Miracle-dz/UniFAR-FTUs huggingface dataset:
Theory III: Dynamics and Generalization in Deep Networks.

We review recent observations on the dynamical systems induced by gradient descent methods used for training deep networks and summarize properties of the solutions they converge to. Recent results illuminate the absence of overfitting in the special case of linear networks for binary classification. They prove that minimization of loss functions such as the logistic, the cross-entropy and the exponential loss yields asymptotic convergence to the maximum margin solution for linearly separable datasets, independently of the initial conditions. Here we discuss the case of nonlinear DNNs near zero minima of the empirical loss, under exponential-type and square losses, for several variations of the basic gradient descent algorithm, including a new NMGD (norm minimizing gradient descent) version that converges to the minimum norm fixed points of the gradient descent iteration. Our main results are: 1) gradient descent algorithms with weight normalization constraint achieve generalization; 2) the fundamental reason for the effectiveness of existing weight normalization and batch normalization techniques is that they are approximate implementations of maximizing the margin under unit norm constraint; 3) without unit norm constraints some level of generalization can still be obtained for not-too-deep networks because the balance of the weights across different layers, if present at initialization, is maintained by the gradient flow. In the perspective of these theoretical results, we discuss experimental evidence around the apparent absence of overfitting, that is the observation that the expected classification error does not get worse when increasing the number of parameters. Our explanation focuses on the implicit normalization enforced by algorithms such as batch normalization. In particular, the control of the norm of the weights is related to Halpern iterations for minimum norm solutions.
ACELL May 47/5

Xu, Wanyan, Betty Jo Wilson, Lin Huang, Emma L. Parkinson, Brent J. F. Hill, and Mark A. Milanick. Probing the extracellular release site of the plasma membrane calcium pump. Am J Physiol Cell Physiol 278: C965– C972, 2000. —The plasma membrane Ca21 pump is known to mediate Ca21/H1 exchange. Extracellular protons activated 45Ca21 efflux from human red blood cells with a half-maximal inhibition constant of 2 nM when the intracellular pH was fixed. An increase in pH from 7.2 to 8.2 decreased the IC50 for extracellular Ca21 from ,33 to ,6 mM. Changing the membrane potential by . 54 mV had no effect on the IC50 for extracellular Ca21. This argues against Ca21 release through a high-field access channel. Extracellular Ni21 inhibited Ca21 efflux with an IC50 of 11 mM. Extracellular Cd21 inhibited with an IC50 of 1.5 mM, . 10 times better than Ca21. The Cd21 IC50 also decreased when the pH was raised from 7.1 to 8.2, consistent with Ca21, Cd21, and H1 competing for the same site. The higher affinity for inhibition by Ni21 and Cd21 is consistent with a histidine or cysteine as part of the release site. The cysteine reagent 2-(trimethylammonium)ethyl methanethiosulfonate did not inhibit Ca21 efflux. Our results are consistent with the notion that the release site contains a histidine.
Inference of high-resolution trajectories in single cell RNA-Seq data from RNA velocity

Trajectory inference methods are used to infer cell developmental trajectories in a continuous biological process, for example, stem cell differentiation. Most of the current trajectory inference methods infer the developmental trajectories based on transcriptome similarity between cells, using single cell RNA-Sequencing (scRNA-Seq) data. These methods are often restricted to certain trajectory structures like linear structure or tree structure, and the directions of the trajectory can only be determined when the root cell is provided. On the other hand, RNA velocity inference method is shown to be a promising alternative in predicting short term cell developmental direction from the sequencing data. Here by we present CellPath , a single cell trajectory inference method that infers developmental trajectories by integrating RNA velocity information. CellPath is able to ﬁnd multiple high-resolution cell developmental paths instead of a single backbone trajectory obtained from traditional trajectory inference methods, and it no longer constrains the trajectory structure to be of any speciﬁc topology. The direction information provided by RNA-velocity also allows CellPath to automatically detect the root cell and the direction of the dynamic process. We evaluate CellPath on both real and synthetic datasets, and show that CellPath ﬁnds more accurate and detailed trajectories compared to the state-of-the-art trajectory inference methods.
Development and Application of a Large‐Scale, Physically Based, Distributed Suspended Sediment Transport Model on the Fraser River Basin, British Columbia, Canada

