Miracle-dz/UniFAR-FTUs huggingface dataset:
A Genome-Wide SNP Scan Reveals Novel Loci for Egg Production and Quality Traits in White Leghorn and Brown-Egg Dwarf Layers

Availability of the complete genome sequence as well as high-density SNP genotyping platforms allows genome-wide association studies (GWAS) in chickens. A high-density SNP array containing 57,636 markers was employed herein to identify associated variants underlying egg production and quality traits within two lines of chickens, i.e., White Leghorn and brown-egg dwarf layers. For each individual, age at first egg (AFE), first egg weight (FEW), and number of eggs (EN) from 21 to 56 weeks of age were recorded, and egg quality traits including egg weight (EW), eggshell weight (ESW), yolk weight (YW), eggshell thickness (EST), eggshell strength (ESS), albumen height(AH) and Haugh unit(HU) were measured at 40 and 60 weeks of age. A total of 385 White Leghorn females and 361 brown-egg dwarf dams were selected to be genotyped. The genome-wide scan revealed 8 SNPs showing genome-wise significant (P<1.51E-06, Bonferroni correction) association with egg production and quality traits under the Fisher's combined probability method. Some significant SNPs are located in known genes including GRB14 and GALNT1 that can impact development and function of ovary, but more are located in genes with unclear functions in layers, and need to be studied further. Many chromosome-wise significant SNPs were also detected in this study and some of them are located in previously reported QTL regions. Most of loci detected in this study are novel and the follow-up replication studies may be needed to further confirm the functional significance for these newly identified SNPs.
The slow (< 1 Hz) oscillation in reticular thalamic and thalamocortical neurons: scenario of sleep rhythm generation in interacting thalamic and neocortical networks

As most afferent axons to the thalamus originate in the cerebral cortex, we assumed that the slow (< 1 Hz) cortical oscillation described in the two companion articles is reflected in reticular (RE) thalamic and thalamocortical cells. We hypothesized that the cortically generated slow rhythm would appear in the thalamus in conjunction with delta and spindle oscillations arising from intrinsic and network properties of thalamic neurons. Intracellular recordings have been obtained in anesthetized cats from RE (n = 51) and cortically projecting (n = 240) thalamic neurons. RE cells were physiologically identified by cortically evoked high-frequency spike bursts and depolarizing spindle oscillations. Thalamocortical cells were recognized by backfiring from appropriate neocortical areas, spindle- related cyclic IPSPs, and hyperpolarization-activated delta oscillation consisting of rhythmic low-threshold spikes (LTSs) alternating with afterhyperpolarizing potentials (AHPs). The slow rhythm (0.3–0.5 Hz) was recorded in 65% of RE neurons. In approximately 90% of oscillating cells, the rhythm consisted of prolonged depolarizations giving rise to trains of single action potentials. DC hyperpolarization increased the synaptic noise and, in a few cells, suppressed the long-lasting depolarizing phase of the slow rhythm, without blocking the fast EPSPs. In approximately 10% of oscillating neurons, the hyperpolarizing phase of the oscillation was much more pronounced, thus suggesting that the slow rhythm was produced by inhibitory sculpturing of the background firing. The slow oscillation was associated with faster rhythms (4–8 Hz) in the same RE neuron. The slow rhythm of RE neurons was closely related to EEG wave complexes recurring with the same frequency, and its strong dependency upon a synchronized state of cortical EEG was observed during shifts in EEG patterns at different levels of anesthesia. In 44% of thalamocortical cells the slow rhythm of depolarizing sequences was apparent and it could coexist with delta or spindle oscillations in the same neuron. The occurrence of the slowly recurring depolarizing envelopes was delayed by the hyperpolarizing spindle sequences or by the LTS-AHP sequences of delta oscillation. The hyperpolarization-activated delta potentials that tended to dampen after a few cycles were grouped in sequences recurring with the slow rhythm. We finally propose a unified scenario of the genesis of the three major sleep rhythms: slow, delta, and spindle oscillations.
The SAFE miner: A fine grained aspect level approach for resolving the sentiment

