Miracle-dz/UniFAR-FTUs huggingface dataset:
Quantifying Foreign Exchange Market Risk at Different Time Horizons

This paper evaluates the performance of the Value-at-Risk models of the foreign exchange rates at different time horizons. It starts with generating different returns at different time horizons from the USD-EUR 5-minute returns and simulates six parametric models (Normal-GARCH, Student-GARCH, Normal-IGARCH, Student-IGARCH, Ornstein-Uhlenbeck volatility, and jump) to assess the possible trading losses on 30 minutes, 6 hours, 12 hours and daily horizons. Using the variance method, Hill estimator, and the Generalized Pareto Distribution, VaR forecasts are obtained. The performance of the selected VaR models along with each VaR technique are evaluated at 1% and 5% confidence level by calculating the violation ratio. The results show that, at both high and low frequencies, the predictive power of the VaR methods display wide variation in assessing the foreign exchange risk. Furthermore, if a return generating process considers the tail fatness and the stochastic volatility structure of the exchange rate returns, the Extreme Value Theory methods will not be superior to the standard variance method.
Turning Points during the Life of Student Project Teams: A Qualitative Study.

In this qualitative study a more flexible alternative of conceptualising changes over time in teams is tested within student project teams. The conceptualisation uses turning points during the lifespan of a team to outline team development, based on work by Erbert, Mearns, & Dena (2005). Turning points are moments that made a significant difference during the course of the collaboration as a team. In this study, they are tracked by means of team interviews and reflection papers of team members. A method of coding was created to collect all information about the turning points, their causes and consequences. By means of a thorough analysis of these coded data an overview of their nature and their effects on the rest of the team process as perceived by the team members themselves is provided. Results show that the development paths of the three teams were differentiated in terms of turning points that occurred and, especially, in the order in which the turning points occurred. However four types of turning points (two at the task level en two at the interpersonal level) were remarkable due to their occurrence in all three project teams.
Skeleton-Indexed Deep Multi-Modal Feature Learning for High Performance Human Action Recognition

This paper presents a new framework for action recognition with multi-modal data. A skeleton-indexed feature learning procedure is developed to further exploit the detailed local features from RGB and optical flow videos. In particular, the proposed framework is built based on a deep Convolutional Network (ConvNet) and a Recurrent Neural Network (RNN) with Long Short Term Memory (LSTM). A skeleton-indexed transform layer is designed to automatically extract visual features around key joints, and a part-aggregated pooling is developed to uniformly regulate the visual features from different body parts and actors. Besides, several fusion schemes are explored to take advantage of multi-modal data. The proposed deep architecture is end-to-end trainable and can better incorporate different modalities to learn effective feature representations. Quantitative experiment results on two datasets, the NTU RGB+D dataset and the MSR dataset, demonstrate the excellent performance of our scheme over other state-of-the-arts. To our knowledge, the performance obtained by the proposed framework is currently the best on the challenging NTU RGB+D dataset.
Improving Color Reproduction Accuracy on Cameras

One of the key operations performed on a digital camera is to map the sensor-specific color space to a standard perceptual color space. This procedure involves the application of a white-balance correction followed by a color space transform. The current approach for this colorimetric mapping is based on an interpolation of pre-calibrated color space transforms computed for two fixed illuminations (i.e., two white-balance settings). Images captured under different illuminations are subject to less color accuracy due to the use of this interpolation process. In this paper, we discuss the limitations of the current colorimetric mapping approach and propose two methods that are able to improve color accuracy. We evaluate our approach on seven different cameras and show improvements of up to 30% (DSLR cameras) and 59% (mobile phone cameras) in terms of color reproduction error.
Match-mismatch dynamics and the relationship between ocean-entry timing and relative ocean recoveries of Central Valley fall run Chinook salmon

