Miracle-dz/UniFAR-FTUs huggingface dataset:
Methylene Blue (and TRx0237)

Cognitive Vitality Reports® are reports written by neuroscientists at the Alzheimer’s Drug Discovery Foundation (ADDF). These scientific reports include analysis of drugs, drugs-indevelopment, drug targets, supplements, nutraceuticals, food/drink, non-pharmacologic interventions, and risk factors. Neuroscientists evaluate the potential benefit (or harm) for brain health, as well as for age-related health concerns that can affect brain health (e.g., cardiovascular diseases, cancers, diabetes/metabolic syndrome). In addition, these reports include evaluation of safety data, from clinical trials if available, and from preclinical models.
Méthodes de Fusion de Cartes de Caractéristiques pour la Détection Multispectrale par Réseaux de Neurones Profonds

L’utilisation d’images multispectrales (par exemple des paires d’images en bandes spectrales visible et infrarouge) peut s’avérer particulièrement utile lorsque l’on cherche à détecter des objets dans des environnements variés (par exemple des scènes extérieures capturées de jour ou de nuit). Pour utiliser ces différentes bandes spectrales, le principal problème technique est la fusion des informations complémentaires issues des différentes bandes. Si cette fusion peut, en théorie, être mise en oeuvre à différents niveaux (précoce, intermédiaire, tardive), les méthodes récentes d’apprentissage profond ont montré que la fusion à mi-parcours (intermédiaire) dans le réseau était celle donnant le meilleur gain de performances. Cet article propose deux nouvelles approches intitulées PS-Fuse et Cyclic Fuse-and-Refine pour fusionner au mieux les caractéristiques multispectrales au sein d’un réseau de neurones profond. Nos expériences montrent que ces deux contributions conduisent à des améliorations significatives de précision par rapport aux méthodes existantes en détection d’objets sur des jeux de données multispectrales.
Prognostic Value of Risk Assessment Tools for Patients with Pulmonary Arterial Hypertension in WHO Functional Class II: A Post Hoc Analysis of the EARLY Trial

Background: Current practice guidelines for patients with Pulmonary Arterial Hypertension (PAH) recommend a multidimensional risk assessment, but World Health Organization (WHO) Functional Class (FC) remains a main criterion for treatment decisions. Objectives: A post hoc analysis was conducted to determine if different risk assessment tools evaluated in PAH registry populations can identify patients in WHO FC II at higher risk of death. Methods: Patients in the randomized, controlled EARLY trial (NCT00091715; N=185), which exclusively enrolled patients in FC II, were stratified into three categories—low-, intermediate-, and high-risk—using the original REVEAL risk calculator, revised REVEAL risk calculator 2.0, COMPERA categorical score, and two FPHN methods (invasive and non-invasive) counting low-risk criteria. Risk of all-cause death was compared between baseline categories. Associations between change in risk category from baseline to month 6 (improved, worsened, or stable) and PAH worsening or death were estimated using a Cox proportional hazards model, adjusting for baseline risk category. Results: Patients classified as intermediate or high risk ranged from 35% using the original REVEAL risk calculator to 89% by the COMPERA method at baseline, and from 37% to 82%, respectively, at month 6. Higher risk category was associated with increased mortality risk. Rates of subsequent PAH worsening and death were higher in patients with worsened risk category and lower in those with improved risk category. Conclusion: Multiparametric assessment has additional prognostic value over FC alone, but different risk assessment tools vary in risk stratification. PAH patients in WHO FC II are not all at low risk, so should be assessed frequently.
NF-κB induction during in vivo hypoxia in dorsocaudal brain stem of rat: effect of MK-801 andl-NAME

