Miracle-dz/UniFAR-FTUs huggingface dataset:
Modeling Congestion in Backbone Routers

Traffic engineering models based on end-to-end loss probabilities and delays do not scale well to fast backbone links. In this paper, we investigate the nature of congestion events in highly aggregated flows. An examination of congestion events shows distinct phases of queue buildup, packet dropping followed later by TCP reaction, and queue clearing. Evidence of the existence and characteristics of these discrete congestion events is presented using active probe data gathered by the Surveyor project. When connections pass through periodic congestion, the aggregate offered load to neighboring links rises and falls in cadence with the congestion events. We named this group of connections a ”flock” and investigate the implications. Flock formation, distinct from the prior notion of synchronization, can scale to larger numbers of connections depending on the diversity of roundtrip times present. Through simulation we illustrate conditions under which flocking occurs. Unlike prior end-to-end studies, this paper takes the viewpoint of a small number of hops along a long path. A model is presented that predicts important characteristics of the congestion events including the quantity, intensity, and duration. Our results suggest that backbone congestion can be modeled as an interaction between flocks of connections. The model effectively explains the prevalence of discrete congestion events in fast links with high multiplexing factors as well as the phases of congestion.
H ∞ structured model reduction algorithms for linear discrete systems via LMI-based optimisation

In this article, H ∞ structured model reduction is addressed for linear discrete systems. Two important classes of systems are considered for structured model reduction, i.e. Markov jump systems and uncertain systems. The problem we deal with is the development of algorithms with the flexibility to allow any structure in the reduced-order system design, such as the structure of an original system, decentralisation of a networked system, pole assignment of the reduced system, etc. The algorithms are derived such that an associated model reduction error guarantees to satisfy a prescribed H ∞ norm-bound constraint. A new condition for the existence of desired reduced-order models preserving a certain structure is presented in a set of linear matrix inequalities (LMI) and non-convex equality constraints. Effective computational algorithms involving LMI are suggested to solve the matrix inequalities characterising a solution of the structured model reduction problem. Numerical examples demonstrate the advantages of the proposed model reduction method.
Zero-Shot Multi-task Hallucination Detection

In recent studies, the extensive utilization of large language models has underscored the importance of robust evaluation methodologies for assessing text generation quality and relevance to specific tasks. This has revealed a prevalent issue known as hallucination, an emergent condition in the model where generated text lacks faithfulness to the source and deviates from the evaluation criteria. In this study, we formally define hallucination and propose a framework for its quantitative detection in a zero-shot setting, leveraging our definition and the assumption that model outputs entail task and sample specific inputs. In detecting hallucinations, our solution achieves an accuracy of 0.78 in a model-aware setting and 0.61 in a model-agnostic setting. Notably, our solution maintains computational efficiency, requiring far less computational resources than other SOTA approaches, aligning with the trend towards lightweight and compressed models.
Congestion Control in Real Time Applications

The amount of traffic generated by RTAs has increased substantially over the years. RTA will face congestion where there is any form of bottleneck restricting traffic. This will result in packet loss or delayed traffic which is unacceptable for RTAs. Therefore it is desirable for RTAs to implement congestion control mechanism to improve the stability of networks. TCP Friendly Rate Control (TFRC) is a congestion control algorithm that provides a smooth transmission rate for RTAs. TFRC is a congestion control mechanism for unicast flows operating in a best effort internet environment. It is reasonably fair when competing for bandwidth with TCP flows in congested network, but has a much lower variation of throughput over time compared with TCP. In this work we use NS2, the network simulator for simulation of TFRC. TFRC is simulated in different environments, limitations are identified and modifications are proposed.
Dermatokinetics as an Important Tool to Assess the Bioavailability of Drugs by Topical Nanocarriers.