The match-mismatch hypothesis suggests there is an optimal window for organisms to undergo key life cycle events. Here, we test the importance of match-mismatch dynamics in the timing of salmon arrival to the ocean, relative to ecosystem phenology, for the ocean survival rates of hatchery-origin fall run Chinook salmon originating from California's Central Valley. Specifi- cally, we considered tag recovery data for releases of coded-wire tagged fish released into the San Francisco Estuary during the years 1978 to 2010. We determined a time lag for each release relative to the local spring transition date (initiation of net upwelling). Additionally, we obtained information on fish condition and size at release, the number of fish released corresponding to distinct tag codes, and yearly stock-specific harvest rate estimates. We used generalized linear models, generalized additive models, and cross-validation to identify the best-supported models for the effects of release timing and other covariates on age-3 ocean fishery recovery rates, a proxy of ocean survival rates. Release time is a useful predictor of within-year variation in survival rates, above and beyond the effects of size at release, presence of disease, and the use of net pens, and the lag relative to spring transition was a slightly better predictor than year-day. The optimal release timing appeared to occur around the end of May, and the optimal time lag appeared to be approximately 70 to 115 d after the spring transition date. However, timing is only one of many factors that affected within- and among-year variation in survival.
KiDS+2dFLenS+GAMA: testing the cosmological model with the EG statistic

We present a new measurement of EG, which combines measurements of weak gravitational lensing, galaxy clustering, and redshift-space distortions. This statistic was proposed as a consistency test of General Relativity (GR) that is insensitive to linear, deterministic galaxy bias, and the matter clustering amplitude. We combine deep imaging data from KiDS with overlapping spectroscopy from 2dFLenS, BOSS DR12, and GAMA and find EG(z = 0.267) = 0.43 ± 0.13 (GAMA), EG(z = 0.305) = 0.27 ± 0.08 (LOWZ+2dFLOZ), and EG(z = 0.554) = 0.26 ± 0.07 (CMASS+2dFHIZ). We demonstrate that the existing tension in the value of the matter density parameter hinders the robustness of this statistic as solely a test of GR. We find that our EG measurements, as well as existing ones in the literature, favour a lower matter density cosmology than the cosmic microwave background. For a flat CDM Universe, we find m(z = 0) = 0.25 ± 0.03. With this paper, we publicly release the 2dFLenS data set at: http://2dflens.swin. edu.au.
Submitted to the Annals of Probability THE CIRCULAR LAW FOR SPARSE NON-HERMITIAN MATRICES By

By Anirban Basak‡ and Mark Rudelson§ Weizmann Institute of Science‡ and University of Michiagn§ For a class of sparse random matrices of the formAn = (ξi,jδi,j) n i,j=1, where {ξi,j} are i.i. d. centered sub-Gaussian random variables of unit variance, and {δi,j} are i.i. d. Bernoulli random variables taking value 1 with probability pn, we prove that the empirical spectral distribution of An/ √ npn converges weakly to the circular law, in probability, for all pn such that pn = ω(log 2 n/n). Additionally if pn satisfies the inequality npn > exp(c √ logn) for some constant c, then the above convergence is shown to hold almost surely. The key to this is a new bound on the smallest singular value of complex shifts of real valued sparse random matrices. The circular law limit also extends to the adjacency matrix of a directed Erdős-Rényi graph with edge connectivity probability pn.
The Association Between Dental Anxiety And Psychiatric Disorders And Symptoms: A Systematic Review

