaluation, we compared emojiSpace performance with two other pre-trained embeddings and demonstrated that emojiSpace outperforms both.
Safe Reinforcement Learning with Contrastive Risk Prediction
Authors: Hanping Zhang, Yuhong Guo
Date: 2022-09-10 00:00:00
Tasks: Prediction, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Safe Exploration, Safe Reinforcement Learning

As safety violations can lead to severe consequences in real-world robotic applications, the increasing deployment of Reinforcement Learning (RL) in robotic domains has propelled the study of safe exploration for reinforcement learning (safe RL). In this work, we propose a risk preventive training method for safe RL, which learns a statistical contrastive classifier to predict the probability of a state-action pair leading to unsafe states. Based on the predicted risk probabilities, we can collect risk preventive trajectories and reshape the reward function with risk penalties to induce safe RL policies. We conduct experiments in robotic simulation environments. The results show the proposed approach has comparable performance with the state-of-the-art model-based methods and outperforms conventional model-free safe RL approaches.
Machine Learning Models Evaluation and Feature Importance Analysis on NPL Dataset
Authors: Rufael Fekadu, Anteneh Getachew, Yishak Tadele, Nuredin Ali, Israel Goytom
Date: 2022-08-28 00:00:00
Tasks: Feature Importance, feature selection

Predicting the probability of non-performing loans for individuals has a vital and beneficial role for banks to decrease credit risk and make the right decisions before giving the loan. The trend to make these decisions are based on credit study and in accordance with generally accepted standards, loan payment history, and demographic data of the clients. In this work, we evaluate how different Machine learning models such as Random Forest, Decision tree, KNN, SVM, and XGBoost perform on the dataset provided by a private bank in Ethiopia. Further, motivated by this evaluation we explore different feature selection methods to state the important features for the bank. Our findings show that XGBoost achieves the highest F1 score on the KMeans SMOTE over-sampled data. We also found that the most important features are the age of the applicant, years of employment, and total income of the applicant rather than collateral-related features in evaluating credit risk.