Miracle-dz/UniFAR-FTUs huggingface dataset:
Ero1L, a thiol oxidase, is required for Notch signaling through cysteine bridge formation of the Lin12-Notch repeats in Drosophila melanogaster

Notch-mediated cell–cell communication regulates numerous developmental processes and cell fate decisions. Through a mosaic genetic screen in Drosophila melanogaster, we identified a role in Notch signaling for a conserved thiol oxidase, endoplasmic reticulum (ER) oxidoreductin 1–like (Ero1L). Although Ero1L is reported to play a widespread role in protein folding in yeast, in flies Ero1L mutant clones show specific defects in lateral inhibition and inductive signaling, two characteristic processes regulated by Notch signaling. Ero1L mutant cells accumulate high levels of Notch protein in the ER and induce the unfolded protein response, suggesting that Notch is misfolded and fails to be exported from the ER. Biochemical assays demonstrate that Ero1L is required for formation of disulfide bonds of three Lin12-Notch repeats (LNRs) present in the extracellular domain of Notch. These LNRs are unique to the Notch family of proteins. Therefore, we have uncovered an unexpected requirement for Ero1L in the maturation of the Notch receptor.
Action retrieval based on generalized dynamic depth data matching

With the great popularity and extensive application of Kinect, the Internet is sharing more and more depth data. To effectively use plenty of depth data would make great sense. In this paper, we propose a generalized dynamic depth data matching framework for action retrieval. Firstly we focus on single depth image matching utilizing both depth and shape feature. The depth feature used in our method is straightforward but proved to be very effective and robust for distinguishing various human actions. Then, we adopt shape context, which is widely used in shape matching, in order to strengthen the robustness of our matching strategy. Finally, we utilize Dynamic Time Warping to measure temporal similarity between two depth video sequences. Experiments based on a dataset of 17 classes of actions from 10 different individuals demonstrate the effectiveness and robustness of our proposed matching strategy.
Data base for protein sequences

Proteins are linear polymers synthesized in living organisms from twenty different kinds of amino acids according to the message carried in the chromosomes. Typically, they have evolved by natural selection over hundreds of millions of years through many small changes in sequence. The present computerized data base includes 77,267 amino acid residues from 767 sequences. We believe that all of the protein structures occurring in living organisms can be combined into fewer than 1,000 groups containing proteins of similar sequence. Each group can be characterized by a few sequences that are known exactly and by a number of evolutionary parameters. The rest of the structures, occurring in organisms not examined, can be described with estimated precision in terms of the number of differences from a known sequence or from sequences inferred to have been present in ancestral forms. The following kinds of information are needed: sequences of proteins from each group, the phylogenetic tree of biological species, a list of protein groups and the gene duplications in each, a description of the quantitative parameters of the evolutionary processes affecting proteins, and methods of estimation of sequences in ancestral species and in living forms phylogenetically close to those investigated. The conceptual tools and the computer programs necessary for the prediction of all of the 1010 to 1011 protein sequences in living species are described. One can readily visualize the separate parts operating as an integrated interactive computerized data base that could predict sequences for specified organisms with an estimated precision based on the collection of known sequences.
Mobilising the sport-based community: the construction of social work through rationales of advanced liberalism

Abstract This article focuses on the forms and conditions of welfare provision and social work in the transforming welfare state. The Sport Programme (SP) is a municipal sport-based social intervention launched in response to segregation causing tensions in society, crime, and social exclusion. The programme constitutes a case for examination. Sport activities for youths at risk in the SP are assumed to foster a sense of community and social cohesion. The article investigates how ‘the community’ is constructed as a space for social inclusion. A variety of statements articulated by policy makers and municipal administrators are analysed from a governmentality perspective. The analysis suggests that ‘community’ is formed by distinguishing the SP from public welfare and social work and by mobilising civil society in the intervention. Public welfare is problematised as bureaucratic and insufficient, whereas civil society is associated with the potency of voluntarism, authentic leadership, and personal relations based on common identity and shared experiences. By involving a social entrepreneur, the SP mobilises and activates civil society as a means of responding to social problems, forming ‘community’ as a space with a ‘human touch’. Such a partnership re-distributes responsibility for responding to social problems to a variety of agencies. It is discussed how the SP enables role model identification as the primary governing rationale and how this incorporates elements of de-professionalisation in social work. Consideration is given to challenges for social work and to how activating ‘community’ illustrates tendencies and transformations in contemporary Swedish welfare provision.
Efficient Belief Propagation for Image Segmentation Based on an Adaptive MRF Model

