Miracle-dz/UniFAR-FTUs huggingface dataset:
IBM Research Report Disambiguation of References to Individuals

We study the problem of disambiguating references to named people in web data. Each name spotted online is shared by several hundred people on average, and teasing apart these references is critical for a new family of person-aware analytical applications. We present and evaluate algorithms for this problem, and give results to indicate that 25% of personal references may be successfully disambiguated with precision in excess of 95%, but that larger fractions cause a significant decline in precision.
Brain Lactate Responses during Visual Stimulation in Fasting and Hyperglycemic Subjects: a 1.5 Tesla 1h-mrs Study

Proton magnetic resonance spectroscopy (1H-MRS) studies showing increased lactate during neural activation support a broader role for lactate in brain energy metabolism than was traditionally recognized. 1H-MRS measures of brain lactate responses have been used to study regional brain metabolism in clinical populations. This study examined whether variations in blood glucose influence the lactate response to visual stimulation in the visual cortex. Six subjects were scanned twice, receiving either saline or 21% glucose intravenously. Using 1H-MRS at 1.5 Tesla with a long echo time (TE=288 msec), the lactate doublet was visible at 1.32 ppm in the visual cortex of all subjects. Lactate increased significantly from resting to visual stimulation. Hyperglycemia had no effect on this increase. The order of the slice-selective gradients for defining the spectroscopy voxel had a pronounced effect on the extent of contamination by signal originating outside the voxel. The results of this preliminary study demonstrate a method for observing a consistent activity-stimulated increase in brain lactate at 1.5 Tesla and show that variations in blood glucose across the normal range have little effect on this response.
TeCFlow – A Temporal Communication Flow Visualizer for Social Network Analysis

This paper introduces an approach for organizational redesign and optimization of communication flows based on temporal analysis of communication patterns in groups of people. Our Temporal Communication Flow Visualizer automatically generates interactive movies of communication flows among individuals by mining e-mail log files and other communication archives. Combining those movies with measures of social network analysis such as the change over time in group betweeness centrality (GBC) and group density leads to deep insights into organizational dynamics. In addition we have defined a contribution index, which measures the activity of an individual as a sender and receiver of messages relative to a team. Based on these findings we can make predictions on the productivity of teams and suggest interventions for improved performance
Learning Sums of Independent Integer Random Variables

Let S = X1+···+Xn be a sum of n independent integer random variables Xi, where each Xi is supported on {0, 1, . , k - 1} but otherwise may have an arbitrary distribution (in particular the Xi's need not be identically distributed). How many samples are required to learn the distribution S to high accuracy? In this paper we show that the answer is completely independent of n, and moreover we give a computationally efficient algorithm which achieves this low sample complexity. More precisely, our algorithm learns any such S to ε-accuracy (with respect to the total variation distance between distributions) using poly(k, 1/ε) samples, independent of n. Its running time is poly(k, 1/ε) in the standard word RAM model. Thus we give a broad generalization of the main result of [DDS12b] which gave a similar learning result for the special case k = 2 (when the distribution S is a Poisson Binomial Distribution). Prior to this work, no nontrivial results were known for learning these distributions even in the case k = 3. A key difficulty is that, in contrast to the case of k = 2, sums of independent {0, 1, 2}-valued random variables may behave very differently from (discretized) normal distributions, and in fact may be rather complicated - they are not log-concave, they can be Θ(n)-modal, there is no relationship between Kolmogorov distance and total variation distance for the class, etc. Nevertheless, the heart of our learning result is a new limit theorem which characterizes what the sum of an arbitrary number of arbitrary independent {0, 1, . , k-1}-valued random variables may look like. Previous limit theorems in this setting made strong assumptions on the “shift invariance” of the random variables Xi in order to force a discretized normal limit. We believe that our new limit theorem, as the first result for truly arbitrary sums of independent {0, 1, . , k-1}-valued random variables, is of independent interest.
Proof of concept for biorefinery approach aiming at two bioenergy production compartments, hydrogen and biodiesel, coupled by an external membrane