Background: A growing amount of evidence suggests that dental anxiety is associated with other psychiatric disorders and symptoms. A systematic review was conducted to critically evaluate the studies of comorbidity of dental anxiety with other specific phobias and other Axis I psychiatric disorders. Objective: The aim of the review was to explore how dental anxiety is associated with other psychiatric disorders and to estimate the level of comorbid symptoms in dental anxiety patients. Methods: The review was conducted and reported in accordance with the MOOSE statement. Data sources included PubMed, PsycInfo, Web of Science and Scopus. Results: The search produced 631 hits, of which 16 unique records fulfilled the inclusion criteria. The number of eligible papers was low. Study populations were heterogeneous including 6,486 participants, and a total of 25 tests and in few cases clinical interviews were used in the evaluation processes. The results enhanced the idea about the comorbidity between dental anxiety and other psychiatric disorders. The effect was found strong in several studies. Conclusion: Patients with a high level of dental anxiety are more prone to have a high level of comorbid phobias, depression, mood disorders and other psychiatric disorders and symptoms.
Real-time Multi-view Depth Generation Using CUDA Multi-GPU

In this paper, we propose a real-time multi-view depth generation method using compute unified device architecture (CUDA) multi-graphics processing units (GPU). The objective is to generate multi-view depth maps in real-time. We employ eight color cameras and three depth cameras. After capturing multi-view color and depth data, we warp the depth information to color camera positions. Then joint bilateral filtering (JBF) is performed to fill empty regions. Such a procedure is accelerated by CUDA which is one of generalpurpose computing on GPU (GPGPU). As a result, depth maps of eight views are generated at a rate of 23 frames per second (fps) on a single GPU computer. When using a multi-GPU computer, depth generation at 34 fps was achieved.
Charging Pattern Optimization for Lithium-Ion Batteries With an Electrothermal-Aging Model

This paper applies advanced battery modeling and multiobjective constrained nonlinear optimization techniques to derive suitable charging patterns for lithium-ion batteries. Three important yet competing charging objectives, including battery health, charging time, and energy conversion efficiency, are taken into account simultaneously. These optimization objectives are first subject to a high-fidelity battery model that is synthesized from recently developed individual electrical, thermal, and aging models. The coupling relationship and multiple timescales among different model dynamics are identified. Furthermore, constraints are imposed explicitly on the current, voltage, state-of-charge, and temperature. Such a complex charging problem is solved by using an ensemble multiobjective biogeography-based optimization approach. As a result, two charging patterns, namely the constant current–constant voltage (CC–CV) and multistage CC–CV, are optimized to balance various combinations of charging objectives. Different tradeoffs and sensitive elements are compared and analyzed based on the Pareto frontiers. Illustrative results demonstrate that the proposed strategy can effectively offer feasible health-conscious charging with desirable tradeoffs among charging speed and energy conversion efficiency under different demand priorities.
Adaptive Cross Approximation for Scattering by Periodic Surfaces

The adaptive cross approximation is applied to boundary element matrices coming from 2D scattering problems by an inflnite periodic surface. This compression technique has the advantage to be applied before the assembly of the matrix. As a result, the computational times for both assembly and solution phases are reduced. Numerical results assess the e-cacy of the method on scattering problems with several periodic surfaces.
Synthesis of iron fertilization experiments: From the iron age in the age of enlightenment

Comparison of eight iron experiments shows that maximum Chl a, the maximum DIC removal, and the overall DIC/Fe efficiency all scale inversely with depth of the wind mixed layer (WML) defining the light environment. Moreover, lateral patch dilution, sea surface irradiance, temperature, and grazing play additional roles. The Southern Ocean experiments were most influenced by very deep WMLs. In contrast, light conditions were most favorable during SEEDS and SERIES as well as during IronEx-2. The two extreme experiments, EisenEx and SEEDS, can be linked via EisenEx bottle incubations with shallower simulated WML depth. Large diatoms always benefit the most from Fe addition, where a remarkably small group of thriving diatom species is dominated by universal response of Pseudo-nitzschia spp. Significant response of these moderate (10–30 μm), medium (30–60 μm), and large (>60 μm) diatoms is consistent with growth physiology determined for single species in natural seawater. The minimum level of “dissolved” Fe (filtrate < 0.2 μm) maintained during an experiment determines the dominant diatom size class. However, this is further complicated by continuous transfer of original truly dissolved reduced Fe(II) into the colloidal pool, which may constitute some 75% of the “dissolved” pool. Depth integration of carbon inventory changes partly compensates the adverse effects of a deep WML due to its greater integration depths, decreasing the differences in responses between the eight experiments. About half of depth-integrated overall primary productivity is reflected in a decrease of DIC. The overall C/Fe efficiency of DIC uptake is DIC/Fe ∼ 5600 for all eight experiments. The increase of particulate organic carbon is about a quarter of the primary production, suggesting food web losses for the other three quarters. Replenishment of DIC by air/sea exchange tends to be a minor few percent of primary CO2 fixation but will continue well after observations have stopped. Export of carbon into deeper waters is difficult to assess and is until now firmly proven and quite modest in only two experiments.
Disjunctive Antecedents for Causal Models

