Miracle-dz/UniFAR-FTUs huggingface dataset:
The Effect of Implicitly Incentivized Faking on Explicit and Implicit Measures of Doping Attitude: When Athletes Want to Pretend an Even More Negative Attitude to Doping

The Implicit Association Test (IAT) aims to measure participants’ automatic evaluation of an attitude object and is useful especially for the measurement of attitudes related to socially sensitive subjects, e.g. doping in sports. Several studies indicate that IAT scores can be faked on instruction. But fully or semi-instructed research scenarios might not properly reflect what happens in more realistic situations, when participants secretly decide to try faking the test. The present study is the first to investigate IAT faking when there is only an implicit incentive to do so. Sixty-five athletes (22.83 years ± 2.45; 25 women) were randomly assigned to an incentive-to-fake condition or a control condition. Participants in the incentive-to-fake condition were manipulated to believe that athletes with lenient doping attitudes would be referred to a tedious 45-minute anti-doping program. Attitudes were measured with the pictorial doping brief IAT (BIAT) and with the Performance Enhancement Attitude Scale (PEAS). A one-way MANOVA revealed significant differences between conditions after the manipulation in PEAS scores, but not in the doping BIAT. In the light of our hypothesis this suggests that participants successfully faked an exceedingly negative attitude to doping when completing the PEAS, but were unsuccessful in doing so on the reaction time-based test. This study assessed BIAT faking in a setting that aimed to resemble a situation in which participants want to hide their attempts to cheat. The two measures of attitude were differentially affected by the implicit incentive. Our findings provide evidence that the pictorial doping BIAT is relatively robust against spontaneous and naïve faking attempts. (B)IATs might be less prone to faking than implied by previous studies.
Non-uniform Motion Deblurring for Bilayer Scenes

We address the problem of estimating the latent image of a static bilayer scene (consisting of a foreground and a background at different depths) from motion blurred observations captured with a handheld camera. The camera motion is considered to be composed of in-plane rotations and translations. Since the blur at an image location depends both on camera motion and depth, deblurring becomes a difficult task. We initially propose a method to estimate the transformation spread function (TSF) corresponding to one of the depth layers. The estimated TSF (which reveals the camera motion during exposure) is used to segment the scene into the foreground and background layers and determine the relative depth value. The deblurred image of the scene is finally estimated within a regularization framework by accounting for blur variations due to camera motion as well as depth.
Capturing Acceptable Variation in Distinguishing Descriptions

Almost all existing referring expression generation algorithms aim to find one best referring expression for a given intended referent. However, human-produced data demonstrates that, for any given entity, many perfectly acceptable referring expressions exist. At the same time, it is not the case that all logically possible descriptions are acceptable; so, if we remove the requirement to produce only one best solution, how do we avoid generating undesirable descriptions? Our aim in this paper is to sketch a framework that allows us to capture constraints on referring expression generation, so that the set of logically possible descriptions can be reduced to just those that are acceptable.
Analyzing registry, log files, and prefetch files in finding digital evidence in graphic design applications

The products of graphic design applications, leave behind traces of digital information which can be used during a digital forensic investigation in cases where counterfeit documents have been created. This paper analyzes the digital forensics involved in the creation of counterfeit documents. This is achieved by first recognizing the digital forensic artifacts left behind from the use of graphic design applications, and then analyzing the files associated with these applications. When analyzing digital forensic artifacts generated by an application, the specific focus is on determining whether the graphic design application was installed, whether the application was used, and determining whether an association can be made between the application’s actions and such a digital crime. This is accomplished by locating such information from the registry, log files and prefetch files. The file analysis involves analyzing files associated with these applications for file signatures and metadata. In the end it becomes possible to determine if a system has been used for creating counterfeit documents or not.
Enveloping grasp feasibility inequality

The enveloping grasp feasibility inequality is formulated in this article using a coordinate transformation based on the concept of frictional degrees-of-freedom. Various analysis and synthesis problems pertinent to enveloping grasp of whole-limb systems can be handled within the same mathematical framework, even under the situation where statically indeterminate contact forces exists. Simulations are also shown to explain the basic idea.
Targeting Melanocortin Receptors as a Novel Strategy to Control Inflammation

Adrenocorticotropic hormone and α-, β-, and γ-melanocyte-stimulating hormones, collectively called melanocortin peptides, exert multiple effects upon the host. These effects range from modulation of fever and inflammation to control of food intake, autonomic functions, and exocrine secretions. Recognition and cloning of five melanocortin receptors (MCRs) has greatly improved understanding of peptide-target cell interactions. Preclinical investigations indicate that activation of certain MCR subtypes, primarily MC1R and MC3R, could be a novel strategy to control inflammatory disorders. As a consequence of reduced translocation of the nuclear factor κB to the nucleus, MCR activation causes a collective reduction of the major molecules involved in the inflammatory process. Therefore, anti-inflammatory influences are broad and are not restricted to a specific mediator. Short half-life and lack of selectivity could be an obstacle to the use of the natural melanocortins. However, design and synthesis of new MCR ligands with selective chemical properties are already in progress. This review examines how marshaling MCR could control inflammation.
Example-based image color and tone style enhancement