BACKGROUND Now-a-days, numerous nanocarrier-based drug products for topical applications are present in the market and number of similar products are being researched. To estimate the amount of drug delivery to skin, the scientists have now established techniques for separation of skin layers for the determination of drug concentrations. This forms the basis of pharmacokinetics of drug(s) in skin, i.e., dermatokinetics. However, dermatokinetic modeling of topical products is still a colossal challenge. Assessment of bioavailability helps in determination of safety and efficacy of topical formulations. OBJECTIVE This article is an attempt to explore the usefulness and methodologies of dermatokinetics for nanocarriermediated topical delivery. It also showcases challenges in methodologies used for determination of dermatokinetic parameters along with advantages. METHOD All the articles (research and review) used for writing the manuscript were collected from various search engines like Science Direct, Google Scholar, PubMed and Eureka Select using keywords like dermatokinetics, novel drug delivery systems, bioequivalence, bioavailability and topical delivery. CONCLUSION As the methods used for determination of pharmacokinetics of oral and intravenous formulations are not useful for dermatokinetic assessment, various methods like tape stripping, microdialysis and vasoconstrictor assays are being used for dermatokinetic assessment. These methods are not only useful to determine the drug concentrations in skin layers, but can also be used to correlate the toxic effects of xenobiotics.
Rule-based visual speech synthesis

A system for rule based audiovisual text-to-speech synthesis has been created. The system is based on the KTH text-to-speech system which has been complemented with a three-dimensional parameterized model of a human face. The face can be animated in real time, synchronized with the auditory speech. The facial model is controlled by the same synthesis software as the auditory speech synthesizer. A set of rules that takes coarticulation into account has been developed. The audiovisual text-to-speech system has also been incorporated into a spoken man-machine dialogue system that is being developed at the department.
CSRL model checking with closed-form bounding distributions

Continuous Stochastic Logic (CSL) which lets to express real time probabilistic properties on Continuous Time Markov Chains (CTMC) has been augmented by reward structures to check also performability measures. Thus Continuous Stochastic Reward Logic (CSRL) defined on Markov Reward Models (MRM) provides a framework to verify performance-related and as well as dependability-related measures. Probabilistic model checking can be provided through bounding transient, steady-state distributions of the underlying Markov chain, since models are checked to see if the considered measures are guaranteed or not. We propose to extend the model checking algorithm that we have proposed for CSL to the CSRL operators. This method is based on the construction of bounding models having closed-form transient and steady-state distributions by means of Stochastic Comparison technique. In the case when the model can be checked by this method we gain significantly in time and memory complexity. However in case when we can not conclude if the considered formula is satisfied or not, we may apply classical model checking algorithms.
Imprecise arithmetic for low power image processing

Sometimes reducing the precision of a numerical processor, by introducing errors, can lead to significant performance (delay, area and power dissipation) improvements without compromising the overall quality of the processing. In this work, we show how to perform the two basic operations, addition and multiplication, in an imprecise manner by simplifying the hardware implementation. With the proposed “sloppy” operations, we obtain a reduction in delay, area and power dissipation, and the error introduced is still acceptable for applications such as image processing.
IFIM: a database of integrated fitness information for microbial genes

Knowledge of an organism’s fitness for survival is important for a complete understanding of microbial genetics and effective drug design. Current essential gene databases provide only binary essentiality data from genome-wide experiments. We therefore developed a new database that Integrates quantitative Fitness Information for Microbial genes (IFIM). The IFIM database currently contains data from 16 experiments and 2186 theoretical predictions. The highly significant correlation between the experiment-derived fitness data and our computational simulations demonstrated that the computer-generated predictions were often as reliable as the experimental data. The data in IFIM can be accessed easily, and the interface allows users to browse through the gene fitness information that it contains. IFIM is the first resource that allows easy access to fitness data of microbial genes. We believe this database will contribute to a better understanding of microbial genetics and will be useful in designing drugs to resist microbial pathogens, especially when experimental data are unavailable. Database URL: http://cefg.uestc. edu.cn/ifim/ or http://cefg.cn/ifim/
Lunch Buddy Mentoring for Children Victimized by Peers: Two Pilot Studies

