Miracle-dz/UniFAR-FTUs huggingface dataset:
Boosting Information Spread: An Algorithmic Approach

The majority of influence maximization (IM) studies focus on targeting influential seeders to trigger substantial information spread in social networks. In this paper, we consider a new and complementary problem of how to further increase the influence spread of given seeders. Our study is motivated by the observation that direct incentives could "boost" users so that they are more likely to be influenced by friends. We study the k-boosting problem which aims to find k users to boost so that the final "boosted" influence spread is maximized. The k-boosting problem is different from the IM problem because boosted users behave differently from seeders: boosted users are initially uninfluenced and we only increase their probability to be influenced. Our work also complements the IM studies because we focus on triggering larger influence spread on the basis of given seeders. Both the NP-hardness of the problem and the non-submodularity of the objective function pose challenges to the k-boosting problem. To tackle the problem, we devise two efficient algorithms with the data-dependent approximation ratio. We conduct extensive experiments using real social networks demonstrating the efficiency and effectiveness of our proposed algorithms. We show that boosting solutions returned by our algorithms achieves boosts of influence that are up to several times higher than those achieved by boosting solutions returned by intuitive baselines, which have no guarantee of solution quality. We also explore the "budget allocation" problem in our experiments. Compared with targeting seeders with all budget, larger influence spread is achieved when we allocation the budget to both seeders and boosted users. This also shows that our study complements the IM studies.
Reducing contention through priority updates

Memory contention can be a serious performance bottleneck in concurrent programs on shared-memory multicore architectures. Having all threads write to a small set of shared locations, for example, can lead to orders of magnitude loss in performance relative to all threads writing to distinct locations, or even relative to a single thread doing all the writes. Shared write access, however, can be very useful in parallel algorithms, concurrent data structures, and protocols for communicating among threads. We study the "priority update" operation as a useful primitive for limiting write contention in parallel and concurrent programs. A priority update takes as arguments a memory location, a new value, and a comparison function >p that enforces a partial order over values. The operation atomically compares the new value with the current value in the memory location, and writes the new value only if it has higher priority according to >p. On the implementation side, we show that if implemented appropriately, priority updates greatly reduce memory contention over standard writes or other atomic operations when locations have a high degree of sharing. This is shown both experimentally and theoretically. On the application side, we describe several uses of priority updates for implementing parallel algorithms and concurrent data structures, often in a way that is deterministic, guarantees progress, and avoids serial bottlenecks. We present experiments showing that a variety of such algorithms and data structures perform well under high degrees of sharing. Given the results, we believe that the priority update operation serves as a useful parallel primitive and good programming abstraction as (1) the user largely need not worry about the degree of sharing, (2) it can be used to avoid non-determinism since, in the common case when >p is a total order, priority updates commute, and (3) it has many applications to programs using shared data.
A Comparison of Lauritzen-Spiegelhalter, Hugin, and Shenoy-Shafer Architectures for Computing Marginals of Probability Distributions

In the last decade, several architectures have been proposed for exact computation of marginals using local computation. In this paper, we compare three architectures--Lauritzen-Spiegelhalter, Hugin, and Shenoy-Shafer--from the perspective of graphical structure for message propagation, message-passing scheme, computational efficiency, and storage efficiency.
An inductively powered scalable 32-channel wireless neural recording system-on-a-chip for neuroscience applications

There has been considerable effort devoted to developing technology for interfacing with the central nervous system in laboratory animals and humans [1–2]. Even though these efforts have led to marvelous technological advancements in circuits and systems, some of the resulting devices may find little use in their application domain, because the specifics of the targeted applications or the realistic needs of the end users may not be taken into account.
Using multiple Gaussian hypotheses to represent probability distributions for mobile robot localization

A new mobile robot localization technique is presented which uses multiple Gaussian hypotheses to represent the probability distribution of the robot location in the environment. Sensor data is assumed to be provided in the form of a Gaussian distribution over the space of robot poses. A tree of hypotheses is built, representing the possible data association histories for the system. Covariance intersection is used for the fusion of the Gaussians whenever a data association decision is taken. However, such a tree can grow without bound and so rules are introduced for the elimination of the least likely hypotheses from the tree and for the proper re-distribution of their probabilities. This technique is applied to a feature-based mobile robot localization scheme and experimental results are given demonstrating the effectiveness of the scheme.
The Peptidyl Prolyl Isomerase Rrd1 Regulates the Elongation of RNA Polymerase II during Transcriptional Stresses

