Miracle-dz/UniFAR-FTUs huggingface dataset:
On the Stable Resolution Limit of Total Variation Regularization for Spike Deconvolution

The stability of spike deconvolution, which aims at recovering point sources from their convolution with a point spread function (PSF), is known to be related to the separation between those sources. When the observations are noisy, it is critical to ensure support stability, where the deconvolution does not lead to spurious, or oppositely, missing estimates of the point sources. In this paper, we study the resolution limit of stably recovering the support of two closely located point sources using the Beurling-LASSO estimator, which is a convex optimization approach based on total variation regularization. We establish a sufficient separation criterion between the sources, depending only on the PSF, above which the Beurling-LASSO estimator is guaranteed to return a stable estimate of the point sources, with the same number of estimated elements as that of the ground truth. Our result highlights the impact of PSF on the resolution limit in the noisy setting, which was not evident in previous studies of the noiseless setting. Towards the end, we show that the same resolution limit applies to resolving two close-located sources in conjunction of other well-separated sources.
RouteNet: Leveraging Graph Neural Networks for Network Modeling and Optimization in SDN

Network modeling is a key enabler to achieve efficient network operation in future self-driving Software-Defined Networks. However, we still lack functional network models able to produce accurate predictions of Key Performance Indicators (KPI) such as delay, jitter or loss at limited cost. In this paper we propose RouteNet, a novel network model based on Graph Neural Network (GNN) that is able to understand the complex relationship between topology, routing, and input traffic to produce accurate estimates of the per-source/destination per-packet delay distribution and loss. RouteNet leverages the ability of GNNs to learn and model graph-structured information and as a result, our model is able to generalize over arbitrary topologies, routing schemes and traffic intensity. In our evaluation, we show that RouteNet is able to predict accurately the delay distribution (mean delay and jitter) and loss even in topologies, routing and traffic unseen in the training (worst case MRE = 15.4%). Also, we present several use cases where we leverage the KPI predictions of our GNN model to achieve efficient routing optimization and network planning.
Cosmic ray interactions in the solar atmosphere

High-energy particles enter the solar atmosphere from Galactic or solar coronal sources, and produce ‘albedo’ emission from the quiet Sun that is now observable across a wide range of photon energies. The interaction of high-energy particles in a stellar atmosphere depends essentially upon the joint variation of the magnetic field and plasma density, which heretofore has been characterized parametrically as P ∝ Bα with P the gas pressure and B the magnitude of the magnetic field. We re-examine that parametrization by using a self-consistent 3D MHD model (Bifrost) and show that this relationship tends to P ∝ B3.5 ± 0.1 based on the visible portions of the sample of open-field flux tubes in such a model, but with large variations from point to point. This scatter corresponds to the strong meandering of the open-field flux tubes in the lower atmosphere, which will have a strong effect on the prediction of the emission anisotropy (limb brightening). The simulations show that much of the open flux in coronal holes originates in weak-field regions within the granular pattern of the convective motions seen in the simulations.
A Pseudo-Likelihood Approach to Linear Regression With Partially Shuffled Data

Abstract Recently, there has been significant interest in linear regression in the situation where predictors and responses are not observed in matching pairs corresponding to the same statistical unit as a consequence of separate data collection and uncertainty in data integration. Mismatched pairs can considerably impact the model fit and disrupt the estimation of regression parameters. In this article, we present a method to adjust for such mismatches under “partial shuffling” in which a sufficiently large fraction of (predictors, response)-pairs are observed in their correct correspondence. The proposed approach is based on a pseudo-likelihood in which each term takes the form of a two-component mixture density. expectation-maximization schemes are proposed for optimization, which (i) scale favorably in the number of samples, and (ii) achieve excellent statistical performance relative to an oracle that has access to the correct pairings as certified by simulations and case studies. In particular, the proposed approach can tolerate considerably larger fraction of mismatches than existing approaches, and enables estimation of the noise level as well as the fraction of mismatches. Inference for the resulting estimator (standard errors, confidence intervals) can be based on established theory for composite likelihood estimation. Along the way, we also propose a statistical test for the presence of mismatches and establish its consistency under suitable conditions. Supplemental files for this article are available online.
Joint Bit Allocation and Hybrid Beamforming Optimization for Energy Efficient Millimeter Wave MIMO Systems

