Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning in Repeated Auctions with Budgets: Regret Minimization and Equilibrium

In online advertising markets, advertisers often purchase ad placements through bidding in repeated auctions based on realized viewer information. We study how budget-constrained advertisers may bid in the presence of competition, when there is uncertainty about future bidding opportunities as well as competitors' heterogenous preferences and budgets. We formulate this problem as a sequential game of incomplete information, where bidders know neither their own valuation distribution, nor the budgets and valuation distributions of their competitors. We introduce a family of dynamic bidding strategies we refer to as "adaptive pacing" strategies, in which advertisers adjust their bids throughout the campaign according to the sample path of observed expenditures. We analyze the performance of this class of strategies under different assumptions on competitors' behavior. Under arbitrary competitors' bids, we establish through matching lower and upper bounds the asymptotic optimality of this class of strategies as the number of auctions grows large. When adopted by all the bidders, the dynamics converge to a tractable and meaningful steady state. Moreover, we show that these strategies constitute an approximate Nash equilibrium in dynamic strategies: The benefit of unilaterally deviating to other strategies, including ones with access to complete information, becomes negligible as the number of auctions and competitors grows large. This establishes a connection between regret minimization and market stability, by which advertisers can essentially follow equilibrium bidding strategies that also ensure the best performance that can be guaranteed off-equilibrium.
DOES CHILDHOOD PREFRONTAL CORTEX VOLUME VARY WITH SOCIOECONOMIC STATUS

I explore the relationship between prefrontal cortex (PFC) volume and socioeconomic status in children. Previous studies have shown that children of lower-SES perform worse than their peers on tests of cognitive functions controlled by the prefrontal cortex, such as executive function, working memory, and IQ. Evidence indicates that the environment associated with low-SES can cause such deficiencies, but the literature thus far has failed to distinguish between these effects and the effects of genetics, a distinction that is of utmost importance if we wish to construct policies to counteract this correlation. My study aims to establish whether differences in the gray matter volume of the PFC are responsible for these differences in cognitive performance. PFC gray matter volume has been shown to govern many of the cognitive functions in which low-SES children are deficient, and it also shows promise as a target for the influence of the low-SES environment. To test the extent to which the PFC gray matter volume is an intermediary in the relationship between SES and cognitive performance, then, I perform a cross-sectional and a differences regression of PFC volume on parental SES for 272 children aged 4-21. My results indicate that while there is indeed a correlation between SES and PFC gray matter volume, it is of a very small magnitude, and hence the PFC gray matter volume is most likely not the primary intermediary in the relationship between SES and cognitive performance.
LocaToR: Locating Passive RFID Tags with the Relative Neighborhood Graph

Passive tags are widely used in many applications, for example, the supply chain, the food industry and ware-house management. In such applications, the location information of tags is very important. However, the uncertain proper-ties of Received Signal Strength Indicators (RSSI), various backscattering events on different power levels and the directivity of readers prevent traditional ranging-based approaches working well in passive RFID systems. In accordance with our observations during experiments, we create a novel approach to locate a specific tag among objects. Although absolute positions are difficult to obtain, we can estimate locations by building a relative relationship between tags. To reduce the effect of the above limitations, we propose a range-free approach named LocaToR to establish a relative neighborhood graph. We implement our method on a real passive system. Taking environmental factors into consideration, we look at two situations: a controlled chamber and a semi-open space. Experimental results show that our approach can obviously improve the accuracy of the localization system as well as save readers' energy.
The Informativeness of Stochastic Frontier and Programming Frontier Efficiency Scores: Cost Efficiency and Other Measures of Bank Holding Company Performance

This paper examines the properties of the X-inefficiencies in U.S. bank holding companies derived from both stochastic and linear programming frontiers. This examination allows the robustness of results across methods to be compared. While we find that calculated programming inefficiency scores are two to three times larger than those estimated using a stochastic frontier, the patterns of the scores across banks and time are similar, and there is a relatively high correlation of the rankings of banks' efficiencies under the two methods. However, when we examine the "informativeness" of the efficiency measured by the two different techniques, we find some large differences. We find evidence that the stochastic frontier scores are more closely related to risk-taking behavior, managerial competence, and bank stock returns. Based on these findings, we conclude that while both methods produce informative efficiency scores, for this data set decision makers should put more weight on the stochastic frontier efficiency estimates.
The language variable in educational research: an exploration of researcher positionality, translation, and interpretation

