Miracle-dz/UniFAR-FTUs huggingface dataset:
Calibrating the Learning Rate for Adaptive Gradient Methods to Improve Generalization Performance

Although adaptive gradient methods (AGMs) have fast speed in training deep neural networks, it is known to generalize worse than the stochastic gradient descent (SGD) or SGD with momentum (S-Momentum). Many works have attempted to modify AGMs so to close the gap in generalization performance between AGMs and S-Momentum, but they do not answer why there is such a gap. We identify that the anisotropic scale of the adaptive learning rate (A-LR) used by AGMs contributes to the generalization performance gap, and all existing modified AGMs actually represent efforts in revising the A-LR. Because the A-LR varies significantly across the dimensions of the problem over the optimization epochs (i.e., anisotropic scale), we propose a new AGM by calibrating the A-LR with a {\em softplus} function, resulting in the \textsc{Sadam} and \textsc{SAMSGrad} methods\footnote{Code is available at https://github.com/neilliang90/Sadam.git. }. These methods have better chance to not trap at sharp local minimizers, which helps them resume the dips in the generalization error curve observed with SGD and S-Momentum. We further provide a new way to analyze the convergence of AGMs (e.g., \textsc{Adam}, \textsc{Sadam}, and \textsc{SAMSGrad}) under the nonconvex, non-strongly convex, and Polyak-{\L}ojasiewicz conditions. We prove that the convergence rate of ADAM also depends on its hyper-parameter epsilon, which has been overlooked in prior convergence analysis. Empirical studies support our observation of the anisotropic A-LR and show that the proposed methods outperform existing AGMs and generalize even better than S-Momentum in multiple deep learning tasks.
Simplicial structures over the 3-sphere and generalized higher order Hochschild homology

In this paper we investigate the simplicial structure of a chain complex associated to the higher order Hochschild homology over the $3$-sphere. We also introduce the tertiary Hochschild homology corresponding to a quintuple $(A,B,C,\varepsilon,\theta)$, which becomes natural after we organize the elements in a convenient manner. We establish these results by way of a bar-like resolution in the context of simplicial modules. Finally, we generalize the higher order Hochschild homology over a trio of simplicial sets, which also grants natural geometric realizations.
A Topological Data Analysis perspective on noncovalent interactions in relativistic calculations

Topological Data Analysis (TDA) is a powerful mathematical theory, largely unexplored in theoretical chemistry. In this work we demonstrate how TDA provides new insights into topological features of electron densities and reduced density gradients, by investigating the effects of relativity on the bonding of the Au4-S-C6H4-S'-Au'4 molecule. Whereas recent analyses of this species carried out with the Quantum Theory of Atoms-In-Molecules [Anderson et al., Chem. Eur. J. 25, 2538, 2019] concluded, from the emergence of new topological features in the electron density, that relativistic effects yielded non-covalent interactions between gold and hydrogen atoms, we show from their low persistence values (which decrease with increased basis set size) these features are not significant. Further analysis of the reduced density gradient confirms no relativity-induced non-covalent interactions in Au4-S-C6H4-S'-Au'4. We argue TDA should be integrated into electronic structure analysis methods, and be considered as a basis for the development of new topology-based approaches.
The Technical Debt Dataset

Technical Debt analysis is increasing in popularity as nowadays researchers and industry are adopting various tools for static code analysis to evaluate the quality of their code. Despite this, empirical studies on software projects are expensive because of the time needed to analyze the projects. In addition, the results are difficult to compare as studies commonly consider different projects. In this work, we propose the Technical Debt Dataset, a curated set of project measurement data from 33 Java projects from the Apache Software Foundation. In the Technical Debt Dataset, we analyzed all commits from separately defined time frames with SonarQube to collect Technical Debt information and with Ptidej to detect code smells. Moreover, we extracted all available commit information from the git logs, the refactoring applied with Refactoring Miner, and fault information reported in the issue trackers (Jira). Using this information, we executed the SZZ algorithm to identify the fault-inducing and -fixing commits. We analyzed 78K commits from the selected 33 projects, detecting 1.8M SonarQube issues, 62K code smells, 28K faults and 57K refactorings. The project analysis took more than 200 days. In this paper, we describe the data retrieval pipeline together with the tools used for the analysis. The dataset is made available through CSV files and an SQLite database to facilitate queries on the data. The Technical Debt Dataset aims to open up diverse opportunities for Technical Debt research, enabling researchers to compare results on common projects.
An Evaluation of Action Recognition Models on EPIC-Kitchens

