Miracle-dz/UniFAR-FTUs huggingface dataset:
Adaptive Social Network Services: The Practice of 9EMBA.COM

Self-promoting plays an essential role for virtual community evolvement. To derive self-promoting and informative social network, we investigate fundamental services that drive the evolvement of social network, and propose the design and implementation of adaptive social network in this work. Our adaptive social network framework integrates content, community, communication and self-branding services with (implicit) tag associations to connect entities and services in the virtual community. To evaluate the presented approach, we realize adaptive social network in practice, developing 9EMBA.COM as a new platform for the virtual community of executive MBA students of nine top universities in Taiwan. A preliminary study on the questionnaire of executive MBA students shows their high satisfaction on 9EMBA.COM.
Frequency of autoimmune disorders and autoantibodies in patients with neuromyelitis optica

Objective The aim of this study was to report the frequency of autoimmune disorders and autoantibodies in 22 patients with neuromyelitis optica (NMO), as well as whether the seropositivity for autoantibodies differs between anti-aquaporin 4 (AQP4) positive and AQP4 negative NMO patients. Methods Demographic, medical records, and a profile of autoantibodies were evaluated in 22 NMO patients, including AQP4, anti-thyroid-stimulating hormone receptor, antinuclear antibodies (ANA), anti-thyroperoxidase (anti-TPO), anti-thyroglobulin (anti-Tg), anti-double-stranded DNA, anti-neutrophil cytoplasmic, anti-cyclic citrullinate peptide, rheumatoid factor, anti-SSA/Ro, anti-SSB/La, anti-Smith antibodies (anti-Sm), anti-ribonucleoprotein, anti-nucleosome, and anti-Scl70. Thyroid-stimulating hormone and free thyroxin were measured. Results The frequency of women was higher than men (95.5% vs. 4.5%) and 68.2% were Afro-Brazilians. Six (27.3%) patients presented other autoimmune disorders, such as Hashimoto thyroiditis (n=2), Graves’ disease (n=1), juvenile idiopathic arthritis (n=1), systemic lupus erythematosus and systemic sclerosis (n=1), and Raynaud’s phenomenon (n=1). The most frequent autoantibodies were anti-AQP4 (54.5%), anti-nucleosome (31.8%), ANA (27.3%), anti-TPO (22.7%), and anti-Tg (22.7%). Difference was not observed in the frequency of autoimmune disorders when the patients were compared according to their anti-AQP4 status. Conclusion The results of the present study underscored that the NMO patients present high frequency of autoantibodies against cellular antigens and the presence of autoimmune disorders. Further studies with large number of NMO patients may contribute to advances in the understanding of NMO disease mechanisms.
Symmetric Temporal Theorem Proving

In this paper we consider the deductive verification of propositional temporal logic specifications of symmetric systems. In particular, we provide a heuristic approach to the scalability problems associated with analysing properties of large numbers of processes. Essentially, we use a temporal resolution procedure to verify properties of a system with few processes and then generalise the outcome in order to reduce the verification complexity of the same system with much larger numbers of processes. This provides a practical route to deductive verification for many systems comprising identical processes.
Acute effects of different light spectra on simulated night-shift work without circadian alignment

ABSTRACT Short-wavelength and short-wavelength-enhanced light have a strong impact on night-time working performance, subjective feelings of alertness and circadian physiology. In the present study, we investigated acute effects of white light sources with varied reduced portions of short wavelengths on cognitive and visual performance, mood and cardiac output.Thirty-one healthy subjects were investigated in a balanced cross-over design under three light spectra in a simulated night-shift paradigm without circadian adaptation.Exposure to the light spectrum with the largest attenuation of short wavelengths reduced heart rate and increased vagal cardiac parameters during the night compared to the other two light spectra without deleterious effects on sustained attention, working memory and subjective alertness. In addition, colour discrimination capability was significantly decreased under this light source.To our knowledge, the present study for the first time demonstrates that polychromatic white light with reduced short wavelengths, fulfilling current lighting standards for indoor illumination, may have a positive impact on cardiac physiology of night-shift workers without detrimental consequences for cognitive performance and alertness.
A New Direction for Streaming Graph Analysis

Applications in computer network security, social media analysis, and other areas rely on analyzing a changing environment. The data is rich in relationships and lends itself to graph analysis. Traditional static graph analysis cannot keep pace with network security applications analyzing nearly one million events per second and social networks like Facebook collecting 500 thousand comments per second. Streaming frameworks like STINGER support ingesting up three million of edge changes per second but there are few streaming analysis kernels that keep up with these rates. Here we introduce a new, non-stop model and use it to decouple the analysis from the data ingest.
Action recognition using multi-layer Depth Motion maps and Sparse Dictionary Learning