Sentiment analysis is the study of classifying human's sentiments, evaluations, attitudes, opinions about some topic, expressed in form of text or speech. In order to improve the customer satisfaction, many e-commerce sites provides the provision to write reviews about products. Instead of manually reading and evaluating numerous reviews, an automated procedure can be deployed to get the overall polarity of the reviews. The goal of the research is to present an approach with the target of deriving qualitative sentiment analysis. Although many techniques have been proposed by the researchers for extracting sentiments from text. This paper introduces the SAFE (sentiment analysis and feature extraction) miner, a fine grained approach for calculating the sentiment score at aspect level. Thus, developing the feature extraction matrix (FEM). FEM will help in determining the features, being commented upon. It will also help in detecting the popular features among the reviews. The paper aims at mining sentiment for each product review and overall sentiment score associated with each product review. The efficient ranking of the features will be deduced, based on the opinions.
Hysteresis Modeling of Magnetic Shape Memory Alloy Actuator Based on Krasnosel'skii-Pokrovskii Model

As a new type of intelligent material, magnetically shape memory alloy (MSMA) has a good performance in its applications in the actuator manufacturing. Compared with traditional actuators, MSMA actuator has the advantages as fast response and large deformation; however, the hysteresis nonlinearity of the MSMA actuator restricts its further improving of control precision. In this paper, an improved Krasnosel'skii-Pokrovskii (KP) model is used to establish the hysteresis model of MSMA actuator. To identify the weighting parameters of the KP operators, an improved gradient correction algorithm and a variable step-size recursive least square estimation algorithm are proposed in this paper. In order to demonstrate the validity of the proposed modeling approach, simulation experiments are performed, simulations with improved gradient correction algorithm and variable step-size recursive least square estimation algorithm are studied, respectively. Simulation results of both identification algorithms demonstrate that the proposed modeling approach in this paper can establish an effective and accurate hysteresis model for MSMA actuator, and it provides a foundation for improving the control precision of MSMA actuator.
Approximate analysis of MAC protocol with multiple self-tokens in a slotted ring

Ring networks are very commonly exploited among local area and metropolitan area networks (LAN/MAN), whereas cells or small fixed-size packets are widely used in synchronized ring networks. In this paper, we present an analytical method for evaluating the delay-throughput performance of a MAC protocol with multiple self-tokens in a slotted ring network under uniform traffic. In our analysis, we introduce the stationary probability, which indicates the number of packets in a node. Also, it is assumed that each node has a sufficiently large amount of self-tokens, and a slotted ring has the symmetry. The analytical results with respect to delay-throughput performance have similar values to computer simulated ones. Furthermore, in order to achieve fair access under non-uniform traffic, we propose an adaptive MAC protocol, where the number of self-tokens in a node dynamically varies, based on the number of packets transmitted within a specified period. In the proposed protocol, when the number of packets transmitted by a node within a specified period is larger than a specified threshold, the node decreases the number of self-tokens in a per-node distributed method. That results in creating free slots in the ring, thus all nodes can obtain an equal opportunity to transmit into the ring. Performance results obtained by computer simulation show that our proposed protocol can maintain throughput fairness under non-uniform traffic.
Some thoughts on critical and tricritical points and deconfinement

We start with a discussion of a phase diagram and three special collision energies: (i) the first touching of the mixed phase; (ii) the softest point; (iii) the critical point. We then proceed to more details about the role a massless critical mode should play in the particle interaction and the elliptic flow. We then enter a discussion of whether high density QGP is or is not strongly coupled, and if so what one can say about color superconductivity domain using universality and data on trapped ultracold fermionic atoms. At the end, we briefly discuss deconfinement, or more specifically on whether there are precursors of the QCD strings at $T>T_c$, as well as possible role of magnetically charged excitations (monopoles and dyons).
The effect multiple query representations on information retrieval system performance

