Miracle-dz/UniFAR-FTUs huggingface dataset:
Dynamic and Public Auditing with Fair Arbitration for Cloud Data

Cloud users no longer physically possess their data, so how to ensure the integrity of their outsourced data becomes a challenging task. Recently proposed schemes such as “provable data possession” and “proofs of retrievability” are designed to address this problem, but they are designed to audit static archive data and therefore lack of data dynamics support. Moreover, threat models in these schemes usually assume an honest data owner and focus on detecting a dishonest cloud service provider despite the fact that clients may also misbehave. This paper proposes a public auditing scheme with data dynamics support and fairness arbitration of potential disputes. In particular, we design an index switcher to eliminate the limitation of index usage in tag computation in current schemes and achieve efficient handling of data dynamics. To address the fairness problem so that no party can misbehave without being detected, we further extend existing threat models and adopt signature exchange idea to design fair arbitration protocols, so that any possible dispute can be fairly settled. The security analysis shows our scheme is provably secure, and the performance evaluation demonstrates the overhead of data dynamics and dispute arbitration are reasonable.
N-terminal Huntingtin (Htt) phosphorylation is a molecular switch regulating Htt aggregation, helical conformation, internalization, and nuclear targeting

Huntington's disease is a fatal neurodegenerative disorder resulting from a CAG repeat expansion in the first exon of the gene encoding the Huntingtin protein (Htt). Phosphorylation of this protein region (Httex1) has been shown to play important roles in regulating the structure, toxicity, and cellular properties of N-terminal fragments and full-length Htt. However, increasing evidence suggests that phosphomimetic substitutions in Htt result in inconsistent findings and do not reproduce all aspects of true phosphorylation. Here, we investigated the effects of bona fide phosphorylation at Ser-13 or Ser-16 on the structure, aggregation, membrane binding, and subcellular properties of the Httex1-Q18A variant and compared these effects with those of phosphomimetic substitutions. We show that phosphorylation at either Ser-13 and/or Ser-16 or phosphomimetic substitutions at both these residues inhibit the aggregation of mutant Httex1, but that only phosphorylation strongly disrupts the amphipathic α-helix of the N terminus and prompts the internalization and nuclear targeting of preformed Httex1 aggregates. In synthetic peptides, phosphorylation at Ser-13, Ser-16, or both residues strongly disrupted the amphipathic α-helix of the N-terminal 17 residues (Nt17) of Httex1 and Nt17 membrane binding. Experiments with peptides bearing different combinations of phosphorylation sites within Nt17 revealed a phosphorylation-dependent switch that regulates the Httex1 structure, involving cross-talk between phosphorylation at Thr-3 and Ser-13 or Ser-16. Our results provide crucial insights into the role of phosphorylation in regulating Httex1 structure and function, and underscore the critical importance of identifying the enzymes responsible for regulating Htt phosphorylation, and their potential as therapeutic targets for managing Huntington's disease.
A Dual-Polarized Magneto-Electric Dipole Array for 60 GHz Applications

A dual linearly polarized antenna array with end-fire radiation is proposed for 60 GHz applications by combining two kinds of magneto-electric (ME) dipole which operate in vertical polarization (V-pol) and horizontal polarization (H-pol) respectively. The array contains $1\times 8$ elements and is constructed by substrate integrated waveguide (SIW). Numerically, proposed array achieve good performance, including large bandwidths of 19.3% and 21.5% for V-pol and H-pol respectively, stable radiation pattern with low cross polarization for both polarizations, and extremely high isolation over 65 dB between two ports. The realized gain is over 14.5 $\text{dBi}$ and radiation efficiency is around 80% for both polarizations in the operation band.
Neuropsychology as a profession in Italy

Abstract The purpose of this study was to analyze the characteristics of individuals working in the field of neuropsychology in Italy, as part of a larger study examining the practice of neuropsychology across various countries. They were asked about their background, professional training, current work situation, types of assessment, preferred diagnostic procedures, as well as the rehabilitation techniques, their targeted populations, teaching responsibilities, and research activities. A total of 154 professionals completed an online survey from April 28, 2016 through June 30, 2016. The majority of participants were women, with a mean age of 42.6 years. Participants reported working for the National Health System, in private practice, or in private rehabilitation facilities. Overall, they reported being very satisfied with their work. Those who identified themselves as neuropsychologists primarily assessed individuals with dementia, stroke, movement disorders, and traumatic brain injury. While the majority of participants declared no problems with the instruments they used, others reported complaints, including but not limited to the financial cost of current neuropsychological tests and the lack of psychometric support. The main perceived obstacles were the lack of willingness to collaborate among professionals, the scarcity of academic training programs, and the lack of clinical training opportunities.
Dimensionality Reduction of Hyperspectral Image Using Spatial Regularized Local Graph Discriminant Embedding