Color and tone adjustments are among the most frequent image enhancement operations. We define a color and tone style as a set of explicit or implicit rules governing color and tone adjustments. Our goal in this paper is to learn implicit color and tone adjustment rules from examples. That is, given a set of examples, each of which is a pair of corresponding images before and after adjustments, we would like to discover the underlying mathematical relationships optimally connecting the color and tone of corresponding pixels in all image pairs. We formally define tone and color adjustment rules as mappings, and propose to approximate complicated spatially varying nonlinear mappings in a piecewise manner. The reason behind this is that a very complicated mapping can still be locally approximated with a low-order polynomial model. Parameters within such low-order models are trained using data extracted from example image pairs. We successfully apply our framework in two scenarios, low-quality photo enhancement by transferring the style of a high-end camera, and photo enhancement using styles learned from photographers and designers.
Investigation of the magnetic field characteristics of Herbig Ae/Be stars: Discovery of the pre-main sequence progenitors of the magnetic Ap/Bp stars

We are investigating the magnetic characteristics of pre-main sequence Herbig Ae/Be stars, with the aim of (1) understanding the origin and evolution of magnetism in intermediate-mass stars, and (2) exploring the influence of magnetic fields on accretion, rotation and mass-loss at the early stages of evolution of A, B and O stars. We have begun by conducting 2 large surveys of Herbig Ae/Be stars, searching for direct evidence of photospheric magnetic fields via the longitudinal Zeeman effect. From observations obtained using FORS1 at the ESO-VLT and ESPaDOnS at the Canada-France-Hawaii Telescope, we report the confirmed detection of magnetic fields in 4 pre-main sequence A- and B-type stars, and the apparent (but as yet unconfirmed) detection of fields in 2 other such stars. We do not confirm the detection of magnetic fields in several stars reported by other authors to be magnetic: HD 139614, HD 144432 or HD 31649. One of the most evolved stars in the detected sample, HD 72106A, shows clear evidence of strong photospheric chemical peculiarity, whereas many of the other (less evolved) stars do not. The magnetic fields that we detect appear to have surface intensities of order 1 kG, seem to be structured on global scales, and appear in about 10% of the stars studied. Based on these properties, these magnetic stars appear to be pre-main sequence progenitors of the magnetic Ap/Bp stars.
Trace complexity of network inference

The network inference problem consists of reconstructing the edge set of a network given traces representing the chronology of infection times as epidemics spread through the network. This problem is a paradigmatic representative of prediction tasks in machine learning that require deducing a latent structure from observed patterns of activity in a network, which often require an unrealistically large number of resources (e.g., amount of available data, or computational time). A fundamental question is to understand which properties we can predict with a reasonable degree of accuracy with the available resources, and which we cannot. We define the trace complexity as the number of distinct traces required to achieve high fidelity in reconstructing the topology of the unobserved network or, more generally, some of its properties. We give algorithms that are competitive with, while being simpler and more efficient than, existing network inference approaches. Moreover, we prove that our algorithms are nearly optimal, by proving an information-theoretic lower bound on the number of traces that an optimal inference algorithm requires for performing this task in the general case. Given these strong lower bounds, we turn our attention to special cases, such as trees and bounded-degree graphs, and to property recovery tasks, such as reconstructing the degree distribution without inferring the network. We show that these problems require a much smaller (and more realistic) number of traces, making them potentially solvable in practice.
Recognition of Defocused Patterns

The paper addresses the recognition problem of defocused patterns. Though recognition algorithms as- sume that the input images are focused and sharp, it does not always hold on actual camera-captured images. Thus, a recognition method that can recognize defocused patterns is required. In this paper, we propose a novel recognition framework for defocused patterns, relying on a single camera without a depth sensor. The framework is based on the coded aperture which can recover a less-degraded image from a defocused image if depth is available. However, in the problem setting of "a single camera without a depth sensor," estimating depth is ill-posed and an assumption is required to estimate the depth. To solve the problem, we introduce a new assumption suitable for pattern recognition; templates are known. It is based on the fact that in pattern recognition, all templates must be available in advance for training. The experiments confirmed that the proposed method is fast and robust to defocus and scaling, especially for heavily defocused patterns.
Towards Adaptive Network Nodes via Service Chain Construction