We benchmark contemporary action recognition models (TSN, TRN, and TSM) on the recently introduced EPIC-Kitchens dataset and release pretrained models on GitHub (this https URL) for others to build upon. In contrast to popular action recognition datasets like Kinetics, Something-Something, UCF101, and HMDB51, EPIC-Kitchens is shot from an egocentric perspective and captures daily actions in-situ. In this report, we aim to understand how well these models can tackle the challenges present in this dataset, such as its long tail class distribution, unseen environment test set, and multiple tasks (verb, noun and, action classification). We discuss the models' shortcomings and avenues for future research.
Attention-guided Low-light Image Enhancement

—Low-light image enhancement is a challenging task since various factors, including brightness, contrast, artifacts and noise, should be handled simultaneously and effectively. To address such a difﬁcult problem, this paper proposes a novel attention-guided enhancement solution and delivers the corresponding end-to-end multi-branch CNNs. The key of our method is the computation of two attention maps to guide the exposure enhancement and denoising respectively. In particular, the ﬁrst attention map distinguishes underexposed regions from normally exposed regions, while the second attention map distinguishes noises from real-world textures. Under their guidance, the proposed multi-branch enhancement network can work in an adaptive way. Other contributions of this paper include the “ decomposition/multi-branch-enhancement/fusion” design of the enhancement network, the reinforcement-net for contrast enhancement, and the proposed large-scale low-light enhancement dataset. We evaluate the proposed method through extensive experiments, and the results demonstrate that our solution outperforms state-of-the-art methods by a large margin. We additionally show that our method is ﬂexible and effective for other image processing tasks.
A remark on the scattering theory for the 2d radial focusing INLS.

We consider the scattering results of the radial solutions below the ground state to the focusing inhomogeneous nonlinear Schr\"odinger equation $$i\partial_tu+\Delta u +|x|^{-b}|u|^{p}u=0$$ in two dimension, where $0<b<1$ and $2-b<p<\infty$. We use a modified version of Arora-Dodson-Murphy's approach [1] to give a new proof that extends the scattering results of [10] and avoids concentration compactness.
A Systematic Review of Second Language Learning with Mobile Technologies

This study provides a systematic literature review of the research done in mobile assisted second language learning (MASLL) published since 2010. Starting from 1424 sources, 54 articles were selected using predefined selection criteria. The documents were analyzed and coded using the categories: educational form and level, study design, location, context, role of technology, pedagogical practice and learning impact. That information allowed an identification of major educational outcomes related to the integration of mobile devices into second language learning. In addition, the study contributes with a set of identified research gaps and recommendations for future research.
Analysis of the p-adic q-Volkenborn integrals: An approach to generalized Apostol-type special numbers and polynomials and their applications

The goal of this paper is to construct some families of generalized Apostol-type special numbers and polynomials attached to the Dirichlet character. Using the p-adic q-Volkenborn integrals including the bosonic and the fermionic p-adic integrals on p-adic integers, we give generating functions for these numbers and polynomials. These numbers and polynomials are associated with some well-known special numbers and polynomials such as the Peters polynomials, the Boole polynomials, the generalized Apostol–Bernoulli numbers and polynomials, the generalized Apostol-Euler numbers and polynomials, the generalized Apostol–Daehee numbers and polynomials, the Stirling numbers and the Bernoulli numbers of the first kind. We investigate some properties of these numbers and polynomials with their generating functions. Using these generating functions and their functional equation, we derive some identities and relations including some special numbers and polynomials. Finally, we give p-adic q-Volkenborn integral representations for these numbers and polynomials with combinatorial sums.
Towards the improvement of event-related potential techniques in developmental populations

Visual event-related potentials (ERPs) have been widely used during the past two decades to study cognition in developmental populations. One of the main challenges related to current infant ERP practices is the need to obtain good-quality ERPs that provide reliable results with which to examine research issues. There are two main aspects that play a role in obtaining a valid ERP trial: the infant’s attention to the stimuli and an EEG signal free of artefacts—primarily eye and body movement artefacts—. The aims of this thesis were to explore alternative methodological approaches that could potentially improve current ERP practice as well as to assess the validity of the current ERP editing methods. Specifically, the first aim was to study the use of eye tracking techniques to improve current ERP data collection methods and ERP final data quality. In Chapter 2, eye tracking technology was used as a mean to study how advanced ERP components are negatively affected by the fixation location distance to the stimulus. Chapter 3 evaluated the changes in attention and visual behaviour of a gaze-contingent paradigm used during ERP data collection in 10-month-old infants. The second aim was to investigate to what extent the current infant ERP editing methods are reliable and to assess the need for their further standardization within the field. Chapter 3 focused on understanding the current editing methods used in ERP infant research for selecting ERP trials free of artefacts. The study analyzed the agreement between three experienced infant ERP human editors and one algorithm for selecting artefact-free ERP trials as well as the EEG signal characteristics that influenced the selection of valid trials. The implications of the results of the three studies presented in this thesis for current infant ERP practices are discussed in Chapter 5. Overall, this thesis highlights the importance and need of the field to work towards the improvement of current methodologies for collecting and analyzing infant ERP data.
Minimal diffeomorphism between hyperbolic surfaces with cone singularities