In the nucleus of the solitary tract, NMDA receptors are critical for the hypoxic ventilatory response while neuronal nitric oxide synthase (NOS) modulates the late component of this response. Nuclear factor (NF)-kappaB is a ubiquitous transcription factor that increases the expression of multiple stress-activated genes. We sought to examine temporal changes in expression of NF-kappaB within the dorsocaudal brain stem of conscious rats after exposures to 10% O2. Time-dependent increases in NF-kappaB occurred with hypoxia and peaked at 60 min. Pretreatment with the N-methyl-D-aspartate (NMDA)-receptor channel antagonist dizocilpine maleate (MK-801) markedly attenuated NF-kappaB complexes during hypoxia. In contrast, after NOS inhibition with NG-nitro-L-arginine methyl ester (L-NAME), although NF-kappaB was diminished in normoxia, increased NF-kappaB expression still occurred with hypoxia. Increased phosphorylation of the NF-kappaB regulatory unit [inhibitory (I)kappaB] was detected by immunoblotting and also peaked at 60 min. Phosphorylation of Ikappa-B during hypoxia was attenuated by MK-801 but not by L-NAME. Thus NMDA-receptor activation in the dorsocaudal brain stem during hypoxia elicits in NF-kappaB activity marked enhancements that are unaffected after NOS blockade.
Historical and Contemporary Factors of the Prostitution: A Quantitative Comparison

The history of prostitution is as old as human history. Prostitution has been both accepted and rejected as a profession. However what has turned prostitution into a controversial and contentious activity is its unstable matrix of adaptability in different eras of human history. Like all other discourses developed both as a concept and activity prostitution has also claimed an intellectual ground of inquiry. Therefore, seen from the perspective of social history ,a discipline that identifies processes of social change as a historical phenomenon, prostitution can also be seen as an institution. To that effect Therefore, from classical to modern world prostitution as a social reality has undergone various transformations. The existing literature on the subject of prostitution shows this difference. The current study is an attempt to compare and contrast the existing and historical configurations responsible for the occurrence of prostitution ensuing a discursive discourse. Since this paper is based on quantitative research design for the collection of the required data a survey was conducted through 270 respondents i.e. prostitution selected on the basis of convenient sampling technique. The findings of the study show that historically economic and religious factors were most instrumental in accomplishing prostitution as a profession. However, in contemporary world psychological, and social issues are also important factors responsible for the growth of prostitution as a profession Most of all, in modern world economics is huge factor behind proliferation of prostitution.
Ultralight and highly flexible aerogels with long cellulose I nanofibers

Ultralight and highly flexible aerogels with long cellulose I nanofibers (CNFs) were produced viapurification and defibrillation of wood fibers into CNF hydrogels, followed by freeze-drying. The aerogels had a web-like entangled structure, low density, and high water uptake capability. With the increase of CNF content in hydrogels from 0.1 to 1.5%, the bulk density of the aerogels increased from 1.3 × 10−3 to 17.0 × 10−3 g cm−3, whereas the water uptake ratio (WUR) decreased from 155 to 54. The microstructure of the aerogels can be transformed from open 3D porous nanofibrillar network to 2D sheet-like skeletons by adjusting the CNF content of the hydrogels. A possible assembly mechanism was proposed based on this transformation. When the transparent supernatant fraction, which has ∼0.018% solid content obtained viacentrifugation of the hydrogels, was subjected to freeze-drying, ultra-low density aerogels (0.2 × 10−3 g cm−3) consisting of nanofibers with lengths above 1 mm and a width range of 40–180 nm, were successfully produced due to the self-assembled of the tiny CNFs and their bundles along the longitudinal direction.
The Effect of Error Type on Pause Length in Post-Editing Machine Translation Output