In this article, we aim to design highly energy efficient end-to-end communication for millimeter wave multiple-input multiple-output systems. This is done by jointly optimizing the digital-to-analog converter (DAC)/analog-to-digital converter (ADC) bit resolutions and hybrid beamforming matrices. The novel decomposition of the hybrid precoder and the hybrid combiner to three parts is introduced at the transmitter (TX) and the receiver (RX), respectively, representing the analog precoder/combiner matrix, the DAC/ADC bit resolution matrix and the baseband precoder/combiner matrix. The unknown matrices are computed as a solution to the matrix factorization problem where the optimal fully digital precoder or combiner is approximated by the product of these matrices. A novel and efficient solution based on the alternating direction method of multipliers is proposed to solve these problems at both the TX and the RX. The simulation results show that the proposed solution, where the DAC/ADC bit allocation is dynamic during operation, achieves higher energy efficiency when compared with existing benchmark techniques that use fixed DAC/ADC bit resolutions.
On the Spectral and Energy Efficiencies of Full-Duplex Cell-Free Massive MIMO

In-band full-duplex (FD) operation is practically more suited for short-range communications such as WiFi and small-cell networks, due to its current practical limitations on the self-interference cancellation. In addition, cell-free massive multiple-input multiple-output (CF-mMIMO) is a new and scalable version of MIMO networks, which is designed to bring service antennas closer to end user equipments (UEs). To achieve higher spectral and energy efficiencies (SE-EE) of a wireless network, it is of practical interest to incorporate FD capability into CF-mMIMO systems to utilize their combined benefits. We formulate a novel and comprehensive optimization problem for the maximization of SE and EE in which power control, access point-UE (AP-UE) association and AP selection are jointly optimized under a realistic power consumption model, resulting in a difficult class of mixed-integer nonconvex programming. To tackle the binary nature of the formulated problem, we propose an efficient approach by exploiting a strong coupling between binary and continuous variables, leading to a more tractable problem. In this regard, two low-complexity transmission designs based on zero-forcing (ZF) are proposed. Combining tools from inner approximation framework and Dinkelbach method, we develop simple iterative algorithms with polynomial computational complexity in each iteration and strong theoretical performance guaranteed. Furthermore, towards a robust design for FD CF-mMIMO, a novel heap-based pilot assignment algorithm is proposed to mitigate effects of pilot contamination. Numerical results show that our proposed designs with realistic parameters significantly outperform the well-known approaches (i.e., small-cell and collocated mMIMO) in terms of the SE and EE. Notably, the proposed ZF designs require much less execution time than the simple maximum ratio transmission/combining.
Improvement of Transient-Fault-Tolerant Scheme for Out-of-Order Superscalar Processors

The feature size of LSI is getting smaller year by year, increasing random variation between the elements. To overcome the problem of the variation, there are researches about detecting and recoverying from runtime timing-faults, and one of them is based on reset function. We focus attention on commit stage of out-of-order superscalar processors, and improve this reset-based scheme. Processors with this scheme can recover not only from timing-fault but from transient-fault.
Blood lactate concentration prediction in critical care patients: handling missing values

Blood lactate concentration is a strong indicator of mortality risk in critically ill patients. While frequent lactate measurements are necessary to assess patient's health state, the measurement is an invasive procedure that can increase risk of hospital-acquired infections. For this reason we formally define the problem of lactate prediction as a clinically relevant benchmark problem for machine learning community so as to assist clinical decision making in blood lactate testing. Accordingly, we demonstrate the relevant challenges of the problem and its data in addition to the adopted solutions. Also, we evaluate the performance of different prediction algorithms on a large dataset of ICU patients from the multi-centre eICU database. More specifically, we focus on investigating the impact of missing value imputation methods in lactate prediction for each algorithm. The experimental analysis shows promising prediction results that encourages further investigation of this problem.
Rehabilitation of the trigeminal nerve

