Miracle-dz/UniFAR-FTUs huggingface dataset:
Probe measurements of electron-energy distributions in plasmas: what can we measure and how can we achieve reliable results?

An electric-probe method for the diagnostics of electron-distribution functions (EDFs) in plasmas is reviewed with emphasis on receiving reliable results while taking into account appropriate probe construction, various measurement errors and the limitations of theories. The starting point is a discussion of the Druyvesteyn method for measurements in weakly ionized, low-pressure and isotropic plasma. This section includes a description of correct probe design, the influence of circuit resistance, ion current and plasma oscillations and probe-surface effects on measurements. At present, the Druyvesteyn method is the most developed, consistent and routine way to measure the EDF. The following section of the review describes an extension of the classical EDF measurements into higher pressures, magnetic fields and anisotropic plasmas. To date, these methods have been used by a very limited number of researchers. Therefore, their verification has not yet been fully completed, and their reliable implementation still requires additional research. Nevertheless, the described methods are complemented by appropriate examples of measurements demonstrating their potential value.
2 Constituent Context Model ( CCM )

Bracketing induction is the unsupervised learning of hierarchical constituents without labeling their syntactic categories such as verb phrase (VP) from natural raw sentences. Constituent Context Model (CCM) is an effective generative model for the bracketing induction, but the CCM computes probability of a constituent in a very straightforward way no matter how long this constituent is. Such method causes severe data sparse problem because long constituents are more unlikely to appear in test set. To overcome the data sparse problem, this paper proposes to define a non-parametric Bayesian prior distribution, namely the Pitman-Yor Process (PYP) prior, over constituents for constituent smoothing. The PYP prior functions as a back-off smoothing method through using a hierarchical smoothing scheme (HSS). Various kinds of HSS are proposed in this paper. We find that two kinds of HSS are effective, attaining or significantly improving the state-ofthe-art performance of the bracketing induction evaluated on standard treebanks of various languages, while another kind of HSS, which is commonly used for smoothing sequences by ngram Markovization, is not effective for improving the performance of the CCM.
Synchronization between motion and music using motion graph

Synchronization between motion and music is an interesting area in computer graphics however this part of work is difficult for generating automatically therefore it is usually done by an animators. Some research are synthesis or edit music to synchronize with motion data and some research are synthesis motion instead. In this paper, we present a scheme for synchronizing music with synthesized motion by using dynamic time warping to change the timing of both. Feature points of music file and motion data are extracted. Then, we pair and synchronize. We also use motion graph, which is a well known method for novice user, to generate long motions and fully automatic process of motion synthesis.
Administration of Silica Prevents Diabetes in BB-Rats

Administration of silica to young BB-rats almost completely prevented the development of spontaneous diabetes. Only 1 of 31 silica-treated rats developed hyperglycemia, whereas 9 of 31 in the untreated group did so. Since silica is highly specific in its action against macrophages, our observations indicate an important role of these cells in the pathogenesis of the disease.
Sar Images Speckle Reduction for Classiication Using Wavelets on Multimatlab

This work presents the results of the implementation on Multi-MATLAB of a speckle reduction algorithm for SAR images using the wavelet transform. Speckle reduction is important for automatic target recognition and classiication algorithms. The method is based on the application of a logarithmic transformation to the image to convert the speckle noise into additive noise. Noise is reduced by applying soft thresholding on the wavelet coeecients. Daubechies wavelets are used to obtain the coeecients. The quality of the procedure is measured in terms of the standard deviation to mean ratio. The speedup obtained by using MultiMATLAB is presented. MultiMATLAB has proven to be a useful problem solving environment for parallel processing.
Shape-Based Online Multitarget Tracking and Detection for Targets Causing Multiple Measurements: Variational Bayesian Clustering and Lossless Data Association

This paper proposes a novel online two-level multitarget tracking and detection (MTTD) algorithm. The algorithm focuses on multitarget detection and tracking for the case of multiple measurements per target and for an unknown and varying number of targets. Information is continuously exchanged in both directions between the two levels. Using the high level target position and shape information, the low level clusters the measurements. Furthermore, the low level features automatic relevance detection (ARD), as it automatically determines the optimal number of clusters from the measurements taking into account the expected target shapes. The high level's data association allows for a varying number of targets. A joint probabilistic data association algorithm looks for associations between clusters of measurements and targets. These associations are used to update the target trackers and the target shapes with the individual measurements. No information is lost in the two-level approach since the measurement information is not summarized into features. The target trackers are based on an underlying motion model, while the high level is supplemented with a filter estimating the number of targets. The algorithm is verified using both simulations and experiments using two sensor modalities, video and laser scanner, for detection and tracking of people and ants.
A review of snow and ice albedo and the development of a new physically based broadband albedo parameterization

