Miracle-dz/UniFAR-FTUs huggingface dataset:
Did Georges de la Tour use optical projections while painting Christ in the Carpenter’s Studio?

Recently it has been theorized that some painters as early as 1420 used concave mirrors (and, later, converging lenses) to project real inverted images onto their supports which they then traced and painted over. We consider a specific painting adduced as evidence for this bold theory, the Lorainnese Baroque master Georges de la Tour’s Christ in the carpenter’s studio (1645). We perform analyses of the reflections and shadows -- “cast” shadows and “form” shadows -- to infer the source(s) of illumination. We find compelling evidence that this source is the candle flame depicted within the painting and held by Christ. We find it implausible that the source is direct solar illumination, which has the intensity demanded by the projection theory, or artificial illumination as hypothesized by theory proponents. Similar analyses of several other paintings by de la Tour uniformly support the conclusion that the illumination is small and artificial within the space of the tableau (i.e., a candle), not extremely powerful illumination from outside the tableau. We created a very simple computer graphics model to test and illustrate part of our conclusions. Our research is the first application of technical shadow analysis to the question whether artists as early as the 15th century used optical projections when painting. Careful reading of the historical record of de la Tour’s working methods supports our technical results and extend the growing image analytic methods and historical sources rebutting the theory.
Graph connectivity in log-diameter steps using label propagation

The fastest deterministic algorithms for connected components take logarithmic time and perform superlinear work on a PRAM. These algorithms require pointer-chasing operations and are limited to shared-memory systems. Another popular method is `leader contraction' where non-leader vertices are contracted to adjacent leaders. The challenge is to select a constant fraction of leaders that are adjacent to a constant fraction of non-leaders with high probability. Instead we investigate whether simple label propagation can be as efficient as the fastest known algorithms for graph connectivity. Label propagation exchanges representative labels within a component. This is attractive for other models because it is deterministic and does not rely on pointer-chasing, but it is inherently difficult to complete in a sublinear number of steps. We are able to solve the problems with label propagation for graph connectivity. We introduce a simple framework for deterministic graph connectivity in log-diameter steps using label propagation that is easily translated to other computational models. We present new algorithms in PRAM, Stream, and MapReduce. Given a simple, undirected graph $G=(V,E)$ with $n=|V|$ vertices, $m=|E|$ edges, and $D$ diameter, all our algorithms complete in $O(\log D)$ steps without pointer operations. We give the first label propagation algorithms that are competitive with the fastest PRAM algorithms, achieving $O(\log D)$ time and $O((m+n)\log D)$ work with $O(m+n)$ processors. Our main contribution is in Stream and MapReduce models. We give an efficient Stream-Sort algorithm that takes $O(\log D)$ passes and $O(\log n)$ memory, and a MapReduce algorithm taking $O(\log D)$ rounds and $O((m+n)\log D)$ communication overall. These are the first $O(\log D)$-step graph connectivity algorithms in Stream and MapReduce models that are also deterministic and simple to implement.
Does acupuncture the day of embryo transfer affect the clinical pregnancy rate? Systematic review and meta-analysis

The effects of acupuncture on IVF outcomes is still unknown. We carried out a systematic review and meta-analysis of RCT to determine whether acupuncture performed at the time of ET improves outcomes. We searched Medline and Embase from January 1990 to June 2017, for the following terms): (acupuncture; acupuncture therapy) and (reproductive techniques, assisted; in vitro fertilization; embryo transfer). We selected RCT that compared acupuncture with sham acupuncture or no treatment. We included only trials in which acupuncture involved the insertion of needles into traditional meridian points. We evaluated the methodological quality of the trials using the Cochrane risk of bias tool. The measure of treatment effect was the pooled odds ratio of achieving a clinical pregnancy, ongoing pregnancy, or live birth for women in the acupuncture group compared with women in the control group. For pooled data, summary test statistics were calculated using the Mantel-Haenszel method, using the Rev-Man software, version 5.1. We analyzed six studies, including 2,376. In all trials, there were no significant differences between the groups concerning the mean numbers of embryos transferred, the mean age of the women undergoing the procedure, diagnose and use of ICSI. Acupuncture performed the day of ET was associated with a reduced risk of clinical pregnancy (0.87, 95% confidence interval 0.77 to 0.98). The pooled rate difference was -0.06 (-0.12 to -0.01) for clinical pregnancy. None of the trials reported significant adverse effects of acupuncture.
Analysis of Japanese undergraduate students' information behavior during academic tasks in a Canadian university

