Miracle-dz/UniFAR-FTUs huggingface dataset:
Introducing convex layers to the Traveling Salesman Problem

In this paper, we will propose convex layers to the Traveling Salesman Problem (TSP). Firstly, we will focus on human performance on the TSP. Experimental data shows that untrained humans appear to have the ability to perform well in the TSP. On the other hand, experimental data also supports the hypothesis of convex hull i.e. human relies on convex hull to search for the optimal tour for the TSP. Secondly, from the paper published by Bonabeau, Dorigo and Theraulaz, social insect behavior would be able to help in some of the optimizing problems, especially the TSP. Thus, we propose convex layers to the TSP based on the argument that, by the analogy to the social insect behavior, untrained humans' cognition should be able to help in the TSP. Lastly, we will use Tour Improvement algorithms on convex layers to search for an optimal tour for a 13-cities problem to demonstrate the idea.
Solving and Explaining Analogy Questions Using Semantic Networks

Analogies are a fundamental human reasoning pattern that relies on relational similarity. Understanding how analogies are formed facilitates the transfer of knowledge between contexts. The approach presented in this work focuses on obtaining precise interpretations of analogies. We leverage noisy semantic networks to answer and explain a wide spectrum of analogy questions. The core of our contribution, the Semantic Similarity Engine, consists of methods for extracting and comparing graph-contexts that reveal the relational parallelism that analogies are based on, while mitigating uncertainty in the semantic network.We demonstrate these methods in two tasks: answering multiple choice analogy questions and generating human readable analogy explanations. We evaluate our approach on two datasets totaling 600 analogy questions. Our results show reliable performance and low false-positive rate in question answering; human evaluators agreed with 96% of our analogy explanations.
Fairness consideration of bandwidth scheduling for real-time services in IEEE 802.16m wireless networks

Although the scheduling problem of the uplink transmission in the IEEE 802.16 broadband wireless access standard has been extensively discussed, most of the results focus on the quality of service (QoS) issues, e.g. throughput and delay requirement. In real-time services, violent variance of bandwidth request usually triggers some problems of resource allocation, especially when the system bandwidth is saturated. In this paper, we introduce a fair scheduling algorithm named contribution-based scheduling algorithm (CSA) for uplink realtime polling service (rtPS). In this algorithm, a mobile subscriber (MS) is endowed with a contribution credit value at the end of each frame, which is based on the MS's contribution to the system resource utilization. The base station (BS) will allocate system resource according to the credit value. With the proposed fair scheduling algorithm, we guarantee the fairness, delay requirement and bandwidth utilization at the same time which cannot be fully achieved in existing studies.
Automated mental stress recognition through mobile thermal imaging

Mental stress is a critical problem in our modern society. This form of stress strongly affects our well being, and technology is needed to help us to manage health problems. The ability to automatically recognize a person's mental stress can be fundamental in supporting stress and health management. This research focuses on the use of mobile thermal imaging, a new and less explored sensor, to merge the measurement of multiple physiological signatures into one sensor and to build a reliable mental stress automatic recognition model. Mobile thermal imaging has greater potentials for real-world applications given that it is small and light weight, and requires low computation cost. To make mobile thermal imaging a robust multimodal stress sensor, we have so far contributed: i) a new robust respiration tracking method; and ii) a novel respiration-based automatic stress recognition model that works in ubiquitous settings. We are currently investigating new thermal signatures from underexplored body regions (i.e. trapezius muscle) and formulating a research framework to fuse multiple thermal signatures for more reliable stress recognition outcomes.
Bug22 influences cilium morphology and the post-translational modification of ciliary microtubules