Rapamycin is an anticancer agent and immunosuppressant that acts by inhibiting the TOR signaling pathway. In yeast, rapamycin mediates a profound transcriptional response for which the RRD1 gene is required. To further investigate this connection, we performed genome-wide location analysis of RNA polymerase II (RNAPII) and Rrd1 in response to rapamycin and found that Rrd1 colocalizes with RNAPII on actively transcribed genes and that both are recruited to rapamycin responsive genes. Strikingly, when Rrd1 is lacking, RNAPII remains inappropriately associated to ribosomal genes and fails to be recruited to rapamycin responsive genes. This occurs independently of TATA box binding protein recruitment but involves the modulation of the phosphorylation status of RNAPII CTD by Rrd1. Further, we demonstrate that Rrd1 is also involved in various other transcriptional stress responses besides rapamycin. We propose that Rrd1 is a novel transcription elongation factor that fine-tunes the transcriptional stress response of RNAPII.
Aromatase inhibitors: a safety comparison

For almost three decades, tamoxifen has been the mainstay of hormonal therapy in breast cancer patients, but now the third-generation aromatase inhibitors (AIs), anastrozole, letrozole and exemestane, are emerging as potential alternatives, associating greater clinical efficacy with a more favourable overall safety profile than tamoxifen. AIs are associated with a lower incidence of thromboembolic events and vaginal bleeding compared with tamoxifen, although they are known to affect bone turnover and possibly lipid metabolism. As the available AIs have similar efficacy, it is likely that safety and tolerability profiles will have an impact on agent selection in clinical practice. Therefore, it is important that differences in the safety profiles of the third-generation AIs are understood.
Study on a Teaching Assistant System Based on Grid Framework

A grid framework based teaching assistant system (EGP-System, Education Grid Platform) was proposed which adopted a bus based resource sharing and remote control technologies. Furthermore, a thin resource encapsulating interface modular was development which can be used to easily implement the inter-connectivity and inter-communication of campus computers and other electronic-resources. As a result of the application of EGP, a remotely collaborative teaching model, and a new highly communicated education environment was proposed. The aim of EGP was to provide students and teachers a virtual, integrated, and peer-to-peer communication environment which will result in the improvement of study efficiency and interesting, and make education keep up with the step of science and technologies. The framework, key technology and functions and application of EGP were addressed in the paper.
A Case Study of Goal-Driven Autonomy in Domination Games

In this paper we present an overview of a variety of efforts to attain good performance of Game AI for Domination games. These are a very popular kind of game sub-genre in which teams compete for control of locations. Due to the rules of the games, good performance is mostly dependant on overall strategy rather than the skill of individual team members.
Glutamate Transporters Regulate Lesion-Induced Plasticity in the Developing Somatosensory Cortex

Glutamate transporters are involved in neural differentiation, neuronal survival, and synaptic transmission. In the present study, we examined glutamate transporter 1 (GLT1) expression in the neonatal somatosensory cortex of C57BL/6 mice, and pursued its role in somatosensory development by comparing barrel development between GLT1 knock-out and control mice. During the first few neonatal days, a critical period for barrels, GLT1 expression is strikingly upregulated in cortical astrocytes, whereas it was downregulated in neuronal elements to below the detection threshold. GLT1 knock-out neonates developed normally in terms of body growth, cortical histoarchitecture, barrel formation, and critical period termination. However, when row C whiskers were lesioned during the critical period, reduction of lesioned row C barrels and reciprocal expansion of intact row B/D barrels were both milder in GLT1 knock-out mice than in control littermates. Accordingly, the map plasticity index, calculated as (B + D)/2C, was significantly lowered in GLT1 knock-out mice. We also found that extracellular glutamate levels in the neonatal somatosensory cortex were significantly elevated in GLT1 knock-out mice. Diminished lesion-induced plasticity was further found in mutant mice lacking glutamate–aspartate transporter (GLAST), an astrocyte-specific glutamate transporter throughout development. Therefore, glutamate transporters regulate critical period plasticity by enhancing expansion of active barrels and shrinkage of inactive barrels. Because cortical contents of glutamate receptors and GLAST were unaltered in GLT1 knock-out mice, this action appears to be mediated, at least partly, by keeping the ambient glutamate level low. Considering an essential role of glutamate receptors in the formation of whisker-related thalamocortical synapse patterning, glutamate transporters thus facilitate their activity-dependent remodeling.
PEX11α Is Required for Peroxisome Proliferation in Response to 4-Phenylbutyrate but Is Dispensable for Peroxisome Proliferator-Activated Receptor Alpha-Mediated Peroxisome Proliferation