We prove the existence of a minimal diffeomorphism isotopic to the identity between two hyperbolic cone surfaces $(\Sigma,g_1)$ and $(\Sigma,g_2)$ when the cone angles of $g_1$ and $g_2$ are different and smaller than $\pi$. When the cone angles of $g_1$ are strictly smaller than the ones of $g_2$, this minimal diffeomorphism is unique.
Anderson localization for the almost Mathieu operator in the exponential regime

For the almost Mathieu operator $(H_{\lambda,\alpha,\theta}u)_n=u_{n+1}+u_{n-1}+2\lambda \cos2\pi(\theta+n\alpha)u_n$, Avila and Jitomirskaya guess that for a.e. $\theta$, $H_{\lambda,\alpha,\theta}$ satisfies Anderson localization if $ |\lambda| > e^{ \beta} $, and they establish this for $ |\lambda| > e^{\frac{16}{9} \beta}$. In the present paper, we extend their result to regime $ |\lambda| > e^{\frac{3}{2} \beta}$.
Vehicle tracking using fuzzy-based vehicle detection window with adaptive parameters

In this paper, fuzzy-based vehicle tracking system is proposed. The proposed system consists of two main processes: vehicle detection and vehicle tracking. In the first process, the Gradient-based Adaptive Threshold Estimation (GATE) algorithm is adopted to provide the suitable threshold value for the sobel edge detection. The estimated threshold can be adapted to the changes of diverse illumination conditions throughout the day. This leads to greater vehicle detection performance compared to a fixed user’s defined threshold. In the second process, this paper proposes the novel vehicle tracking algorithms namely Fuzzy-based Vehicle Analysis (FBA) in order to reduce the false estimation of the vehicle tracking caused by uneven edges of the large vehicles and vehicle changing lanes. The proposed FBA algorithm employs the average edge density and the Horizontal Moving Edge Detection (HMED) algorithm to alleviate those problems by adopting fuzzy rule-based algorithms to rectify the vehicle tracking. The experimental results demonstrate that the proposed system provides the high accuracy of vehicle detection about 98.22%. In addition, it also offers the low false detection rates about 3.92%.
QoS based Web Service Selection and Multi-Criteria Decision Making Methods

With the continuing proliferation of web services offering similar efficacies, around the globe, it has become a challenge for a user to select the best web service. In literature, this challenge is exhibited as a 0-1 knapsack problem of multiple dimensions and multiple choices, known as an NP-hard problem. Multi-Criteria Decision Making (MCDM) method is one of the ways which suits this problem and helps the users to select the best service based on his/her preferences. In this regard, this paper assists the researchers in two conducts: Firstly, to witness the performance of different MCDM methods for large number of alternatives and attributes. Secondly, to perceive the possible deviation in the ranking obtained from these methods. For carrying out the experimental evaluation, in this paper, five different well-known MCDM methods have been implemented and compared over two different scenarios of 50 as well as 100 web services, where their ranking is defined on an account of several Quality of Service (QoS) parameters. Additionally, a Spearman’s Rank Correlation Coefficient has been calculated for different pairs of MCDM methods in order to provide a clear depiction of MCDM methods showing the least deviation in their ranking. The experimental results comfort web service users in conquering an appropriate decision on the selection of suitable service.
Improving Classifiers for Semantic Annotation of Software Requirements with Elaborate Syntactic Structure

A user generally writes software requirements in ambiguous and incomplete form by using natural language; therefore, a software developer may have difficulty in clearly understanding what the meanings are. To solve this problem with automation, we propose a classifier for semantic annotation with manually pre-defined semantic categories. To improve our classifier, we carefully designed syntactic features extracted by constituency and dependency parsers. Even with a small dataset and a large number of classes, our proposed classifier records an accuracy of 0.75, which outperforms the previous model, REaCT.
he contingency effects of environmental uncertainty on the relationship etween supply chain integration and operational performance

This paper extends prior supply chain research by building and empirically testing a theoretical model of the contingency effects of environmental uncertainty (EU) on the relationships between three dimensions of supply chain integration and four dimensions of operational performance. Based on the contingency and organizational information processing theories, we argue that under a high EU, the associations between supplier/customer integration, and delivery and flexibility performance, and those between
Engaging Primary School Children in Computational Thinking: Designing and Developing Videogames

