Miracle-dz/UniFAR-FTUs huggingface dataset:
Two New Piggybacking Designs with Lower Repair Bandwidth

Piggybacking codes are a special class of MDS array codes that can achieve small repair bandwidth with small sub-packetization by first creating some instances of an $(n,k)$ MDS code, such as a Reed-Solomon (RS) code, and then designing the piggyback function. In this paper, we propose a new piggybacking coding design which designs the piggyback function over some instances of both $(n,k)$ MDS code and $(n,k')$ MDS code, when $k\geq k'$. We show that our new piggybacking design can significantly reduce the repair bandwidth for single-node failures. When $k=k'$, we design piggybacking code that is MDS code and we show that the designed code has lower repair bandwidth for single-node failures than all existing piggybacking codes when the number of parity node $r=n-k\geq8$ and the sub-packetization $\alpha<r$. Moreover, we propose another piggybacking codes by designing $n$ piggyback functions of some instances of $(n,k)$ MDS code and adding the $n$ piggyback functions into the $n$ newly created empty entries with no data symbols. We show that our code can significantly reduce repair bandwidth for single-node failures at a cost of slightly more storage overhead. In addition, we show that our code can recover any $r+1$ node failures for some parameters. We also show that our code has lower repair bandwidth than locally repairable codes (LRCs) under the same fault-tolerance and redundancy for some parameters.
Temporal Latent Bottleneck: Synthesis of Fast and Slow Processing Mechanisms in Sequence Learning

Recurrent neural networks have a strong inductive bias towards learning temporally compressed representations, as the entire history of a sequence is represented by a single vector. By contrast, Transformers have little inductive bias towards learning temporally compressed representations, as they allow for attention over all previously computed elements in a sequence. Having a more compressed representation of a sequence may be beneficial for generalization, as a high-level representation may be more easily re-used and re-purposed and will contain fewer irrelevant details. At the same time, excessive compression of representations comes at the cost of expressiveness. We propose a solution which divides computation into two streams. A slow stream that is recurrent in nature aims to learn a specialized and compressed representation, by forcing chunks of $K$ time steps into a single representation which is divided into multiple vectors. At the same time, a fast stream is parameterized as a Transformer to process chunks consisting of $K$ time-steps conditioned on the information in the slow-stream. In the proposed approach we hope to gain the expressiveness of the Transformer, while encouraging better compression and structuring of representations in the slow stream. We show the benefits of the proposed method in terms of improved sample efficiency and generalization performance as compared to various competitive baselines for visual perception and sequential decision making tasks.
Differentiable Point-Based Radiance Fields for Efficient View Synthesis

We propose a differentiable rendering algorithm for efficient novel view synthesis. By departing from volume-based representations in favor of a learned point representation, we improve on existing methods more than an order of magnitude in memory and runtime, both in training and inference. The method begins with a uniformly-sampled random point cloud and learns per-point position and view-dependent appearance, using a differentiable splat-based renderer to train the model to reproduce a set of input training images with the given pose. Our method is up to 300 × faster than NeRF in both training and inference, with only a marginal sacrifice in quality, while using less than 10 MB of memory for a static scene. For dynamic scenes, our method trains two orders of magnitude faster than STNeRF and renders at a near interactive rate, while maintaining high image quality and temporal coherence even without imposing any temporal-coherency regularizers.
Measuring and mitigating voting access disparities: a study of race and polling locations in Florida and North Carolina

Voter suppression and associated racial disparities in access to voting are long-standing civil rights concerns in the United States. A history of violent explicit discouragement has shifted to more subtle access limitations that can include long lines and wait times, long travel times to reach a polling station, and other logistical barriers to voting. Our focus in this work is on quantifying disparities in voting access pertaining to the overall time-to-vote, and how they could be remedied via a better choice of polling location or provisioning more sites where voters can cast ballots. However, appropriately calibrating access disparities is difficult because of the need to account for factors such as population density and different community expectations for reasonable travel times. In this paper, we perform one of the first large-scale studies of voter access to polling locations, using real-world voter data from Florida and North Carolina in the 2020 general election. We develop a methodology for the calibrated measurement of disparities in polling location "load" and distance to polling locations based on a novel normalized distance metric to model the voter experience of distance. We find that voter turnout is reduced when this normalized distance to polling locations increases, and that non-white voters had to travel further to the polls in Florida (using this normalized distance) than White voters. We also introduce algorithms, with modifications to handle scale, that can reduce these disparities by suggesting new polling locations from a given list of identified public locations (including schools and libraries). The developed voting access measurement methodology and algorithmic remediation technique demonstrates that better polling location placement is possible.
The Euro Area sovereign debt crisis: Some implications of its systemic dimension