Post-editing machine translated output is a common practice in the translation industry. Post-editing refers to the editing of machine translated text to publishable content. Especially for large amounts of text it can save various resources. However, not every type of error in post-editing is treated the same way and takes the same amount of time to edit. Therefore, it is hard to estimate the editing time, as it largely depends on the error types that the software produces. Not only the post-editing takes more time for certain errors, but also the pause time can vary. Pause time represents the time after the last cursor positioning and before the first editing movement. Currently, the two most commonly used approaches in machine translation are Statistical Machine Translation (SMT) and Neural Machine Translation (NMT). This thesis focuses on three key constructs: (1) the amount of errors and the error types produced by SMT and NMT, (2) the pause length prior to editing errors in SMT and NMT as well as (3) the pause length prior to five annotated error types. In a first analysis, it is shown that NMT software produces output with fewer absolute errors than SMT software. Moreover, in a second analysis, it is shown that, in general, the pause length in post-editing prior to errors is shorter for NMT output than for SMT output. In addition, an exploratory approach is taken in order to investigate whether the five annotated error types yield differences in the pause length prior to editing. The results of this analysis show that Addition errors and Inflectional Morphology errors require the shortest pause length prior to editing the error, while a Mistranslation error evokes the longest pause prior to the edit.
Reviews on Eye blink detection

we are surrounded by technologies; spend more and more time with visual display unit (VDU) devices such as computers, laptops, TV screens, mobile phones and tablets. Use of VDU devices is often associated with eye and visual problems. Normally eye should blink 10 – 15 times per minute. But the rate of spontaneous eye blinking while using computer reduces significantly (up to 60% reduction). Blinking helps us to spread the tear film, moisten and disinfect the surface of the eye, due to which the reduced blink rate causes dryness of our eyes which causes dryness, redness, burning, sandy-gritty eye irritation or sensitivity to light and eye fatigue. These symptoms are also known as Dry Eye, which is a major part of the Computer Vision Syndrome (CVS). CVS is a set of problems related to the computer use including dry eyes, eyestrain, headache, blurred vision, neck pain and backache. The easiest way to avoid the symptoms of Dry Eye is to blink regularly. There are only few available hardware or software solutions which try to prevent from dry eye symptoms. This paper gives a brief overview of Gabor’s filters and Circular Hough Transformation for a method for extracting circles from images.
Efficiently Controlling Wireless Data Centers with Exact Topology Adjustment Strategy

As wireless transmission technology advances, wireless data centers (WDCs) are more and more widely used as computing infrastructures beneﬁting from its ﬂexibility and low complexity of cabling. Software-deﬁned networking (SDN) brings new convenience in topology design of wireless data center networks (DCNs) which has a great effect on the performance of data centers. In this paper, we introduce the controllability as a new consideration for wireless data center designers. We deﬁned a new measure for controllability evaluation and compared the existing WDC architectures, uncovered their diversity on properties of dynamics. Then we propose a novel exact topology adjustment strategy to control WDCs more efﬁciently. Mathematical reasoning and experimental results show the correctness and effectiveness of our method. Our method can not only signiﬁcantly improve the controllability of existing WDCs, but also applicable in accurate architecture design of wireless network systems.
An Efﬁcient Pyramid Network for reﬁned edge detection with or without semantic category awareness

Category-aware semantic edge detection can be seen as an extension of edge detection where edge maps distinguish categories of objects in the scene. Obtaining both precise shape and well categorized boundaries is a challenging double task. This paper presents an improved architecture for semantic edge detection called Edge Pyra-mid Network (EPN), which effectively combines low-level and high-level features to produce smoother edges. Obser-ving the impact of misalignment and loss functions on training effectiveness, we propose a ﬁne-tuning strategy which incrementally improves edge detection accuracy through alternated training cycles using the weighted negative and the unweighted loss functions. Before applying the ﬁne-tuning strategy, our network already outperforms state-of-the-art semantic edge detection networks on the benchmark datasets SBD and Cityscapes. The performance is further improved using our ﬁne-tuning strategy (+0.85% and +1.78% compared to STEAL on SBD and Cityscapes data-sets, respectively). Furthermore, we show that EPN architecture achieves competitive performance (ODS F-measure of .830) against the state-of-the-art category-agnostic edge detection networks on the BSDS500 dataset.
Statistical errors in molecular dynamics averages