Sartorio [4] argues convincingly that disjunctive causes exist. To treat disjunctive causes within Halpern and Pearl [2]’s framework of causal models, we extend their causal model semantics by disjunctive antecedents and propose a refinement of their definition of actual causation.
An Improved Probabilistic Simulation Framework for Gene Family Evolution

Phylogenetic trees are frequently used to represent the evolution of species, genes, and protein domains. Gene family evolution is usually represented in a framework where gene trees evolve inside a species tree. The recent Domain-Gene-Species model of evolution presents a framework where protein domains also evolve inside one or more gene trees, each of which evolves inside a species tree [1]. The Duplication-Transfer-Loss (DTL) reconciliation and the Domain-GeneSpecies (DGS) reconciliation algorithms allow for a parsimony-based approach to inferring the evolutionary histories of a given set of species, genes, and, in the case of DGS reconciliation, protein domains. However, in the absence of biological data regarding the true evolutionary histories of these species, genes, and domains, we must rely on simulated data to validate the accuracy of such phylogenetic reconciliation methods. Although numerous probabilistic simulation frameworks exist for gene family evolution, such as PrIME-GenPhyloData and SimPhy, none of these existing frameworks account for certain aspects of gene family evolution, such as the presence of both additive and replacing horizontal gene transfers and the possibility that the gene family might not be present at the root of the species tree [2–5]. Furthermore, no existing simulation framework can simulate sub-gene level events such as partial gene transfers and the evolution of domain families. In this work, we modify the PrIME-GenPhyloData simulation framework to simulate both replacing and additive horizontal gene transfers, account for phylogenetic distance bias in choosing transfer recipients, and randomly select the location of gene birth in the species tree. In addition, we introduce the ability to simulate sub-gene level events such as partial gene transfers through the simulated evolution of protein domains within gene families, creating the first probabilistic simulation framework of its kind. To demonstrate the utility of our new simulation framework, we systematically evaluate the accuracy of DTL reconciliation on simulated datasets that contain both additive and replacing transfers. Our results from this simulation study indicate that DTL reconciliation, which assumes that all transfers are additive, is surprisingly robust to the presence of replacing transfers.
Phenotypic and Genetic Characterization of a Maize Association Mapping Panel Developed for the Identification of New Sources of Resistance to Aspergillus flavus and Aflatoxin Accumulation

Maize (Zea mays L.) susceptibility to ear rot by Aspergillus flavus (Link:Fr) and aflatoxin accu- mulation causes global economic and human health damage. Host plant resistance is an ideal solution, but commercial varieties lack sufficient resistance to solve the problem. Due to gen - eral lack of resistant maize germplasm, no cur - rently available association mapping panels are expected to contain significant variation for the trait. A new association mapping panel contain - ing the majority of aflatoxin accumulation resis - tant maize lines at the time of compilation is pre - sented here along with genetic and phenotypic characterization data. p henotypic data from testcrossed lines include aflatoxin levels, days to silking, and corn earworm ( Helicoverpa zea
Tactile Sensors for Friction Estimation and Incipient Slip Detection—Toward Dexterous Robotic Manipulation: A Review