Summary Cilia and flagella are organelles essential for motility and sensing of environmental stimuli. Depending on the cell type, cilia acquire a defined set of functions and, accordingly, are built with an appropriate length and molecular composition. Several ciliary proteins display a high degree of conservation throughout evolution and mutations in ciliary genes are associated with various diseases such as ciliopathies and infertility. Here, we describe the role of the highly conserved ciliary protein, Bug22, in Drosophila. Previous studies in unicellular organisms have shown that Bug22 is required for proper cilia function, but its exact role in ciliogenesis has not been investigated yet. Null Bug22 mutant flies display cilia-associated phenotypes and nervous system defects. Furthermore, sperm differentiation is blocked at the individualization stage, due to impaired migration of the individualization machinery. Tubulin post-translational modifications (PTMs) such as polyglycylation, polyglutamylation or acetylation, are determinants of microtubule (MT) functions and stability in centrioles, cilia and neurons. We found defects in the timely incorporation of polyglycylation in sperm axonemal MTs of Bug22 mutants. In addition, we found that depletion of human Bug22 in RPE1 cells resulted in the appearance of longer cilia and reduced axonemal polyglutamylation. Our work identifies Bug22 as a protein that plays a conserved role in the regulation of PTMs of the ciliary axoneme.
A programming by demonstration system for human-robot collaborative assembly tasks

Programming by demonstration (PbD) has been one of the promising approaches to robot programming, where a robot learns how to operate by observing human demonstrations. While most of existing PbD research dealt with single-agent tasks, this paper deals with collaborative tasks, in which a robot and a human collaborate to assemble a structure by coordinating their operations. The system has three parts: demonstration observation parts, task modeling parts, and task execution parts. A scene recognition system is developed for reliable action recognition in human demonstrations. The developed PbD system has successfully been applied to a collaborative assembly tasks with more than twenty assembly steps.
Residual stresses, defects and non-destructive evaluation of FSW joints

Summary This article describes some of the research activities in progress at the German Aerospace Centre, DLR, on the friction stir welding process. It shows in detail the principal defects that can be found in FSW joints, particularly those defects that are due to incomplete filling, incomplete penetration and root discontinuities. The non-destructive ultrasonic method has been used as a promising technique to detect and characterise any of the imperfections mentioned above. The results obtained are confirmed by both light and scanning electron microscopy (SEM). The second part of the analysis focuses on the quantitative evaluation of the residual stresses using the ‘cut compliance’ method, and the mathematical tools provided by fracture mechanics. Finally, two examples of fatigue life predictions for welded joints are presented that are considered to be significant in light of the above considerations. One concerns the propagation of defects in a specimen FSW weld in the presence of a residual stress field and variable amplitude loads, and the other concerns the prediction of the fatigue behaviour of defective FSW specimens without the influence of residual stresses. The results obtained showed good agreement with the experimental data.
Heterogeneous Bayesian ensembles for classifying spam emails

Spam emails have become a major problem in internet communication and can cause potentially serious adverse effects on the recipients if unidentified. Many spam filters have been developed to filter out certain spam emails, but as spammers continuously improve their spamming techniques, the exiting filters may become less effective. This paper presents a heterogeneous ensemble approach that combines several methodologically different filters to work collectively to improve accuracy and reliability in identifying spam emails. A special procedure for building heterogeneous and homogeneous ensembles with Bayesian filter as base learner has been devised and a framework has been designed and implemented. After verifying the framework intensively with 10 other benchmark data sets, it was applied to identify spam emails. The experiments with a spam benchmark corpus indicated that the heterogeneous ensembles achieved more accurate and reliable classifications than the individual and other ensemble filters.
Global analysis of mRNA decay and abundance in Escherichia coli at single-gene resolution using two-color fluorescent DNA microarrays