This poster session will report preliminary results from doctoral research that investigates how Japanese students, as a sample of international students in Canadian universities, perform their academic tasks and to what extent they are information literate. The number of international students, particular from non-North American and non-European countries, has greatly increased in recent decades in North American post-secondary academic institutions. Although their intellectual and financial contributions are significant, their experience after enrolment is often neglected, which leads to dissatisfaction. Thus, it is important for academic institutions to improve international students' experience to facilitate their academic success. Among various potential solutions, improvement of information literacy skills is especially important for students' academic success in today's information intense environment. However, academic libraries struggle to answer the question of how they can provide support for international students with diverse needs, experiences, and expectations. Before planning strategies to improve their skills, it is critical for academic institutions to understand how students conduct research and to what extent they are information literate. Using multiple qualitative methodologies, Japanese undergraduate students' information behavior will be investigated and assessed using information behavior models (how students behaved) and information literacy standards (how well the behavior was conducted). This research will generate knowledge of how they perform their research and the extent to which their behaviors during their research tasks are information literate. An understanding of how various factors affect their behavior will deepen understanding of why students are or are not information literate. The research findings will contribute to the capacity of North American academic libraries to facilitate international students' success in academia.
Quiet sleep detection in preterm infants using deep convolutional neural networks

Objective. Neonates spend most of their time asleep. Sleep of preterm infants evolves rapidly throughout maturation and plays an important role in brain development. Since visual labelling of the sleep stages is a time consuming task, automated analysis of electroencephalography (EEG) to identify sleep stages is of great interest to clinicians. This automated sleep scoring can aid in optimizing neonatal care and assessing brain maturation. Approach. In this study, we designed and implemented an 18-layer convolutional neural network to discriminate quiet sleep from non-quiet sleep in preterm infants. The network is trained on 54 recordings from 13 preterm neonates and the performance is assessed on 43 recordings from 13 independent patients. All neonates had a normal neurodevelopmental outcome and the EEGs were recorded between 27 and 42 weeks postmenstrual age. Main results. The proposed network achieved an area under the mean and median ROC curve equal to 92% and 98%, respectively. Significance. Our findings suggest that CNN is a suitable and fast approach to classify neonatal sleep stages in preterm infants.
Sources and pattern of protein intake and risk of overweight or obesity in young UK twins

Abstract High protein intake in young children is associated with excess gains in weight and body fat, but the specific role of different protein sources has yet to be described. The study aimed to investigate the role of different types of protein in the post-weaning stage on weight, BMI and overweight/obesity at 60 months. Intakes of animal, dairy and plant protein and a dietary pattern characterising variation in protein types at 21 months of age were estimated using a 3-d diet diary in a cohort of 2154 twins; weight and height were recorded every 3 months from birth to 60 months. Longitudinal mixed-effect models investigated the associations between sources of protein intake or dietary pattern scores and BMI, weight and overweight/obesity from 21 months up to 60 months. Adjusting for confounders, dairy protein intake at 21 months was positively associated with greater weight (46 (95 % CI 21, 71) g and BMI up to 60 months (0·04 (95 % CI 0·004, 0·070) kg/m2) and the odds of overweight/obesity at 3 years (OR 1·12; 95 % CI 1·00, 1·24). Milk showed associations of similar magnitude. A dietary pattern low in dairy protein and high in plant protein was associated with lower weight gain up to 60 months, but not overweight/obesity. Intake of dairy products in early childhood is most strongly associated with weight gain, compared with other protein sources. A dietary pattern characterised by lower protein intake and greater protein source diversity at 2 years may confer a lower risk of excess weight gain.
TLR2 Promotes Glioma Immune Evasion by Downregulating MHC Class II Molecules in Microglia

In the glioma tumor microenvironment, TLR2 activation of microglia induces downregulation of microglial MHC class II expression. Impaired MHC class II expression limits T cell–dependent antitumor immunity. Gliomas, the most common primary neoplasms in the brain, are notorious for their ability to evade the immune response. Despite microglial infiltration in gliomas, expression of MHC class II molecules in those microglia is compromised. Here, we report that Toll-like receptor 2 (TLR2) activation downregulated expression of MHC class II molecules in microglia in an orthotopic murine glioma model. TLR2-induced microglial impairment hindered the proliferation and activation of CD4+ T cells, which facilitated glioma immune evasion. TLR2-induced downregulation of MHC class II molecules was caused by suppression of the master regulator of MHC class II molecule transcription, Ciita. TLR2 activation triggered downstream MAPK/ERK1/2 signaling and loss of histone H3 acetylation at Ciita promoters, which in turn inhibited Ciita expression. In glioblastoma tissues, various endogenous TLR2 ligands, including the heat shock proteins that are endogenous TLR2 ligands, were upregulated, a response that correlated with CIITA inhibition. Thus, TLR2 promotes glioma immune-system evasion. These results advance our understanding of microglia as antigen-presenting cells in the context of glioma. In the glioma tumor microenvironment, TLR2 activation of microglia induces downregulation of microglial MHC class II expression. Impaired MHC class II expression limits T-cell–dependent antitumor immunity. Cancer Immunol Res; 6(10); 1220–33. ©2018 AACR.
Learning to Exploit Invariances in Clinical Time-Series Data using Sequence Transformer Networks