Modeling sediment transport through large basins presents a challenging problem. The relation between water flux and sediment load is complex, and substantial erosion and transport can occur over small spatial and temporal scales. Analysis of large‐scale basins often relies on lumped empirical models that do not consider spatial or subannual variability. In this study, we adapt a small‐scale, mechanistic, distributed suspended sediment transport model for application to large basins. The model is integrated into the Terrestrial Hydrology Model with Biochemistry to make use of the Terrestrial Hydrology Model with Biochemistry's dynamic water routing. The coupled model is applied to the 230,000‐km2 Fraser River Basin in British Columbia, Canada, using climatic and hydrological inputs provided by a historical run of the Variable Infiltration Capacity model. Hourly simulations are aggregated into monthly and long‐term averages which are compared against observations. Simulated long‐term lake sedimentation values are within an order of magnitude of observations, and monthly load simulations have an average R2 of 0.70 across the five study stations with available data. Model results indicate that sediment loads from tributaries do not heavily influence dynamics along the main stem and suggest the importance of network connectivity. Sensitivity analysis indicates that models may benefit from characterizing bed load irrespective of its contribution to total sediment load. Historical simulations over the 1965–2004 period reveal important changes in sediment dynamics that could not be captured with a lumped model, including a decrease in basin sediment load interannual variability driven by changes in runoff and load variability within a key subbasin.
Concern for Information Privacy and Online Consumer Purchasing in China

Individuals’ concern for information privacy (CFIP) impacts beliefs, intentions, and behaviors in a variety of contexts, including consumer electronic commerce. Most empirical studies on the impact of CFIP on electronic commerce have been conducted using consumers in the United States. Despite China’s growing economy and increasing importance in the global economy, to date, there has been no empirical study of CFIP’s impact on Chinese consumers’ willingness to engage in transactions online. The purpose of this study is to test a widely-referenced model of CFIP’s role in consumer e-commerce in the context of China. We conducted surveys of Chinese consumers’ willingness to engage in transactions with two online merchants, a familiar merchant (Taobao) and a less-familiar merchant (Amazon). For both merchants, CFIP had only mediated impacts on consumers’ willingness to transact with online merchants. While there were similarities between our results and those reported in the original study, there were also differences. Our findings provide a number of contributions for research and practice.
Delft University of Technology Modeling of information diffusion on social networks with applications to WeChat

Traces of user activities recorded in online social networks open new possibilities to systematically understand the information diffusion process on social networks. From the online social network WeChat, we collected a large number of information cascade trees, each of which tells the spreading trajectory of a message/information such as which user creates the information and which users view or forward the information shared by which neighbors. In this work, we propose two heterogeneous non-linear models, one for the topologies of the information cascade trees and the other for the stochastic process of information diffusion on a social network. Both models are validated by the WeChat data in reproducing and explaining key features of cascade trees. Specifically, we apply the Random Recursive Tree (RRT) to model the growth of cascade trees. The RRT model could capture key features, i.e. the average path length and degree variance of a cascade tree in relation to the number of nodes (size) of the tree. Its single identified parameter quantifies the relative depth or broadness of the cascade trees and indicates that information propagates via a star-like broadcasting or viral-like hop by hop spreading. The RRT model explains the appearance of hubs, thus a possibly smaller average path length as the cascade size increases, as observed in WeChat. We further propose the stochastic Susceptible View Forward Removed (SVFR) model to depict the dynamic user behavior including cre∗Corresponding author Email address: h.wang@tudelft.nl (Huijuan Wang) Preprint submitted to Physica A March 20, 2019 © 2018 Manuscript version made available under CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/
Ensembles of Text and Time-Series Models for Automatic Generation of Financial Trading Signals

Event Studies in Finance have focused on traditional news headlines to assess the impact an event has on a traded company. The increased proliferation of news and information produced by social media content has disrupted this trend. Although researchers have begun to identify trading opportunities from social media platforms, such as Twitter, almost all techniques use a general sentiment from large collections of tweets. Though useful, general sentiment does not provide an opportunity to indicate specific events worthy of affecting stock prices. This work presents an event clustering algorithm, utilizing natural language processing techniques to generate newsworthy events from Twitter, which have the potential to influence stock prices in the same manner as traditional news headlines. The event clustering method addresses the effects of pre-news and lagged-news, two peculiarities that appear when connecting trading and news, regardless of the medium. Pre-news signifies a finding where stock prices move in advance of a news release. Lagged-news refers to follow-up or late-arriving news, adding redundancy in making trading decisions. For events generated by the proposed clustering algorithm, we have designed and implemented novel language and time-series techniques – incorporating Event Studies and Machine Learning to produce an actionable system that can guide trading decisions. Of the various methods considered, the emphasis was particularly on the state-of-the-art established methods versus modern Deep Learning techniques. The recommended prediction algorithms provide investing strategies with profitable risk-adjusted returns.
Communications of the Association for Information Systems Communications of the Association for Information Systems