A molecular dynamics calculation produces a time‐dependent fluctuating signal whose average is a thermodynamic quantity of interest. The average of the kinetic energy, for example, is proportional to the temperature. A procedure is described for determining when the molecular dynamics system is in equilibrium with respect to a given variable, according to the condition that the mean and the bandwidth of the signal should be sensibly constant in time. Confidence limits for the mean are obtained from an analysis of a finite length of the equilibrium signal. The role of serial correlation in this analysis is discussed. The occurence of unstable behavior in molecular dynamics data is noted, and a statistical test for a level shift is described.
Environment sensor for home sleep monitoring

The sleep is a very important period of everyone’s life cycle. It has importance in the regeneration and preparation for the next day’s activity. Therefore, the quality of the sleep is important the effectiveness of the next day or long-term ineffective sleep will degrade the concentration of the person which can be serious problem in certain professions like bus drivers, pilots. The objective measurement, monitoring and evaluation of the sleep quality is important either in the sleep laboratory or even in the patient home. There are several type of sleep sensors, based on different working principle, contact, contactless and indirect. They provide important information about the human condition while asleep. However, it turned out the environment monitoring also provides valuable information about the sleep quality. Especially in the case of home sleep monitoring the environment monitoring is essential. This article explains the home sleep monitoring and introduces an environment monitoring device developed for home sleep monitoring.
Optimization of ingredients and process conditions for the preparation of puri using response surface methodology

Tandoori roti is a wheat based Indian bread product similar in appearance to Arabic bread. Relationships between the ingredient variables (water and salt) and processing variables (mixing time, and baking time and temp.) that affect product quality were studied, and a set of optimum processing conditions was established. A model capable of predicting the quality of tandoori roti was developed using response surface methodology. Optimum conditions that produced max. sensory score were: moisture content, 720 ml/kg flour; salt level, 11 g/kg flour; mixing time, 3.7 min (in a Hobart mixer at 58 rpm); baking time, 37 s; and baking temp. , 425C.
Characterization and Field‐Emission Properties of Vertically Aligned ZnO Nanonails and Nanopencils Fabricated by a Modified Thermal‐Evaporation Process

Vertically aligned ZnO nanonails and nanopencils are synthesized on a silicon substrate using a modified thermal‐evaporation process, without using a catalyst or predeposited buffer layers. An adiabatic layer is used to provide an abrupt temperature decrease and high gas concentration for the nanostructures growth. The structure and morphology of the as‐synthesized ZnO nanonails and nanopencils are characterized using X‐ray diffraction, and scanning and transmission electron microscopies. Raman and photoluminescence properties are also investigated at room temperature. Field‐emission characterization shows that the turn‐on fields for the vertically aligned ZnO nanonails and nanopencils are 7.9 and 7.2 V μm–1, respectively.
Investigating Diffusion of Scientific Knowledge on Twitter: A Study of Topic Networks of Opioid Publications

One way to assess the value of scientific research is to measure the attention it receives on social media. While previous research has mostly focused on the "number of mentions" of scientific research on social media, the current study uses "topic networks" to measure public attention to scientific research on Twitter. Topic networks in this research are the "co-occurring author keywords" in scientific publications and the "co-occurring hashtags" in the tweets mentioning scientific publications. Since bots (automated social media accounts) may significantly influence public attention, this study also investigates whether the topic networks based on the tweets by all accounts (bot and non-bot accounts) differ from the topic networks by non-bot accounts. Our analysis is based on a set of opioid scientific publications from 2011 to 2019 and the tweets associated with them. We use co-occurrence network analysis to generate topic networks. Results indicated that the public has mostly used generic terms to discuss opioid publications. Results confirmed that topic networks provide a legitimate method to visualize public discussions of (health-related) scientific publications, and how the public discusses (health-related) scientific research differently from the scientific community. There was a significant overlap between the topic networks based on the tweets by all accounts and non-bot accounts. This result indicates that in generating topic networks, bot accounts do not need to be excluded as they have negligible impact on the results.
Investigating Bi-Level Optimization for Learning and Vision From a Unified Perspective: A Survey and Beyond

