Miracle-dz/UniFAR-FTUs huggingface dataset:
RepNet: Cutting Tail Latency in Data Center Networks with Flow Replication

Data center networks need to provide low latency, especially at the tail, as demanded by many interactive applications. To improve tail latency, existing approaches require modifications to switch hardware and/or end-host operating systems, making them difficult to be deployed. We present the design, implementation, and evaluation of RepNet, an application layer transport that can be deployed today. RepNet exploits the fact that only a few paths among many are congested at any moment in the network, and applies simple flow replication to mice flows to opportunistically use the less congested path. RepNet has two designs for flow replication: (1) RepSYN, which only replicates SYN packets and uses the first connection that finishes TCP handshaking for data transmission, and (2) RepFlow which replicates the entire mice flow. We implement RepNet on {\tt node.js}, one of the most commonly used platforms for networked interactive applications. {\tt node}'s single threaded event-loop and non-blocking I/O make flow replication highly efficient. Performance evaluation on a real network testbed and in Mininet reveals that RepNet is able to reduce the tail latency of mice flows, as well as application completion times, by more than 50\%.
Overview of the BioCreative VI Precision Medicine Track Mining protein interactions and mutations for precision medicine

The Precision Medicine Track in BioCreative VI aims to bring together the biomedical text mining community and the biocuration community for a novel challenge composed of two tasks: 1) Triage task focused on identifying scientific articles that describe protein-protein interactions (PPI) being disrupted or significantly affected by the presence of genetic mutations, and 2) Relation extraction task focused on extracting the affected protein pairs. This is a novel challenge for the BioNLP community and, to assist system developers and the assessment of such an endeavor, we created the first large-scale manually annotated corpus of scientific articles that describe such information relevant to precision medicine initiative. The training corpus contained 4,082 articles annotated for triage, of which 598 PubMed articles were annotated for relations. The testing corpus contained 1,464 PubMed articles annotated for both triage and relations. Ten teams worldwide participated in the triage task and sent in results for 22 distinct text mining models. Six teams participated in the relation extraction task and sent in results from 14 different text mining systems. When comparing the text mining system predictions with human annotations, for the triage task, the best F-score was 69.5%, the best precision was 61.6%, the best recall was 97.9% and the best average precision was 72.8%. For the relation extraction task, when we account for similar gene identifiers with HomoloGene database, the best F-score was 37.3%, the best precision was 45.4%, and the best recall was 53.9%. Given the level of participation and team results we find our task to be successful in engaging the text-mining research community, producing a first-of-its-kind, large, manually annotated corpus of scientific articles relevant for precision medicine, and providing the first results of automatically identifying PubMed articles that describe PPI affected by mutations, and extracting the affected relations. Keywords—precision medicine, corpus annotation, relation extraction, protein-protein interaction, mutation, information extraction.
Two Birds and One Stone: Gaussian Interference Channel with a Shared Out-of-Band Relay

The two-user Gaussian interference channel with a shared out-of-band relay is considered. The relay observes a linear combination of the source signals and broadcasts a common message to the two destinations, through a perfect link of fixed rate R_0 bits per channel use. The out-of-band nature of the relay is reflected by the fact that the common relay message does not interfere with the received signal at the two destinations. We are interested in designing "oblivious" relay strategies that incrementally improve upon the communication strategy in place, i.e., the source encoding strategy (apart from rate allocation) is independent of the relay presence. An ideal relay strategy would have a two-for-one gain, i.e., for every common bit relayed, the rate of each user is increased by one bit. We show that this ideal upper bound cannot be always achieved. Still, for a range of weak interference regimes and for R_0 below a certain threshold depending on signal-to-noise and interference-to-noise ratios (SNR and INR), we show an oblivious relay strategy that incrementally improves the sum rate by 2R_0-Delta bits per channel use, where Delta=1.95 is a constant for all channel coefficients and R_0. For a symmetric interference channel, the approximate two-for-one gain is achieved in the regime of log INR / log SNR < 2/3. A similar general result is also found for an asymmetric interference channel. It is further shown that an oblivious relay strategy achieves the entire capacity region of the interference-relay channel model at hand to within the constant gap Delta = 1.95 bits, for R_0 below a threshold that depend on the ratio log INR / log SNR. Instrumental to this result is the design of a suitable quantize-and-forward (QF) strategy at the relay, that can benefit both destinations.
Counting triangles in real-world graph streams: Dealing with repeated edges and time windows