: Affect and emotions play an important role in how individuals form judgments. Yet, the literature on technological judgments has primarily relied on the cognitive belief perspective. By segregating emotions into positive and negative affect, we incorporate affect in addition to cognitions to understand what drives perceptions about IS quality and, specifically, e-government website quality. Grounding our discussion in the affect infusion model (AIM) and prospect theory, we examine the mechanisms through which positive and negative affect infuse into IS quality judgments. We also theorize that both positive and negative affect have a moderating role in the relationships between cognitions and IS quality perceptions. We tested the model via surveying e-government website users and found that affect had a significant direct role in how they judged IS quality. While negative affect significantly moderated the relationship between experienced usefulness and how individuals perceived the three IS quality measures (i.e., information quality, system quality, and service quality), positive affect did not moderate this relationship. Finally, we theorize about the differential role that affect has on how individuals perceive the three IS quality measures depending on their affect infusion potential. We conclude by discussing our study’s theoretical and practical implications.
Generalized Digital Trees and their Di(cid:11)erence{di(cid:11)erential Equations

Consider a tree partitioning process in which n elements are split into b at the root of a tree (b a design parameter), the rest going recursively into two subtrees with a binomial probability distribution. This extends some familiar tree data structures of computer science like the digital trie and the digital search tree. The exponential generating function for the expected size of the tree satis(cid:12)es a di(cid:11)erence{ di(cid:11)erential equation of order b,
Full-Text version Title : A Novel and Efficient Session Spanning Biometric and Password based Three-factor Authentication Protocol for Consumer USB Mass Storage Devices

This paper proposes a key agreement scheme after secure authentication to prevent the unauthorized access of the data stored in a Universal Serial Bus (USB) Mass Storage Device (MSD). Due to the system architecture of this proposed scheme, authorized users can store their data in a secure encrypted form after performing authentication. The novelty of this work is that users can retrieve the encrypted data in not only the current session but also across different sessions, thus reducing the required communications overhead. This paper then analyses the security of the proposed protocol through a formal analysis to demonstrate that the information has been stored securely and is also protected offering strong resilience to relevant security attacks. The computational and communication costs of the proposed scheme is analyzed and compared to related works to show that the proposed scheme has an improved tradeoff for computational cost, communication cost and security.
Luxapose: indoor positioning with mobile phones and visible light

We explore the indoor positioning problem with unmodified smartphones and slightly-modified commercial LED luminaires. The luminaires-modified to allow rapid, on-off keying-transmit their identifiers and/or locations encoded in human-imperceptible optical pulses. A camera-equipped smartphone, using just a single image frame capture, can detect the presence of the luminaires in the image, decode their transmitted identifiers and/or locations, and determine the smartphone's location and orientation relative to the luminaires. Continuous image capture and processing enables continuous position updates. The key insights underlying this work are (i) the driver circuits of emerging LED lighting systems can be easily modified to transmit data through on-off keying; (ii) the rolling shutter effect of CMOS imagers can be leveraged to receive many bits of data encoded in the optical transmissions with just a single frame capture, (iii) a camera is intrinsically an angle-of-arrival sensor, so the projection of multiple nearby light sources with known positions onto a camera's image plane can be framed as an instance of a sufficiently-constrained angle-of-arrival localization problem, and (iv) this problem can be solved with optimization techniques. We explore the feasibility of the design through an analytical model, demonstrate the viability of the design through a prototype system, discuss the challenges to a practical deployment including usability and scalability, and demonstrate decimeter-level accuracy in both carefully controlled and more realistic human mobility scenarios.
Large-Scale Inference: Empirical Bayes Methods for Estimation, Testing, and Prediction

Apart from a more philosophical paragraph about the distinction between machine learning and statistical analysis in the final chapter, with the drawback of using neural nets and such as black-box methods (Page 185), there is relatively little coverage of nonparametric models in the book, the choice of “parametric formulations” (Page 96) being openly chosen. I can somehow understand this perspective for simpler settings, namely that nonparametric models offer little explanation of the production of the data. However, in more complex models, nonparametric components often are a convenient way to evacuate burdensome nuisance parameters. Again, technical aspects are not the focus of Principles of Applied Statistics, so this also explains why it does not dwell intently on nonparametric models.
AN EXPERIMENTAL APPROACH FOR TEACHING STATISTICS IN THE EGYPTIAN SCHOOLS

Teachers of mathematics in Egyptian schools almost depend on traditional lectures and discussions in teaching the statistics units which concentrate often on either mere computational aspects or theoretical concepts and distributions. To change this situation, an experimental approach is suggested to be used in Egyptian schools as a useful base for teaching and learning the subject in these schools. Questions interspersed throughout the experiments intend to promote statistical thinking among students, to help them to discuss the results obtained and to formulate final conclusions. One of the basic features of the approach is to give students the chance to use some of the methods used by real statisticians, then to find out relationships, new meanings and findings for themselves. By using such an approach, students can collect, analyze data and discover things by themselves. A field study conducted for preparatory stage students in Egypt (11 - 14 years old) has revealed the effectiveness of the suggested approach in achieving its goals.
STUDENTS' USE OF MODAL CLUMPS TO SUMMARIZE DATA