Recently, researchers have started applying convolutional neural networks (CNNs) with one-dimensional convolutions to clinical tasks involving time-series data. This is due, in part, to their computational efficiency, relative to recurrent neural networks and their ability to efficiently exploit certain temporal invariances, (e.g., phase invariance). However, it is well-established that clinical data may exhibit many other types of invariances (e.g., scaling). While preprocessing techniques, (e.g., dynamic time warping) may successfully transform and align inputs, their use often requires one to identify the types of invariances in advance. In contrast, we propose the use of Sequence Transformer Networks, an end-to-end trainable architecture that learns to identify and account for invariances in clinical time-series data. Applied to the task of predicting in-hospital mortality, our proposed approach achieves an improvement in the area under the receiver operating characteristic curve (AUROC) relative to a baseline CNN (AUROC=0.851 vs. AUROC=0.838). Our results suggest that a variety of valuable invariances can be learned directly from the data.
Representation Learning for Classification in Heterogeneous Graphs with Application to Social Networks

We address the task of node classification in heterogeneous networks, where the nodes are of different types, each type having its own set of labels, and the relations between nodes may also be of different types. A typical example is provided by social networks where node types may for example be users, content, or films, and relations friendship, like, authorship. Learning and performing inference on such heterogeneous networks is a recent task requiring new models and algorithms. We propose a model, Labeling Heterogeneous Network (LaHNet), a transductive approach to classification that learns to project the different types of nodes into a common latent space. This embedding is learned so as to reflect different characteristics of the problem such as the correlation between node labels, as well as the graph topology. The application focus is on social graphs, but the algorithm is general and can be used for other domains. The model is evaluated on five datasets representative of different instances of social data.
Throughput Improvement of MPTCP by Selective Bicasting with Cross-Layer Control in Wireless Environment

A mobile device equipped with multiple wireless network interfaces has capability to improve throughput and availability by means of Multipath TCP (MPTCP) communication with alternating the primary (active) interface according to the current wireless network condition. To realize this ability, we proposed MPTCP selective bicasting which bicasts only retransmission request packets and retransmission packets. However, this method as a problem that it would not improve throughput so much when the condition of wireless communication deteriorates since it bicasts packets only after the transport layer detects packet losses. In this paper, we propose a method to improve throughput which bicasts packets before the transport layer detects packet losses by means of cross-layer control. Based on the frame loss rate in the data link layer, this method sends dropping packets via the alternative interface earlier than the previous method. Simulation results shows that the proposed method improves throughput without sending so many packets via the alternative interface.
Compensatory increases of select proteostasis networks after Hsp70 inhibition in cancer cells

ABSTRACT Cancer cells thrive when challenged with proteotoxic stress by inducing components of the protein folding, proteasome, autophagy and unfolded protein response (UPR) pathways. Consequently, specific molecular chaperones have been validated as targets for anti-cancer therapies. For example, inhibition of Hsp70 family proteins (hereafter Hsp70) in rhabdomyosarcoma triggers UPR induction and apoptosis. To define how these cancer cells respond to compromised proteostasis, we compared rhabdomyosarcoma cells that were sensitive (RMS13) or resistant (RMS13-R) to the Hsp70 inhibitor MAL3-101. We discovered that endoplasmic reticulum-associated degradation (ERAD) and autophagy were activated in RMS13-R cells, suggesting that resistant cells overcome Hsp70 ablation by increasing misfolded protein degradation. Indeed, RMS13-R cells degraded ERAD substrates more rapidly than RMS cells and induced the autophagy pathway. Surprisingly, inhibition of the proteasome or ERAD had no effect on RMS13-R cell survival, but silencing of select autophagy components or treatment with autophagy inhibitors restored MAL3-101 sensitivity and led to apoptosis. These data indicate a route through which cancer cells overcome a chaperone-based therapy, define how cells can adapt to Hsp70 inhibition, and demonstrate the value of combined chaperone and autophagy-based therapies. This article has an associated First Person interview with the first author of the paper. Summary: We demonstrate that cancer cells can compensate for Hsp70 inhibition by inducing autophagy, emphasizing the role of autophagy/chaperone-based therapies.
Resource Allocation for Cooperative D2D-Enabled Wireless Caching Networks