[1] We present a computationally simple, theoretically based parameterization for the broadband albedo of snow and ice that can accurately reproduce the theoretical broadband albedo under a wide range of snow, ice, and atmospheric conditions. Depending on its application, this parameterization requires between one and five input parameters. These parameters are specific surface area of snow/ice, concentration of light-absorbing carbon, solar zenith angle, cloud optical thickness, and snow depth. The parameterization is derived by fitting equations to albedo estimates generated with a 16-stream plane-parallel, discrete ordinates radiative transfer model of snow and ice that is coupled to a similar model of the atmosphere. Output from this model is also used to establish the physical determinants of the spectral albedo of snow and ice and evaluate the characteristics of spectral irradiance over snow-covered surfaces. Broadband albedo estimates determined from the radiative transfer model are compared with results from a selection of previously proposed parameterizations. Compared to these parameterizations, the newly proposed parameterization produces accurate results for a much wider range of snow, ice, and atmospheric conditions.
Taming Large Events: Optimal Portfolio Theory for Strongly Fluctuating Assets

We propose a method of optimization of asset allocation in the case where the stock price variations are supposed to have "fat" tails represented by power laws. Generalizing over previous works using stable Levy distributions, we distinguish three distinct components of risk described by three different parts of the distributions of price variations: unexpected gains (to be kept), harmless noise inherent to financial activity, and unpleasant losses, which is the only component one would like to minimize. The independent treatment of the tails of distributions for positive and negative variations and the generalization to large events of the notion of covariance of two random variables provide explicit formulae for the optimal portfolio. The use of the probability of loss (or equivalently the Value-at-Risk), as the key quantity to study and minimize, provides a simple solution to the problem of optimization of asset allocations in the general case where the characteristic exponents are different for each asset.
Expressive Text-to-Speech: A user-centred approach to sound design in voice-enabled mobile applications

Recent improvements in speech technology are expected to change the way we communicate, facilitating access to web services and applications (voice portals, multimodal email clients or games) in various mobile situations. In order to be attractive for users, however, speech output needs to be more expressive to increase naturalness, meaningfulness and ease of listening. Conventional Text-to-Speech (TTS) systems do not express non-verbal elements occurring in written text. This paper describes our user-centred approach to design non-speech sounds to express several non-linguistic elements in an email reader. Furthermore, it reports on a user study in which the effectiveness and satisfaction regarding the sound-enhanced emails was investigated. The results indicate that in most cases the addition of tailored non-speech sounds increases the user’s ability to perceive the structure and meaning of the audio content and decreases the subjective workload, without leading to a higher user annoyance. As a consequence, non-speech sound should be integrated into market-ready TTS systems in a systematic way.
Combining Gabor Local Texture Pattern and Wavelet Global Features for Fingerprint Matching

Fingerprint verification is one of the most well-known and publicized biometrics. Fingerprints have been matched over the decades using minutiae features. However, minutiae extraction from poor-quality or incomplete images is not reliable and often gives erroneous results in matching. Further, due to variations caused by the users, minutiae alignment and matching may not be exact. Fingerprints exhibit oriented texture-like patterns. Gabor filters can optimally capture local frequency and orientation information even from poor-quality or incomplete images. Like-wise, global texture information can be efficiently extracted using wavelet transform. This paper proposes a hybrid fingerprint verification system based on local texture pattern obtained using Gabor filtering and wavelet global features obtained by multiresolution analysis of a fingerprint. Experimental results show that the proposed hybrid system is efficient and suitable for real-time authentication applications with a small size database.
A Double-Edged Fork: Motivating and De-Motivating Pro-Environmental Food Behavior

Climate change is a consequence of human behavior, but people tend to construe climate change as an unfathomable, abstract phenomenon that is irrelevant to their individual actions. In the present studies, the high-impact, underrepresented behavior of dietary choices was communicated with numerical information that varied in its frame of reference. We present initial evidence that presenting the footprint of human behavior at a global level, compared to at an individual level, demoralizes individual choices and weakens behavioral intentions to change diet. In addition, we find that participants reported reductions in their meat consumption when an implementation intention intervention was combined with our ‘frame of reference’ intervention. Presenting nation-wide consequences of human behavior is a double-edged sword: Framing in a large scale might reveal the relationship between collective actions and environmental issues, but it hinders the belief that individual actions make a difference.
A Catalog of 203 Galaxy Clusters Serendipitously Detected in the ROSAT PSPC Pointed Observations