Graphs in the real-world are often temporal and can be represented as a "stream" of edges. Estimating the number of triangles in a graph observed as a stream of edges is a fundamental problem in data mining. Our goal is to design a single pass space-efficient streaming algorithm for estimating triangle counts. While there are numerous algorithms for this problem, they all (implicitly or explicitly) assume that the stream does not contain duplicate edges. However, real graph streams are rife with duplicate edges. The work around is typically an extra unaccounted pass (storing all the edges! ) just to "clean up" the data. Furthermore, previous work tends to aggregate all edges to construct a graph, discarding the temporal information. It will be much more informative to investigate temporal windows, especially multiple time windows simultaneously. Can we estimate triangle counts for multiple time windows in a single pass even when the stream contains repeated edges? In this work, we give the first algorithm for estimating the triangle count of a multigraph stream of edges over arbitrary time windows. We build on existing "wedge sampling" work for triangle counting. Duplicate edges create significant biasing issues for small space streaming algorithms, which we provably resolve through a subtle debiasing mechanism. Moreover, our algorithm seamlessly handles multiple time windows. The final result is theoretically provable and has excellent performance in practice. Our algorithm discovers fascinating transitivity and triangle trends in real-world temporal graphs.
Studying Maximum Information Leakage Using Karush-Kuhn-Tucker Conditions

When studying the information leakage in programs or protocols, a natural question arises: “what is the worst case scenario? ”. This problem of identifying the maximal leakage can be seen as a channel capacity problem in the information theoretical sense. In this paper, by combining two powerful theories: Information Theory and Karush‐Kuhn‐Tucker conditions, we demonstrate a very general solution to the channel capacity problem. Examples are given to show how our solution can be applied to practical contexts of programs and anonymity protocols, and how this solution generalizes previous approaches to this problem.
Frequency-Domain Compressive Channel Estimation for Frequency-Selective Hybrid Millimeter Wave MIMO Systems

Channel estimation is useful in millimeter wave (mm-wave) MIMO communication systems. Channel state information allows optimized designs of precoders and combiners under different metrics, such as mutual information or signal-to-interference noise ratio. At mm-wave, MIMO precoders and combiners are usually hybrid, since this architecture provides a means to trade-off power consumption and achievable rate. Channel estimation is challenging when using these architectures, however, since there is no direct access to the outputs of the different antenna elements in the array. The MIMO channel can only be observed through the analog combining network, which acts as a compression stage of the received signal. Most of the prior work on channel estimation for hybrid architectures assumes a frequency-flat mm-wave channel model. In this paper, we consider a frequency-selective mm-wave channel and propose compressed sensing-based strategies to estimate the channel in the frequency domain. We evaluate different algorithms and compute their complexity to expose tradeoffs in complexity overhead performance as compared with those of previous approaches.
On empirical cumulant generating functions of code lengths for individual sequences

We consider the problem of lossless compression of individual sequences using finite-state (FS) machines, from the perspective of the best achievable empirical cumulant generating function (CGF) of the code length, i.e., the normalized logarithm of the empirical average of the exponentiated code length. Since the probabilistic CGF is minimized in terms of the Renyi entropy of the source, one of the motivations of this study is to derive an individual-sequence analogue of the Renyi entropy, in the same way that the FS compressibility is the individual-sequence counterpart of the Shannon entropy. We consider the CGF of the code-length both from the perspective of fixed-to-variable (F-V) length coding and the perspective of variable-to-variable (V-V) length coding, where the latter turns out to yield a better result, that coincides with the FS compressibility. We also extend our results to compression with side information, available at both the encoder and decoder. In this case, the V-V version no longer coincides with the FS compressibility, but results in a different complexity measure.
F eb 2 01 6 HORNET : High-speed Onion Routing at the Network Layer

We present HORNET, a system that enables high-speed end-toend anonymous channels by leveraging next-generation network architectures. HORNET is designed as a low-latency onion routing system that operates at the network layer thus enabling a wide ra nge of applications. Our system uses only symmetric cryptograp hy for data forwarding yet requires no per-flow state on interme diat routers. This design enables HORNET routers implemented on offthe-shelf hardware to process anonymous traffic at over 93 Gb /s. HORNET is also highly scalable, adding minimal processing o verhead per additional anonymous channel.
To Frontalize or Not To Frontalize: Do We Really Need Elaborate Pre-Processing to Improve Face Recognition Performance?