After the beginning of the euro area, countries in its periphery engaged in weighty borrowing from foreign private investors, allowing domestic spending to outpace incomes. Now, these countries face debt crises reflecting a loss of creditor confidence in the sustainability of their finances from which results an abrupt end in private foreign lending to these economies. The debt crisis made evident the asymmetry between core and periphery countries, which is visible in trends in saving, consumption and investment. These divergent patterns have contributed to view the debt crisis as a problem of the PIIGS (Portugal, Ireland, Italy, Greece, Spain) that can be contained in the periphery, or as a new version of the fable of the Grasshopper and the Ant. We dispute this reductionism showing that the debt crisis is systemic and its solution cannot be found with more fiscal rules and austerity in peripheral countries alone. It will imply, if not an increase in fiscal and political integration, at least a higher coordination at the political and economic front and a new governance structure.
Advantageous Selection Without Moral Hazard (with an Application to Life Care Annuities)

Les documents de travail contiennent des travaux souvent préliminaires et/ou partiels. Ils sont publiés pour encourager et stimuler les discussions. Toute référence à ces documents devrait tenir compte de leur caractère provisoire. Les opinions exprimées dans les documents de travail sont celles de leurs auteurs et elles ne reflètent pas nécessairement celles du Département des sciences économiques ou de l'ESG. De courts extraits de texte peuvent être cités et reproduits sans permission explicite des auteurs à condition de faire référence au document de travail de manière appropriée. Abstract Advantageous (or propitious) selection occurs when an increase in the premium of an insurance contract induces high-cost agents to quit, thereby reducing the average cost among remaining buyers. Hemenway (1990) and many subsequent contributions motivate its ad-vent by diﬀerences in risk-aversion among agents, implying diﬀerent prevention eﬀorts. We argue that it may also appear in the absence of moral hazard, when agents only diﬀer in riskiness and not in (risk) preferences. We ﬁrst show that proﬁt-maximization implies that advantageous selection is more likely when markup rates and the elasticity of insurance demand are high. We then move to standard settings satisfying the single-crossing property and show that advantageous selection may occur when several contracts are oﬀered, when agents also face a non-insurable background risk, or when agents face two mutually exclusive risks that are bundled together in a single insurance contract. We exemplify this last case with life care annuities, a product which bundles long-term care insurance and annuities, and we use Canadian survey data to provide an example of a contract facing advantageous selection.
Speech emotion recognition using deep learning

Speech Emotion Recognition (SER) has a broad range of applications and there has been a signiﬁcant amount of research in this fascinating area in recent years. However, the entertainment sector suﬀers from a lack of study in this research.The Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) architectures will be utilized to categorize the emotions in audio recordings captured by actors expressing various emotions. An innovative method will be discussed that combines 2D CNN+LSTM with MFCC features extracted from audio data. Multiple experiments are used to determine the reliability of such systems that use deep learning. The model is based on four widely used datasets in SER: SAVEE, RAVDESS, TESS, and CREMA-D, and has a validation accuracy of 67.58%. Ad-ditionally, this model was evaluated on an unknown dataset that included audio samples in the German language and achieved a testing accuracy of 71.28%.
Dymore User’s Manual The generalized- α time integration scheme

The generalized-α [1] scheme generalizes the classical Hilber-Hughes-Taylor [2] time integrations scheme, which is widely used for structural dynamics problems. For linear problems, these schemes achieve high-frequency numerical dissipation while minimizing unwanted lowfrequency dissipation. Both methods have been successfully used for both linear and nonlinear problems, although unconditional stability is proved for linear systems only. For instance, Cardona and Géradin [3] used the Hilber-Hughes-Taylor scheme for integrating the equations of motion in mechanism analysis.
Supplementary Material for the paper: “Tracking People with 3D Representations”

3D Location approximation: Originally, the camera prediction is done in the bounding box space, assuming a weak perspective camera, as done by HMR (9). When doing the conversion to the view coordinate 3D space corresponding to the full image, we assume a constant focal length f and a fully perspective camera model. To get a reasonable approximation for the translation T of the person with respect to the camera, we consider the root joint (i.e., pelvis) of the person. More specifically, when projected under the weak perspective camera of the (local) bounding box, the root joint projects to the pixel (xi, yi) of the bounding box. We now require that, when projected under the fully perspective camera of the full image, the root joint projects to the corresponding pixel of the full image, i.e., (xi + xc − b/2, yi + yc − b/2) Solving this set of equation gives us the approximate location T indicated in the main manuscript.
A Generalization of Pricing Options with Discrete Dividends in Markets with Daily Price Limits