We present a catalog of 203 clusters of galaxies serendipitously detected in 647 ROSAT PSPC high Galactic latitude pointings covering 158 deg2. This is one of the largest X-ray-selected cluster samples, comparable in size only to the ROSAT All-Sky Survey sample of nearby clusters (Ebeling et al. 1997). We detect clusters in the inner 17. ′5 of the ROSAT PSPC field of view using the spatial extent of their X-ray emission. Fluxes of detected clusters range from 1.6 × 10-14 to 8 × 10-12 ergs s-1 cm-2 in the 0.5-2 keV energy band. X-ray luminosities range from 1042 ergs s-1, corresponding to very poor groups, to ~5 × 1044 ergs s-1, corresponding to rich clusters. The cluster redshifts range from z = 0.015 to z > 0.5. The catalog lists X-ray fluxes, core radii, and spectroscopic redshifts for 73 clusters and photometric redshifts for the remainder. Our detection method, optimized for finding extended sources in the presence of source confusion, is described in detail. Selection effects necessary for a statistical analysis of the cluster sample are comprehensively studied by Monte Carlo simulations. We have optically confirmed 203 of 223 X-ray sources as clusters of galaxies. Of the remaining 20 sources, 19 are likely false detections arising from blends of unresolved point X-ray sources. Optical identifications of the remaining object are hampered by a bright nearby star. Above a flux of 2 × 10-13 ergs s-1 cm-2, 98% of extended X-ray sources are optically confirmed clusters. The number of false detections and their flux distribution are in perfect agreement with simulations. The log N-log S relation for clusters derived from our catalog shows excellent agreement with counts of bright clusters derived from the Einstein Extended Medium Sensitivity Survey and ROSAT All-Sky Survey. At fainter fluxes, our log N-log S relation agrees with the smaller area WARPS survey. Our cluster counts appear to be systematically higher than those from a 50 deg2 survey by Rosati et al. In particular, at a flux of 2 × 10-13 ergs s-1 cm-2, we find a surface density of clusters of 0.57 ± 0.07 deg-2, which is a factor of 1.3 more than was found by Rosati et al. This difference is marginally significant at the ~2 σ level. The large area of our survey makes it possible to study the evolution of the X-ray luminosity function in the high luminosity range inaccessible with other, smaller area ROSAT surveys.
The MUSiC performance measurement method

Abstract This paper reports a method for measuring usability in terms of task performance-achievement of frequent and critical task goals by particular users in a context simulating the work environment. The terms usability and quality in use are defined in international standards as the effectiveness, efficiency and satisfaction with which goals are achieved in a specific context of use. The performance measurement method gives measures which, in combination with measures of satisfaction, operationalize these definitions. User performance is specified and assessed by measures including task effectiveness (the quantity and quality of task performance) and User efficiency (effectiveness divided by tasktime). Measures are obtained with users performing tasks in a context of evaluation which matches the intended context of use. This can also reveal usability problems which may not become evident if the evaluator interacts with the user. The method is supported by tools which make it practical in commercial t.
Streaming self-scaling histograms with stability and optimality guarantees

Histograms provide discrete efficient representation of continuous data and can be constrained to fit various purposes. We provide the first offline sub-quadratic algorithm which applies to any sub-additive histogram cost functions. Sub-additive cost functions includes in particular all the `p norms. This algorithm outputs a histogram with at most 2m buckets and cost at most (1 + ε) the optimal cost achievable with m buckets. Then we show how to adapt our algorithm in a streaming context but restricted for a certain cost function that bounds the maximum error for one-dimensional aggregate range queries. An advantage of our approach is that is only uses O(m) space and O(log(m)) per item processing time, m being the number of buckets, independent from N the number of values processed. We also prove that the histogram outputs of our streaming algorithm are very stable. While reading the streams, the output histogram undergoes at most O(m log(N)) boundary displacements. To our knowledge, this concept of stability has never been studied for streaming histograms. Stability implies that extra properties associated to the buckets don’t have to be reconstructed too often, strengthening their quality and accuracy. Our empirical evaluations confirm the good behavior of our streaming algorithm in practice.
Embedded system for real-time person detecting in infrared images/videos using super-resolution and Haar-like feature techniques

This paper describes a real time person detection system using near infrared images/videos. This novel system integrates person detection and super resolution algorithms performing person recognition. Additionally, we use a detector Haar-like features and for increasing resolution we use classical algorithms like Nearest neighbor interpolation, Bilineal interpolation and bicubic interpolation. The detector is trained using Adataboost and cascade classifiers and the implementation is performed in the embeded system Raspberry pi2 with the Noir Pi Camera. The implemented embedded system runs at about 20 frames/second.
Toward a Power-Efficient Backbone Network: The State of Research