Five independently generated Boolean query formulations for ten different TREC topics were produced by ten different expert online searchers. These different formulations were grouped, and the groups, and combinations of them, were used as searches against the TREC test collection, using the INQUERY probabilistic inference network retrieval engine, Results show that progressive combination of query formulations leads to progressively improving retrieval performance, Results were compared against the performance of INQUERY natural language based queries, and in combination with them. The issue of recall as a performance measure in large databases was raised, since overlap between the searches conducted in this study, and the TREC-1 searches, was smaller than expected.
The fast quasiadiabatic approach to optical waveguide design

Optical waveguide devices based on adiabatic mode evolution have large bandwidth and fabrication tolerances, but they have longer device lengths in general. We define a suitable adiabaticity parameter for optical waveguides and derive the formulae for engineering the adiabaticity along the waveguide. The fast quasiadiabatic dynamics (FAQUAD) protocol can then be applied to redistribute adiabaticity over the length of the device, speeding up mode evolution in adiabatic waveguide devices. By homogeneously distributing the adiabaticity along the length of the devices, shortcuts to adiabaticity are found for adiabatic waveguide devices, resulting in significant length reductions from the conventional designs. Devices on silicon, including mode (de)multiplexers, polarization splitter-rotators and 3 dB couplers using the FAQUAD approach are discussed. The FAQUAD approach leads to devices that are compact, broadband, and have good fabrication tolerances.
A Mindfulness Based Intervention to Enhance University Student Wellbeing in Saudi Arabia

The Mindful Jeddah Training Program (MJTP) is a culturally and religiously inclusive three-week mindfulness-training pilot program designed to help university student’s deal with everyday stress, anxiety and depression while increasing levels of life satisfaction. A quasi-experiment was designed to compare the effectiveness of the MJTP with a control condition. Twenty-six female participants from a private university in Jeddah, Saudi Arabia were randomly assigned to treatment or control group participation. A comparison of group means showed that the MJTP group had marginally decreased levels of stress, anxiety, depression and increased mental well-being and mindfulness. Yet, a dependent t-test showed that the MJTP group had statistical significantly increased levels of life satisfaction as well. An independent t-test between the two groups revealed no significant results. The control group showed decreased mental well-being while slightly increasing depression and anxiety which reinforces the idea that analyzing and over-thinking are detrimental to psychological health. The MJTP can be an effective way to meet well-being aims in Saudi Arabia and in a manner that is culturally and religiously congruent.
Correcting Access Restrictions to a Consequence More Flexibly

Recent research has shown that labeling ontologies can be useful for restricting the access to some of the axioms and their implicit consequences. However, the labeling of the axioms is an error-prone and highly sensible task. In previous work we have shown how to correct the access restrictions if the security administrator knows the precise access level that a consequence must receive, and axioms are relabeled to that same access level. In this paper, we look at a more general situation in which access rights can be granted or denied to some specific users, without having to fully specify the precise access level. We also allow a more flexible labeling function, where the new access level of the relabeled axioms may differ from the level of the restriction. We provide black-box algorithms for computing suggestions of axioms to be relabeled.
Personal Digital Information Resources Management Behavior

With the growth and the increasing complexity of digital resources, it is clear that individuals may have to depend on various types of information management tools to organize and retrieve information needed to carry out their personal or professional tasks. With new web applications, “users are provided with the tools and platforms to undertake their own collaborative content creation and distribution”. However, it may also worsen the information retrieval problems, for more information resources means higher needs for proper management in order to find needed information in the right format at right time. We assume that user’s PIM behavior with PCs will have impact on how they will perform PIM in other environments. There is a need to reassess the existing PIM tools and testing formerly claimed theories of PIM behavior in the context of today’s digital information environment. This paper reports on the findings of a survey conducted with 118 computer users to investigate their information organization and retrieval strategy and the factors that affect their choices of adopting different strategies. The results should shed some light of personal information behavior that may aid the design and implementation of the future PIM tools that optimize advanced technology.
Software Design Challenges in Time Series Prediction Systems Using Parallel Implementation of Artificial Neural Networks