Network functional combination is a promising direction in enhancing Internet adaptability. It decomposes the current layered network into fine-grained building blocks and combines them on demand. However, what legacy functions should be decomposed and how to combine them in an optimal way are unclear. We propose a novel adaptive architecture called reconstructive network architecture (RECON) based on the principles of the Complex Adaptive System. This study has three main contributions. First, RECON decomposes functions of the protocol stack at layers 3 and 4 into fine-grained building blocks, called atomic capabilities to open the network core functions unlike existing solutions. Second, RECON can customize different service chains on demand by combining atomic capabilities in an optimal way. We formulate the atomic capability combination into a nonlinear integer optimization problem with the proposed algorithm to reach an appropriate tradeoff between the optimal solution and computation cost. Finally, we implement a proof-of-concept for RECON in the network node. Results are corroborated by several numerical simulations.
Voluntarily Reported Immunization Registry Data: Reliability and Feasibility to Predict Immunization Rates, San Diego, California, 2013

Objectives: Accurate data on immunization coverage levels are essential to public health program planning. Reliability of coverage estimates derived from immunization information systems (IISs) in states where immunization reporting by medical providers is not mandated by the state may be compromised by low rates of participation. To overcome this problem, data on coverage rates are often acquired through random-digit-dial telephone surveys, which require substantial time and resources. This project tested both the reliability of voluntarily reported IIS data and the feasibility of using these data to estimate regional immunization rates. Methods: We matched telephone survey records for 553 patients aged 19-35 months obtained in 2013 to 430 records in the San Diego County IIS. We assessed concordance between survey data and IIS data using κ to measure the degree of nonrandom agreement. We used multivariable logistic regression models to investigate differences among demographic variables between the 2 data sets. These models were used to construct weights that enabled us to predict immunization rates in areas where reporting is not mandated. Results: We found moderate agreement between the telephone survey and the IIS for the diphtheria, tetanus, and acellular pertussis (κ = 0.49), pneumococcal conjugate (κ = 0.49), and Haemophilus influenzae type b (κ = 0.46) vaccines; fair agreement for the varicella (κ = 0.39), polio (κ = 0.39), and measles, mumps, and rubella (κ = 0.35) vaccines; and slight agreement for the hepatitis B vaccine (κ = 0.17). Conclusions: Consistency in factors predicting immunization coverage levels in a telephone survey and IIS data confirmed the feasibility of using voluntarily reported IIS data to assess immunization rates in children aged 19-35 months.
Mechanically Versatile Soft Machines through Laminar Jamming

There are two major structural paradigms in robotics: soft machines, which are conformable, durable, and safe; and traditional rigid robots, which are fast, precise, and capable of applying high forces. Here, the paradigms are bridged by enabling soft machines to behave like traditional rigid robots on command. This task is accomplished via laminar jamming, a structural phenomenon in which a laminate of compliant strips becomes strongly coupled through friction when a pressure gradient is applied, causing dramatic changes in mechanical properties. Rigorous analytical and finite element models of laminar jamming are developed, and jamming structures are experimentally characterized to show that the models are highly accurate. Then jamming structures are integrated into soft machines to enable them to selectively exhibit the stiffness, damping, and kinematics of traditional rigid robots. The models allow jamming structures to efficiently meet arbitrary performance specifications, and the physical demonstrations illustrate how to construct systems that can behave like either soft machines or traditional rigid robots at will, such as continuum manipulators that can rapidly have joints appear and disappear. This study aims to foster a new generation of mechanically versatile machines and structures that cannot simply be classified as “soft” or “rigid. ”
A multimarker regression‐based test of linkage for affected sib‐pairs at two linked loci

We address the analytical problem of evaluating the evidence for linkage at a test locus while taking into account the effect of a known linked disease locus. The method we propose is a multimarker regression approach that models the identity‐by‐descent states for affected sib‐pairs at a series of linked markers in terms of the identity‐by‐descent state at the known disease locus. Our method allows analysis to be performed at a test location (or a series of locations) without the requirement that identity‐by‐descent be directly observed at either the test or the known conditioning locus. An advantage of our method is that identity‐by‐descent states from multiple markers are included simultaneously in the test of linkage, without recourse to multipoint imputation. The properties and power of the method are examined under various null and alternative hypotheses. The method is applied to data from a study of 1056 type 1 diabetes families to examine the evidence for an additional putative locus (IDDM15) on chromosome 6q, linked to IDDM1 in the HLA region on chromosome 6p. After accounting for the strong effect of IDDM1 and the differing rates of male and female recombination in the region, we find only marginal evidence for IDDM15 (P=0.03 to 0.002, using different methods) approximately 15 cM centromeric of the original localisation. Genet. Epidemiol. 2006. © 2005 Wiley‐Liss, Inc.
Exact Moment Matching for Efficient Importance Functions in SMC Methods