ABSTRACT In qualitative educational research, the languages spoken by the participants and the researchers can greatly impact the quality of the data collected. This paper will explore the advantages and disadvantages of being an insider researcher by virtue of speaking the same language(s) as the participants. Whether the researcher is a linguistic insider or outsider, he or she will need to make important decisions with regard to translation and interpretation when conducting cross-language research. Such linguistic decisions can also impact the quality of the data collected and the trustworthiness of the research. The advantages and disadvantages of translation and interpretation in qualitative educational research will be explored. This paper offers researchers and students suggestions for conducting cross-language educational research in an ethical and transparent manner.
Efficient Web Video Classification via Cross-modality Knowledge Transferring

This paper puts forward a novel method for classifying Web videos with high efficiency. Instead of analyzing the videos or extracting complicated visual features, which are both computationally expensive, we only utilize the related textual information of the to-be-classified Web videos. To address the sparsity of the textual features, we propose to exploit knowledge from auxiliary data of diverse modalilies during training, such that more informative features can be constructed. We carried out extensive experiments on MCG-WEB dataset collected from YouTube for video classification. The results demonstrate that our method can outperform several related state-of-the-art methods markedly and is quite fast, validating its effectiveness and efficiency.
Fructan metabolism and changes in fructan composition during cold acclimation in perennial ryegrass

Perennial ryegrass (Lolium perenne L.) produces high levels of fructans as a mixture of oligosaccharides and polysaccharides with different degrees of polymerization (DP). The present study describes the analysis of the compositional changes in the full spectrum of fructans, fructan distribution between above ground biomass (top) and the roots, and the transcription of candidate genes involved in fructan metabolism during cold acclimation in perennial ryegrass variety “Veyo” and ecotype “Falster” from distinct geographical origins. We observed changes in fructan composition and induction of low-DP fructans, especially DP = 4, in both the top and the roots of “Veyo” and “Falster” in response to low-temperature stress. The accumulation of DP > 50 fructans was only apparent in the top tissues where the Lp1-FFT expression is higher compared to the roots in both “Veyo” and “Falster. ” Our results also show the accumulation and depolymerization of fructans with different DP, together with the induction of genes encoding fructosyltransferases and fructan exohydrolases in both “Veyo” and “Falster” during cold acclimation, supporting the hypothesis that fructan synthesis and depolymerization occurring simultaneously. The ecotype “Falster,” adapted to cold climates, increased total fructan content and produced more DP > 7 fructans in the roots than the variety “Veyo,” adapted to warmer climates. This indicates that high-DP fructan accumulation in roots may be an adaptive trait for plant recovery after abiotic stresses.
Numerical investigation of powder trajectories and deposition in tribo-charge powder coating

An iterative numerical algorithm for simulating particle trajectories in the tribo-charge powder coating process is presented in the paper. The electric field distribution is determined by means of the finite element method. The particle trajectories are predicted by integrating the differential equation of motion, resulting from the Newton equation. The density of the space charge in the powder cloud is estimated using the particle-in-cell algorithm. All problems are solved iteratively until a self-consistent solution is found for all the involved parameters. The algorithm has been applied to a few different models of the problem assuming mono- and polydispersed powders. The effect of powder parameters on the wrap-round and Faraday cage effects has been investigated.
Peak-constrained least-squares optimization

We presented the basic concepts for peak-constrained least-squares (PCLS) optimization in previous papers. We present advanced PCLS optimization concepts in this paper. This paper discusses only a few of the many ways that PCLS design problems can be formulated. We believe that the PCLS optimality criterion can be customized to fit virtually any practical application. For example, PCLS optimization can readily be used to design equalizers in the prefilter-equalizer or interpolated FIR implementations of filters.
Immune-Inspired Collaborative Filtering Technology for Rating-Based Recommendation System

A novel technology inspired by the adaptive and self-organizing immune nature is applied to the task of rating-based recommendation technology. Unlike present vector-space model, user's multiple interest model is introduced to be the representation of antigen and antibody. The artificial immune networks model, which is a type of competitive learning algorithm is capable of extracting relevant features contained in antigens, useful predictions and recommendations are made from the memory antibody cells which represent an "internal image" of the antigens. It provides better recommendation quality owing to solving to data sparse problem. Experimental results indicate the effectiveness and wide applicability. The advantages are expected to be its ease of adaptation to the dynamic environment.
Performance on the Gollin Incomplete Figures