Dimensionality reduction (DR) is an important preprocessing step for hyperspectral image (HSI) classification. Recently, graph-based DR methods have been widely used. Among various graph-based models, the local graph discriminant embedding (LGDE) model has shown its effectiveness due to the complete use of label information. Besides spectral information, an HSI also contains rich spatial information. In this paper, we propose a regularization method to incorporate the spatial information into the LGDE model. Specifically, an oversegmentation method is first employed to divide the original HSI into nonoverlapping superpixels. Then, based on the observation that pixels in a superpixel often belong to the same class, intraclass graphs are constructed to describe such spatial information. Finally, the constructed superpixel-level intraclass graphs are used as a regularization term, which can be naturally incorporated into the LGDE model. Besides, to sufficiently capture the nonlinear property of an HSI, the linear LGDE model is further extended into its kernel counterpart. To demonstrate the effectiveness of the proposed method, experiments have been established on three widely used HSIs acquired by different hyperspectral sensors. The obtained results show that the proposed method can achieve higher classification performance than many state-of-the-art graph embedding models, and the kernel extension model can further improve the classification performance.
The phosphorylation of Tudor‐SN mediated by JNK is involved in the regulation of milk protein synthesis induced by prolactin in BMECs

Tudor staphylococcal nuclease (Tudor‐SN) is a multifunctional protein involved in a variety of cellular processes and plays a critical role in the regulation of gene expression. Recently, Tudor‐SN was found to be upregulated in mammary epithelial cells during lactation in response to prolactin, which further to regulate milk protein synthesis. However, the detailed regulatory mechanism of Tudor‐SN to milk protein still remains to be elucidated. In our study, we observed that the levels of Tudor‐SN and phosphor‐Tudor‐SN (Thr103) were both enhanced upon prolactin stimulation. Immunofluorescence assays demonstrated that prolactin treatment facilitated the nuclear transport of Tudor‐SN. Further study revealed that the phosphorylation of Tudor‐SN was depended on activated JNK. Coimmunoprecipitation assays disclosed that Tudor‐SN might be phosphorylated directly by JNK. Using gene mutation assays, we further discovered that mutation of Thr to Ala at site of 103 prevented the nuclear transport of Tudor‐SN. Thus, these results suggested the essential mechanism of the activated Tudor‐SN in milk protein regulation in response to prolactin, which may provide some new sights into improve milk protein production.
MeCo-TSM: Multi-Entity Complex Process-Oriented Service Modeling Method

In the modern service industry, both service processes and data structures are becoming increasingly diverse and complex. In addition, interdependences exist among data, such that the use of "shoe size" data must be based on the "type of goods" data returning "shoe". This is also observed for the functions and interfaces in a system, as one can use the function "order payment" only after the function "order generation". This kind of phenomenon is rather common in service systems nowadays, especially when the service is a transboundary service such as the new retail proposed by Jack Ma. Traditional modeling methods have difficulties in handling such scenarios. There have been studies on service modeling over the past several years, and they have focused mainly on the service processes and interactions among services. In this work, we construct MeCo-TSM based on three sub-models to handle multi-entity complex service process. We verify our model in the real processes of our cooperation company and compare it with related works. MeCo-TSM supports the service better in our cases and shows satisfactory efficiency, effectiveness and reusability.
Fault Isolation of 2.5D and 3D Packages through Analysis Across Entire System

The need to increase transistor packing density beyond Moore's Law and the need for expanding functionality, real-estate management and faster connections has pushed the industry to develop complex 2.5D and 3D package technology which includes System-in-Package (SiP), wafer-level packaging, through-silicon-vias (TSV), stacked-die packages. The high level of functional integration and the complex package architecture in these, pose a significant challenge for conventional Fault Isolation (FI) and Failure analysis (FA) methods. Various FA tools available the industry provide key data for the fault isolation in packages. Very often, one need to correlate the package level results from tool across the entire system for accurate fault isolation. This means, the results must be taken across the system and perform analysis at each level in the system. In this paper, we are presenting case studies for different methods to perform analysis across the system for accurate fault isolation.
Awareness and knowledge of Chikungunya infection following its outbreak in Pakistan among health care students and professionals: a nationwide survey