Belief propagation (BP) over Pairwise Markov random field (MRF) has been successfully applied to some computer vision problems. However, Conventional Pairwise MRF model is still insufficient to capture natural image statistical characteristics. To solve this problem, we proposed an adaptive MRF model for image segmentation problem. The proposed model adaptively model the local features according to local region information of the image and the local feature parameters will be efficiently estimated. Then we develop an efficient BP algorithm for image segmentation. The convergence region messages are passed among the local regions over the proposed model. Experimental results show that the proposed BP algorithm generates more accurate segmentation results, and also can efficiently restrain effect of image noise and texture mutation for segmentation.
Recruitment of Occipital Cortex during Sensory Substitution Training Linked to Subjective Experience of Seeing in People with Blindness

Over three months of intensive training with a tactile stimulation device, 18 blind and 10 blindfolded seeing subjects improved in their ability to identify geometric figures by touch. Seven blind subjects spontaneously reported ‘visual qualia’, the subjective sensation of seeing flashes of light congruent with tactile stimuli. In the latter subjects tactile stimulation evoked activation of occipital cortex on electroencephalography (EEG). None of the blind subjects who failed to experience visual qualia, despite identical tactile stimulation training, showed EEG recruitment of occipital cortex. None of the blindfolded seeing humans reported visual-like sensations during tactile stimulation. These findings support the notion that the conscious experience of seeing is linked to the activation of occipital brain regions in people with blindness. Moreover, the findings indicate that provision of visual information can be achieved through non-visual sensory modalities which may help to minimize the disability of blind individuals, affording them some degree of object recognition and navigation aid.
On The De…ning Number For Vertex Colorings Of A Family Of Graphs

In a given graph G = (V;E) a set S of vertices with an assignment of colors to them is called a de…ning set for vertex colorings of G if there exists a unique extension of S to a c � �(G) coloring of the vertices of G. A de…ning set with minimum cardinality is called a minimum de…ning set, and its cardinality is the de…ning number. In this paper, we study the de…ning number for vertex colorings of graphs arising from applying Mycielski’s construction to Ladder graphs.
Automatic Clustering via Outward Statistical Testing on Density Metrics

Clustering is one of the research hotspots in the field of data mining and has extensive applications in practice. Recently, Rodriguez and Laio [1] published a clustering algorithm on Science that identifies the clustering centers in an intuitive way and clusters objects efficiently and effectively. However, the algorithm is sensitive to a preassigned parameter and suffers from the identification of the “ideal” number of clusters. To overcome these shortages, this paper proposes a new clustering algorithm that can detect the clustering centers automatically via statistical testing. Specifically, the proposed algorithm first defines a new metric to measure the density of an object that is more robust to the preassigned parameter, further generates a metric to evaluate the centrality of each object. Afterwards, it identifies the objects with extremely large centrality metrics as the clustering centers via an outward statistical testing method. Finally, it groups the remaining objects into clusters containing their nearest neighbors with higher density. Extensive experiments are conducted over different kinds of clustering data sets to evaluate the performance of the proposed algorithm and compare with the algorithm in Science. The results show the effectiveness and robustness of the proposed algorithm.
A novel multirate adaptive FIR filtering algorithm and structure