The Gollin test (measuring recognition thresholds for fragmented line drawings of everyday objects and animals) has traditionally been regarded as a test of incomplete figure perception or c̀losure', though there is a debate about how such closure is achieved. Here, figural incompleteness is considered to be the result of masking, such that absence of contour elements of a fragmented figure is the result of the influence of an `invisible' mask. It is as though the figure is partly obscured by a mask having parameters identical to those of the background. This mask is `invisible' only consciously, but for the early stages of visual processing it is real and has properties of multiplicative noise. Incomplete Gollin figures were modeled as the figure covered by the mask with randomly distributed transparent and opaque patches. We adjusted the statistical characteristics of the contour image and empty noise patches and processed those using spatial and spatial-frequency measures. Across 73 figures, despite inter-subject variability, mean recognition threshold was always approximately 15% of total contour in naive observers. Recognition worsened with increasing spectral similarity between the figure and the `invisible' mask. Near threshold, the spectrum of the fragmented image was equally similar to that of the `invisible' mask and complete image. The correlation between spectral parameters of figures at threshold and complete figures was greatest for figures that were most easily recognised. Across test sessions, thresholds reduced when either figure or mask parameters were familiar. We argue that recognition thresholds for Gollin stimuli in part reflect the extraction of signal from noise. DOI:10.1068/p5366
Releaving the Hidden Secret with LSB Steganography

: Data hiding is the art of hiding data for various purposes such as; to maintain private data, secure confidential data and so on. There are lots of techniques used for data hiding and the well known technique is the Steganography. Steganography is one of the most powerful techniques to conceal the existence of hidden secret data inside a cover object. Images are the most popular cover medium used in steganography. Embedding secret information inside images requires intensive computations, and therefore, designing steganography in hardware speeds up steganography. This paper provides a hardware design of Least Significant Bit (LSB) steganography technique. The design utilizes the Spartan III FPGA kit of the Altera family and 2/3-LSB steganography algorithm to perform the steganography steps. The design balances the tradeoffs such as imperceptibility, quality and capacity.
Developing and testing adapted measures of children’s self-efficacy, intentions, and behaviors associated with childhood obesity

ABSTRACT Updated reliable and valid instruments are needed to evaluate interventions to combat childhood obesity. The Healthy Living for Kids Survey (HLKS) includes adapted existing measures of children’s physical activity, nutritional behaviors, dietary habits, nutritional intentions, screen time behaviors, and self-efficacy regarding nutrition, physical activity, and screen time. 96 fourth and fifth graders completed the HLKS. Internal reliability was minimally acceptable for five scales (α = 0.63–0.80), with adequate test-retest reliability coefficients (r = 0.63–0.78) for three of these scales. The Dietary Habits, Nutritional Self-Efficacy, and Nutritional Intentions scales demonstrated acceptable reliability and validity at this stage of development.
Overexpressing the Multiple-Stress Responsive Gene At1g74450 Reduces Plant Height and Male Fertility in Arabidopsis thaliana

A subset of genes in Arabidopsis thaliana is known to be up-regulated in response to a wide range of different environmental stress factors. However, not all of these genes are characterized as yet with respect to their functions. In this study, we used transgenic knockout, overexpression and reporter gene approaches to try to elucidate the biological roles of five unknown multiple-stress responsive genes in Arabidopsis. The selected genes have the following locus identifiers: At1g18740, At1g74450, At4g27652, At4g29780 and At5g12010. Firstly, T-DNA insertion knockout lines were identified for each locus and screened for altered phenotypes. None of the lines were found to be visually different from wildtype Col-0. Secondly, 35S-driven overexpression lines were generated for each open reading frame. Analysis of these transgenic lines showed altered phenotypes for lines overexpressing the At1g74450 ORF. Plants overexpressing the multiple-stress responsive gene At1g74450 are stunted in height and have reduced male fertility. Alexander staining of anthers from flowers at developmental stage 12–13 showed either an absence or a reduction in viable pollen compared to wildtype Col-0 and At1g74450 knockout lines. Interestingly, the effects of stress on crop productivity are most severe at developmental stages such as male gametophyte development. However, the molecular factors and regulatory networks underlying environmental stress-induced male gametophytic alterations are still largely unknown. Our results indicate that the At1g74450 gene provides a potential link between multiple environmental stresses, plant height and pollen development. In addition, ruthenium red staining analysis showed that At1g74450 may affect the composition of the inner seed coat mucilage layer. Finally, C-terminal GFP fusion proteins for At1g74450 were shown to localise to the cytosol.
AN UNSUPERVISED LEARNING METHOD FOR AN ATTACKER AGENT IN ROBOT SOCCER COMPETITIONS BASED ON THE KOHONEN NEURAL NETWORK