Software development life cycle has been characterized by destructive disconnects between activities like planning, analysis, design, and programming. Particularly software developed with prediction based results is always a big challenge for designers. Time series data forecasting like currency exchange, stock prices, and weather report are some of the areas where an extensive research is going on for the last three decades. In the initial days, the problems with financial analysis and prediction were solved by statistical models and methods. For the last two decades, a large number of Artificial Neural Networks based learning models have been proposed to solve the problems of financial data and get accurate results in prediction of the future trends and prices. This paper addressed some architectural design related issues for performance improvement through vectorising the strengths of multivariate econometric time series models and Artificial Neural Networks. It provides an adaptive approach for predicting exchange rates and it can be called hybrid methodology for predicting exchange rates. This framework is tested for finding the accuracy and performance of parallel algorithms used.
Neural-network control of nonaffine nonlinear system with zero dynamics by state and output feedback

This paper focuses on adaptive control of nonaffine nonlinear systems with zero dynamics using multilayer neural networks. Through neural network approximation, state feedback control is firstly investigated for nonaffine single-input-single-output (SISO) systems. By using a high gain observer to reconstruct the system states, an extension is made to output feedback neural-network control of nonaffine systems, whose states and time derivatives of the output are unavailable. It is shown that output tracking errors converge to adjustable neighborhoods of the origin for both state feedback and output feedback control.
Fractional Immunization in Networks

Preventing contagion in networks is an important problem in public health and other domains. Targeting nodes to immunize based on their network interactions has been shown to be far more effective at stemming infection spread than immunizing random subsets of nodes. However, the assumption that selected nodes can be rendered completely immune does not hold for infections for which there is no vaccination or effective treatment. Instead, one can confer fractional immunity to some nodes by allocating variable amounts of infection-prevention resource to them. We formulate the problem to distribute a fixed amount of resource across nodes in a network such that the infection rate is minimized, prove that it is NP-complete and derive a highly effective and efficient linear-time algorithm. We demonstrate the efficiency and accuracy of our algorithm compared to several other methods using simulation on realworld network datasets including US-MEDICARE and state-level interhospital patient transfer data. We find that concentrating resources at a small subset of nodes using our algorithm is up to 6 times more effective than distributing them uniformly (as is current practice) or using network-based heuristics. To the best of our knowledge, we are the first to formulate the problem, use truly nation-scale network data and propose effective algorithms.
Epidermal Growth Factor Receptor Mutation and Anaplastic Lymphoma Kinase Gene Fusion: Detection in Malignant Pleural Effusion by RNA or PNA Analysis

Analyzing EGFR mutations and detecting ALK gene fusion are indispensable when planning to treat pulmonary adenocarcinoma. Malignant pleural effusion (MPE) is a devastating complication of lung cancer and sometimes the only source for mutation analysis. The percentage of tumor cells in the pleural effusion may be low; therefore, mutant enrichment is required for a successful analysis. The EGFR mutation status in MPE was determined using three methods: (1) PCR sequencing of genomic DNA (direct sequencing), (2) mutant-enriched PCR sequencing of genomic DNA using peptide nucleic acid (PNA-sequencing), and (3) PCR sequencing of cDNA after reverse transcription for cellular RNA (RNA-sequencing). RT-PCR was also used to test cases for ALK gene fusion. PNA-sequencing and RNA-sequencing had similar analytical sensitivities (< 1%), which indicates similar enrichment capabilities. The clinical sensitivity in 133 cases when detecting the common EGFR exon 19 and exon 21 mutations was 56.4% (75/133) for direct sequencing, 63.2% (84/133) for PNA-sequencing, and 65.4% (87/133) for RNA-sequencing. RT-PCR and sequencing showed 5 cases (3.8%) with ALK gene fusion. All had wild-type EGFR. For EGFR analysis of MPE, RNA-sequencing is at least as sensitive as PNA-sequencing but not limited to specific mutations. Detecting ALK fusion can be incorporated in the same RNA workflow. Therefore, RNA is a better source for comprehensive molecular diagnoses in MPE.
Representation of Subspaces and Enumerative Encoding of the Grassmannian Space