In this paper, we study the resource allocation problem for a cooperative device-to-device (D2D)- enabled wireless caching network, where each user randomly caches popular contents to its memory and shares the contents with nearby users through D2D links. In order to enhance the throughput of spectrum-sharing D2D links, which may be severely limited by the interference among D2D links, we enable the cooperation among some of the D2D links to eliminate the interference among them. We formulate a joint link scheduling and power allocation problem to maximize the overall throughput of cooperative D2D links (CDLs) and non- cooperative D2D links (NDLs), which is NP-hard. To solve the problem, we decompose it into two sub- problems, which maximize the sum rates of the CDLs and the NDLs, respectively. For CDL optimization, we propose a semi-orthogonal-based algorithm for joint user scheduling and power allocation. For NDL optimization, we propose a novel low-complexity algorithm to perform link scheduling and develop a Difference of Convex functions (D.C.) programming method to solve the non-convex power allocation problem. Simulation results show that cooperative transmission can significantly improve both the number of served users and the overall system throughput.
On Graph Representation with Smallest Numerical Encoding

The study of succinct representation of graphs has received relevant attention to allow efficiency in modeling interconnected systems. Related work on graph representation has achieved compact encodings by benefiting from structural regularities, such as triangularity, separability, planarity, symmetry and sparsity; whereas the case of arbitrary unstructured graphs has remained elusive. In this paper, we propose an scheme to represent arbitrary graphs by using the smallest integer number. We believe our approach is useful to represent graphs efficiently.
A Projection-Based Approach for Memory Leak Detection

One of the major software safety issues is memory leak. Moreover, detecting memory leak vulnerabilities is challenging in static analysis. Existing static detection tools find bugs by collecting programs' information in the process of scanning source code. However, the current detection tools are weak in efficiency and accuracy, especially when the targeted program contains complex branches. This paper proposes a projection-based approach to detect memory leaks in C source code with complex control flows. According to the features of memory allocation and deallocation in C source code, this approach projects the original control flow graph of a program to a simpler one, and it reduces the analysis complexity. Besides, this paper implements a memory-leak detection tool—PML_Checker, and evaluates the tool by comparing with three open-source static detection tools on both public benchmarks and study test cases. The experimental results show that PML_Checker reports the most memory leak vulnerabilities among the four existing tools with complex control flows and complex data types, and PML_Checker obtains higher efficiency and accuracy on public benchmarks.
A Study and Estimation a Lost Person Behavior in Crowded Areas Using Accelerometer Data from Smartphones

As smartphones become more popular, applications are being developed with new and innovative ways to solve problems in the day-to-day lives of users. One area of smartphone technology that has been developed in recent years is human activity recognition (HAR). This technology uses various sensors that are built into the smartphone to sense a person's activity in real time. Applications that incorporate HAR can be used to track a person's movements and are very useful in areas such as health care. We use this type of motion sensing technology, specifically, using data collected from the accelerometer sensor. The purpose of this study is to study and estimate the person who may become lost in a crowded area. The application is capable of estimating the movements of people in a crowded area, and whether or not the person is lost in a crowded area based on his/her movements as detected by the smartphone. This will be a great benefit to anyone interested in crowd management strategies. In this paper, we review related literature and research that has given us the basis for our own research. We also detail research on lost person behavior. We looked at the typical movements a person will likely make when he/she is lost and used these movements to indicate lost person behavior. We then evaluate and describe the creation of the application, all of its components, and the testing process.
The opioid receptor mu 1 (OPRM1) rs1799971 and catechol-O-methyltransferase (COMT) rs4680 as genetic markers for placebo analgesia

Abstract The placebo effect is considered the core example of mind–body interactions. However, individual differences produce large placebo response variability in both healthy volunteers and patients. The placebo response in pain, placebo analgesia, may be dependent on both the opioid system and the dopaminergic system. Previous studies suggest that genetic variability affects the function of these 2 systems. The aim of this study was therefore to address the interaction between the single nucleotide polymorphisms opioid receptor mu 1 (OPRM1) rs1799971 and catechol-O-methyltransferase (COMT) rs4680 on placebo analgesia. Two hundred ninety-six healthy volunteers participated in a repeated-measures experimental design where thermal heat pain stimuli were used as pain stimuli. Participants were randomized either to a placebo group receiving placebo cream together with information that the cream would reduce pain, or to a natural history group receiving the same pain stimuli as the placebo group without any application of cream or manipulation of expectation of pain levels. The results showed that the interaction between OPRM1 rs1799971 and COMT rs4680 was significantly associated with the placebo analgesic response. Participants with OPRM1 Asn/Asn combined with COMT Met/Met and Val/Met reported significant pain relief after placebo administration, whereas those with other combinations of the OPRM1 and COMT genotypes displayed no significant placebo effect. Neither OPRM1 nor COMT had any significant influence on affective changes after placebo administration. As shown in this study, genotyping with regard to OPRM1 and COMT may predict who will respond favorably to placebo analgesic treatment.