This paper presents the results of a project on Computational Thinking education for primary school pupils. During the project – called Computational Thinking for children education – 81 students from a primary school in Italy have been guided to the design and development of computer games through the Microsoft Kodu game development platform. Different activities have been proposed to the pupils with the aim of promoting Computational Thinking abilities and skills. A narrative approach has been adopted throughout the project. Preliminary results of the educational experience highlight as the adoption of narrative learning and physical reproduction of manipulative programming objects provide an affordance for the development of Computational Thinking abilities. Furthermore, constancy during the learning process affects the acquisition of game development skills; finally, the design and implementation of computer games using Kodu have had a significant positive influence on the perception of computer programming.
Large-eddy simulations of sandstorms as charged-particle suspensions in turbulent boundary layers

Intense electric fields and lightning have been observed in sandstorms. In this study, the physical mechanisms necessary for production and sustenance of large-scale electric fields in sandstorms are investigated. A turbulent two-phase boundary layer of sand particles suspended in air is chosen as a model problem for sandstorms. Large-Eddy Simulations of this problem are performed with a stretched vortex subgrid-scale model, a virtual wall model and a recycling inflow boundary-condition procedure. Sand particles are modeled with an Eulerian approach that includes the effect of Coulomb forces. This framework is employed to examine transport of sand particles and to investigate the resulting levels of electric fields in the boundary layer.
Financial Valuation and Risk Management Working Paper No . 183 The Pricing of Interest Rate and Credit Risks in Equity Returns : An Empirical Cross-Country Comparison

In this study, we examine whether changes in the investment opportunity set stemming from interest rate and credit risks are priced in the US, the UK and the Swiss equity premia by estimating both two-factor and threefactor versions of Merton’s ICAPM. The systematic pricing of credit risk is confirmed for all three markets and for alternative specifications of the interest rate variables. The credit risk premium represents between 20 and 30% of the total equity premium in each country and is thus an important decision variable for portfolio management purposes. Finally, we find that market and credit risks are jointly positively and significantly priced in the US and Swiss equity risk premia. JEL classification: G12,G15.
PROCEEDINGS OF THE TWENTY-SEVENTH ANNUAL KECK RESEARCH SYMPOSIUM IN GEOLOGY April 2014

Modern dolomite (CaMg(CO 3 ) 2 ) precipitation is extremely limited and occurs in few locales, despite oversaturation in many environments. The significant quantity of dolomite present in Deep Springs Lake, CA provides an opportunity to test the hypothesis that bacteria surmount the barriers that normally prevents dolomite precipitation. Analysis of stable carbon and oxygen isotope ratios of modern sediment in five cores obtained near springs in the lake test the hypothesis that biological activity induces dolomite formation. The δ 13 C average value from bulk carbonate sediment at all studied locations (3.8‰) is consistent with equilibration with atmospheric CO 2 . This dolomite could not have precipitated from inorganic carbon dissolved in pore water adjacent to a methane-rich spring, which has greatly enriched average δ 13 C values (9.4‰). For an environment in equilibrium, the inorganic precipitation of carbonates would normally create enriched δ 13 C values in comparison to the pore water. For spring site 9S#11, the offset between carbon isotopes of carbonate and organic carbon in sediment cores (Δ 13 C = δ 13 C carbonate - δ 13 C organic ) was homogeneous throughout the depth profile and averaged 26.9‰. Both lines of evidence suggest that dolomite precipitation in Deep Springs Lake does not rely upon bacterial mediation.
1 Autonomic Management of Networked Small-Medium Factories

This Chapter addresses SMEs that are qualified to perform complementary manufacturing activities as producers, subcontractors or suppliers. They are used to participate in different supply chains taking the form of networks where every node manages in turn its own supply chain. More precisely, there are at least three main models (or viewpoints) to take into account:  Medium-large company and its supply chain members. When issuing an order the medium-large company should select the most convenient supplier depending on factors like cost, lead time, capacity, supply conditions, distance. These factors are variable over time since every supply chain member has other customers and often its own supply chain to manage.  Small-medium company and its scattered customers. Many manufacturing companies are moving still more from mass production to small customised batches frequently requested by their numerous and demanding customers. In order to preserve these customers it is necessary to assure faster and more accurate replies to their requests and orders.  Cluster of small-medium companies behaving as a virtual factory. The cluster intends to show one face to its customers, and to this purpose it must behave as a single organisation. The critical point is actually assuring a fast reaction to customer requests and orders even though decisions are taken through intense communications between partners.