Miracle-dz/UniFAR-FTUs huggingface dataset:
Block-Compressed-Sensing-Based Multiuser Detection for Uplink Grant-Free NOMA Systems

Grant-free non-orthogonal multiple access (NOMA) has recently gained significant attention for reducing signaling overhead in machine-type communications (MTC). In this context, compressed sensing (CS) has been identified as a good candidate for joint activity and data detection due to the inherent sparsity nature of user activity. This paper augments activity and data detection for frame based multi-user uplink scenarios where users are (in)active for the duration of a frame, namely frame-wise joint sparsity model. Firstly, we formulate the block CS (BCS)-based sparse signal recovery framework, by fully extracting and exploiting the underlying frame-wise joint sparsity of the user activity. Then, to make explicit use of the block sparsity inherent in the equivalent block-sparse model and consider that the user sparsity level should be unknown for multiuser detection, two enhanced BCS- based greedy algorithms are developed, i.e., threshold aided block sparsity adaptive subspace pursuit (TA-BSASP) and cross validation aided block sparsity adaptive subspace pursuit (CVA- BSASP). Specifically, the proposed TA-BSASP algorithm can approach the oracle least squares (LS) performance, by reasonably setting the threshold based on the AWGN noise floor. And the proposed CVA-BSASP algorithm is a highly practical algorithm design that does not require any prior knowledge, by adopting the statistical and machine learning mechanism cross validation (CV) to determine the stopping condition of the algorithm. Superior performance of the proposed algorithms is demonstrated by numerical experiments.
Layer 4 Optimizer (L4O) for Enhancing Battery Life in Smart Devices

Applications (Apps) in Smart devices make our life connected to Internet all the time. But, the battery capacity is a major setback for Internet connected Smart phones. We know that TCP is the backbone protocol for these Internet data transmissions. In this paper, we studied the impacts of TCP connection flow on Smart phone battery. We have conducted extensive experiments and collected data from various operator networks in different countries. We observe that TCP delayed closures at the client and prolonged TCP zero Window probes (ZWP) from the server lead to increased signaling overhead and radio ON time. These phenomenon cause unwanted battery drain at the smart phones. To address these problems, we propose a solution that prevents unnecessary packet communication due to TCP delayed closures and ZWPs. Our solution has been evaluated for Android devices such as Samsung Galaxy S7 and S8 variants and Tizen device such as Samsung Z3. We have found that, our solution consistently achieves 10 to 20% of power usage reduction irrespective of varying network and traffic conditions.
ADAM-ADMM: A Unified, Systematic Framework of Structured Weight Pruning for DNNs

Weight pruning methods of deep neural networks have been demonstrated to achieve a good model pruning ratio without loss of accuracy, thereby alleviating the significant computation/storage requirements of large-scale DNNs. Structured weight pruning methods have been proposed to overcome the limitation of irregular network structure and demonstrated actual GPU acceleration. However, the pruning ratio and GPU acceleration are limited when accuracy needs to be maintained. In this work, we overcome pruning ratio and GPU acceleration limitations by proposing a unified, systematic framework of structured weight pruning for DNNs, named ADAM-ADMM. It is a framework that can be used to induce different types of structured sparsity, such as filter-wise, channel-wise, and shape-wise sparsity, as well non-structured sparsity. The proposed framework incorporates stochastic gradient descent with ADMM, and can be understood as a dynamic regularization method in which the regularization target is analytically updated in each iteration. A significant improvement in structured weight pruning ratio is achieved without loss of accuracy, along with fast convergence rate. With a small sparsity degree of 33.3% on the convolutional layers, we achieve 1.64% accuracy enhancement for the AlexNet model. This is obtained by mitigation of overfitting. Without loss of accuracy on the AlexNet model, we achieve 2.58x and 3.65x average measured speedup on two GPUs, clearly outperforming the prior work. The average speedups reach 2.77x and 7.5x when allowing a moderate accuracy loss of 2%. In this case the model compression for convolutional layers is 13.2x, corresponding to 10.5x CPU speedup. Our experiments on ResNet model and on other datasets like UCF101 and CIFAR-10 demonstrate the consistently higher performance of our framework. Our models and codes are released at this https URL
Social-IoT Enabled Identifier/Locator Splitting: Concept, Architecture, and Performance Evaluation

