Miracle-dz/UniFAR-FTUs huggingface dataset:
AN ANALYSIS OF BODY MASS, WING LENGTH, AND VISIBLE FAT DEPOSITS OF DARK-EYED JUNCOS WINTERING AT DIFFERENT LATITUDES

During winter, many bird species at temperate latitudes increase their stored fat (see King and Farner 1966, King 1972 for review of older literature; O'Connor 1973, Evans and Smith 1975, Biebach 1977, Vincent and Bedard 1976, Carey et al. 1978, Blem 1978 for more recent findings). Winter fattening is considered an adaptive response to the increases in thermoregulatory costs, duration of nocturnal fasting, and risk that in severe climates snow or ice storms will curtail or prevent daylight feeding. If climate (including day length) is the ultimate cause of winter fattening, then in much of the world populations of the same species wintering at different latitudes should differ in amount of fat stored (King and Mewaldt 1981). In certain sedentary species, higher body mass (wet weight) and thus perhaps greater fat stores have been found to be characteristic of more northern populations (e.g., the Great Tit [Parus major], compare van Balen 1967 and Haftorn 1976; and the House Sparrow [Passer domesticus] Blem 1973); but studies of migratory birds have produced conflicting results. Several species of shorebirds exhibit the expected trend (Pienkowski et al. 1975, Dick and Pienkowski 1979), but neither White-crowned Sparrows (Zonotrichia leucophrys gambelii) (King and Mewaldt 1981) nor American Goldfinches (Carduelis tristis) (Wiseman 1975, Carey et al. 1978) are heavier farther north. The European Starling (Sturnus vulgaris), a partial migrant, exhibits still a different pattern, carrying more fat at midlatitudes than it does to the north or south (Blem 1981). In this paper we analyze, according to sex and age, variation in body mass of 1835 migratory Dark-eyed Juncos (Junco hyemalis) from six wintering locations in the eastern United States. Juncos have been the subject of numerous studies of body mass in the laboratory and field (Helms and Drury 1960, Johnston 1962, Weise 1963, Farrar 1966, Helms et al. 1967, Knowles 1972, Swanson 1975, Holmes 1976, Prescott 1978), but sex and/ or age was not determined in most of these. Juncos are known to fatten in winter (Helms et al. 1967, Bower and Helms 1967, Holmes 1976); and in one geographic comparison, mass was greater at a higher latitude (Massachusetts) than at a lower (Delaware), even when temperatures were warmer at the more northerly location (Helms and Drury 1960, Knowles 1972). Here we ask whether body mass varies with latitude and whether any variation that is found can be attributed to differences in fat storage.
Reducing the risk of disordered eating among female college students: A test of alternative interventions.

The purpose of this study was to test the effectiveness of a cognitive-dissonance based intervention in reducing disordered eating attitudes and behaviors. The intervention program created dissonance through discussion, exercises, and homework aimed at addressing and countering internalized sociocultural pressures, beliefs and values about women's bodies, attractiveness, and worth in the U.S. Seventy-seven female undergraduates were randomly assigned to one of four conditions: cognitive-dissonance, combined cognitive-dissonance, healthy weight placebo control, and wait-list control To determine effectiveness of the intervention, MANCOVA procedures were used, with Time 1 scores serving as the covariate. Overall, the women who received the dissonance based interventions produced the strongest effects among measures assessing sociocultural pressures, internalization, and body dissatisfaction in comparison to the control group, and experienced significant reductions in dieting behaviors and bulimic symptoms over the course of the study, suggesting that the creation of dissonance via the intervention assisted the women in reducing eating disorder risk factors.
The Moderating Effects of Cultural Context in Buyer-Seller Negotiation

E. T. Hall's (1976) conceptualization of high and low context cultures has surfaced in the literature as a driving force in determining buyers' and sellers' negotiation styles. We explore the effect low and high cultural contexts have on the relationship between international sales representatives' demographic characteristics and their cooperative negotiating styles. Exporters from the United States (low context) and the Philippines (high context) were sampled to study the effects of gender, educational level, and experience on the cooperative problem-solving approach to negotiation. Results indicated problem-solving levels between high and low context sample groups were significantly different but no gender differences were noted within the same cultural context. Our findings also suggested that cultural context moderates the relationship between the negotiator's experience and negotiation style. Negotiator experience had a greater positive effect on the cooperative style when negotiators were from the low context culture country.
Objective quality assessment of MPEG-2 video streams by using CBP neural networks