ABSTRACT The PEX11 peroxisomal membrane proteins promote peroxisome division in multiple eukaryotes. As part of our effort to understand the molecular and physiological functions of PEX11 proteins, we disrupted the mouse PEX11α gene. Overexpression of PEX11α is sufficient to promote peroxisome division, and a class of chemicals known as peroxisome proliferating agents (PPAs) induce the expression of PEX11α and promote peroxisome division. These observations led to the hypothesis that PPAs induce peroxisome abundance by enhancing PEX11α expression. The phenotypes of PEX11α−/− mice indicate that this hypothesis remains valid for a novel class of PPAs that act independently of peroxisome proliferator-activated receptor alpha (PPARα) but is not valid for the classical PPAs that act as activators of PPARα. Furthermore, we find that PEX11α−/− mice have normal peroxisome abundance and that cells lacking both PEX11α and PEX11β, a second mammalian PEX11 gene, have no greater defect in peroxisome abundance than do cells lacking only PEX11β. Finally, we report the identification of a third mammalian PEX11 gene, PEX11γ, and show that it too encodes a peroxisomal protein.
Scale-Invariant Memory Representations Emerge from Moiré Interference between Grid Fields That Produce Theta Oscillations: A Computational Model

The dorsomedial entorhinal cortex (dMEC) of the rat brain contains a remarkable population of spatially tuned neurons called grid cells (Hafting et al., 2005). Each grid cell fires selectively at multiple spatial locations, which are geometrically arranged to form a hexagonal lattice that tiles the surface of the rat's environment. Here, we show that grid fields can combine with one another to form moiré interference patterns, referred to as “moiré grids,” that replicate the hexagonal lattice over an infinite range of spatial scales. We propose that dMEC grids are actually moiré grids formed by interference between much smaller “theta grids,” which are hypothesized to be the primary source of movement-related theta rhythm in the rat brain. The formation of moiré grids from theta grids obeys two scaling laws, referred to as the length and rotational scaling rules. The length scaling rule appears to account for firing properties of grid cells in layer II of dMEC, whereas the rotational scaling rule can better explain properties of layer III grid cells. Moiré grids built from theta grids can be combined to form yet larger grids and can also be used as basis functions to construct memory representations of spatial locations (place cells) or visual images. Memory representations built from moiré grids are automatically endowed with size invariance by the scaling properties of the moiré grids. We therefore propose that moiré interference between grid fields may constitute an important principle of neural computation underlying the construction of scale-invariant memory representations.
New Fast Fall Detection Method Based on Spatio-Temporal Context Tracking of Head by Using Depth Images

In order to deal with the problem of projection occurring in fall detection with two-dimensional (2D) grey or color images, this paper proposed a robust fall detection method based on spatio-temporal context tracking over three-dimensional (3D) depth images that are captured by the Kinect sensor. In the pre-processing procedure, the parameters of the Single-Gauss-Model (SGM) are estimated and the coefficients of the floor plane equation are extracted from the background images. Once human subject appears in the scene, the silhouette is extracted by SGM and the foreground coefficient of ellipses is used to determine the head position. The dense spatio-temporal context (STC) algorithm is then applied to track the head position and the distance from the head to floor plane is calculated in every following frame of the depth image. When the distance is lower than an adaptive threshold, the centroid height of the human will be used as the second judgment criteria to decide whether a fall incident happened. Lastly, four groups of experiments with different falling directions are performed. Experimental results show that the proposed method can detect fall incidents that occurred in different orientations, and they only need a low computation complexity.
Models and Inference for Prefix-Constrained Machine Translation

We apply phrase-based and neural models to a core task in interactive machine translation: suggesting how to complete a partial translation. For the phrase-based sys-tem, we demonstrate improvements in suggestion quality using novel objective functions, learning techniques, and inference algorithms tailored to this task. Our contributions include new tunable metrics, an improved beam search strategy, an n -best extraction method that increases suggestion diversity, and a tuning procedure for a hierarchical joint model of alignment and translation. The combination of these techniques improves next-word suggestion accuracy dramatically from 28.5% to 41.2% in a large-scale English-German experiment. Our recurrent neural translation system increases accuracy yet further to 53.0%, but inference is two orders of magnitude slower. Manual error analysis shows the strengths and weaknesses of both approaches.
Visual Inspection Reliability for Precision Manufactured Parts