The Identifier/Locator Splitting (ILS) paradigm has been proposed in the future Internet research arena to address the semantic overload of IP addresses. In this paper the integration of the Social Internet of Things (SIoT) concept into ILS solutions is investigated. Indeed, SIoT has been recently argued as a promising approach to improve the performance of identifier-to-locator mapping procedures. More specifically, we describe the general approach, propose an architecture and show some preliminary performance results obtained through simulations. The proposed SIoT-enabled ILS architecture relies on distributed repositories of virtual counterparts of entities that store Friendship Tables specifically introduced to enable the mapping information to be retrieved by surfing the social graph. Achieved results in a smart campus scenario show that the proposed approach achieves better performance, in terms of number of hops, than a mapping solution based on Distributed Hash Tables (DHT).
Consciousness without report: insights from summary statistics and inattention ‘blindness’

We contrast two theoretical positions on the relation between phenomenal and access consciousness. First, we discuss previous data supporting a mild Overflow position, according to which transient visual awareness can overflow report. These data are open to two interpretations: (i) observers transiently experience specific visual elements outside attentional focus without encoding them into working memory; (ii) no specific visual elements but only statistical summaries are experienced in such conditions. We present new data showing that under data-limited conditions observers cannot discriminate a simple relation (same versus different) without discriminating the elements themselves and, based on additional computational considerations, we argue that this supports the first interpretation: summary statistics (same/different) are grounded on the transient experience of elements. Second, we examine recent data from a variant of ‘inattention blindness’ and argue that contrary to widespread assumptions, it provides further support for Overflow by highlighting another factor, ‘task relevance’, which affects the ability to conceptualize and report (but not experience) visual elements. This article is part of the theme issue ‘Perceptual consciousness and cognitive access’.
Multi-bin Trainable Linear Unit for Fast Image Restoration Networks

Tremendous advances in image restoration tasks such as denoising and super-resolution have been achieved using neural networks. Such approaches generally employ very deep architectures, large number of parameters, large receptive fields and high nonlinear modeling capacity. In order to obtain efficient and fast image restoration networks one should improve upon the above mentioned requirements. In this paper we propose a novel activation function, the multi-bin trainable linear unit (MTLU), for increasing the nonlinear modeling capacity together with lighter and shallower networks. We validate the proposed fast image restoration networks for image denoising (FDnet) and super-resolution (FSRnet) on standard benchmarks. We achieve large improvements in both memory and runtime over current state-of-the-art for comparable or better PSNR accuracies.
Multi-Granularity Hierarchical Attention Fusion Networks for Reading Comprehension and Question Answering

This paper describes a novel hierarchical attention network for reading comprehension style question answering, which aims to answer questions for a given narrative paragraph. In the proposed method, attention and fusion are conducted horizontally and vertically across layers at different levels of granularity between question and paragraph. Specifically, it first encode the question and paragraph with fine-grained language embeddings, to better capture the respective representations at semantic level. Then it proposes a multi-granularity fusion approach to fully fuse information from both global and attended representations. Finally, it introduces a hierarchical attention network to focuses on the answer span progressively with multi-level soft-alignment. Extensive experiments on the large-scale SQuAD, TriviaQA dataset validate the effectiveness of the proposed method. At the time of writing the paper, our model achieves state-of-the-art on the both SQuAD and TriviaQA Wiki leaderboard as well as two adversarial SQuAD datasets.
Gedunin, A Neem Limonoid in Combination with Epalrestat Inhibits Cancer Hallmarks by Attenuating Aldose Reductase-Driven Oncogenic Signaling in SCC131 Oral Cancer Cells.