The increasing use of compression standards in broadcasting digital TV has raised the need for established criteria to measure perceived quality. Novel methods must take into account the specific artifacts introduced by digital compression techniques. This paper presents a methodology using circular backpropagation (CBP) neural networks for the objective quality assessment of motion picture expert group (MPEG) video streams. Objective features are continuously extracted from compressed video streams on a frame-by-frame basis; they feed the CBP network estimating the corresponding perceived quality. The resulting adaptive modeling of subjective perception supports a real-time system for monitoring displayed video quality. The overall system mimics perception but does not require an analytical model of the underlying physical phenomenon. The ability to process compressed video streams represents a crucial advantage over existing approaches, as avoiding the decoding process greatly enhances the system's real-time performance. Experimental evidence confirmed the approach validity. The system was tested on real test videos; they included different contents ranging from fiction to sport. The neural model provided a satisfactory, continuous-time approximation for actual scoring curves, which was validated statistically in terms of confidence analysis. As expected, videos with slow-varying contents such as fiction featured the best performances.
A new vertical edge detection algorithm and its application

Edge detection is a very important process for many image processing applications, especially in Car License Plate Detection and Recognition Systems(CLPDRS). The need to distinguish the desired details is a very important pre-process in order to give good results in short time processing. We proposed a new and fast vertical edge detection algorithm (VEDA) which is based on the contrast between the gray scale values. Once, input gray image was binarized by using adaptive threshold, unwanted lines elimination algorithm (ULEA) was proposed and applied. After that, a VEDA was applied for experimental images. Then, implementation on the application is performed and discussed in order to confirm that VEDA is robust for highlighting license plate details easily. The results revealed accurate edge detection performance and demonstrated the great efficiency of using VEDA in order to highlight license plate details. Finally, VEDA showed that it is faster than Sobel operator by about 7-9 times.
Adaptive residual filtering for safe human-robot collision detection under modeling uncertainties

This paper presents an innovative collision detection strategy for robot manipulators in the context of the human-robot interaction. Classical approaches consisting of a comparison of the applied motor torques with those provided by a dynamic model can be sensitive to model uncertainties, leading to conservative detection thresholds. In this work, a “gray-box” model is designed based on a use-case study to shape the on-line evaluation of the residuals. This approach takes into account unstructured uncertainties relative to the speed-dependent non-linearities (e.g. friction phenomena) and the acceleration, both of particular interest when dealing with highly time-varying dynamics. Taking advantage of proprioceptive measurements of the robot state, the residual is adaptively filtered regarding these model uncertainties, and the evaluation step is improved by considering a dynamic threshold. The proposed multi-variable algorithm is implemented on the CEA robot arm ASSIST and the experimental results illustrate the enhancement of the detection sensitivity.
Optimising Incremental Dialogue Decisions Using Information Density for Interactive Systems

Incremental processing allows system designers to address several discourse phenomena that have previously been somewhat neglected in interactive systems, such as backchannels or barge-ins, but that can enhance the responsiveness and naturalness of systems. Unfortunately, prior work has focused largely on deterministic incremental decision making, rendering system behaviour less flexible and adaptive than is desirable. We present a novel approach to incremental decision making that is based on Hierarchical Reinforcement Learning to achieve an interactive optimisation of Information Presentation (IP) strategies, allowing the system to generate and comprehend backchannels and barge-ins, by employing the recent psycholinguistic hypothesis of information density (ID) (Jaeger, 2010). Results in terms of average rewards and a human rating study show that our learnt strategy outperforms several baselines that are not sensitive to ID by more than 23%.
Twitter hashtags: Joint Translation and Clustering

The popularity of microblogging platforms, such as Twitter, renders them valuable real-time information resources for tracking various aspects of worldwide events, e.g., earthquakes, political elections, etc. Such events are usually characterized in microblog posts via the use of hashtags (#). As microbloggers come from different backgrounds, and express themselves in different languages, we witness different "translations" of hashtags which, however, are about the same event. Language-dependent variants of hashtags can possibly lead to issues in content-analysis. In this paper, we propose a method for translating hashtags, which builds on methods from information retrieval. The method introduced is source and target language independent. Our method is desirable, either instead of, or complimentary, to the direct translation of the hashtag for three reasons. First we return a list of hashtags on the same topic, which takes into account the plurality and variability of hashtags used by microbloggers for assigning posts to a topic. Second, our framework accounts for the problem that microbloggers in different languages will refer to the same topic using different tokens. Finally, our method does not require special preprocessing of hashtags, reducing barriers to real-world implementation. We present proof-of-concept results for the given Spanish hashtag #33mineros.
Exploiting the Nephrotoxic Effects of Venom from the Sea Anemone, Phyllodiscus semoni, to Create a Hemolytic Uremic Syndrome Model in the Rat

In the natural world, there are many creatures with venoms that have interesting and varied activities. Although the sea anemone, a member of the phylum Coelenterata, has venom that it uses to capture and immobilise small fishes and shrimp and for protection from predators, most sea anemones are harmless to man. However, a few species are highly toxic; some have venoms containing neurotoxins, recently suggested as potential immune-modulators for therapeutic application in immune diseases. Phyllodiscus semoni is a highly toxic sea anemone; the venom has multiple effects, including lethality, hemolysis and renal injuries. We previously reported that venom extracted from Phyllodiscus semoni induced acute glomerular endothelial injuries in rats resembling hemolytic uremic syndrome (HUS), accompanied with complement dysregulation in glomeruli and suggested that the model might be useful for analyses of pathology and development of therapeutic approaches in HUS. In this mini-review, we describe in detail the venom-induced acute renal injuries in rat and summarize how the venom of Phyllodiscus semoni could have potential as a tool for analyses of complement activation and therapeutic interventions in HUS.
Self-Confrontation of Teachers.