Background The World Health Organization (WHO) declares Chikungunya (CHIK) infection to be endemic in South Asia. Despite its first outbreak in Pakistan, no documented evidence exists which reveals the knowledge or awareness of healthcare students and workers (HCSW) regarding CHIK, its spread, symptoms, treatment and prevention. Since CHIK is an emergent infection in Pakistan, poor disease knowledge may result in a significant delay in diagnosis and treatment. The current study was aimed to evaluate the awareness and knowledge of CHIK among HCSW. Methods A cross-sectional study was conducted among HCSW from teaching institutes and hospitals in seven provinces of Pakistan. We collected information on socio-demographic characteristics of the participants and their knowledge by using a 30-item questionnaire. The cumulative knowledge score (CKS) was calculated by correct answers with maximum score of 22. The relationship between demographics and knowledge score was evaluated by using appropriate statistical methods. Results There were 563 respondents; mean age 25.2 ± 5.9 years with female preponderance (62.5%). Of these, 319 (56.7%) were aware of CHIK infection before administering the survey. The average knowledge score was 12.8 ± 4.1 (% knowledge score: 58.2%). Only 31% respondents had good disease knowledge while others had fair (36.4%) and poor (32.6%) knowledge. Out of five knowledge domains, domain III (vector, disease spread and transmission) and V (prevention and treatment) scored lowest among all i.e. percent score 44.5% and 54.1%, respectively. We found that socio-demographic characteristics had no influence on knowledge score of the study participants. Conclusion Approximately one-half of participants were not aware of CHIK infection and those who were aware had insufficient disease knowledge. Findings of the current study underscore the dire need of educational interventions not only for health care workers but also for students, irrespective to the discipline of study.
Template Oriented Text Summarization via Knowledge Graph

People are flooded with massive semi-structured and unstructured texts in their daily work life. The fast-paced lifestyle has forced us to get more focused information from these large amounts of text more quickly. So people urgently need a technology that can automatically extract abstracts from text. The traditional extractive automatic abstract method can only extract keywords or key sentences. Although the current popular sequence-to-sequence extraction methods have greatly improved compared with the traditional methods, they cannot be combined with the background information to obtain higher level abstraction. Therefore, we propose a method based on knowledge graph technology to automatically extract abstract texts. This method can not only obtain higher-level extraction from the text, but also can select template and question and answer to obtain a personalized abstract. We experimented on the CNN DAILYMAIL dataset. The results show that the abstract obtained by this method can reflect more textual information, and more in line with human reading habits, and can achieve personalized extraction, and can obtain close to the best ROUGE index results.
Free as in Free Word Order: An Energy Based Model for Word Segmentation and Morphological Tagging in Sanskrit

The configurational information in sentences of a free word order language such as Sanskrit is of limited use. Thus, the context of the entire sentence will be desirable even for basic processing tasks such as word segmentation. We propose a structured prediction framework that jointly solves the word segmentation and morphological tagging tasks in Sanskrit. We build an energy based model where we adopt approaches generally employed in graph based parsing techniques (McDonald et al., 2005a; Carreras, 2007). Our model outperforms the state of the art with an F-Score of 96.92 (percentage improvement of 7.06%) while using less than one tenth of the task-specific training data. We find that the use of a graph based approach instead of a traditional lattice-based sequential labelling approach leads to a percentage gain of 12.6% in F-Score for the segmentation task.
Robust Continuous Wheel Slip Control With Reference Adaptation: Application to the Brake System With Decoupled Architecture

Modern and coming generations of electric and automated vehicles are characterized by higher requirements to robust and fault-tolerant operation of chassis systems independently from driving situations and road conditions. In this regard, this paper introduces an adaptive continuous wheel slip control (WSC) developed for the sport utility vehicle equipped with a high-dynamic decoupled electrohydraulic brake system. The system architecture, mathematical formulation of the WSC and state estimator as well as the experimental WSC validation is described in this paper. The focus is given on three continuous WSC strategies based on proportional integral (PI), sliding-mode PI and integral-sliding-mode control techniques. The proposed WSC also includes the state and parameter estimator for the adaptation of the reference wheel slip depending on current road conditions and using the standard on-board vehicle sensors and extremum-seeking algorithm. Adaptability and robustness of all WSC configurations were confirmed by the road experiments performed on low- and high-$ \mu$ surfaces with mandatory condition of the same controls adjustments for all test cases. Tests show an enhancement of the vehicle safety and ride quality, compared to the vehicle with the rule-based WSC control.
Effective Control for Wireless Sensor and Mobile Actuator Network in Regulation of Environmental Density Function

This paper proposes an environmental regulating system by using a multi-robot system in cooperation with wireless sensor networks. Wireless sensors pre-deployed in the environment and embedded in mobile robots can gather sensory information to estimate the distribution of environmental variable. The environment distribution is illustrated by a density function which is constructed by Gaussian mixture model based on Expectation Maximization (EM) algorithm to estimate real condition. Subsequently, a gradient decent coverage control is proposed to drive the multi-robot system to cover the optimal distribution based on the estimated density function. Meanwhile, the actuators embedded on mobile robots are designed to regulate the environment to achieve a desired value of density function. Numerical examples are illustrated to show the performance of the proposed control system.Keywords: Wireless sensor and actuator networks (WSANs), coverage control, expectation maximization estimation, gradient descent algorithm, Lyapunov-based controller.
Efficiently Processing Complex-Valued Data in Homomorphic Encryption