A new class of FIR filtering algorithms and VLSI architectures based on the multirate approach were recently proposed. They not only reduce the computational complexity in FIR filtering, but also retain attractive implementation-related properties such as regularity and multiply-and-accumulate structure. In addition, the multirate feature can be applied to low-power/high-speed VLSI implementation. These properties make the multirate FIR filtering very attractive in many DSP and communication applications. In this paper, we propose a novel adaptive filter based on this new class of multirate FIR filtering structures. The proposed adaptive filter inherits the advantages of the multirate structures such as low computational complexity and low-power/high-speed applications. Moreover, the multirate feature helps to improve the convergence property of the adaptive filters.
Thermodynamics of anomaly-driven cosmology

The Friedmann equations of general relativity can be derived from the first law of thermodynamics when the entropy of the apparent horizon of a spatially isotropic universe is given by the Bekenstein–Hawking entropy. We point out that if the entropy of the apparent horizon receives a logarithmic correction, the first law of thermodynamics leads to a modified Friedmann equation which corresponds precisely to the time–time component of the semi-classical Einstein field equations sourced by the trace anomaly of the super-Yang–Mills theory. This correspondence allows for a thermodynamic description of the dynamics of the Randall–Sundrum braneworld scenario.
State Observer Design for a Class of Takagi-Sugeno Discrete-Time Descriptor Systems

This paper deals with the design of explicit observers for a class of discrete-time dynamical implicit systems described by Takagi-Sugeno (TS) model in the two cases where the premise variable are measurable and the premise variables are unmeasurable. The idea of the proposed approach is based on the singular value decomposition. The convergence of the state estimation error is studied using the Lyapunov theory and the stability conditions are given in terms of Linear Matrix Inequalities (LMIs). Finally, an example is given to illustrate the proposed approach.
A 26-Gb/s 1.80-pJ/b CMOS-driven transmitter for 850-nm common-cathode VCSELs

A 26-Gb/s, 1.80-pJ/b transmitter composed of a 65-nm bulk-CMOS driver and an 850nm VCSEL array in a QFN package is demonstrated. The VCSEL's anode is directly modulated without a negative supply voltage at 0.82-dBm OMA.
Bucket Wheel Reclaimer modeling as a robotic arm

Bucket Wheel Reclaimer (BWR) has been widely used for stacking/reclaiming bulk materials (i.e. iron ore) onto/from stockpiles in mining industry. Generally speaking, current BWRs are manually operated, remotely operated, or automated to simply follow predefined trajectory patterns. BWRs are very large in size, heavy in weight, expensive in price and slow in motion. It is commonly agreed in the industry that the current stacking/reclaiming efficiency can be largely improved leading to huge amount of savings in dollar terms. However, as BWRs are always heavily engaged in production and can not be easily spared for the required study and development for efficiency improvement, a close to real BWR model would be highly desirable and greatly beneficial to carry out necessary studies and preparations. This paper presents the work carried out for the modelling of a typical BWR using Matlab/Simulink including not only the kinematics and dynamics of a typical BWR but also the friction forces from its joints, limitation of its encoder resolutions, and random disturbances to cover the un-modelled dynamics. Besides, a hybrid controller, which consists of a fuzzy logic controller on top of a model based portion, is developed to control the motion of the BWR. Simulations are conducted, and the results demonstrate satisfactory performance and prove the developed model can be used for further studies to improve relevant production efficiencies.
A Bi-modal Handwritten Text Corpus

A description is given of compositions comprising a chlorine-containing polymer and at least one compound of the general formula Iwhere Y is S or O, andR1 and R2 independently of one another are unsubstituted or C1-C4-alkyl-, C1-C4-alkoxy- and/or hydroxyl-substituted phenyl, phenyl-C1-C4-alkyl which is unsubstituted or substituted on the phenyl ring by C1-C4-alkyl, C1-C5-alkoxy and/or hydroxyl, or are C3-C6-alkenyl, C5-C8-cycloalkyl, or C2-C10-alkyl interrupted by at least 1 oxygen atom, or, if R1 and R2 are different from one another, one of the two radicals can be C2-C12-alkyl and the other can be C2-C12-alkyl or one of the above radicals, or, if R1 and R2 are identical, both can also be C5-C12-alkyl, and, if Y is sulfur, R1 and R2 cannot be octyl.
Fast Non Rigid Matching by Gradient Descent: Study and Improvements of the "Demons" Algorithm