Simply presenting informa#ion that displays clearly any discrepancy between a teacher's ideal and actual classroom performance does not, in itself, promise constructive change. This is true in part because teachers confronted with such discrepancies often experience anxiety. Teachers who wish to change often find it difficult to behave differently in the classroom for a variety of reasons. This paper discusses some pSychological reactions that can be expected by teachers when they are confronted with information about their own classroom behavior. It also scojgests some actions that a cacilitator might take to help a teacher focus energies on self-improvement. (Author) U S DEPARTMENT OF HEALTH EDUCATION S. WELFARE NATIONAL INSTITUTE OF EDUCATION no, , r N PPC. I"E PE 14,C.N Otft,,N Pl). N '1. 01 F 0111C N, DO NO, NE ,111 CifIC :A N1 0, ( N.Irf7NA, r :If E ION ,,IL071,17f OW y
Parsing Syntactic and Semantic Dependencies with Two Single-Stage Maximum Entropy Models

This paper describes our system to carry out the joint parsing of syntactic and semantic dependencies for our participation in the shared task of CoNLL-2008. We illustrate that both syntactic parsing and semantic parsing can be transformed into a word-pair classification problem and implemented as a single-stage system with the aid of maximum entropy modeling. Our system ranks the fourth in the closed track for the task with the following performance on the WSJ+Brown test set: 81.44% labeled macro F1 for the overall task, 86.66% labeled attachment for syntactic dependencies, and 76.16% labeled F1 for semantic dependencies.
DedupT: Deduplication for tape systems

Deduplication is a commonly-used technique on disk-based storage pools. However, deduplication has not been used for tape-based pools: tape characteristics, such as high mount and seek times combined with data fragmentation resulting from deduplication create a toxic combination that leads to unacceptably high retrieval times. This work proposes DedupT, a system that efficiently supports deduplication on tape pools. This paper (i) details the main challenges to enable efficient deduplication on tape libraries, (ii) presents a class of solutions based on graph-modeling of similarity between data items that enables efficient placement on tapes; and (iii) presents the design and evaluation of novel cross-tape and on-tape chunk placement algorithms that alleviate tape mount time overhead and reduce on-tape data fragmentation. Using 4.5 TB of real-world workloads, we show that DedupT retains at least 95% of the deduplication efficiency. We show that DedupT mitigates major retrieval time overheads, and, due to reading less data, is able to offer better restore performance compared to the case of restoring non-deduplicated data.
Authentication and Key Agreement via Memorable Password

This paper presentsa new passwor d authentication and key agreementprotocol called AMP in a provablemanner . The intrinsic problemwith password authenticationis a passwor d, associatedwith each user, haslow entropyso that (1) thepasswor d is hard to transmitsecurelyoveraninsecurechannel and(2) thepasswor d file is hard to protect.Our solution to this complex problemis theamplifiedpassword proof idea along with the amplifiedpasswor d file. A party commitsthe high entropy information andamplifiesher passwor d with that informationin the amplifiedpasswor d proof. Shenever showsany informationexceptthat sheknowsit for her proof. Our amplifiedpasswor d proof idea is similar to the zero-knowledge proof in that sense. A serverstores amplifiedverifiers in theamplifiedpasswor d file that is secureagainsta serverfile compromiseanda dictionary attack. AMP mainlyprovidesthepasswor dverifierbasedauthenticationandtheDiffie-Hellman basedkey agreement,securely andefficiently. AMP is simple and actually the most efficient protocol amongtherelatedprotocols.
Lawyer, Know Thyself: A Review of Empirical Research on Attorney Attributes Bearing on Professionalism

Professor Daicoff collects and reviews almost 40 years of previously unorganized empirical research on attorneys and law students. She argues that there is a set of empirically- demonstrated personality traits, attributes, characteristics, goals, motivations, attitudes, psychological needs, and decision making styles which are unique to lawyers and law students. For example, lawyers may differ from the general population in the rational, logical way in which they approach problems and make decisions, in what they value, and in what motivates them. While some of these traits are intensified in law school, at least some appear to be pre-existing prior to legal education, suggesting that individuals with the lawyer personality may self-select into the law. She explores the extent to which these attorney attributes are responsible for a tripartite crisis in the legal profession, in the areas of professionalism, low public opinion of attorneys, and lawyer distress and dissatisfaction. She concludes that the "crisis" has been caused in part by the "attorney personality;" however, at least some of the attorney attributes may be adaptive, desirable, or even necessary for the effective practice of law.
A RESOLVED MAP OF THE INFRARED EXCESS IN A LYMAN BREAK GALAXY AT z = 3