BACKGROUND AND OBJECTIVES Aldose Reductase (AR), a polyol pathway enzyme that mediates diabetic complications is implicated in tumour development and progression. This study was undertaken to determine whether gedunin, a neem limonoid prevents the hallmarks of cancer by inhibiting AR and the associated downstream PI3K/Akt/mTOR/ERK/NF-κB signalling axis in the SCC131 oral cancer cell line. METHODS The expression of AR and key molecules involved in cell proliferation, apoptosis, autophagy, invasion and angiogenesis was analysed by qRT-PCR, and immunoblotting. ROS generation and cell cycle were analysed by FACS. Alamar blue assay and scratch assay were used to evaluate cell proliferation and migration in the endothelial cell line Eahy926. RESULTS Gedunin and the AR inhibitor epalrestat inhibited AR expression and ROS generation. Cell cycle arrest at G1/S was associated with cell death by autophagy with subsequent switch over to apoptosis. Furthermore, hypoxia-induced cell migration was inhibited in Eahy926 cells with downregulation of pro-invasive and proangiogenic proteins in SCC131 as well as Eahy926 cells. Co-inactivation of Akt and ERK was coupled with abrogation of IKK/NF-κB signaling. However, the combination of gedunin and epalrestat was more effective than single agents. CONCLUSION Inhibition of AR-mediated ROS signalling may be a key mechanism by which gedunin and epalrestat exert their anticancer effects. Our results provide compelling evidence that the combination of gedunin and epalrestat modulates expression of key oncogenic signalling kinases and transcription factors primarily by influencing phosphorylation and subcellular localisation. AR inhibitors such as gedunin and epalrestat are novel candidate agents for cancer prevention and therapy.
Training Neural Machines with Trace-Based Supervision

We investigate the effectiveness of trace-based supervision methods for training existing neural abstract machines. To define the class of neural machines amenable to trace-based supervision, we introduce the concept of a differential neural computational machine (∂NCM) and show that several existing architectures (NTMs, NRAMs) can be described as ∂NCMs. We performed a detailed experimental evaluation with NTM and NRAM machines, showing that additional supervision on the interpretable portions of these architectures leads to better convergence and generalization capabilities of the learning phase than standard training, in both noise-free and noisy scenarios.
Property-Driven Dynamic Call Graph Exploration

Analyzing and visualizing call relations can provide useful insights into the connectivities and linkings of certain parts of a software system. This can in particular be a good strategy to find software system parts that are interlinked a lot while others typically occur as more or less stand-alone components not called by many others. The challenging problem with call relation data comes from the dynamics of the data, i.e., a call graph can be changing either during the development of a software system or during the execution of the software. The second case mostly leads to long graph sequences changing on a fine-granular temporal scale requiring a suitable overview-based dynamic graph visualization technique. Moreover, identifying certain temporal patterns in the graph evolution can help to detect certain phases of either the evolution of a software system or phases during the execution that can show which components are connected while someone interacts with the runnable software for example. This can particularly be based on graph, layout, or attribute properties, all providing different perspectives on the dynamics of the graph data. We illustrate the usefulness of our visualization technique by applying it to the open source software project JHotDraw. The call graphs are recorded during runtime while typical user interactions are applied.
Longitudinal Epidemiological Study of Autism Subgroups Using Autism Treatment Evaluation Checklist (ATEC) Score

Here we report the results of the subgroup analyses of an observational cohort of children whose parents completed the Autism Treatment Evaluation Checklist (ATEC) over the period of several years. A linear mixed effects model was used to evaluate longitudinal changes in ATEC scores within different patient subgroups. All groups decreased their mean ATEC score over time indicating improvement of symptoms, however there were significant differences between the groups. Younger children improved more than the older children. Children with milder ASD improved more than children with more severe ASD in the Communication subscale. There was no difference in improvement between females vs. males. One surprising finding was that children from developed English-speaking countries improved less than children from non-English-speaking countries.
Fairly Allocating Many Goods with Few Queries