Bi-Level Optimization (BLO) is originated from the area of economic game theory and then introduced into the optimization community. BLO is able to handle problems with a hierarchical structure, involving two levels of optimization tasks, where one task is nested inside the other. In machine learning and computer vision fields, despite the different motivations and mechanisms, a lot of complex problems, such as hyper-parameter optimization, multi-task and meta learning, neural architecture search, adversarial learning and deep reinforcement learning, actually all contain a series of closely related subproblms. In this paper, we first uniformly express these complex learning and vision problems from the perspective of BLO. Then we construct a best-response-based single-level reformulation and establish a unified algorithmic framework to understand and formulate mainstream gradient-based BLO methodologies, covering aspects ranging from fundamental automatic differentiation schemes to various accelerations, simplifications, extensions and their convergence and complexity properties. Last but not least, we discuss the potentials of our unified BLO framework for designing new algorithms and point out some promising directions for future research. A list of important papers discussed in this survey, corresponding codes, and additional resources on BLOs are publicly available at: https://github.com/vis-opt-group/BLO.
Streaming Erasure Codes over the Multiple Access Relay Channel

—Applications where multiple users communicate with a common server and desire low latency are common and increasing. This paper studies a network with two source nodes, one relay node and a destination node, where each source nodes wishes to transmit a sequence of messages, through the relay, to the destination, who is required to decode the messages with a strict delay constraint T . The network with a single source node has been studied in [1]. We start by introducing two important tools: the delay spectrum, which generalizes delay-constrained point-to-point transmission, and concatenation, which, similar to time sharing, allows combinations of different codes in order to achieve a desired regime of operation. Using these tools, we are able to generalize the two schemes previously presented in [1], and propose a novel scheme which allows us to achieve optimal rates under a set of well-deﬁned conditions. Such novel scheme is further optimized in order to improve the achievable rates in the scenarios where the conditions for optimality are not met.
OffCon3: What is state of the art anyway?

Two popular approaches to model-free continuous control tasks are SAC and TD3. At first glance these approaches seem rather different; SAC aims to solve the entropy-augmented MDP by minimising the KL-divergence between a stochastic proposal policy and a hypotheical energy-basd soft Q-function policy, whereas TD3 is derived from DPG, which uses a deterministic policy to perform policy gradient ascent along the value function. In reality, both approaches are remarkably similar, and belong to a family of approaches we call `Off-Policy Continuous Generalized Policy Iteration'. This illuminates their similar performance in most continuous control benchmarks, and indeed when hyperparameters are matched, their performance can be statistically indistinguishable. To further remove any difference due to implementation, we provide OffCon$^3$ (Off-Policy Continuous Control: Consolidated), a code base featuring state-of-the-art versions of both algorithms.
Towards a Standard Feature Set of NIDS Datasets

Network Intrusion Detection Systems (NIDSs) datasets are essential tools used by researchers for the training and evaluation of Machine Learning (ML)-based NIDS models. There are currently ﬁve datasets, known as NF-UNSW-NB15, NF-BoT-IoT, NF-ToN-IoT, NF-CSE-CIC-IDS2018 and NF-UQ-NIDS, which are made up of a common feature set. However, their performances in classifying network traﬃc, mainly using the multi-classiﬁcation method, is often unreliable. Therefore, this paper proposes a standard NetFlow feature set, to be used in future NIDS datasets due to the tremendous beneﬁts of having a common feature set. NetFlow has been widely utilised in the networking industry for its practical scaling properties. The evaluation is done by extracting and labelling the proposed features from four well-known datasets. The newly generated datasets are known as NF-UNSW-NB15-v2, NF-BoT-IoT-v2, NF-ToN-IoT-v2, NF-CSE-CIC-IDS2018-v2 and NF-UQ-NIDS-v2. Their performances have been compared to their respective original datasets using an Extra Trees clas-siﬁer, showing a great improvement in the attack detection accuracy. They have been made publicly available to use for research purposes [1].