Much of the information available about factors that affect mRNA decay in Escherichia coli, and by inference in other bacteria, has been gleaned from study of less than 25 of the ≈4,300 predicted E. coli messages. To investigate these factors more broadly, we examined the half-lives and steady-state abundance of known and predicted E. coli mRNAs at single-gene resolution by using two-color fluorescent DNA microarrays. An rRNA-based strategy for normalization of microarray data was developed to permit quantitation of mRNA decay after transcriptional arrest by rifampicin. We found that globally, mRNA half-lives were similar in nutrient-rich media and defined media in which the generation time was approximately tripled. A wide range of stabilities was observed for individual mRNAs of E. coli, although ≈80% of all mRNAs had half-lives between 3 and 8 min. Genes having biologically related metabolic functions were commonly observed to have similar stabilities. Whereas the half-lives of a limited number of mRNAs correlated positively with their abundance, we found that overall, increased mRNA stability is not predictive of increased abundance. Neither the density of putative sites of cleavage by RNase E, which is believed to initiate mRNA decay in E. coli, nor the free energy of folding of 5′ or 3′ untranslated region sequences was predictive of mRNA half-life. Our results identify previously unsuspected features of mRNA decay at a global level and also indicate that generalizations about decay derived from the study of individual gene transcripts may have limited applicability.
Ndufc2 Gene Inhibition Is Associated With Mitochondrial Dysfunction and Increased Stroke Susceptibility in an Animal Model of Complex Human Disease

Background The genetic basis of stroke susceptibility remains to be elucidated. STR1 quantitative trait locus (STR1/QTL) was identified on rat chromosome 1 of stroke‐prone spontaneously hypertensive rat (SHRSP) upon Japanese‐style stroke‐permissive diet (JD), and it contributes to 20% of the stroke phenotype variance. Methods and Results Nine hundred eighty‐six probe sets mapping on STR1 were selected from the Rat RAE230A array and screened through a microarray differential expression analysis in brains of SHRSP and stroke‐resistant SHR (SHRSR) fed with either regular diet or JD. The gene encoding Ndufc2 (NADH dehydrogenase [ubiquinone] 1 subunit), mapping 8 Mb apart from STR1/QTL Lod score peak, was found significantly down‐regulated under JD in SHRSP compared to SHRSR. Ndufc2 disruption altered complex I assembly and activity, reduced mitochondrial membrane potential and ATP levels, and increased reactive oxygen species production and inflammation both in vitro and in vivo. SHRSR carrying heterozygous Ndufc2 deletion showed renal abnormalities and stroke occurrence under JD, similarly to SHRSP. In humans, T allele variant at NDUFC2/rs11237379 was associated with significant reduction in gene expression and with increased occurrence of early‐onset ischemic stroke by recessive mode of transmission (odds ratio [OR], 1.39; CI, 1.07–1.80; P=0.012). Subjects carrying TT/rs11237379 and A allele variant at NDUFC2/rs641836 had further increased risk of stroke (OR=1.56; CI, 1.14–2.13; P=0.006). Conclusions A significant reduction of Ndufc2 expression causes complex I dysfunction and contributes to stroke susceptibility in SHRSP. Moreover, our current evidence may suggest that Ndufc2 can contribute to an increased occurrence of early‐onset ischemic stroke in humans.
Role of the Human Immunodeficiency Virus Type 1 Envelope Gene in Viral Fitness

ABSTRACT A human host offers a variety of microenvironments to the infecting human immunodeficiency virus type 1 (HIV-1), resulting in various selective pressures, most of them directed against the envelope (env) gene. Therefore, it seems evident that the replicative capacity of the virus is largely related to viral entry. In this study we have used growth competition experiments and TaqMan real-time PCR detection to measure the fitness of subtype B HIV-1 primary isolates and autologous env-recombinant viruses in order to analyze the contribution of wild-type env sequences to overall HIV-1 fitness. A significant correlation was observed between fitness values obtained for wild-type HIV-1 isolates and those for the corresponding env-recombinant viruses (r = 0.93; P = 0.002). Our results suggest that the env gene, which is linked to a myriad of viral characteristics (e.g., entry into the host cell, transmission, coreceptor usage, and tropism), plays a major role in fitness of wild-type HIV-1. In addition, this new recombinant assay may be useful for measuring the contribution of HIV-1 env to fitness in viruses resistant to novel antiretroviral entry inhibitors.
GENETIC DIVESITY AND MOLECULAR CHARACTERIZATION OF MUNGBEANGENOTYPES (Vigna radiata (L.) Wilczek)