We investigate the query complexity of the fair allocation of indivisible goods. For two agents with arbitrary monotonic valuations, we design an algorithm that computes an allocation satisfying envy-freeness up to one good (EF1), a relaxation of envy-freeness, using a logarithmic number of queries. We show that the logarithmic query complexity bound also holds for three agents with additive valuations. These results suggest that it is possible to fairly allocate goods in practice even when the number of goods is extremely large. By contrast, we prove that computing an allocation satisfying envyfreeness and another of its relaxations, envy-freeness up to any good (EFX), requires a linear number of queries even when there are only two agents with identical additive valuations.
Ceftazidime-Avibactam Susceptibility Breakpoints against Enterobacteriaceae and Pseudomonas aeruginosa

Clinical susceptibility breakpoints against Enterobacteriaceae and Pseudomonas aeruginosa for the ceftazidime-avibactam dosage regimen of 2,000/500 mg every 8 h (q8h) by 2-h intravenous infusion (adjusted for renal function) have been established by the FDA, CLSI, and EUCAST as susceptible (MIC, ≤8 mg/liter) and resistant (MIC, >8 mg/liter). The key supportive data from pharmacokinetic/pharmacodynamic analyses, in vitro surveillance, including molecular understanding of relevant resistance mechanisms, and efficacy in regulatory clinical trials are collated and analyzed here. ABSTRACT Clinical susceptibility breakpoints against Enterobacteriaceae and Pseudomonas aeruginosa for the ceftazidime-avibactam dosage regimen of 2,000/500 mg every 8 h (q8h) by 2-h intravenous infusion (adjusted for renal function) have been established by the FDA, CLSI, and EUCAST as susceptible (MIC, ≤8 mg/liter) and resistant (MIC, >8 mg/liter). The key supportive data from pharmacokinetic/pharmacodynamic analyses, in vitro surveillance, including molecular understanding of relevant resistance mechanisms, and efficacy in regulatory clinical trials are collated and analyzed here.
Infants' Pain Recognition Based on Facial Expression: Dynamic Hybrid Descriptions

SUMMARY The accurate assessment of infants’ pain is important for understanding their medical conditions and developing suitable treatment. Pediatric studies reported that the inadequate treatment of infants’ pain might cause various neuroanatomical and psychological problems. The fact that infants can not communicate verbally motivates increasing interests to develop automatic pain assessment system that provides continuous and accurate pain assessment. In this paper, we propose a new set of pain fa- cial activity features to describe the infants’ facial expression of pain. Both dynamic facial texture feature and dynamic geometric feature are extracted from video sequences and utilized to classify facial expression of infants as pain or no pain. For the dynamic analysis of facial expression, we con- struct spatiotemporal domain representation for texture features and time series representation (i.e. time series of frame-level features) for geometric features. Multiple facial features are combined through both feature fusion and decision fusion schemes to evaluate their e ﬀ ectiveness in infants’ pain assessment. Experiments are conducted on the video acquired from NICU infants, and the best accuracy of the proposed pain assessment approaches is 95.6%. Moreover, we ﬁnd that although decision fusion does not perform better than that of feature fusion, the False Negative Rate of decision fusion (6.2%) is much lower than that of feature fusion (25%).
Unsupervised Semantic Abstractive Summarization

Automatic abstractive summary generation remains a significant open problem for natural language processing. In this work, we develop a novel pipeline for Semantic Abstractive Summarization (SAS). SAS, as introduced by Liu et. al. (2015) first generates an AMR graph of an input story, through which it extracts a summary graph and finally, creates summary sentences from this summary graph. Compared to earlier approaches, we develop a more comprehensive method to generate the story AMR graph using state-of-the-art co-reference resolution and Meta Nodes. Which we then use in a novel unsupervised algorithm based on how humans summarize a piece of text to extract the summary sub-graph. Our algorithm outperforms the state of the art SAS method by 1.7% F1 score in node prediction.
Lymphocytic choriomeningitis virus Clone 13 infection causes either persistence or acute death dependent on IFN-1, cytotoxic T lymphocytes (CTLs), and host genetics