We have observed the dust continuum of 10 z = 3.1 Lyman break galaxies with the Atacama Large Millimeter/submillimeter Array at ∼450 mas resolution in Band 7. We detect and resolve the 870 μm emission in one of the targets with a flux density of S870 = 192 ± 57 μJy, and measure a stacked 3σ signal of S870 = 67 ± 23 μJy for the remaining nine. The total infrared luminosities are L8–1000 = (8.4 ± 2.3) × 1010 L⊙ for the detection and L8–1000 = (2.9 ± 0.9) × 1010 L⊙ for the stack. With Hubble Space Telescope Advanced Camera for Surveys I-band imaging we map the rest-frame UV emission on the same scale as the dust, effectively resolving the “infrared excess” (IRX = LFIR/LUV) in a normal galaxy at z = 3. Integrated over the galaxy we measure IRX = 0.56 ± 0.15, and the galaxy-averaged UV slope is β = −1.25 ± 0.03. This puts the galaxy a factor of ∼10 below the IRX–β relation for local starburst nuclei of Meurer et al. However, IRX varies by more than a factor of 3 across the galaxy, and we conclude that the complex relative morphology of the dust relative to UV emission is largely responsible for the scatter in the IRX–β relation at high-z. A naive application of a Meurer-like dust correction based on the UV slope would dramatically overestimate the total star formation rate, and our results support growing evidence that when integrated over the galaxy, the typical conditions in high-z star-forming galaxies are not analogous to those in the local starburst nuclei used to establish the Meurer relation.
Speculative Symbolic Execution

Symbolic execution is an effective path oriented and constraint based program analysis technique. Recently, there is a significant development in the research and application of symbolic execution. However, symbolic execution still suffers from the scalability problem in practice, especially when applied to large-scale or very complex programs. In this paper, we propose a new fashion of symbolic execution, named Speculative Symbolic Execution (SSE), to speed up symbolic execution by reducing the invocation times of constraint solver. In SSE, when encountering a branch statement, the search procedure may speculatively explore the branch without regard to the feasibility. Constraint solver is invoked only when the speculated branches are accumulated to a specified number. In addition, we present a key optimization technique that enhances SSE greatly. We have implemented SSE and the optimization technique on Symbolic Pathfinder (SPF). Experimental results on six programs show that, our method can reduce the invocation times of constraint solver by 20.7% to 48.7% (with an average of 29.9%), and save the search time from 23.6% to 43.6% (with an average of 30%).
eyeDentify: Multimedia Cyber Foraging from a Smartphone

The recent introduction of smartphones has resulted in an explosion of innovative mobile applications. The computational requirements of many of these applications, however, can not be met by the smartphone itself. The compute power of the smartphone can be enhanced by distributing the application over other compute resources. Existing solutions comprise of a light weight client running on the smartphone and a heavy weight compute server running on, for example, a cloud. This places the user in a dependent position, however, because the user only controls the client application. In this paper, we follow a different model, called cyber foraging, that gives users full control over all parts of the application. We have implemented the model using the Ibis middleware. We evaluate the model using an innovative application in the domain of multimedia computing, and show that cyber foraging increases the application's responsiveness and accuracy whilst decreasing its energy usage.
Exact Post-selection Inference for Forward Stepwise and Least Angle Regression

In this paper we propose new inference tools for forward stepwise and least angle regression. We first present a general scheme to perform valid inference after any selection event that can be characterized as the observation vector y falling into some polyhedral set. This framework then allows us to derive conditional (post-selection) hypothesis tests at any step of the forward stepwise and least angle regression procedures. We derive an exact null distribution for our proposed test statistics in finite samples, yielding p-values with exact type I error control. The tests can also be inverted to produce confidence intervals for appropriate underlying regression parameters. Application of this framework to general likelihood-based regression models (e.g., generalized linear models and the Cox model) is also discussed.
The Natural Induction System AQ21 and its Application to Data Describing Patients with Metabolic Syndrome: Initial Results

This paper briefly describes the AQ21 learning system that implements a simple form of natural induction, an approach to learning that generates hypotheses in forms resembling natural language descriptions, and by that easy to understand and interpret. The system was applied to the analysis of aggregated data obtained from non-invasive tests performed on different groups of patients with metabolic syndrome. The discovered patterns were very simple and were evaluated by an expert as potentially medically significant.