In this article we introduce a new proposal distribution to be used in conjunction with the sequential Monte Carlo (SMC) method of solving non-linear filtering problem. The proposal distribution incorporates all the information about the to be estimated current state form both the available state and observation processes. This makes it more effective than the state transition density which is more commonly used but ignores the recent observation. Because of its Gaussian nature it is also very easy to implement. We show further that the introduced proposal performs better than other similar importance functions which also incorporate both state and observations.
Genomic Scans of Zygotic Disequilibrium and Epistatic SNPs in HapMap Phase III Populations

Previous theory indicates that zygotic linkage disequilibrium (LD) is more informative than gametic or composite digenic LD in revealing natural population history. Further, the difference between the composite digenic and maximum zygotic LDs can be used to detect epistatic selection for fitness. Here we corroborate the theory by investigating genome-wide zygotic LDs in HapMap phase III human populations. Results show that non-Africa populations have much more significant zygotic LDs than do Africa populations. Africa populations (ASW, LWK, MKK, and YRI) possess more significant zygotic LDs for the double-homozygotes (DAABB) than any other significant zygotic LDs (DAABb, DAaBB, and DAaBb), while non-Africa populations generally have more significant DAaBb’s than any other significant zygotic LDs (DAABB, DAABb, and DAaBB). Average r-squares for any significant zygotic LDs increase generally in an order of populations YRI, MKK, CEU, CHB, LWK, JPT, CHD, TSI, GIH, ASW, and MEX. Average r-squares are greater for DAABB and DAaBb than for DAaBB and DAABb in each population. YRI and MKK can be separated from LWK and ASW in terms of the pattern of average r-squares. All population divergences in zygotic LDs can be interpreted with the model of Out of Africa for modern human origins. We have also detected 19735-95921 SNP pairs exhibiting strong signals of epistatic selection in different populations. Gene-gene interactions for some epistatic SNP pairs are evident from empirical findings, but many more epistatic SNP pairs await evidence. Common epistatic SNP pairs rarely exist among all populations, but exist in distinct regions (Africa, Europe, and East Asia), which helps to understand geographical genomic medicine.
Abnormal Contrast Responses in the Extrastriate Cortex of Blindsight Patients

When the human primary visual cortex (V1) is damaged, the dominant geniculo-striate pathway can no longer convey visual information to the occipital cortex. However, many patients with such damage retain some residual visual function that must rely on an alternative pathway directly to extrastriate occipital regions. This residual vision is most robust for moving stimuli, suggesting a role for motion area hMT+. However, residual vision also requires high-contrast stimuli, which is inconsistent with hMT+ sensitivity to contrast in which even low-contrast levels elicit near-maximal neural activation. We sought to investigate this discrepancy by measuring behavioral and neural responses to increasing contrast in patients with V1 damage. Eight patients underwent behavioral testing and functional magnetic resonance imaging to record contrast sensitivity in hMT+ of their damaged hemisphere, using Gabor stimuli with a spatial frequency of 1 cycle/°. The responses from hMT+ of the blind hemisphere were compared with hMT+ and V1 responses in the sighted hemisphere of patients and a group of age-matched controls. Unlike hMT+, neural responses in V1 tend to increase linearly with increasing contrast, likely reflecting a dominant parvocellular channel input. Across all patients, the responses in hMT+ of the blind hemisphere no longer showed early saturation but increased linearly with contrast. Given the spatiotemporal parameters used in this study and the known direct subcortical projections from the koniocellular layers of the lateral geniculate nucleus to hMT+, we propose that this altered contrast sensitivity in hMT+ could be consistent with input from the koniocellular pathway.
Movement generation using dynamical systems : a humanoid robot performing a drumming task

The online generation of trajectories in humanoid robots remains a difficult problem. In this contribution, we present a system that allows the superposition, and the switch between, discrete and rhythmic movements. Our approach uses nonlinear dynamical systems for generating trajectories online and in real time. Our goal is to make use of attractor properties of dynamical systems in order to provide robustness against small perturbations and to enable online modulation of the trajectories. The system is demonstrated on a humanoid robot performing a drumming task.
Criticality of spreading dynamics in hierarchical cluster networks without inhibition

An essential requirement for the representation of functional patterns in complex neural networks, such as the mammalian cerebral cortex, is the existence of stable network activations within a limited critical range. In this range, the activity of neural populations in the network persists between the extremes of quickly dying out, or activating the whole network. The nerve fibre network of the mammalian cerebral cortex possesses a modular organization extending across several levels of organization. Using a basic spreading model without inhibition, we investigated how functional activations of nodes propagate through such a hierarchically clustered network. The simulations demonstrated that persistent and scalable activations could be produced in clustered networks, but not in random networks of the same size. Moreover, the parameter range yielding critical activations was substantially larger in hierarchical cluster networks than in small-world networks of the same size. These findings indicate that a hierarchical cluster architecture may provide the structural basis for the stable and diverse functional patterns observed in cortical networks.