Significance T cell exhaustion and successful therapy to restore T cell function initially uncovered with lymphocytic choriomeningitis virus (LCMV) Clone (Cl) 13 infection is important for understanding and treating persistent viral infections and cancers in mice and humans. Here, we report that Cl 13 infection in multiple inbred mouse strains elicits opposite phenotypes: acute death in 7 to 9 d associated with a robust T cell response contrasted to suppression of T cell response leading to persistent infection with normal life span. Death was due to pulmonary vascular leakage of fluids and cells into the lung, leading to respiratory failure. Death can be aborted by blocking interferon-1 signaling or deleting CD8 T cells. Understanding of T cell exhaustion and successful therapy to restore T cell function was first described using Clone (Cl) 13 variant selected from the lymphocytic choriomeningitis virus (LCMV) Armstrong (ARM) 53b parental strain. T cell exhaustion plays a pivotal role in both persistent infections and cancers of mice and humans. C57BL/6, BALB, SWR/J, A/J, 129, C3H, and all but one collaborative cross (CC) mouse strain following Cl 13 infection have immunosuppressed T cell responses, high PD-1, and viral titers leading to persistent infection and normal life spans. In contrast, the profile of FVB/N, NZB, PL/J, SL/J, and CC NZO mice challenged with Cl 13 is a robust T cell response, high titers of virus, PD-1, and Lag3 markers on T cells. These mice all die 7 to 9 d after Cl 13 infection. Death is due to enhanced pulmonary endothelial vascular permeability, pulmonary edema, collapse of alveolar air spaces, and respiratory failure. Pathogenesis involves abundant levels of Cl 13 receptor alpha-dystroglycan on endothelial cells, with high viral replication in such cells leading to immunopathologic injury. Death is aborted by blockade of interferon-1 (IFN-1) signaling or deletion of CD8 T cells.
Multi-Temporal Remote Sensing Image Registration Using Deep Convolutional Features

Registration of multi-temporal remote sensing images has been widely applied in military and civilian fields, such as ground target identification, urban development assessment, and geographic change assessment. Ground surface change challenges feature point detection in amount and quality, which is a common dilemma faced by feature-based registration algorithms. Under severe appearance variation, detected feature points may contain a large proportion of outliers, whereas inliers may be inadequate and unevenly distributed. This paper presents a convolutional neural network (CNN) feature-based multi-temporal remote sensing image registration method with two key contributions: (i) we use a CNN to generate robust multi-scale feature descriptors and (ii) we design a gradually increasing selection of inliers to improve the robustness of feature point registration. Extensive experiments on feature matching and image registration are performed over a multi-temporal satellite image data set and a multi-temporal unmanned aerial vehicle image dataset. Our method outperforms four state-of-the-art methods in most scenarios.
And/or trees: A local limit point of view

We present here a new and universal approach for the study of random and/or trees, unifying in one framework many different models, including some novel ones not yet understood in the literature. An and/or tree is a Boolean expression represented in (one of) its tree shapes. Fix an integer k, take a sequence of random (rooted) trees of increasing size, say (tn)n≥1 , and label each of these random trees uniformly at random in order to get a random Boolean expression on k variables. We prove that, under rather weak local conditions on the sequence of random trees (tn)n≥1 , the distribution induced on Boolean functions by this procedure converges as n tends to infinity. In particular, we characterize two different behaviors of this limit distribution depending on the shape of the local limit of (tn)n≥1 : a degenerate case when the local limit has no leaves; and a non‐degenerate case, which we are able to describe in more details under stronger conditions. In this latter case, we provide a relationship between the probability of a given Boolean function and its complexity. The examples covered by this unified framework include trees that interpolate between models with logarithmic typical distances (such as random binary search trees) and other ones with square root typical distances (such as conditioned Galton–Watson trees).