The present investigation was undertaken to examine the genetic divergence in 50 mungbean germplasm lines for 13 characters using Mahalanobis D2 statistics. The genotypes grouped into eight clusters. Cluster VII had maximum intra-cluster distance while inter-cluster distance was highest between clusters V and VII. Cluster means indicated that none of the clusters was superior for all the characters studied. Therefore, hybridization between genotypes belonging to different clusters is suggested for development of superior genotypes. 10 SSR primers were used for molecular study of which only one gave slight difference among 19 mungbean genotypes. The quality and quantity of DNA used for amplification by PCR is the key to reproducible results and success of genotyping. Especially, DNA purity is extremely crucial for obtaining clear and discriminate patterns. DNA extraction from mungbean is difficult due to presence of contaminants such as phenols. Therefore, the present study was under taken to obtain high quality and pure DNA in mungbean. With few modifications four different DNA extraction protocols were tried in the present study to obtain high quality and pure DNA viz., (I) Doyle and Doyle (1987), (ii) Method of Murray and Thompson (1980), (iii) Porebski et al.(1997), and (iv) Lin et al. (2001). Out of the four methods tried for DNA extraction, the method of Lin et al. (2001) was found most efficient, as the DNA obtained through this protocol was relatively pure which gave amplyfying products in the PCR. The genotype used for the standardization was MGG -361. Molecular characterization of 19 randomly chosen mungbean genotypes was attempted with the eight standardized primers. None of the primers showed scorable polymorphism. The primers VR4, VR5 and VR9, exhibited non specific bands, in addition to the monomorphic bands
Modeling the Dynamics of Disease States in Depression

Major depressive disorder (MDD) is a common and costly disorder associated with considerable morbidity, disability, and risk for suicide. The disorder is clinically and etiologically heterogeneous. Despite intense research efforts, the response rates of antidepressant treatments are relatively low and the etiology and progression of MDD remain poorly understood. Here we use computational modeling to advance our understanding of MDD. First, we propose a systematic and comprehensive definition of disease states, which is based on a type of mathematical model called a finite-state machine. Second, we propose a dynamical systems model for the progression, or dynamics, of MDD. The model is abstract and combines several major factors (mechanisms) that influence the dynamics of MDD. We study under what conditions the model can account for the occurrence and recurrence of depressive episodes and how we can model the effects of antidepressant treatments and cognitive behavioral therapy within the same dynamical systems model through changing a small subset of parameters. Our computational modeling suggests several predictions about MDD. Patients who suffer from depression can be divided into two sub-populations: a high-risk sub-population that has a high risk of developing chronic depression and a low-risk sub-population, in which patients develop depression stochastically with low probability. The success of antidepressant treatment is stochastic, leading to widely different times-to-remission in otherwise identical patients. While the specific details of our model might be subjected to criticism and revisions, our approach shows the potential power of computationally modeling depression and the need for different type of quantitative data for understanding depression.
Throughput and Delay Analysis in Video Streaming Over Block-Fading Channels

We study video streaming over a slow-fading wireless channel. In a streaming application, video packets are required to be decoded and displayed in the order they are transmitted as the transmission goes on. This results in per-packet delay constraints, and the resulting channel can be modeled as a physically degraded fading broadcast channel with as many virtual users as the number of packets. In this paper, we study two important quality of user experience (QoE) metrics, namely throughput and interdecoding delay. We introduce several transmission schemes, and compare their throughput and maximum interdecoding delay performances. We also introduce a genie-aided scheme, which provides theoretical bounds on the achievable performance. We observe that adapting the transmission rate at the packet level, i.e., periodically dropping a subset of the packets, leads to a good tradeoff between the throughput and the maximum interdecoding delay. We also show that an approach based on initial buffering leads to an asymptotically vanishing packet loss rate at the expense of a relatively large initial delay. For this scheme, we derive a condition on the buffering time that leads to throughput maximization.
Algorithmic and Domain Centralization in Distributed Constraint Optimization Problems