This paper proposes solutions for pricing options on stocks which pay discrete dividends in markets with daily price limits. We first extend the intraday density function of Guo and Chang (2020) to a multi-day one and use the framework of Haug et al. (2003) to value European options on stocks paying discrete dividends. Next, we adopt the fast Fourier transform (FFT) to derive accurate and efficient formulae for American options and, further, employ the three-point Richardson extrapolation to accelerate the computation. Finally, the accuracy of our proposed methods is verified by simulations.
Discussion on Leadership of BIDV Bank in Vietnam - And Analyzing Roles of Marketing 4P Strategy of the Bank

Our study shows that BIDV, who became Best retail bank in Vietnam in recent years, has made very positive contributions to the overall achievements of the banking industry, need to clearly define the customers they want to focus on so that they can offer specific products that suit the needs of customers. Determining the right target audience also helps banks in conducting accurate pricing of products corresponding to the market segments they are brought to users, as well as distribution channels, distribute products and forms of marketing advertising to achieve the highest efficiency. We also suggest that BIDV bank focuses more on marketing 4P and 7P strategy in new context. Last but not the least, By data collection method through statistics, analysis, synthesis, comparison, quantitative analysis to generate qualitative comments and discussion; using econometric method to perform regression equation and evaluate quantitative results, the article analyzed and evaluated the impacts of many macroeconomic factors on BIDV stock price and we would suggest the government, Ministry of Finance and State Bank of Vietnam consider to control inflation more rationally. And increasing cost and Rf little bit to increase stock price as well.
WIRELESS SENSOR NETWORKS: OPTIMAL ROUTING STRATEGY BY BLUETOOTH MESH LOW POWER NODES USING ACO ALGORITHM

As smart homes, factories, and farms developed, it has used sensors that communicate with wireless signals such as Bluetooth mesh-low power node (M-LPN) with the wireless sensor network (WSN) system, which causes them to be interfered with by different signals coming from different sources, which causes them not to function properly. The Bluetooth M-LPN is critical that enables the wireless sensor network to function. t makes up the inter-node controller of a WSN and collects information about nodes and sends commands to them on behalf of the founder/client node. A mesh system uses each node as a sensor and repeater. This means that each node must always listen for, relay, and route network traffic. This most likely results in decreased battery life. This proposed takes advantage of Bluetooth M-LPNWSN systems and algorithm-based efficient approach by using label-based Ant Colony Optimization (ACO) in the clustering process for allocating routing. Theoretical analysis and simulation results show that the proposed algorithm has a better performance in terms of total routing length, number of hops, and communication load when compared with the other existing algorithms such as Artificial Neural Networks (ANNs) that were used with the Bluetooth low
Quantitative Structure-Activity Relationships: Analysis of Interactions of 2,3,7,8-Tetrachlorodibenzo-p-dioxin and 2-Substituted Analogues with Rat, Mouse, Guinea Pig, and Hamster Cytosolic Receptor1

The competitive receptor binding affinities of thirteen 2-substituted 3,7,8-trichlorodibenzo-p-dioxins to hepatic cytosol from rat, mouse, guinea pig, and hamster were determined with [3H]-2,3,7,8-tetrachloro-dibenzo-p-dioxinas the radioligand.Multiple parameterlinearregression analysis of the binding data from the four species gave the following equations: where Â»,A/,,. HB, aÂ°,and V, are physicochemical parameters for substituent lipophilicity, steric effect, hydrogenbondingcapacity, electronegativity, and vander Waals volume(relative to H), respectively. These equations demonstratethat there are importantspecies differences in the receptor protein bindingsite interactions with the substituted analogues. These data, coupled with the knownspecies differences in the molecular propertiesof the receptorproteins, areevidence for a heterologous nature of the receptor between mammalian species. Multiple parameter linear regression analysis of the relative aryl hydrocarbonhydroxylase (AHH) induction potencies of these analogues in rat hepatoma H-4-II E-cells in culture gave the following equation. The correlation between receptor bindingand AHH inductionwas dependent on a steric parameter(AB* STERIMOL) and the results suggest that an additional substituent-dependent process (e.g., an activation step) may be required after initial ligand-receptor binding for the ultimate expression of the receptor-mediated response (i.e., AHH induction). mg). 2-Bromo-3,7,8-TrCDD (25 was treated with cuprous cyanide (100 mg) in pyridine (2 ml) for 4 h with stirring at 120*C. The reaction was diluted with water, acidified, extracted with chloroform and/or purified by preparative thin-layer chromatography to give 2-cyano- 3,7,8-TrCDD (10 mg). The purities of all the 2 sulistinited 3,7,8-TrCDDs were determined by gas-liquid chromatography by using a Tracor Model 565 instrument equipped with an 0.6-cm x 1.2-m glass column and packed with 3% OV 101 Ultrabonded Carbowax 20 (80-100 mesh, RFR Corp., Hope, RI) by using a flame ionization detector. The purity of all the substituted dibenzo-/>-dioxins was >97%. Mass spectra were determined by gas chromatography-mass spectrometry with the use of a Finnigan OAW 1000 instrument and 220-MHz proton magnetic resonance spectra were recorded on a VarÃ anspectrometer.
Defragmentation Based Dynamic Storage Allocation in Cloud