Codes in the Grassmannian space have found recently application in network coding. Representation of k- dimensional subspaces of F n has generally an essential role in solving coding problems in the Grassmannian, and in particular in encoding subspaces of the Grassmannian. Different represen- tations of subspaces in the Grassmannian are presented. We use two of these representations for enumerative encoding of the Grassmannian. One enumerative encoding is based on a Ferrers diagram representation of subspaces; and another is based on an identifying vector and a reduced row echelon form representation of subspaces. A third method which combines the previous two is more efficient than the other two enumerative encodings. Each enumerative encoding is induced by some ordering of the Grassmannian. These orderings also induce lexicographic codes in the Grassmannian. Some of these codes suggest a new method to generate error-correcting codes in the Grassmannian with larger size than the current known codes.
Solving the Fermion Flavor Problem using Renormalization Group Flow

A long-standing puzzle of the current Standard Model for particle physics is that both leptons and quarks arise in replicated patterns. Our work suggests that the number of fermion flavors may be directly derived from the dynamics of Renormalization Group (RG) equations. Specifically, we argue that the number of flavors results from demanding stability of the RG flow about its fixed-point solution.
CASPaR: Congestion avoidance shortest path routing for delay tolerant networks

Unlike traditional transmission control protocol/Internet protocol–based networks, delay/disruption tolerant networks may experience connectivity disruptions and guarantee no end-to-end connectivity between source and destination. As the popularity of delay/disruption tolerant networks continues to rise, so does the need for a robust and low-latency routing protocol. A one-copy, shortest path delay/disruption tolerant network routing protocol that addresses congestion avoidance and maximizes total network bandwidth utility is crucial to efficient packet delivery in high-load networks and is the motivation behind the development of the congestion avoidance shortest path routing. Congestion avoidance shortest path routing is designed to either route undeliverable packets “closer” to their destinations or hold onto them when advantageous to do so. Congestion avoidance and bottleneck minimization are integrated into its design. Moreover, congestion avoidance shortest path routing negotiates node queue differentials between neighbors similar to backpressure algorithms and maps shortest paths without any direct knowledge of node connectivity outside of its own neighborhood. Simulation results show that congestion avoidance shortest path routing outperforms well-known protocols in terms of packet delivery probability and latency and is still quite efficient in terms of overhead requirements. Finally, we explore the effectiveness of modeling a variant of congestion avoidance shortest path routing based on the resistance to current flow of electrical circuits in an attempt to further reduce network congestion. Preliminary results indicate a noticeable performance increase when this method is used.
A hybrid approach to finding negated and uncertain expressions in biomedical documents

More and more biomedical documents are digitally written and stored. To make the most of the rich resources, it is crucial to precisely locate the information pertinent to users' interests. One of the obstacles in finding information in natural language text is negations, which deny or reverse the meaning of a sentence or clause. This is especially problematic in the biomedical domain since scientific findings and clinical records often contain negated expressions to explicitly state negative effects or the absence of symptoms. Ignoring such negated expressions result in more irrelevant information and may even lead to false conclusions. Therefore, identifying negative words and their scopes are important sub-tasks in biomedical information processing. This paper reports on our ongoing work on a hybrid approach to negation identification combining statistical and heuristic approaches. Our approach is evaluated on three types of biomedical documents in comparison with an existing machine learning approach. In addition, the empirical results are manually analyzed to better understand the nature of the problems.