Most iconic methods for rigid matching consist in finding and minimizing a registration criterion specifically chosen to solve a given problem. For non-rigid matching, attention has rather focussed on the type of smoothing or physical model of deformation to be used. In this report, we propose to place the non-rigid matching problem into a minimization framework. We have developped our theoretical idea in the case of the least squares criterion, corresponding to the assumption that the intensities of points do not change over time, and we have implemented a first order gradient descent which, along with a multiresolution approach, minimizes this criterion- . We also prove that the «demons» algorithm, thought of until now as an as hoc matching technique, can be seen as an approximation of a second order gradient descent on this criterion. Analysis of the mechanisms of this gradient descent incites us to introduce two different weightings into the filters used to smooth the solution, which we called an a priori weighting improves the solution found for the minimization problem, which is shown by comparing results in a distance-roughness space, while the a posteriori weighting helps tackle the appearance or disappearance of matter and occlusions, both sensitive issues for non-rigid iconic methods.
Patterns of Cognitive Dysfunction in Progressive MS

Background: Progressive MS is associated with a high frequency of cognitive impairment. However, it is not clear to what extent this reflects global dysfunction, or independent deficits in specific functions. Objective: To characterise patterns of cognitive impairment in progressive MS on a multi-dimensional cognitive assessment tool well established in neurodegenerative diseases. Methods: Patients with secondary (SPMS; n = 60) and primary progressive MS (PPMS; n = 28) were assessed using the Addenbrooke’s Cognitive Examination-Revised (ACE-R) multi-dimensional cognitive assessment scale. Independent dimensions of impairment and their relative contribution to the overall burden of cognitive dysfunction were then determined by factor analysis. Results: Two independent dimensions of impairment were seen: frontal-executive (attention, verbal fluency, recall) on one hand, and language and visuospatial functions on the other. These accounted for 55% and 45% respectively of the variance not explained by a global influence (14.2% and 11.6% respectively of total variance). Isolated language and visuospatial dysfunction was seen in both groups, whereas isolated impairment in frontal-executive functions was underrepresented in SPMS (p = 0.001) and not seen in PPMS patients (p = 0.040). Conclusions: In addition to a prominent global influence on cognitive performance, patients with progressive MS commonly exhibit language and visuospatial deficits. Evaluation of these abilities should therefore be included in clinical assessment of cognition in progressive MS.
A scan-line-based data compression approach for point clouds: Lossless and effective

Light Detection and Ranging (LIDAR) is an attractive and effective technique to obtain spatial information of terrain features. While the vast amount of points presents problems for data exchange, distribution, and storage. Thus, a data compression approach for point clouds is proposed in this paper to solve these problems. In our proposed approach, JPEG2000 standard is exploited to compress the color information. To compress the 3D coordinate, a distance based predictor is employed to predict the forthcoming point using the information of previous points. And a compression and decompression algorithm based on arithmetic coding is proposed to compress or decompress the predictor. The experiment results reveal that our approach is effective and efficiency.
Application of Artificial Neural Networks for electrical losses estimation in three-phase transformer

This paper proposes an application of Artificial Neural Networks (ANN) for estimation of electrical losses in the three-phase distribution transformer during construction stages. The Artificial Neural Networks (ANN) is employed as an estimator in order to identify the electrical loss of the distribution transformer during design process. The related parameters such as input current, core loss, copper loss, resistance of transformer windings, and ambient temperature were collected from the measuring of 100 transformers. Some of these data are used to train ANN and test. The trained ANN is then tested by 20 data sets from the collected data. The simulations which are compared to the measured values of the test sets provide satisfactory estimation of electrical loss with an acceptable error.
RepliCHI: the workshop