Cloud computing is an emerging computing paradigm that supply’s users everything as a service. Cloud computing arise as an efficient way toallocate resources like storage, memory and processor for execution of task and services for a geographically dispersed providers from different organizations. Focus is on resource management problem is transformed to resource virtualization and allocation instead of job decomposition and scheduling. Public clouds sell capacity in the form of pre-defined virtual machine (VM) configurations to their users. This force’s the users to buy the VM configuration based on the peak usage.This reduces the value proposition of moving to apublic cloud as compared to doing consolidation in a privatevirtualized datacentre. Ideally we would like the cloudtenants to buy capacity in bulk and benefit from statisticalmultiplexing among workloads. This requires dynamic allocationof bulk capacity among VMs of a user that may berunning on different servers across different datacentres.In this paper, we have used a Basic Base plus Proportional Excess model which is capable of adaptively adjusting storage space in which owner plays important role in storage allocation. We see several remarkable observations of the relationships between users and providers and owners.
Application of Optimization Methods in Profiled School Scheduling Methodologies

: Sustainable development does not focus solely on environmental issues. Education and training build the critical foundation for sustainable development. One of the priorities of the Lithuanian national sustainable development strategy is the enhancement of accessibility and effectiveness of education and science systems. The main problem investigated in the paper is the enhancement of school management by the means of creation and optimization of a profiled school schedule. This kind of task does not have any algorithms of polynomial complexity that is why the principal attention is paid to heuristic methods. The paper reports on the results of experimentation with the local search techniques (such as Local, Local Random, Monte-Carlo, Simulated Annealing and Bayes) for optimization of the profiled school schedule. A new element of this work is optimization of Simulated Annealing (SA) parameters using special Bayes (BA) methods. Another new element is application of vectorial optimization theory by fixing Pareto optimal schedules such that would satisfy the subjective criteria of the parameters according to that particular condition of the spot. Both these new elements make the given work distinct from all the other earlier works on the profiled school schedule optimization. The results of each technique are analyzed.
Design, analyses, and ﬁeld test of a 0.7 m conical shaped charge

This paper describes the design, analysis and ﬁeld test of a 0.7 m (28 in) conical shaped charge (CSC). The shaped charge was designed and analyzed using an analytical code for preliminary analyses and parametric studies to determine an approximate design that could satisfy the program requirements as well as the geometrical and weight constraints. The goal was to design a shaped charge that could produce approximately a 25 cm (9.8 in) diameter, 6 m (19.7 ft) deep hole in rock and concrete. Preliminary results, obtained with the analytical code, were veriﬁed independently with a hydrocode. After the shaped charge was designed and analyzed, it was fabricated and ﬁeld tested by ﬁring it into Tuff rock. The shaped charge fulﬁlled the program requirements and the test results closely agreed with the analyses.
Clinical assessment and voxel-based morphometry study of untreated Adult Attention deficit hyperkinetic disorders patients

Purpose Adult ADHD is one of the most undiagnosed diseases mainly because of the misperception that ADHD is a childhood disease. In this study, we assess the characteristic features of adult ADHD using clinical assessment and structural Magnetic resonance imaging (sMRI) Methods We obtained structural MRI data from the UCLA Consortium for Neuropsychiatric Phenomics for 21 untreated adult ADHD patients and 21 age and gender propensity-matched control patients. For clinical assessment, we compared the scores of Barrat's impulsivity score, Dickman impulsivity inventory II, and Eysenck's Impulsivity Inventory. We then compared grey matter volume (GMV) between ADHD and control patients using a two-sample t-test. We also assessed the correlation between different clinical assessments and GMV. Results Based on our results, adult ADHD showed significantly higher impulsivity scores, however, no significant difference in functional impulsivity scores or empathy summary scores. For sMRI, there was a significant decrease of GMV of the left cuneus in female ADHD patients. For clinical assessment scales, only the motor impulsiveness subdomain showed a significant positive correlation with the GMV of the left precuneus. Conclusions In this study, we assessed the characteristic sMRI features and clinical assessment scores for untreated adult ADHD. Our results show that a study with a bigger sample size can identify diagnostic features for adult ADHD.