ABSTRACT A biorefinery is a facility that integrates biomass conversion processes and equipment to produce fuels, power and chemicals from biomass. This concept is applied here to hydrogen production by anaerobic fermentation. Its biological production is increasingly perceived as a potential pathway for developing renewable sources of energy, due to hydrogen's high energy content (142 kJ g−1) and the absence of harmful emissions during utilization in a hydrogen fuel cell. The main secondary metabolites produced during dark fermentation (volatile fatty acids, VFAs) are building blocks and can also serve as an alternate carbon source for lipid production from oleaginous yeast strains for biodiesel production. The concept proposed here is the coupling of two main compartments. In the first one, lignocellulosic biomass is transformed by dark fermentation into hydrogen and VFAs. The medium used in the first compartment is transferred via an immersed membrane bioreactor to the second compartment where the production of single-cell oil with oleaginous yeast occurs using VFAs as carbon source. This study aims at presenting the feasibility of this bioprocess, through hydrogen production from glucose followed by lipid production by the oleaginous yeast Cryptococcus curvatus grown on the VFA-enriched supernatant resulting from the dark fermentation step.
Centromeres of the Yeast Komagataella phaffii (Pichia pastoris) Have a Simple Inverted-Repeat Structure

Centromere organization has evolved dramatically in one clade of fungi, the Saccharomycotina. These yeasts have lost the ability to make normal eukaryotic heterochromatin with histone H3K9 methylation, which is a major component of pericentromeric regions in other eukaryotes. Following this loss, several different types of centromere emerged, including two types of sequence-defined ("point") centromeres, and the epigenetically-defined "small regional" centromeres of Candida albicans. Here we report that centromeres of the methylotrophic yeast Komagataella phaffii (formerly called Pichia pastoris) are structurally-defined. Each of its four centromeres consists of a 2-kb inverted repeat (IR) flanking a 1-kb central core (mid) region. The four centromeres are unrelated in sequence. CenH3 (Cse4) binds strongly to the cores, with a decreasing gradient along the IRs. This mode of organization resembles Schizosaccharomyces pombe centromeres but is much more compact and lacks the extensive flanking heterochromatic otr repeats. Different isolates of K. phaffii show polymorphism for the orientation of the mid regions, due to recombination in the IRs. CEN4 is located within a 138-kb region that changes orientation during mating-type switching, but switching does not induce recombination of centromeric IRs. The existing genetic toolbox for K. phaffii should facilitate analysis of the relationship between the IRs and the establishment and maintenance of centromeres in this species.
The effect of interparticle interactions on spin glass and hyperthermia properties of Fe3O4 nanoparticles

Water dispersible Fe3O4 nanoparticles (NPs) were prepared, forming a durable suspension for several days. The zero field cooled magnetization peak temperature is ~4.5 times larger than the average blocking temperature, suggesting the presence of interparticle interactions. Frequency dependent AC magnetic susceptibility revealed superspin glass state at low temperatures. Also a signature of size dependent surface spin glass state was observed associated with a hump in in-phase and a peak in out-of phase susceptibility curves. Magnetic hyperthermia studies indicated that interparticle interactions influence the heat generation of NPs in an AC magnetic field. The temperature rise of 7 nm particles obtained was 8 times larger than that of 5 nm particles. This study shows the important role of interparticle interactions on magnetic and hyperthermia properties of magnetic NPs.
Neisseria conserved protein DMP19 is a DNA mimic protein that prevents DNA binding to a hypothetical nitrogen-response transcription factor