—Automatic face recognition performance has improved remarkably in the last decade. Much of this success can be attributed to the development of deep learning techniques like convolutional neural networks (CNNs). But the training process for CNNs requires a large amount of clean and well-labelled training data. If a CNN is intended to work with non-frontal face images, should this training data be diverse in terms of facial poses, or should face images be frontalized as a pre-processing step? We address this question in this paper. We evaluate a set of popular facial landmarking and pose frontalization algorithms to understand their effect on facial recognition performance. We also introduce a new automatic frontalization scheme that operates over a single image without the need for a subject-speciﬁc 3D model, and perform a comparative analysis between the new scheme and other methods in the literature. A CNN trained on face images frontalized using different pre-processing methods is used to extract features from the Point and Shoot Challenge (PaSC) video dataset. The veriﬁcation and identiﬁcation performance of the CNN serves to quantify the effectiveness of each landmarking and frontalization scheme. We ﬁnd that frontalization, although an intuitive pre-processing strategy, does not signiﬁcantly improve face recognition performance when compared with a simple 2D face alignment.
AD -MINISTER

Considering the importance of web 3.0 enterprises in online retailing, this article addresses how these enterprises operate in the Iranian context. DigiKala is an online retailer that has been one of the most successful Start-ups in Iran and has gained the 85-90% of the online retailers market of the country. This enterprise has started to evolve its value chain to a platform business. On the other hand, twitter as a free social networking microblogging has been one of the most important tools in the online market, as customers, especially on digital markets, share their positive and negative comments in regard with their purchase experience. In this research, firstly social network analysis (SNA) method used to understand the relationships and individual nodes in the network. Then, the thematic analysis method applied to analyze the sent tweets to achieve users’ behavioral patterns. Using different social media, like Twitter, and sharing the feedback is also valid for Digikala as a web 3.0 enterprises. This research studied the network of tweets in a special event, namely Black Friday, which DigiKala offered considerable discounts for the customers. Some implications of our study are also presented at the end of this article.
MANCaLog: A Logic for Multi-Attribute Network Cascades (Extended Abstract)

Paulo Shakarian Network Science Center and Dept. of Electrical Engineering and Computer Science U.S. Military Academy West Point, NY 10996 paulo@shakarian.net Gerardo I. Simari Dept. of Computer Science University of Oxford Wolfson Building, Parks Road Oxford OX1 3QD, UK gerardo.simari@cs.ox. ac.uk Robert Schroeder CORE Lab Defense Analysis Dept. Naval Postgraduate School Monterey, CA 93943 rcschroe@nps.edu
More complex encoder is not all you need

U-Net and its variants have been widely used in medical image segmentation. However, most current U-Net variants confine their improvement strategies to building more complex encoder, while leaving the decoder unchanged or adopting a simple symmetric structure. These approaches overlook the true functionality of the decoder: receiving low-resolution feature maps from the encoder and restoring feature map resolution and lost information through upsampling. As a result, the decoder, especially its upsampling component, plays a crucial role in enhancing segmentation outcomes. However, in 3D medical image segmentation, the commonly used transposed convolution can result in visual artifacts. This issue stems from the absence of direct relationship between adjacent pixels in the output feature map. Furthermore, plain encoder has already possessed sufficient feature extraction capability because downsampling operation leads to the gradual expansion of the receptive field, but the loss of information during downsampling process is unignorable. To address the gap in relevant research, we extend our focus beyond the encoder and introduce neU-Net (i.e., not complex encoder U-Net), which incorporates a novel Sub-pixel Convolution for upsampling to construct a powerful decoder. Additionally, we introduce multi-scale wavelet inputs module on the encoder side to provide additional information. Our model design achieves excellent results, surpassing other state-of-the-art methods on both the Synapse and ACDC datasets.
Off-line Arabic Handwriting Recognition : A Survey

The automatic recognition of text on scanned images has enabled many applications such as searching for words in large volumes of documents, automatic sorting of postal mail, and convenient editing of previously printed documents. The domain of handwriting in the Arabic script presents unique technical challenges and has been addressed more recently than other domains. Many different methods have been proposed and applied to various types of images. This paper provides a comprehensive review of these methods. It is the first survey to focus on Arabic handwriting recognition and the first Arabic character recognition survey to provide recognition rates and descriptions of test data for the approaches discussed. It describes background on the field, discussion of the methods, and future research directions.
Breathing and Oxygen Carrying Capacity in Ts65Dn and Down Syndrome