Objective: Sandia National Laboratories conducted an experiment for the National Nuclear Security Administration to determine the reliability of visual inspection of precision manufactured parts used in nuclear weapons. Background: Visual inspection has been extensively researched since the early 20th century; however, the reliability of visual inspection for nuclear weapons parts has not been addressed. In addition, the efficacy of using inspector confidence ratings to guide multiple inspections in an effort to improve overall performance accuracy is unknown. Further, the workload associated with inspection has not been documented, and newer measures of stress have not been applied. Method: Eighty-two inspectors in the U.S. Nuclear Security Enterprise inspected 140 parts for eight different defects. Results: Inspectors correctly rejected 85% of defective items and incorrectly rejected 35% of acceptable parts. Use of a phased inspection approach based on inspector confidence ratings was not an effective or efficient technique to improve the overall accuracy of the process. Results did verify that inspection is a workload-intensive task, dominated by mental demand and effort. Conclusion: Hits for Nuclear Security Enterprise inspection were not vastly superior to the industry average of 80%, and they were achieved at the expense of a high scrap rate not typically observed during visual inspection tasks. Application: This study provides the first empirical data to address the reliability of visual inspection for precision manufactured parts used in nuclear weapons. Results enhance current understanding of the process of visual inspection and can be applied to improve reliability for precision manufactured parts.
Nicotinic Receptor Gene CHRNA4 Interacts with Processing Load in Attention

Background Pharmacological studies suggest that cholinergic neurotransmission mediates increases in attentional effort in response to high processing load during attention demanding tasks [1]. Methodology/Principal Findings In the present study we tested whether individual variation in CHRNA4, a gene coding for a subcomponent in α4β2 nicotinic receptors in the human brain, interacted with processing load in multiple-object tracking (MOT) and visual search (VS). We hypothesized that the impact of genotype would increase with greater processing load in the MOT task. Similarly, we predicted that genotype would influence performance under high but not low load in the VS task. Two hundred and two healthy persons (age range = 39–77, Mean = 57.5, SD = 9.4) performed the MOT task in which twelve identical circular objects moved about the display in an independent and unpredictable manner. Two to six objects were designated as targets and the remaining objects were distracters. The same observers also performed a visual search for a target letter (i.e. X or Z) presented together with five non-targets while ignoring centrally presented distracters (i.e. X, Z, or L). Targets differed from non-targets by a unique feature in the low load condition, whereas they shared features in the high load condition. CHRNA4 genotype interacted with processing load in both tasks. Homozygotes for the T allele (N = 62) had better tracking capacity in the MOT task and identified targets faster in the high load trials of the VS task. Conclusion The results support the hypothesis that the cholinergic system modulates attentional effort, and that common genetic variation can be used to study the molecular biology of cognition.
Functional analysis of aromatic biosynthetic pathways in Pseudomonas putida KT2440

Pseudomonas putida KT2440 is a non‐pathogenic prototrophic bacterium with high potential for biotechnological applications. Despite all that is known about this strain, the biosynthesis of essential chemicals has not been fully analysed and auxotroph mutants are scarce. We carried out massive mini‐Tn5 random mutagenesis and screened for auxotrophs that require aromatic amino acids. The biosynthesis of aromatic amino acids was analysed in detail including physical and transcriptional organization of genes, complementation assays and feeding experiments to establish pathway intermediates. There is a single pathway from chorismate leading to the biosynthesis of tryptophan, whereas the biosynthesis of phenylalanine and tyrosine is achieved through multiple convergent pathways. Genes for tryptophan biosynthesis are grouped in unlinked regions with the trpBA and trpGDE genes organized as operons and the trpI, trpE and trpF genes organized as single transcriptional units. The pheA and tyrA gene‐encoding multifunctional enzymes for phenylalanine and tyrosine biosynthesis are linked in the chromosome and form an operon with the serC gene involved in serine biosynthesis. The last step in the biosynthesis of these two amino acids requires an amino transferase activity for which multiple tyrB‐like genes are present in the host chromosome.
On asymmetric FIR interpolators with minimum Lperror

The synthesis approach presented in this paper allows one to produce discrete-time filters whose approximation to an arbitrary delay is optimal in a minimax sense. These interpolators exhibit an equal-ripple behavior in their amplitude and/or phase characteristics. In contrast to linear phase FIR designs, the resulting impulse response is in general nonsymmetric. The synthesis method is based on the Fletcher-Powell minimization algorithm applied to the FIR filter tap weights. The technique may be profitably extended to the synthesis of any FIR characteristic having a desired phase response which is other than a multiple of T/2 delay.
Optical Flow Requires Multiple Strategies (but Only One Network)

We show that the matching problem that underlies optical flow requires multiple strategies, depending on the amount of image motion and other factors. We then study the implications of this observation on training a deep neural network for representing image patches in the context of descriptor based optical flow. We propose a metric learning method, which selects suitable negative samples based on the nature of the true match. This type of training produces a network that displays multiple strategies depending on the input and leads to state of the art results on the KITTI 2012 and KITTI 2015 optical flow benchmarks.