Humans can handle and manipulate objects with ease; however, human dexterity has yet to be matched by artificial systems. Receptors in our fingers and hands provide essential tactile information to the motor control system during dexterous manipulation such that the grip force is scaled to the tangential forces according to the coefficient of friction. Likewise, tactile sensing will become essential for robotic and prosthetic gripping performance as applications move toward unstructured environments. However, most existing research ignores the need to sense the frictional properties of the sensor–object interface, which (along with contact forces and torques) is essential for finding the minimum grip force required to securely grasp an object. Here, we review this problem by surveying the field of tactile sensing from the perspective that sensors should: 1) detect gross slip (to adjust the grip force); 2) detect incipient slip (dependent on the frictional properties of the sensor–object interface and the geometries and mechanics of the sensor and the object) as an indication of grip security; or 3) measure friction on contact with an object and/or following a gross or incipient slip event while manipulating an object. Recommendations are made to help focus future sensor design efforts toward a generalizable and practical solution to sense, and hence control grip security. Specifically, we propose that the sensor mechanics should encourage incipient slip, by allowing parts of the sensor to slip while other parts remain stuck, and that instrumentation should measure displacement and deformation to complement conventional force, pressure, and vibration tactile sensing.
Encoder-Decoder Methods for Text Normalization

Text normalization is the task of mapping non-canonical language, typical of speech transcription and computer-mediated communication, to a standardized writing. It is an up-stream task necessary to enable the subsequent direct employment of standard natural language processing tools and indispensable for languages such as Swiss German, with strong regional variation and no written standard. Text normalization has been addressed with a variety of methods, most successfully with character-level statistical machine translation (CSMT). In the meantime, machine translation has changed and the new methods, known as neural encoder-decoder (ED) models, resulted in remarkable improvements. Text normalization, however, has not yet followed. A number of neural methods have been tried, but CSMT remains the state-of-the-art. In this work, we normalize Swiss German WhatsApp messages using the ED framework. We exploit the flexibility of this framework, which allows us to learn from the same training data in different ways. In particular, we modify the decoding stage of a plain ED model to include target-side language models operating at different levels of granularity: characters and words. Our systematic comparison shows that our approach results in an improvement over the CSMT state-of-the-art.
A Comprehensive Analysis of Partial Shading Effect on Output Parameters of a Grid-connected PV System

One of the issues of grid-connected photovoltaic systems is the effect of the partial shading on the key parameters and performance of the system. In practice, a share of the entire PV panel may shadded because of the various reasons, inevitably. In this case, the key parameters of the system output are affected with respect to the shading extent and paradigm. In this paper, the effects of the various partial shading patterns on the ouput of the system are examined. This is performed by deriving relevant equations and appropriate modeling of the system and defining different scenarios. The analysis on the system performance is carried out on the dominant output parameters including panel voltage, panel power, and total harmonic distortion (THD) of the inverter. Also, the study considers the effect of using bypass diodes in the panels or not. Addintionally, to compare derived conclusions, the study is implementd on a practical system. The set up is made up of a 7-level multi-level inverter, a Z-source converter, and 1 kW lateral circuitry. The real world test results of the study demonstrate a negligible deviation compared to the simulation results.
Speech Emotion Recognition Based on SVM and GMM-HMM Hybrid System

Speech emotion recognition is one of the latest challenges in speech processing, we implemented a hybrid system for speech emotion recognition. Two methods were proposed, compared and combined. At first, we utilized GMM-HMM model fed with MFCC feature to exploit the dynamics of emotional signals. Then a balanced SVM classifier was applied on the static LLD feature. In order to take advantage of generative model and discriminate model, these two methods were combined by taking the probability representation of the GMM-HMM model as the additional features for the SVM classifier. This hybrid system makes full use of both MFCC feature and LLD feature, the performance of the hybrid system also proves the effectiveness of our proposed system.