RoboCup competition as a great test-bed, has turned to a worldwide popular domains in recent years. The main object of such competitions is to deal with complex behavior of systems which consist of multiple autonomous agents. The rich experience of human soccer player can be used as a valuable reference for a robot soccer player. However, because of the differences between real and simulated soccer world, it needs to fit such precious transcendental knowledge to use in the simulated soccer game. On the other hand, Reinforcement Learning (RL) as a common method in this domain because of its trial-and-error nature does not have great performance in using transcendental knowledge. Thus, this method is limited to complex multi-agent learning problems. Among various frameworks of intelligences, in general, Artificial Neural Networks (ANN) and specially Kohonen neural networks with its feed-forward architecture and its ability in discovering any relationships of interest that may exist in the input data may be considered as a powerful tool in clustering. This paper puts forward an unsupervised learning method based on Kohonen network to create a powerful Tactics layer in decision-making section for an attacker agent. The approach presented in this paper is based on the combination of expert's knowledge and data obtained from the simulated world. This system is applied to the attacker agents of ULA 2006 soccer team. Simulation results revealed that the chosen approach is superior with respect to the other intelligent techniques.
A virtual test facility for simulating the dynamic response of materials

The goal of the Caltech Center is to construct a virtual test facility (VTF): a problem solving environment for full 3D parallel simulation of the dynamic response of materials undergoing compression due to shock waves. The objective is to design a software environment that will: facilitate computation in a variety of experiments in which strong shock waves impinge on targets comprising various combinations of materials; compute the target materials' subsequent dynamic response; and validate these computations against experimental data. Successfully constructing such a facility requires modeling of the highest accuracy. We must model at atomistic scales to correctly describe the material properties of the target materials and high explosives; at intermediate (meso) scales to understand the micromechanical response of the target materials; and at continuum scales to capture properly the evolution of macroscopic effects. The article outlines such a test facility. Although it is a very simplified version of the facilities found in a shock-compression laboratory, our VTF includes all the basic features, offering a problem solving environment for validating experiments and facilitating further development of simulation capabilities.
Large vocabulary continuous speech recognition using WFST-based linear classifier for structured data

This paper describes a discriminative approach that further advances the framework for Weighted Finite State Transducer (WFST) based decoding. The approach introduces additional linear models for adjusting the scores of a decoding graph composed of conventional information source models (e.g., hidden Markov models and N-gram models), and reviews the WFSTbased decoding process as a linear classifier for structured data (e.g., sequential multiclass data). The difficulty with the approach is that the number of dimensions of the additional linear models becomes very large in proportion to the number of arcs in a WFST, and our previous study only applied it to a small task (TIMIT phoneme recognition). This paper proposes a training method for a large-scale linear classifier employed in WFSTbased decoding by using a distributed perceptron algorithm. The experimental results show that the proposed approach was successfully applied to a large vocabulary continuous speech recognition task, and achieved an improvement compared with the performance of the minimum phone error based discriminative training of acoustic models. Index Terms: speech recognition, weighted finite state transducer, linear classifier, distributed perceptron, large vocabulary continuous speech recognition
The SuperCOSMOS Sky Survey . Paper I : Introduction and Description

In this, the first in a series of three papers concerning the SuperCOSMOS Sky Survey (SSS), we give an introduction and user guide to the survey programme. We briefly describe other wide–field surveys and compare with our own. We give examples of the data, and make a comparison of the accuracies of the various image parameters available with those from the other surveys providing similar data; we show that the SSS database and interface offer advantages over these surveys. Some science applications of the data are also described and some limitations discussed. The series of three papers constitutes a comprehensive description and user guide for the SSS.
Hybrid Feature and Adaptive Particle Filter for Robust Object Tracking

A hybrid feature based adaptive particle filter algorithm is presented for object tracking in real scenarios with static camera. The hybrid feature is combined by two effective features: the Grayscale Arranging Pairs (GAP) feature and the color histogram feature. The GAP feature has high discriminative ability even under conditions of severe illumination variation and dynamic background elements, while the color histogram feature has high reliability to identify the detected objects. The combination of two features covers the shortage of single feature. Furthermore, we adopt an updating target model so that some external problems such as visual angles can be overcame well. An automatic initialization algorithm is introduced which provides precise initial positions of objects. The experimental results show the good performance of the proposed method. Keywords—Hybrid feature, adaptive Particle Filter, robust Object Tracking, Grayscale Arranging Pairs (GAP) feature.