The authors describe two pilot studies examining the potential benefits of Lunch Buddy (LB) mentoring, a manualized approach to school-based mentoring. LB mentoring takes place during the school lunch period and is designed to promote positive changes in children's peer relationships. In both studies, changes in peer victimization were assessed after one and three semesters of LB mentoring. In the first study, LB mentors were paired with elementary school children (N = 24) identified by child and teacher reports as victimized by peers. Results revealed significant declines in self- and teacher-rated victimization after one and three semesters of mentoring. In the second study, LB mentors were paired with victimized children who also engaged in bullying behavior (N = 23). Results indicated significant declines in self- and teacher-ratings of peer victimization after three semesters of mentoring. In both studies, ratings of perceived harm were low and ratings of satisfaction were high. Discussed are the implications of these findings for future research and for efforts to develop a nonharmful way to help children who are repeatedly victimized.
Towards Lossless ANN-SNN Conversion under Ultra-Low Latency with Dual-Phase Optimization

Spiking neural network (SNN) operating with asynchronous discrete events shows higher energy efﬁciency. A popular approach to implement deep SNNs is ANN-SNN conversion combining both efﬁcient training in ANNs and efﬁcient inference in SNNs. However, the previous works mostly re-quired thousands of time steps to achieve lossless conversion. In this paper, we ﬁrst identify the un-derlying cause, i.e., misrepresentation of the negative or overﬂow residual membrane potential in SNNs. Furthermore, we systematically analyze the conversion error between SNNs and ANNs, and then decompose it into three folds: quantization error, clipping error, and residual membrane potential representation error. With such insights, we propose a dual-phase conversion algorithm to minimize those errors. As a result, our model achieves SOTA in both accuracy and accuracy-delay tradeoff with deep architectures (ResNet and VGG net). Speciﬁcally, we report SOTA accuracy within 16 × speedup compared with the latest results. Mean-while, lossless conversion is performed with at least 2 × faster reasoning performance.
Human Behavior Analysis Using Deformable Triangulations

This paper presents a new posture classification system to analyze different human behaviors directly from video sequences using the technique of triangulation. For well analyzing each posture in the video sequences, we propose a triangulation-based method to triangulate it to different triangle meshes from which two important posture features are then extracted, i.e., the ones of skeleton and centroid context. The first one is used for a coarse search and the second one is for a finer classification to classify postures in more details. For the first descriptor, we take advantages of a dfs (depth-first search) scheme to extract the skeleton features of a posture from its triangulation result. Then, with the help of skeleton information, we can define a new shape descriptor, i.e., centroid context, to describe a posture up to a semantic level. That is, the centroid context is a finer descriptor to describe a posture not only from its whole shape but also from its body parts. Since the two descriptors are complement to each other, all desired human postures can be compared and classified very accurately. The nice ability of posture classification can help us generate a set of key postures for transferring a behavior sequence to a set of symbols. Then, a novel string matching scheme is proposed to analyze different human behaviors. Experimental results have proved that the proposed method is robust, accurate, and powerful in human behavior analysis
Sex Determining Region Y-Box 2 (SOX2) Amplification Is an Independent Indicator of Disease Recurrence in Sinonasal Cancer