The replication of, or perhaps the replicability of, research is often considered to be a cornerstone of scientific progress. Yet unlike many other disciplines, like medicine, physics, or mathematics, we have almost no drive and barely any reason to consider replicating the work of other HCI researchers. Our community is driven to publish novel results in novel spaces using novel designs, and to keep up with evolving technology. The aim of this workshop is to trial a new venue that embodies the plans made in previous SIGs and panels, such that we can begin to give people an outlet to publish experiences of attempting to replicate HCI research, and challenge or confirm its findings.
Characterization of auditory synaptic inputs to gerbil perirhinal cortex

The representation of acoustic cues involves regions downstream from the auditory cortex (ACx). One such area, the perirhinal cortex (PRh), processes sensory signals containing mnemonic information. Therefore, our goal was to assess whether PRh receives auditory inputs from the auditory thalamus (MG) and ACx in an auditory thalamocortical brain slice preparation and characterize these afferent-driven synaptic properties. When the MG or ACx was electrically stimulated, synaptic responses were recorded from the PRh neurons. Blockade of type A gamma-aminobutyric acid (GABA-A) receptors dramatically increased the amplitude of evoked excitatory potentials. Stimulation of the MG or ACx also evoked calcium transients in most PRh neurons. Separately, when fluoro ruby was injected in ACx in vivo, anterogradely labeled axons and terminals were observed in the PRh. Collectively, these data show that the PRh integrates auditory information from the MG and ACx and that auditory driven inhibition dominates the postsynaptic responses in a non-sensory cortical region downstream from the ACx.
Social Innovation for Poverty Reduction and Sustainable Development: Some Governance and Policy Perspectives

The systematic deployment of social innovation has been largely overlooked by policy makers concerned to reduce poverty and promote sustainable development. According to research funded by the European Commission, the policies of international organizations rarely refer to the potential of social innovation, neither generally nor in the context of achieving the 2030 Sustainable Development Agenda. However, a huge number of initiatives around the global are successfully using social innovation approaches, but most do so without using this term. They thereby do not fully understand the potential and strength that a more systematic deployment of social innovation theories, methods and practices could bring. This paper aims to at least partially redress this balance by reporting on evidence derived from the on-going empirical and theoretical research undertaken by the SI-DRIVE research project in relation to policy reduction and sustainable development, in particular by focusing on the governance and policy implications.
Metadata Distribution and Consistency Techniques for Large-Scale Cluster File Systems

Most supercomputers nowadays are based on large clusters, which call for sophisticated, scalable, and decentralized metadata processing techniques. From the perspective of maximizing metadata throughput, an ideal metadata distribution policy should automatically balance the namespace locality and even distribution without manual intervention. None of existing metadata distribution schemes is designed to make such a balance. We propose a novel metadata distribution policy, Dynamic Dir-Grain (DDG), which seeks to balance the requirements of keeping namespace locality and even distribution of the load by dynamic partitioning of the namespace into size-adjustable hierarchical units. Extensive simulation and measurement results show that DDG policies with a proper granularity significantly outperform traditional techniques such as the Random policy and the Subtree policy by 40 percent to 62 times. In addition, from the perspective of file system reliability, metadata consistency is an equally important issue. However, it is complicated by dynamic metadata distribution. Metadata consistency of cross-metadata server operations cannot be solved by traditional metadata journaling on each server. While traditional two-phase commit (2PC) algorithm can be used, it is too costly for distributed file systems. We proposed a consistent metadata processing protocol, S2PC-MP, which combines the two-phase commit algorithm with metadata processing to reduce overheads. Our measurement results show that S2PC-MP not only ensures fast recovery, but also greatly reduces fail-free execution overheads.