Abstract We introduce a new homomorphic encryption scheme that is natively capable of computing with complex numbers. This is done by generalizing recent work of Chen, Laine, Player and Xia, who modified the Fan–Vercauteren scheme by replacing the integral plaintext modulus t by a linear polynomial X − b. Our generalization studies plaintext moduli of the form Xm + b. Our construction significantly reduces the noise growth in comparison to the original FV scheme, so much deeper arithmetic circuits can be homomorphically executed.
Randomized Incremental Construction of Net-Trees

Net-trees are a general purpose data structure for metric data that have been used to solve a wide range of algorithmic problems. We give a simple randomized algorithm to construct net-trees on doubling metrics using $O(n\log n)$ time in expectation. Along the way, we define a new, linear-size net-tree variant that simplifies the analyses and algorithms. We show a connection between these trees and approximate Voronoi diagrams and use this to simplify the point location necessary in net-tree construction. Our analysis uses a novel backwards analysis that may be of independent interest.
Wetting controls of droplet formation in step emulsification

Significance Step emulsification is one of the few methods that enables upscaled production of droplets with a monodispersity desired for many industrial processes. However, the physical mechanisms of the droplet formation process are poorly understood. We study the droplet breakup by experiments and simulations and find a simple theory predicting the transition from dripping to jetting and the minimal contact angle of α = 2π/3 = 120° for which droplets form. These results have important consequences for the development and design of novel microfluidic systems and reactors that address the growing demand for tools to manipulate fluids at the submillimeter scale. The formation of droplets is ubiquitous in many natural and industrial processes and has reached an unprecedented level of control with the emergence of milli- and microfluidics. Although important insight into the mechanisms of droplet formation has been gained over the past decades, a sound understanding of the physics underlying this phenomenon and the effect of the fluid’s flow and wetting properties on the droplet size and production rate is still missing, especially for the widely applied method of step emulsification. In this work, we elucidate the physical controls of microdroplet formation in step emulsification by using the wetting of fluidic channels as a tunable parameter to explore a broad set of emulsification conditions. With the help of high-speed measurements, we unequivocally show that the final droplet pinch-off is triggered by a Rayleigh–Plateau-type instability. The droplet size, however, is not determined by the Rayleigh–Plateau breakup, but by the initial wetting regime, where the fluid’s contact angle plays a crucial role. We develop a physical theory for the wetting process, which closely describes our experimental measurements without invoking any free fit parameter. Our theory predicts the initiation of the Rayleigh–Plateau breakup and the transition from dripping to jetting as a function of the fluid’s contact angle. Additionally, the theory solves the conundrum why there is a minimal contact angle of α = 2π/3 = 120° for which droplets can form.
Quantifying Consumers' Willingness to Pay for Electric Vehicle Charging

Poor availability of a nationwide, publicly accessible charging infrastructure in Europe is seen as a major impediment for a stronger market penetration of electric vehicles (EVs). Making privately owned charging points for electric vehicles accessible to the public in a Peer-to-Peer (P2P) charging network may present a remedy to this problem. Developing such a P2P service, among others, requires knowledge on the customers' willingness to pay for EV charging. We survey customers of a utility company in a mid-sized European city to assess their willingness to pay for EV charging. Using choice-based conjoint analysis, we obtain customers' willingness to pay differentiated by charging point's characteristics and its location. The estimates are subsequently implemented in form of price recommendations into an actual web-based P2P platform that enables private individuals to provide and access EV charging services. This research contributes to the knowledge on the design of payment models in general and on the design of EV charging infrastructure services in particular. Poor availability of a nationwide, publicly accessible charging infrastructure in Europe is seen as a major impediment for a stronger market penetration of electric vehicles (EVs). Making privately owned charging points for electric vehicles accessible to the public in a Peer-to-Peer (P2P) charging network may present a remedy to this problem. Developing such a P2P service, among others, requires knowledge on the customers' willingness to pay for EV charging. We survey customers of a utility company in a mid-sized European city to assess their willingness to pay for EV charging. Using choice-based conjoint analysis, we obtain customers' willingness to pay differentiated by charging point's characteristics and its location. The estimates are subsequently implemented in form of price recommendations into an actual web-based P2P platform that enables private individuals to provide and access EV charging services. This research contributes to the knowledge on the design of payment models in general and on the design of EV charging infrastructure services in particular.