DNA mimic proteins occupy the DNA binding sites of DNA-binding proteins, and prevent these sites from being accessed by DNA. We show here that the Neisseria conserved hypothetical protein DMP19 acts as a DNA mimic. The crystal structure of DMP19 shows a dsDNA-like negative charge distribution on the surface, suggesting that this protein should be added to the short list of known DNA mimic proteins. The crystal structure of another related protein, NHTF (Neisseria hypothetical transcription factor), provides evidence that it is a member of the xenobiotic-response element (XRE) family of transcriptional factors. NHTF binds to a palindromic DNA sequence containing a 5′-TGTNAN11TNACA-3′ recognition box that controls the expression of an NHTF-related operon in which the conserved nitrogen-response protein [i.e. (Protein-PII) uridylyltransferase] is encoded. The complementary surface charges between DMP19 and NHTF suggest specific charge–charge interaction. In a DNA-binding assay, we found that DMP19 can prevent NHTF from binding to its DNA-binding sites. Finally, we used an in situ gene regulation assay to provide evidence that NHTF is a repressor of its down-stream genes and that DMP19 can neutralize this effect. We therefore conclude that the interaction of DMP19 and NHTF provides a novel gene regulation mechanism in Neisseria spps.
A T Helper Cell 2 (Th2) Immune Response against Non-self Antigens Modifies the Cytokine Profile of Autoimmune T Cells and Protects against Experimental Allergic Encephalomyelitis

Experimental allergic encephalomyelitis (EAE) is an autoimmune disease of the central nervous system (CNS), and the most commonly used experimental model for multiple sclerosis. It is mediated by autoreactive T cell clones exhibiting a T helper cell (Th) 1 cytokine profile. Nonencephalitogenic T lymphocytes specific for self or exogenous antigens have been found to suppress encephalitogenic T cell responses and to protect against autoimmune disease. The mechanisms by which exogenous antigens modulate autoimmunity are not fully understood. In this study, we tested the hypothesis that a Th2-type immune response against an exogenous, nonself antigen, keyhole limpet hemocyanin (KLH), by releasing IL-4 in the microenvironment, could shift the cytokine profile of encephalitogenic T cells from an inflammatory Th1 to a protective Th2 type. SJL/J mice were preimmunized with the KLH in incomplete Freund's adjuvant to induce a population of Th2 memory cells that would be expected to release Th2 cytokines when activated by the specific antigen at the time of EAE induction. Four weeks later, mice received an encephalitogenic challenge containing guinea pig myelin in complete Freund's adjuvant with or without KLH. All KLH primed animals not receiving the exogenous antigen at the time of EAE induction developed a severe clinical disease indistinguishable from control mice not KLH primed. In contrast, animals preimmunized and challenged with the encephalitogenic inoculum containing KLH showed either no, or markedly reduced, clinical signs. Enzyme-linked immunospot analysis demonstrated that KLH-specific T cells in the primed mice were producing IL-4 characteristic of Th2 cells. In the KLH-primed and restimulated mice, the cytokine profile of the autoreactive, myelin basic protein–specific T cells was shifted from an inflammatory Th1 towards a protective Th2 type. We infer that the presence of IL-4 secreted by KLH-specific memory Th2 cells in the lymphoid system microenvironment in which the autoreactive T cells were engaged by the encephalitogenic stimulus were able to bias their cytokine profile towards a protective Th2 phenotype. This interpretation is supported by the observation that the protective effect of preimmunization with KLH was overcome by rm– IL-12, which inhibited the production of IL-4 by the Th1 cells and biased the autoimmune response to a predominantly Th1 type. Since IL-4 mRNA could not be detected by reverse transcriptase PCR in the CNS, the protective effect was inferred to be mediated by Th2 cells in the lymphoid system, and not the target organ. We conclude that exogenous, nonself antigens that can induce Th2 responses, can modify the cytokine environment sufficiently to alter the cytokine phenotype of inflammatory, autoreactive T cell clones, and ultimately, to provide significant protection against EAE and possibly other T cell–mediated autoimmune diseases.
Spectral compressive sensing with polar interpolation