Abstract : A class of problems known as Distributed Constraint Optimization Problems (DCOP) has become a growing research interest in computer science because of its difficulty (NP-Complete) and many real-world applications (meeting scheduling, sensor networks, military planning). In this thesis we identify two types of centralization relevant to DCOPs: algorithmic centralization, in which a DCOP algorithm actively centralizes part (or all) of the problem structure, and domain centralization, in which inherent centralization already exists in the domain specification. We explore algorithmic centralization by empirically studying Adopt and OptAPO, two DCOP algorithms which differ in the amount of centralization they use. Our results show that centralizing a problem's structure decreases communication overhead, but increases local computation. We compare the algorithms through our contribution of a new performance metric, Cycle-Based Runtime, which takes both communication costs and local computation time into account. We then explore domain centralization by studying meeting scheduling, which has problem structure clustered at scheduling agents. We present a novel variant of Adopt, called AdoptMVA, which uses a centralized search within agents to take advantage of the partially centralized structure. We show that when agent ordering is controlled for, AdoptMVA outperforms Adopt in situations where communication costs are high. We contribute a Branch & Bound search heuristic which works well for meeting scheduling problems with multiple variables per agent. We also empirically experiment with meeting scheduling, showing that meeting size is in some cases a better indicator of solution difficulty than the number of agents in a problem.
Proliferation of lens epithelial cells on the Acrysof intraocular lens: clinical and histological features of a case

Postoperative deposits on the surface of intraocular lenses (IOLs) have been demonstrated in vivo for many IOL material types. The Acrysof acrylic lens develops a granular material extending from the capsulorhexis margin onto the IOL's anterior surface during the first 4 weeks after surgery in a significant proportion of cases. Complete resolution of this membrane occurs by 3 months in almost all cases. The case is presented of an 81‐year‐old woman who had a persisting membrane covering the IOL surface at 6 months. This membrane was removed surgically and pathology showed the constituent cells to be of lens epithelial cell origin.
Learning optimal ranking with tensor factorization for tag recommendation

Tag recommendation is the task of predicting a personalized list of tags for a user given an item. This is important for many websites with tagging capabilities like last.fm or delicious. In this paper, we propose a method for tag recommendation based on tensor factorization (TF). In contrast to other TF methods like higher order singular value decomposition (HOSVD), our method RTF ('ranking with tensor factorization') directly optimizes the factorization model for the best personalized ranking. RTF handles missing values and learns from pairwise ranking constraints. Our optimization criterion for TF is motivated by a detailed analysis of the problem and of interpretation schemes for the observed data in tagging systems. In all, RTF directly optimizes for the actual problem using a correct interpretation of the data. We provide a gradient descent algorithm to solve our optimization problem. We also provide an improved learning and prediction method with runtime complexity analysis for RTF. The prediction runtime of RTF is independent of the number of observations and only depends on the factorization dimensions. Besides the theoretical analysis, we empirically show that our method outperforms other state-of-the-art tag recommendation methods like FolkRank, PageRank and HOSVD both in quality and prediction runtime.
Distributed cooperative learning over networks via fuzzy logic systems

In this paper, we expand the scope of research on distributed cooperative learning (DCL) via fuzzy logic systems (FLSs) over an undirected and connected network, that is, each node (or learner) cooperatively learn an unknown pattern (or function) and finally reach consensus through local information interaction with their one-hop neighbors. Based on the approximation of FLSs, we present continuous-time and discrete-time DCL algorithms with respect to the aforementioned problem. The optimal coefficient matrix of the FLS is trained by the two proposed algorithms, respectively. And we use algebraic graph theory and Lyapunov approach to prove that the weight coefficient matrix in the algorithms has an exponential convergence rate, respectively. Moreover, the algorithms reduce communication cost and bandwidth compared with the centralized learning (CL) algorithms. Some simulation results illustrate the effectiveness and advantages of the proposed algorithms.