In this paper, we propose a new spatio-temporal feature based method for human action recognition using depth image sequence. Fist, Layered Depth Motion maps (LDM) are utilized to capture the temporal motion feature. Next, multi-scale HOG descriptors are computed on LDM to characterize the structural information of actions. Then sparse coding is applied for feature representation. Extending Sparse fisher Discriminative Dictionary Learning (SDDL) model and its corresponding classification scheme are also introduced. In SDDL model, the sub-dictionary is updated class by class, leading to class-specific compact discriminative dictionaries. The proposed method is evaluated on public MSR Action3D datasets and demonstrates great performance, especially in cross subject test.
Interference Modeling in an Urban Microcell With Full-Dimensional MIMO

In this paper, we study the uplink performance in a three-dimensional (3-D) urban microcell. The single-antenna users are distributed according to a 3-D Poisson point process inside the cell. Each user has a probability to experience a line-of-sight (LoS) channel or a fading channel, and transmits to the multiantenna base station (BS) utilizing fractional power control. The BS deploys a large 2-D planar antenna array and performs 3-D beamforming (BF) to receive the signals of all users in the cell sector. Under such a setting, we study the interference incurred by the cochannel users in the cell sector to a randomly located target user. In particular, we show that the interference incurred by the LoS interferers to a LoS target user is associated with the 3-D BF antenna pattern. This type of interference is determined by the separation between the locations of the target user and interferer, and hence it is heavily dependent on the cell geometry. We exploit the massive MIMO regime and present a good approximation to model the LoS interference. We further resort to stochastic geometry to analyze the coverage probability and the rate per user in the cell.
Evaluation of the Potentials of Some Cassava Varieties in Nigeria for Bio-Ethanol Production

Suitability of four cassava varieties (98/2101, 98/0505, TME 419, and TMS 4(2) 1425) for ethanol production was investigated. The total starch and amylose contents of variety 98/2101 were higher than those of the other three varieties. Variety 98/2101 also gave the highest ethanol productivity and yield. This was followed by variety 98/0505 while TMS 4(2) 1425 gave very low ethanol concentration and yield. The final ethanol concentration and yield were influenced by the age of variety 98/2101. The optimum age for harvesting this variety for ethanol production is 12 months. Above 12 months, some of the tubers started rotting and even ethanol yield per cassava flour decreased. On the whole, the starch contents of the varieties investigated were lower than the values reported for other varieties in other parts of the world. Efforts should therefore be made to develop cassava varieties with higher starch contents in Nigeria. Keywords: Cassava varieties, Fuel ethanol, Koji, Amylase enzymes, Bio-fuels
A New Packet Scheduling Algorithm for Real-Time Multimedia Streaming

Delivering the real-time services over converged networks is a big challenge. Real-time services need to high Quality of Service (QoS). For this purpose, bandwidth reservation and packet prioritization techniques are used. Thus, real-time data packets can be reached to their targets with minimum delays and losses. But, this situation creates unintended consequences for other internet services such as HTTP and FTP. In this case, establishing a balance between the real-time services and the other services is a must. In this study we introduce a new research question: how to transport real-time multimedia IP packets just in time? Just in time means that transportation of the packets neither early, nor late. For this purpose we developed a scheduling/prioritizing algorithm called just in time transport (JITT). Following a cross-layer design approach, JITT controls delay and jitter over whole communication path. We evaluated JITT on the different simulations and one experimental testbed for performance analysis. Our findings support that JITT provides stable delay and low jitter and transports the packets nearly just in time.
Lazygraph: lazy data coherency for replicas in distributed graph-parallel computation

Replicas 1 of a vertex play an important role in existing distributed graph processing systems which make a single vertex to be parallel processed by multiple machines and access remote neighbors locally without any remote access. However, replicas of vertices introduce data coherency problem. Existing distributed graph systems treat replicas of a vertex v as an atomic and indivisible vertex, and use an eager data coherency approach to guarantee replicas atomicity. In eager data coherency approach, any changes to vertex data must be immediately communicated to all replicas of v, thus leading to frequent global synchronizations and communications. In this paper, we propose a lazy data coherency approach, called LazyAsync, which treats replicas of a vertex as independent vertices and maintains the data coherency by computations, rather than communications in existing eager approach. Our approach automatically selects some data coherency points from the graph algorithm, and maintains all replicas to share the same global view only at such points, which means the replicas are enabled to maintain different local views between any two adjacent data coherency points. Based on PowerGraph, we develop a distributed graph processing system LazyGraph to implement the LazyAsync approach and exploit graph-aware optimizations. On a 48-node EC2-like cluster, LazyGraph outperforms PowerGraph on four widely used graph algorithms across a variety of real-world graphs, with a speedup ranging from 1.25x to 10.69x.
Signaling and Gene Regulatory Networks Governing Definitive Endoderm Derivation From Pluripotent Stem Cells