Existing approaches to compressive sensing of frequency-sparse signals focuses on signal recovery rather than spectral estimation. Furthermore, the recovery performance is limited by the coherence of the required sparsity dictionaries and by the discretization of the frequency parameter space. In this paper, we introduce a greedy recovery algorithm that leverages a band-exclusion function and a polar interpolation function to address these two issues in spectral compressive sensing. Our algorithm is geared towards line spectral estimation from compressive measurements and outperforms most existing approaches in fidelity and tolerance to noise.
SoftEPC — Dynamic instantiation of mobile core network entities for efficient resource utilization

Existing mobile network infrastructures, for example the Evolved Packet Core (EPC), are based on mission specific, specialized network nodes/entities that are interconnect via standardized interfaces in a static manner, each providing a specific service/function. One common practice adopted by many Mobile Network Operators (MNOs) in order to cater to increasing traffic demand is the over provisioning of network and processing resources. However, this approach results in increased CAPEX and OPEX and in view of future load forecasts this approach is no longer considered feasible or economical. In this paper we address this specific issue and propose the concept of softEPC, which leverages the recent advances in cloud technology concept such as Infrastructure as a Service (IaaS) and virtualization techniques in the realm of mobile networks. A softEPC is considered as a virtual network of Evolved Packet Core (EPC) functions over a physical transport network topology. This will enable the on-demand and load-aware dynamic instantiation of network functions and services at appropriate locations in response to the actual traffic demand. The main objective of this approach is to increase the utilization of network resources by flexibly and dynamically placing the network functions/services where most appropriate to provide optimum service and where resources are available to increase the number of services provided to mobile users. The benefits and gains of this approach will be demonstrated by means of simulation results.
DW-MAC: a low latency, energy efficient demand-wakeup MAC protocol for wireless sensor networks

Duty cycling is a widely used mechanism in wireless sensor networks (WSNs) to reduce energy consumption due to idle listening, but this mechanism also introduces additional latency in packet delivery. Several schemes have been proposed to mitigate this latency, but they are mainly optimized for light traffic loads. A WSN, however, could often experience bursty and high traffic loads, such as due to broadcast or convergecast traffic. In this paper, we present a new MAC protocol, called Demand Wakeup MAC (DW-MAC), that introduces a new low-overhead scheduling algorithm that allows nodes to wake up on demand during the Sleep period of an operational cycle and ensures that data transmissions do not collide at their intended receivers. This demand wakeup adaptively increases effective channel capacity during an operational cycle as traffic load increases, allowing DW-MAC to achieve low delivery latency under a wide range of traffic loads including both unicast and broadcast traffic. We compare DW-MAC with S-MAC (with and without adaptive listening) and with RMAC using ns-2 and show that DW-MAC outperforms these protocols, with increasing benefits as traffic load increases. For example, under high unicast traffic load, DW-MAC reduces delivery latency by 70% compared to S-MAC and RMAC, and uses only 50% of the energy consumed with S-MAC with adaptive listening. Under broadcast traffic, DW-MAC reduces latency by more than 50% on average while maintaining higher energy efficiency.
Concurrent reachability games

An open system can be modeled as a two-player game between the system and its environment. At each round of the game, player 1 (the system) and player 2 (the environment) independently and simultaneously choose moves, and the two choices determine the next state of the game. Properties of open systems can be modeled as objectives of these two-player games. For the basic objective of reachability-can player 1 force the game to a given set of target states? -there are three types of winning states, according to the degree of certainty with which player 1 can reach the target. From type-1 states, player 1 has a deterministic strategy to always reach the target. From type-2 states, player 1 has a randomized strategy to reach the target with probability 1. From type-3 states, player 1 has for every real /spl epsi/>0 a randomized strategy to reach the target with probability greater than 1-/spl epsi/. We show that for finite state spaces, all three sets of winning states can be computed in polynomial time: type-1 states in linear time, and type-2 and type-3 states in quadratic time. The algorithms to compute the three sets of winning states also enable the construction of the winning and spoiling strategies. Finally, we apply our results by introducing a temporal logic in which all three kinds of winning conditions can be specified, and which can be model checked in polynomial time. This logic, called Randomized ATL, is suitable for reasoning about randomized behavior in open (two-agent) as well as multi-agent systems.
A General Framework for Expressing Preferences in Causal Reasoning and Planning