When it comes to restoring impaired neural function by means of surgical reconstruction, sensory nerves have always been in the role of the neglected child when compared with motor nerves. Especially in the head and neck area, with its either sensory, motor or mixed cranial nerves, an impaired sensory function can cause severe medical conditions. When performing surgery in the head and neck area, sustaining neural function must not only be highest priority for motor but also for sensory nerves. In cases with obvious neural damage to sensory nerves, an immediate neural repair, if necessary with neural interposition grafts, is desirable. Also in cases with traumatic trigeminal damage, an immediate neural repair ought to be considered, especially since reconstructive measures at a later time mostly require for interposition grafts. In terms of the trigeminal neuralgia, commonly thought to arise from neurovascular brainstem compression, a pharmaceutical treatment is considered as the state of the art in terms of conservative therapy. A neurovascular decompression of the trigeminal root can be an alternative in some cases when surgical treatment is sought after. Besides the above mentioned therapeutic options, alternative treatments are available.
A Method to Maintain the Field Coverage by Static and Mobile Sensor Nodes Using Wireless Charging

: In wireless sensor networks (WSNs), sensor nodes periodically sense, record, and transmit environmental information. WSNs require long lifetime and adequate field coverage, which can be problematic under certain conditions. Several studies have addressed these problems using energy harvesting, wireless charging, or mobile sensor nodes. In particular, mobile nodes are effective for adequate field coverage: however, appropriate node movement is critical. In addition, mobile nodes with wireless charging devices can charge the batteries of other nodes. We formulate the problem to extend lifetime and maintain field coverage by determining the positions of mobile nodes that can cover the field effectively and charge the batteries of other nodes simultaneously. We propose a coverage algorithm that can cover a field with a minimal number of nodes and a movement algorithm that determines an efficient mobile node movement schedule to charge static nodes. Simulation results, confirm that the energy charging system used by the proposed method can extend WSN lifetime up to 66%.
Interactive local Super-Resolution Reconstruction of whole-body MRI mouse data: applications to bone and kidney metastases

A resolution enhancing post-processing technique called super-resolution reconstruction (SRR) has recently been demonstrated to improve visualization and localization of micro-structures in small animal MRI. In such cases, however, the size of the animal under investigation relative to the size of the structures of interest can be very large. This results in high-resolution images of tens of millions of voxels. In such cases, solving the SRR problem becomes very expensive, in terms of both computation time and memory requirements. In this paper we introduce local SRR to overcome these problems. We present a novel method that combines state-of-the-art image processing techniques from the areas of articulated atlas-based segmentation, planar reformation and SRR and allows researchers to efficiently capture both global and local scales in whole-body small animal MRI. The approach is validated in two case studies involving CT, BLI and MRI data of bone and kidney tumors in a mouse model. Using only a few low-resolution images, and a total acquisition time compatible with in vivo experiments, we have produced SRR MR images from which detailed information about the metastases can be inferred. We show that local SRR MRI is a computationally efficient complementary imaging modality for the precise description of tumor metastases, and that it provides a high-resolution alternative to conventional MRI.
HireNet: A Hierarchical Attention Model for the Automatic Analysis of Asynchronous Video Job Interviews