The generation of definitive endoderm (DE) from pluripotent stem cells (PSCs) is a fundamental stage in the formation of highly organized visceral organs, such as the liver and pancreas. Currently, there is a need for a comprehensive study that illustrates the involvement of different signaling pathways and their interactions in the derivation of DE cells from PSCs. This study aimed to identify signaling pathways that have the greatest influence on DE formation using analyses of transcriptional profiles, protein–protein interactions, protein–DNA interactions, and protein localization data. Using this approach, signaling networks involved in DE formation were constructed using systems biology and data mining tools, and the validity of the predicted networks was confirmed experimentally by measuring the mRNA levels of hub genes in several PSCs‐derived DE cell lines. Based on our analyses, seven signaling pathways, including the BMP, ERK1‐ERK2, FGF, TGF‐beta, MAPK, Wnt, and PIP signaling pathways and their interactions, were found to play a role in the derivation of DE cells from PSCs. Lastly, the core gene regulatory network governing this differentiation process was constructed. The results of this study could improve our understanding surrounding the efficient generation of DE cells for the regeneration of visceral organs. J. Cell. Physiol. 231: 1994–2006, 2016. © 2016 Wiley Periodicals, Inc.
Meta-Reinforcement Learning of Structured Exploration Strategies

Exploration is a fundamental challenge in reinforcement learning (RL). Many of the current exploration methods for deep RL use task-agnostic objectives, such as information gain or bonuses based on state visitation. However, many practical applications of RL involve learning more than a single task, and prior tasks can be used to inform how exploration should be performed in new tasks. In this work, we explore how prior tasks can inform an agent about how to explore effectively in new situations. We introduce a novel gradient-based fast adaptation algorithm -- model agnostic exploration with structured noise (MAESN) -- to learn exploration strategies from prior experience. The prior experience is used both to initialize a policy and to acquire a latent exploration space that can inject structured stochasticity into a policy, producing exploration strategies that are informed by prior knowledge and are more effective than random action-space noise. We show that MAESN is more effective at learning exploration strategies when compared to prior meta-RL methods, RL without learned exploration strategies, and task-agnostic exploration methods. We evaluate our method on a variety of simulated tasks: locomotion with a wheeled robot, locomotion with a quadrupedal walker, and object manipulation.
Squeakr: An Exact and Approximate k-mer Counting System

Motivation k-mer-based algorithms have become increasingly popular in the processing of high-throughput sequencing (HTS) data. These algorithms span the gamut of the analysis pipeline from k-mer counting (e.g., for estimating assembly parameters), to error correction, genome and transcriptome assembly, and even transcript quantification. Yet, these tasks often use very different k-mer representations and data structures. In this paper, we set forth the fundamental operations for maintaining multisets of k-mers and classify existing systems from a data-structural perspective. We then show how to build a k-mer-counting and multiset-representation system using the counting quotient filter (CQF), a feature-rich approximate membership query (AMQ) data structure. We introduce the k-mer-counting/querying system Squeakr (Simple Quotient filter-based Exact and Approximate Kmer Representation), which is based on the CQF. This off-the-shelf data structure turns out to be an efficient (approximate or exact) representation for sets or multisets of k-mers. Results Squeakr takes 2×−3;4.3× less time than the state-of-the-art to count and perform a random-point-query workload. Squeakr is memory-efficient, consuming 1.5X–4.3X less memory than the state-of-the-art. It offers competitive counting performance, and answers point queries (i.e. queries for the abundance of a particular k-mer) over an order-of-magnitude faster than other systems. The Squeakr representation of the k-mer multiset turns out to be immediately useful for downstream processing (e.g., de Bruijn graph traversal) because it supports fast queries and dynamic k-mer insertion, deletion, and modification. Availability https://github.com/splatlab/squeakr Contact ppandey@cs.stonybrook. edu
Compositional Proofs in Differential Dynamic Logic dL

Modularity and composability are essential properties to facilitate and scale the design of cyber-physical systems from the specification of hybrid, discrete and continuous, components. Modularity is essential to break down a system model into comprehensible and manageable component specifications. Composability is essential to design a system from component models while preserving their verified properties, expressed as assume-guarantee contracts. In this paper, we address the specification of hybrid system using Platzer's differential dynamic logic (dL). Our contribution is threefold: (1) We define a new composition operator in dL and prove that it is associative and commutative (AC). Prior notions of composition in dL were not associative. (2) We provide a theorem which characterizes necessary conditions to automate the proof that composed components satisfy the composition of their individual contracts, enabling modular and compositional verification. (3) We case-study our AC composition operator by considering the modular and detailed specification of a cruise controller in KeYmaeraX, the latest implementation of dL, to demonstrate the proof automation capability of our contribution and exemplify a compositional design methodology.
Global learning network analytics to enhance PLN understanding