We consider the problem of representing arbitrary preferences in causal reasoning and planning systems. In planning, a preference may be seen as a goal or constraint that is desirable, but not necessary, to satisfy. To begin, we define a very general query language for histories, or interleaved sequences of world states and actions. Based on this, we specify a second language in which preferences are defined. A single preference defines a binary relation on histories, indicating that one history is preferred to the other. From this, one can define global preference orderings on the set of histories, the maximal elements of which are the preferred histories. The approach is very general and flexible; thus it constitutes a ‘base’ language in terms of which higher-level preferences may be defined. To this end, we investigate two fundamental types of preferences that we call choice and temporal preferences. We consider concrete strategies for these types of preferences and encode them in terms of our framework. We suggest how to express aggregates in the approach, allowing, e.g. the expression of a preference for histories with lowest total action costs. Last, our approach can be used to express other approaches and so serves as a common framework in which such approaches can be expressed and compared. We illustrate this by indicating how an approach due to Son and Pontelli can be encoded in our approach, as well as the language PDDL3.
Polymer Electrode Material for Microbial Bioelectrochemical Systems

Bioelectrochemical systems based on polymer-bacteria thin film modified electrode were explored. The prepared polymer-bacteria modified copper electrode was characterized with voltametric methods, as cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS). The proposed electrode indicated a definite redox response, high conductivity and electrochemical stability. The experimental results revealed that the prepared electrode could be a feasible for degradation of hazardous phenol pollutants
Distinguishing text/non-text natural images with Multi-Dimensional Recurrent Neural Networks

In this paper, we focus on the text/non-text classification problem: distinguishing images that contain text from a lot of natural images. To this end, we propose a novel neural network architecture, termed Convolutional Multi-Dimensional Recurrent Neural Network (CMDRNN), which distinguishes text/non-text images by classifying local image blocks, taking both region pixels and dependencies among blocks into account. The network is composed of a Convolutional Neural Network (CNN) and a Multi-Dimensional Recurrent Neural Network (MDRNN). The CNN extracts rich and high-level image representation, while the MDRNN analyzes dependencies along multiple directions and produces block-level predictions. By evaluating CMDRNN on a public dataset, we observe improvements over prior arts in terms of both speed and accuracy.
Contributions of Sensory Coding and Attentional Control to Individual Differences in Performance in Spatial Auditory Selective Attention Tasks

Listeners with normal hearing thresholds (NHTs) differ in their ability to steer attention to whatever sound source is important. This ability depends on top-down executive control, which modulates the sensory representation of sound in the cortex. Yet, this sensory representation also depends on the coding fidelity of the peripheral auditory system. Both of these factors may thus contribute to the individual differences in performance. We designed a selective auditory attention paradigm in which we could simultaneously measure envelope following responses (EFRs, reflecting peripheral coding), onset event-related potentials (ERPs) from the scalp (reflecting cortical responses to sound) and behavioral scores. We performed two experiments that varied stimulus conditions to alter the degree to which performance might be limited due to fine stimulus details vs. due to control of attentional focus. Consistent with past work, in both experiments we find that attention strongly modulates cortical ERPs. Importantly, in Experiment I, where coding fidelity limits the task, individual behavioral performance correlates with subcortical coding strength (derived by computing how the EFR is degraded for fully masked tones compared to partially masked tones); however, in this experiment, the effects of attention on cortical ERPs were unrelated to individual subject performance. In contrast, in Experiment II, where sensory cues for segregation are robust (and thus less of a limiting factor on task performance), inter-subject behavioral differences correlate with subcortical coding strength. In addition, after factoring out the influence of subcortical coding strength, behavioral differences are also correlated with the strength of attentional modulation of ERPs. These results support the hypothesis that behavioral abilities amongst listeners with NHTs can arise due to both subcortical coding differences and differences in attentional control, depending on stimulus characteristics and task demands.