New technologies drastically change recruitment techniques. Some research projects aim at designing interactive systems that help candidates practice job interviews. Other studies aim at the automatic detection of social signals (e.g. smile, turn of speech, etc.) in videos of job interviews. These studies are limited with respect to the number of interviews they process, but also by the fact that they only analyze simulated job interviews (e.g. students pretending to apply for a fake position). Asynchronous video interviewing tools have become mature products on the human resources market, and thus, a popular step in the recruitment process. As part of a project to help recruiters, we collected a corpus of more than 7000 candidates having asynchronous video job interviews for real positions and recording videos of themselves answering a set of questions. We propose a new hierarchical attention model called HireNet that aims at predicting the hirability of the candidates as evaluated by recruiters. In HireNet, an interview is considered as a sequence of questions and answers containing salient socials signals. Two contextual sources of information are modeled in HireNet: the words contained in the question and in the job position. Our model achieves better F1-scores than previous approaches for each modality (verbal content, audio and video). Results from early and late multimodal fusion suggest that more sophisticated fusion schemes are needed to improve on the monomodal results. Finally, some examples of moments captured by the attention mechanisms suggest our model could potentially be used to help finding key moments in an asynchronous job interview.
Time Domain Properties of Non-maximal Length Sequences

Linear Feedback Shift Register (LFSR) sequences find applications in spread spectrum communications, radars, system identification and cryptography. Among LFSR sequences, maximal length sequences are in variably used in these application due to their desirable properties. However maximal length sequences are available in limited number of lengths for a given number of shift register stages. On the other hand, the non-maximal length sequences can be generated in varieties of lengths. It appears that the NMLS were not investigated into, to the extent they deserve. The author conjectures that several applications other than communications and ranging might benefit from non-maximal length (NML) sequences. In this paper, NLM sequences are generated and their time domain properties are investigated in reference to the properties maximal length sequences. Simulations are carried out to generate NML sequences corresponding to polynomials of degrees from 6 to 20 and time domain properties such as Runs, One-zero Balance, Correlation, etc are investigated.
Reliable and Robust Animal-Component-Free hMSC-BM Expansion on Ready-to-Use Eppendorf CCCadvanced TM FN 1 Motifs Surface

In the last decade, human mesenchymal stem cells (hMSCs) have generated increasing interest in the scientific world. Many fetal and adult tissues harbor potential multipotent MSCs, which hold a long-term in vitro culturing capacity across several passages without losing their essential characteristics. Prior to their use as a powerful tool for research applications, hMSCs must be expanded in order to reach an adequate number of cells without losing their homing ability, multi-lineage potential, secretion of anti-inflammatory molecules and immunoregulatory effects. Experiments require stable and completely defined hMSC culture systems consisting of growth surface and culture medium. The novel Eppendorf CCCadvancedTM FN1 motifs surface represents a completely synthetic cell adhesion-promoting growth surface for the long-term cultivation of hMSCs in xeno-free and restrictive culture conditions, providing a defined culture system without any animal and human components. This ready-to-use surface is made up of fibronectin-derived motifs to support cell attachment in various serum-free media by mimicking native extra-cellular matrix proteins without additional preparation of the surface. With its unique properties, the FN1 motifs surface combines convenience with reliable hMSC cultivation: the ready-to-use consumable significantly reduces labor time and effort for scientists while offering a fully synthetic growth surface with a high level of consistency during long-term hMSC expansion. APPLICATION NOTE I No. 390 I Page 2
Transcriptomic analysis reveals the roles of 2 detoxification systems in response to mercury in 3 Chromera velia 4

Heavy metal pollution is an increasing global concern. Among heavy metals, mercury 15 (Hg) is especially dangerous because of its massive release into the environment and high toxicity, 16 especially for aquatic organisms. The molecular response mechanisms of algae to Hg exposure are 17 mostly unknown. Here, we combine physiological, biochemical, and transcriptomic analysis to 18 provide, for the first time, a comprehensive view on the pathways activated in Chromera velia in 19 response to toxic levels of Hg. Production of hydrogen peroxide and superoxide anion, two 20 reactive oxygen species (ROS), showed opposite patterns in response to Hg2+ while reactive 21 nitrogen species (RNS) levels did not change. A deep RNA sequencing analysis generated a total of 22 307,738,790 high-quality reads assembled in 122,874 transcripts, representing 89,853 unigenes 23 successfully annotated in databases. Detailed analysis of the differently expressed genes 24 corroborate the biochemical results observed in ROS production and suggests novel putative 25 molecular mechanisms in the algal response to Hg2+. Moreover, we indicated that important 26 transcription factor (TF) families associated with stress responses differentially expressed in C. velia 27 cultures under Hg stress. Our study presents the first in-depth transcriptomic analysis of C. velia, 28 focusing on the expression of genes involved in different detoxification defense systems in 29 response to heavy metal stress. 30
COMMON SENSE BASED TEXT DOCUMENT CLUSTERING ALGORITHM BY COARSE AND FINE GRAINED CLUSTERING TECHNIQUES