In the 21st century learning increasingly happens on the social web. Learning has evolved into an interactive social process producing large amounts of data across a multitude of inhomogeneous systems. To identify the role of individual actors and groups of actors the whole global learning network needs to be analyzed. The work presented in this paper ingests learning data into a cloud hosted distributed temporal graph model with a supporting distributed processing framework to calculate global graph metrics. The presented simple architectural approach builds upon the xAPI specification to ensure compatibility and flexibility. Based on the global graph metrics we can detect communities and identify information brokers. This information enhances the understanding of the learner's personal learning network and its development over time. It contributes to ongoing efforts to guide learners' through the tangled undergrowth of the global social learning network towards individuals and communities relevant to their interests, skills and aptitudes.
The Impact of Alexithymia on Desire for Alcohol during a Social Stress Test

ABSTRACT Background: Alexithymia is a personality construct comprising difficulty in identifying and describing emotions and externally oriented thinking. Its role in heavy and problematic alcohol consumption is well documented, together with its relationship with social stress. However, little research has examined whether social stress has any effect on desire for alcohol among alexithymic individuals. Objectives: In this experimental study, we explored the relationship between alexithymia and desire for alcohol in response to an experimental social stressor. Methods: One hundred and thirty eight social drinkers completed the Toronto Alexithymia Scale, self-report measures of alcohol consumption and a stress-inducing task. Desire for alcohol was measured at three time points: baseline, stressor and recovery. Results: Correlation analysis demonstrated that alexithymia was associated with significantly higher rates of alcohol consumption and higher levels of desire for alcohol. Mixed measures ANOVA demonstrated a significant main effect of alexithymia and a significant group by time effect of alexithymia on desire for alcohol. Conclusions/Importance: The findings demonstrate increased desire for alcohol before, during and after a social stressor among alexithymic participants. These findings offer an insight into the relationship between alexithymia, social stress and alcohol consumption.
Power System Structural Vulnerability Assessment Based on an Improved Maximum Flow Approach

With the increasing complexity of the power grid and the concerns on major blackouts, there is an urgent need for an efficient and effective tool to assess the power system structural vulnerability. To tackle this challenge, this paper proposes a maximum flow-based complex network approach to identify the critical lines in a system. The proposed method consists of two major steps. First, the power network is modeled as a graph with edges (transmission lines, transformers, etc.) and nodes (buses, substations, etc.). The critical scenarios are identified by using the principal component analysis and convex hull. Then the second step is to use an improved maximum flow-based complex network approach for topology analysis. Weighted vertices in the network are considered, enabling taking the selected operating conditions into consideration when identifying the vulnerable lines. The proposed method is validated using the western Danish power system. The vulnerable lines in the network are ranked. Simulation results demonstrate the effectiveness of the method by intentional attacks and comparison with the planning strategy from the system operator.
Use of acoustic emission for defect detection in adhesively bonded cylindrical joints loaded in torsion

The Acoustic Emission (AE) technique has been applied in order to detect and recognise the presence and size of defects in cylindrical adhesively bonded joints loaded in torsion. The application of a Design of Experiment procedure showed that load rate and curing time mainly affect the resistance of the joint. Statistical correlation coefficients confirm this result. Acoustic Emission also are more correlated to load rate and curing time than to defect size. In the tests performed at strictly constant load rate and curing time, a very good correlation between defect size and Acoustic Emission parameters (counts, events and amplitude distributions) has been found. In conclusion a test procedure for defect detection in adhesively bonded joints, based on Acoustic Emission application, is proposed.
Clinical Review of Adult Tracheostomy

Tracheostomy is a surgical procedure which is increasingly being performed in the intensive care unit (ICU) rather than the operating room (Griffiths et al 2005, Delaney et al 2006). Procedural knowledge including postoperative care is essential for ENT surgeons and ICU practitioners alike. Our article aims to highlight the operative technique, surgical complications and various types of tracheostomy tubes available, including their management.
Application of DIROEF Algorithm for Noncollinear Multiple CCD Array Stitching of the Chinese Mapping Satellite 1-02

The Chinese Mapping Satellite 1-02 (MS1-02) was launched on May 6, 2012, and can provide wide-swath remote sensing images through stitching together subimages acquired by a panchromatic sensor (PAN). However, the limited stitching precision caused by the particular configuration of the PAN reduces the geometric quality of images, which restricts their application. In this letter, we propose a new image-space stitching approach based on the differential recursion optimal estimation filter for PAN images and describe it in detail. To validate the correctness and advantages of our method, experiments were run on several sets of matching points with different characteristics and images with different geographic conditions. The results show that images with seamless visual effect and subpixel-level stitching precision can be obtained.