Objectives The transcription factor SOX2 (3q26.3-q27) is an embryonic stem cell factor contributing to the induction of pluripotency in terminally differentiated somatic cells. Recently, amplification of the SOX2 gene locus has been described in squamous cell carcinoma (SCC) of different organ sites. Aim of this study was to investigate amplification and expression status of SOX2 in sinonasal carcinomas and to correlate the results with clinico-pathological data. Materials and Methods A total of 119 primary tumor samples from the sinonasal region were assessed by fluorescence in-situ hybridization and immunohistochemistry for SOX2 gene amplification and protein expression, respectively. Of these, 59 were SSCs, 18 sinonasal undifferentiated carcinomas (SNUC), 10 carcinomas associated with an inverted papilloma (INVC), 19 adenocarcinomas (AD) and 13 adenoid cystic carcinomas (ACC). Results SOX2 amplifications were found in subsets of SCCs (37.5%), SNUCs (35.3%), INVCs (37.5%) and ADs (8.3%) but not in ACCs. SOX2 amplification resulted in increased protein expression. Patients with SOX2-amplified sinonasal carcinomas showed a significantly higher rate of tumor recurrences than SOX2 non-amplified tumors. Conclusion This is the first study assessing SOX2 amplification and expression in a large cohort of sinonasal carcinomas. As opposed to AD and ACC, SOX2 amplifications were detected in more than 1/3 of all SCCs, SNUCs and INVCs. We therefore suggest that SNUCs are molecularly closely related to SCCs and INVCs and that these entities represent a subgroup of sinonasal carcinomas relying on SOX2 acquisition during oncogenesis. SOX2 amplification appears to identify sinonasal carcinomas that are more likely to relapse after primary therapy, suggesting that these patients might benefit from a more aggressive therapy regime.
Native and Non-Native Plants Provide Similar Refuge to Invertebrate Prey, but Less than Artificial Plants

Non-native species introductions are widespread and can affect ecosystem functioning by altering the structure of food webs. Invading plants often modify habitat structure, which may affect the suitability of vegetation as refuge and could thus impact predator-prey dynamics. Yet little is known about how the replacement of native by non-native vegetation affects predator-prey dynamics. We hypothesize that plant refuge provisioning depends on (1) the plant’s native status, (2) plant structural complexity and morphology, (3) predator identity, and (4) prey identity, as well as that (5) structurally similar living and artificial plants provide similar refuge. We used aquatic communities as a model system and compared the refuge provided by plants to macroinvertebrates (Daphnia pulex, Gammarus pulex and damselfly larvae) in three short-term laboratory predation experiments. Plant refuge provisioning differed between plant species, but was generally similar for native (Myriophyllum spicatum, Ceratophyllum demersum, Potamogeton perfoliatus) and non-native plants (Vallisneria spiralis, Myriophyllum heterophyllum, Cabomba caroliniana). However, plant refuge provisioning to macroinvertebrate prey depended primarily on predator (mirror carp: Cyprinus carpio carpio and dragonfly larvae: Anax imperator) and prey identity, while the effects of plant structural complexity were only minor. Contrary to living plants, artificial plant analogues did improve prey survival, particularly with increasing structural complexity and shoot density. As such, plant rigidity, which was high for artificial plants and one of the living plant species evaluated in this study (Ceratophyllum demersum), may interact with structural complexity to play a key role in refuge provisioning to specific prey (Gammarus pulex). Our results demonstrate that replacement of native by structurally similar non-native vegetation is unlikely to greatly affect predator-prey dynamics. We propose that modification of predator-prey interactions through plant invasions only occurs when invading plants radically differ in growth form, density and rigidity compared to native plants.
Critical Contribution of Adenosine A2A Receptors in Bone Marrow–Derived Cells to White Matter Lesions Induced by Chronic Cerebral Hypoperfusion

Abstract Adenosine A2A receptors (A2ARs) in distinct cellular types may exert different and even opposite effects on many neurologic disorders; A2ARs in bone marrow–derived cells (BMDCs) have been shown to play important roles in various brain injuries. We previously showed that global A2AR inactivation aggravates chronic cerebral hypoperfusion–induced white matter lesions (WMLs); however, the specific cell populations responsible for A2AR-mediated signaling remain unknown. In the present study, we developed chimeric mice in which A2ARs were either selectively inactivated or reconstituted in BMDCs by transplanting bone marrow from global A2AR gene knockout or wild-type mice into wild-type or gene knockout mice, respectively. Chimeric mice were subsequently subjected to chronic cerebral hypoperfusion by bilateral common carotid artery stenosis, and the effects of BMDC A2ARs on WMLs were evaluated. The selective inactivation of A2AR in BMDCs aggravated chronic cerebral hypoperfusion–induced WMLs, promoted microglial activation, and increased proinflammatory cytokine expression, whereas the selective reconstitution or activation of A2AR in BMDCs using the agonist CGS21680 produced the opposite effects. These results demonstrate that A2ARs in BMDCs are important modulators of WMLs induced by chronic cerebral hypoperfusion; this modulation might be associated with the regulation of inflammatory cytokine production.
A Related Entity based Approach for Knowledge Base Acceleration