Text documents occupy the major source of data and hence it is important to keep the data in an organized fashion. Clustering is one of the ways for data organization, which tends to group similar documents together. In spite of the presence of numerous existing clustering algorithms, still there is an emergent need for accurate clustering algorithms. Additionally, most of the clustering algorithms work by distance based measures, which is the reason for lack of accuracy. In order to overcome these issues, this work presents a double layered text document clustering algorithm. The entire system is categorized into phases such as document pre-processing, representation, clustering and cluster labelling. The document pre-processing phase prepares the document in such a way that it is suitable for the forthcoming processes. The document representation phase is to standardize the structure of the document and this is done by Document Index Graph (DIG) model. The documents are then clustered by cosine similarity and rough set of clusters are formed. The second level of cluster refinement is achieved by ConceptNet, which works on the basis of common sense reasoning. Finally, the clusters are labelled by picking the top ranked key-phrase. This work is tested over BBCSport and 20 NewsGroup dataset and the proposed approach proves better results in terms of F-measure, purity and entropy.
Assessment of PrEP eligibility and uptake among at-risk MSM participating in a HIV-1 vaccine feasibility cohort in coastal Kenya

Introduction: Pre-exposure prophylaxis (PrEP) is provided free of costs to at-risk populations in Kenya, including men who have sex with men (MSM), but anal intercourse is not an eligibility criterion. We set out to determine PrEP eligibility, uptake and predictors of PrEP uptake among MSM enrolled in an HIV-1 vaccine feasibility cohort in coastal Kenya. Methods: We compared the number of MSM identified as eligible for PrEP from June-December 2017 by Kenyan Ministry of Health (MoH) criteria, which do not include reported anal intercourse, to those identified as eligible by a published MSM cohort-derived HIV-1 risk score (CDHRS). We determined PrEP uptake and assessed factors associated with uptake at first offer among eligible MSM followed up monthly. Results: Out of 167 MSM assessed for PrEP eligibility, 118 (70.7%) were identified by both MoH and CDHRS eligibility criteria; 33 (19.8%) by CDHRS alone, 11 (6.6%) by MoH criteria alone, and 5 (3.0%) by neither criterion. Of the men identified by CDHRS alone, the majority (24 or 72.7%) reported receptive anal intercourse (RAI). Of the 162 MSM eligible for PrEP, 113 (69.7%) accepted PrEP at first offer. Acceptance of PrEP was higher for men reporting RAI (adjusted prevalence ratio [aPR], 1.4; 95% confidence interval [CI], 1.0–1.9), having paid for sex (aPR, 1.3; 95% CI, 1.1–1.6) and group sex (aPR, 1.4; 95% CI, 1.1–1.8), after adjustment for sociodemographic factors. Conclusions: Assessing PrEP eligibility using the CDHRS identified 20% more at-risk MSM for PrEP initiation than when Kenyan MoH criteria were used. Approximately 70% of eligible men accepted PrEP at first offer, suggesting that PrEP is acceptable among at-risk MSM. MSM reporting RAI, group sex, or paying for sex were more likely to accept PrEP. Incorporating RAI into MoH PrEP eligibility criteria would enhance the impact of PrEP programming in Kenya.