An underwater weapons system having an encapsulated torpedo and an anchor assembly which may be planted in the water by a submarine, aircraft, or surface ship. Shortly after water-entry the anchor separates, leaving the encapsulated torpedo vertically moored at a predetermined depth. Detection equipment on the casing listens passively for a target, and upon target detection, an active transducer is activated to classify the potential target. Upon classification, the casing opens, releasing and activating a homing torpedo having self-contained target-seeking capabilities.
COMPUTATIONAL MODELS FOR CARDIAC FLOW AND VIRTUAL CARDIOGRAPHY

We propose a multiphysics simulation approach for predicting cardiac flows as well as conducting virtual echo (ECHO)and phono-cardiography (PC) of these flows. Intraventricular blood flow is simulated by solving the three-dimensional incompressible NavierStokes equations with an immersed boundary method. Using this computational hemodynamic data, echoand phono cardiographic signals are synthesized by separate simulations that model the physics of ultrasound wave scattering and flow-induced sound, respectively. For virtual ECHO, a Doppler ultrasound image is reproduced through Lagrangian particle tracking of red blood cells and application of sound wave scattering theory. For virtual PC, the generation and propagation of blood-flow induced sounds is directly simulated by a computational hemo-acoustics model. The virtual ECHO is applied to reproduce an M-mode Doppler image for the left ventricle and the virtual PC is used to model systolic murmurs caused by hypertrophic cardiomyopathy(HCM). This "virtual" cardiography analysis provides data that can be compared and verified directly against clinically acquired data. Also the development of detailed correlations between blood-flow dynamics and the cardiographic data is expected to provide valuable information for the improvement of diagnostic tools based on these modalities. J. H. Seo, V. Vedula, and R. Mittal 2
( SVM ) Results subtask 1 Rule based system for Negation and Speculation Results subtask 3 1 43 2

The BioNLP’09 Shared Task on Event Extraction is a challenge which concerns the detection of bio-molecular events from text. In this paper, we present a detailed account of the challenges encountered during the construction of a machine learning framework for participation in this task. We have focused our work mainly around the filtering of false positives, creating a high-precision extraction method. We have tested techniques such as SVMs, feature selection and various filters for data preand post-processing, and report on the influence on performance for each of them. To detect negation and speculation in text, we describe a custom-made rule-based system which is simple in design, but effective in performance.
MATTE: anti-noise module alignedment for phenotype-gene-related

Purpose Although many transcriptome analysis methods find fundamental interactions or markers of some phenotypes, preservation of module or network is still a challenge. Methods The study developed a method to directly compare the transcriptome data of phenotypes and present the differences modularly, called Module alignedment of TranscripTomE(MATTE). Results MATTE performs better under high noise than differential co-expression(DC) clustering in the simulation experiments but still detects differential expression(DE) and DC genes. After subsequent annotation of cell types in single-cell data, MATTE obtained the best scores in both supervised and unsupervised learning, i. e. MATTE found meaningful markers. Finally, we apply MATTE in analyzing the transcriptome of Breast Cancer(BRCA). We have found five BRCA subtypes, and the characteristic of one subtype is detected in the form of a module network. Conclusion MATTE can find meaningful genes and modules, thus facilitating the downstream analysis task to obtain insight into biology.
City Research Online

Computational modelling of music similarity is an increasingly important task for personalisation and optimisation in Music Information Retrieval and for research in music perception and cognition. Relative similarity ratings provide a new and promising approach to this task as they avoid problems associated with absolute ratings. In this article, we use relative ratings from the MagnaTagATune dataset to develop a complete learning and evaluation process with state-of-the-art algorithms and provide the ﬁrst comprehensive and rigorous evaluation of this approach. We compare diﬀerent high and low level audio features, genre data, dimensionality eﬀects, weighted similarity ratings, and diﬀerent sampling methods. For model adaptation, we compare SVM-based metric learning, Metric-Learning-to-Rank (MLR), including a diagonal and a novel weighted MLR variant, and similarity learning with Neural Networks. Our results show that music similarity measures learnt on relative ratings are signiﬁcantly better than a standard metric, depending on the choice of learning algorithm, feature set and application scenario. We implemented a testing framework in Matlab R (cid:13) , which we made publicly available 1 to ensure reproducibility of our results. 2