The year-by-year increase in electricity consumption significantly affects all nations today from environmental, social, and economic perspectives. Various studies show that, among the contributors of this increase, power consumption of the equipment forming the Internet infrastructure is significant. Consequently, considerable attention is given to finding solutions to reduce power consumption of the systems that run the Internet. Current literature suggests that the optical backbone network is responsible for the majority of the consumed power, particularly at high traffic loads. This highlights the importance of formulating solutions to reduce power consumption in backbone networks. In this paper, we provide a comprehensive survey of the most relevant research efforts on minimizing power consumption of backbone networks. We categorize our review into four broad approaches: network redesign, traffic engineering, power-aware networking, and load-adaptive operation. We highlight the findings of relevant studies and provide an analysis of their limitations. We also evaluate the technological differences of similar work and identify a number of future directions. Finally, we demonstrate that the adoption of these technologies is gathering pace among prominent research bodies, device manufacturers, and network operators.
Effect of Handrail Height on Sit-To-Stand Movement

Background Care-needing older adults and disabled individuals often require handrails for assistance of movements, such as sit-to-stand movements. Handrails must be set at the appropriate position; however, the effects of handrail height on joint movement and center-of-gravity movements during sit-to-stand movement remain unclear. In the present study, we sought to clarify the effects of handrail height on joint movement, center-of-gravity, and floor reaction force during sit-to-stand movement. Methods Subjects included 16 healthy young adults and 25 older adults who require long-term care. Kinetic and kinematic measurements during sit-to-stand movement of young adults were conducted using a 3-D motion analyzer and a force plate. Trunk forward tilt angle during sit-to-stand movement of older adults was measured using a still image from a video recording. Results Using low handrails, sit-to-stand movement resulted in an increased hip flexion angle, ankle dorsiflexion angle, and trunk forward tilt angle and a greater forward center-of-gravity shift than when not using handrails in young adults during seat-off. In contrast, using high handrails resulted in a smaller hip flexion angle and trunk forward tilt angle in young adults. The backward force on the floor was decreased in the low handrail condition, and was increased in the high handrail condition rather than that of sit-to-stand movement without handrails in young adults. The effect of handrail height on trunk forward tilt angle was the same in both healthy young adults and care-needing older adults during seat-off. Conclusion Because handrail height affects joint movement and shift in the center-of-gravity during sit-to-stand movement, handrail position should be selected to match the status of older adults with functional impairment.
Intrathecal Delivery of Hepatocyte Growth Factor From Amyotrophic Lateral Sclerosis Onset Suppresses Disease Progression in Rat Amyotrophic Lateral Sclerosis Model

Hepatocyte growth factor (HGF) is one of the most potent survival-promoting factors for motor neurons. We showed that introduction of the HGF gene into neurons of G93A transgenic mice attenuates motor neuron degeneration and increases the lifespan of these mice. Currently, treatment regimens using recombinant protein are closer to clinical application than gene therapy. To examine its protective effect on motor neurons and therapeutic potential we administered human recombinant HGF (hrHGF) by continuous intrathecal delivery to G93A transgenic rats at doses of 40 or 200 μg and 200 μg at 100 days of age (the age at which pathologic changes of the spinal cord appear, but animals show no clinical weakness) and at 115 days (onset of paralysis), respectively, for 4 weeks each. Intrathecal administration of hrHGF attenuates motor neuron degeneration and prolonged the duration of the disease by 63%, even with administration from the onset of paralysis. Our results indicated the therapeutic efficacy of continuous intrathecal administration of hrHGF in transgenic rats and should lead to the consideration for further clinical trials in amyotrophic lateral sclerosis using continuous intrathecal administration of hrHGF.
Endothelial cell–oligodendrocyte interactions in small vessel disease and aging

Cerebral small vessel disease (SVD) is a prevalent, neurological disease that significantly increases the risk of stroke and dementia. The main pathological changes are vascular, in the form of lipohyalinosis and arteriosclerosis, and in the white matter (WM), in the form of WM lesions. Despite this, it is unclear to what extent the key cell types involved–the endothelial cells (ECs) of the vasculature and the oligodendrocytes of the WM–interact. Here, we describe the work that has so far been carried out suggesting an interaction between ECs and oligodendrocytes in SVD. As these interactions have been studied in more detail in other disease states and in development, we explore these systems and discuss the role these mechanisms may play in SVD.
Redundancy in Linked Data Partitioning for Efficient Query Evaluation

The problem of efficient querying large amount of linked data using Map-Reduce is investigated in this paper. The proposed approach is based on the following assumptions: a) Data graphs are arbitrarily partitioned in the distributed file system is such a way that replication of data triples between the data segments is allowed. b) Data triples are replicated is such a way that answers to a special form of queries, called subject-object star queries, can be obtained from a single data segment. c) Each query posed by the user, can be transformed into a set of subject-object star sub queries. We propose a one and a half phase, scalable, Map-Reduce algorithm that efficiently computes the answers of the initial query by computing and appropriately combining the sub query answers. We prove that, under certain conditions, query can be answered in a single map-reduce phase.