Abstract Individuals with Down syndrome (Ds) are at increased risk of respiratory infection, aspiration pneumonia, and apnea. The Ts65Dn mouse is a commonly used model of Ds, but there have been no formal investigations of awake breathing and respiratory muscle function in these mice. We hypothesized that breathing would be impaired in Ts65Dn vs. wild-type (WT), and would be mediated by both neural and muscular inputs. Baseline minute ventilation was not different at 3, 6, or 12 mo of age. However, VT/Ti, a marker of the neural drive to breathe, was lower in Ts65Dn vs. WT and central apneas were more prevalent. The response to breathing hypoxia was not different, but the response to hypercapnia was attenuated, revealing a difference in carbon dioxide sensing, and/or motor output in Ts65Dn. Oxygen desaturations were present in room air, demonstrating that ventilation may not be sufficient to maintain adequate oxygen saturation in Ts65Dn. We observed no differences in arterial PO2 or PCO2, but Ts65Dn had lower hemoglobin and hematocrit. A retrospective medical record review of 52,346 Ds and 52,346 controls confirmed an elevated relative risk of anemia in Ds. We also performed eupneic in-vivo electromyography and in-vitro muscle function and histological fiber typing of the diaphragm, and found no difference between strains. Overall, conscious respiration is impaired in Ts65Dn, is mediated by neural mechanisms, and results in reduced hemoglobin saturation. Oxygen carrying capacity is reduced in Ts65Dn vs. WT, and we demonstrate that individuals with Ds are also at increased risk of anemia.
Influence of polymorphisms in candidate genes on carcass and meat quality traits in rabbits

Candidate gene is a powerful approach to study gene-trait association and offers valuable information for genetic improvement using marker-assisted selection. The current work aimed to study the polymorphisms of four single nucleotide polymorphism (SNPs) at located growth hormone (GH), insulin-like growth factor-II (IGF-II), fat mass and obesity-associated (FTO), and insulin receptor substrate-1 (IRS-1) genes, and their association with the carcass, and meat quality traits in rabbits. The SNPs were genotyped using RFLP-PCR in New Zealand White and local Baladi rabbits. The results revealed that the heterozygous genotype was the most frequent in all cases, except for the FTO SNP in LB rabbits. There was a significant effect for GH genotypes on meat lightness after slaughter and hind-part weight. While, IGF-II mutation significantly affected slaughter, hot carcass, commercial carcass, and hind-part weights. The FTO SNP was associated with cooking loss and intramuscular fat weight, and the IRS-1 SNP was significantly associated with drip loss and intramuscular fat. Specific-breed effects were obtained for IGF-II SNP on cooking loss, and for the intramuscular fat. Although the results suggested that these mutations are useful candidate genes for selection, more research for detecting more variants associated with carcass and meat quality traits in rabbits are recommended.
RASTER PROCEDURES FOR MULTI-CRITERIA/MULTI-OBJECTIVE DECISIONS

Decisions about the allocation of land typically involve the evaluation of multiple criteria according to several, often conflicting, objective. With the advent of GIS, we now have the opportunity for a more explicity reasoned environmental decision making process. However, GIS has been slow to develop decision support tools, more typically relying on procedures outside the GIS software. In this paper the issues of multi-criteria/multi- objective decision making are discussed, along with an exploration of a new set of decision support tools appropriate for the large data-handling needs of raster GIS. A case study is used to illustrate these tools as developed for the IDRISI geographic analysis software system
excessive daytime sleepiness and adherence to antihypertensive medications among Blacks: analysis of the counseling African Americans to control hypertension (cAATch) trial

Background: Excessive daytime sleepiness (EDS) often occurs as a result of insufficient sleep, sleep apnea, illicit substance use, and other medical and psychiatric conditions. This study tested the hypothesis that blacks exhibiting EDS would have poorer self-reported adherence to hypertensive medication using cross-sectional data from the Counseling African-Americans to Control Hypertension (CAATCH) trial. Methods: A total of 1,058 hypertensive blacks (average age 57 ± 12 years) participated in CAATCH, a randomized controlled trial evaluating the effectiveness of a multilevel intervention for participants who receive care from community health centers in New York City. Data analyzed in this study included baseline sociodemographics, medical history, EDS, and medication adherence. We used the Epworth Sleepiness Scale, with a cutoff score of $ 10, to define EDS. Medication adherence was measured using an abbreviated Morisky Medication Adherence scale, with a score . 0 indicating nonadherence. Results: Of the sample, 71% were female, 72% received at least a high school education, 51% reported a history of smoking, and 33% had a history of alcohol consumption. Overall, 27% of the participants exhibited EDS, and 44% of those who exhibited EDS were classified as adherent to prescribed antihypertensive medications. Multivariable logistic regression analysis, adjusting for effects of age, body mass index, sex, education, and smoking and drinking history indicated that participants who exhibited EDS were more than twice as likely to be nonadherent (odds ratio 2.28, 95% confidence interval 1.42–3.67, P , 0.001). Conclusion: Analysis of the CAATCH data showed a high prevalence of EDS among hypertensive blacks. EDS is a significant predictor of nonadherence to prescribed medications for hypertension. These findings point to a modifiable variable that can be targeted in future interventions focusing on medication adherence.