In this paper we present the overview of our work in the TREC 2013 KBA Track. The goal is to find documents which may contribute to the update of knowledge base entries (e.g., Wikipedia or Freebase articles). Two tasks are introduced in this year’s track: (1) Cumulative Citation Recommendation (CCR), (2) Streaming Slot Filling (SSF). Particularly, we focus on the CCR task, follow our previous work on TREC 2012 KBA Track [3] and propose an improved approach by incorporating weighting to related entities. Our approach is based on the framework of leveraging related entities for document filtering. In particular, we pool related entities from the profile page (i.e., Wikipedia) of target entity, estimate the weight of each related entities based on the training data, and apply the weighted related entities to estimate the confidence scores of streaming documents. We explore three methods based on different weighting strategies: (1) all related entities get zero weight, which is equivalent to exact matching against the target entity only. (2) all related entities get same weight. (3) related entities are weighted based on training data. Experiments on the KBA Stream Corpus 2013 reveal the effectiveness of our approach.
Role of maternal serum ferritin in prediction of preterm labour

Abstract The aim of this study was to evaluate the value of measurement of serum ferritin level in pregnant women to predict preterm labour. The study included 236 women whose haemoglobin (Hb) levels were ≥10.5 gm/dl and gestational age (GA) was less than 30 weeks. Serum ferritin levels were measured at 30 weeks of gestational age. At the end of the study, 23 women delivered with preterm premature rupture of membrane (PPROM) and 17 women delivered before 37 weeks but without PROM (study group). The rest of the pregnant women (196 women) delivered between 37 and 40 weeks (control group). We found a significant difference between the two groups with respect to serum ferritin level. The cut off value of serum ferritin between the two groups was 31 ng/ml with sensitivity 92.8%, specificity 99.4%, positive predictive value 97.5%, negative predictive value 98.4% and accuracy 98.3%. Impact statement What is already known on this subject: maternal serum ferritin has been found to be elevated in women who delivered preterm. What the results of this study add: In this study, we have shown that serum ferritin 31 ng/ml is the optimal cut-point between preterm and full-term women. What the implications are of these findings for clinical practice and/or further research: Serum ferritin 31 ng/ml could be proposed as a potential helpful marker to predict preterm labour.
Construction of a novel oligonucleotide array‐based transcription factor interaction assay platform and its uses for profiling STAT1 cofactors in mouse fibroblast cells

Here, we describe a novel oligonucleotide array‐based transcription factor (TF) interaction assay platform that can directly identify cointeracting TF complexes following binding to their regulatory DNA elements. This platform that combines immuno‐coprecipitation technology with our previously reported oligonucleotide array‐based TF assay (OATFA), is named targeted immuno‐coprecipitation OATFA (TIC‐OATFA). We illustrate use of the system to identify interaction partners of STAT1 (signal transducer and activator of transcription proteins 1) in mouse fibroblasts. Several previously known partners of STAT1, as well as new partners, were identified by TIC‐OATFA, including the upstream stimulatory factors 1 and 2 (USF1, USF2), nuclear factor of activated T cells, TATA box‐binding protein, nuclear factor erythroid‐derived 2, nuclear factor‐kappa B, and nuclear factor 1. Both USF1 and nuclear factor‐kappa B are well known to interact with STAT1, but the other five TFs are previously unreported STAT1 interaction partners. We examined interactions between one new TF, USF2, and STAT1 in detail. USF2 belongs to the group of bHLH‐zip transcription factors, which in a number of diseases including cancers, has enhanced activity. In summary, a novel oligonucleotide array‐based assay platform was developed and used to study interactions between STAT1 and functional TF binding partners, revealing that USF2 and potentially four other new TFs are partners of STAT1 in an IFN‐γ stimulated mouse fibroblast cell line.