Miracle-dz/UniFAR-FTUs huggingface dataset:
Relevance of regulatory T cell promotion of donor-specific tolerance in solid organ transplantation

Current clinical strategies to control the alloimmune response after transplantation do not fully prevent induction of the immunological processes which lead to acute and chronic immune-mediated graft rejection, and as such the survival of a solid organ allograft is limited. Experimental research on naturally occurring CD4+CD25highFoxP3+ Regulatory T cells (Tregs) has indicated their potential to establish stable long-term graft acceptance, with the promise of providing a more effective therapy for transplant recipients. Current approaches for clinical use are based on the infusion of freshly isolated or ex vivo polyclonally expanded Tregs into graft recipients with an aim to redress the in vivo balance of T effector cells to Tregs. However mounting evidence suggests that regulation of donor-specific immunity may be central to achieving immunological tolerance. Therefore, the next stages in optimizing translation of Tregs to organ transplantation will be through the refinement and development of donor alloantigen-specific Treg therapy. The altering kinetics and intensity of alloantigen presentation pathways and alloimmune priming following transplantation may indeed influence the specificity of the Treg required and the timing or frequency at which it needs to be administered. Here we review and discuss the relevance of antigen-specific regulation of alloreactivity by Tregs in experimental and clinical studies of tolerance and explore the concept of delivering an optimal Treg for the induction and maintenance phases of achieving transplantation tolerance.
Cued Speech for Enhancing Speech Perception and First Language Development of Children With Cochlear Implants

Nearly 300 million people worldwide have moderate to profound hearing loss. Hearing impairment, if not adequately managed, has strong socioeconomic and affective impact on individuals. Cochlear implants have become the most effective vehicle for helping profoundly deaf children and adults to understand spoken language, to be sensitive to environmental sounds, and, to some extent, to listen to music. The auditory information delivered by the cochlear implant remains non-optimal for speech perception because it delivers a spectrally degraded signal and lacks some of the fine temporal acoustic structure. In this article, we discuss research revealing the multimodal nature of speech perception in normally-hearing individuals, with important inter-subject variability in the weighting of auditory or visual information. We also discuss how audio-visual training, via Cued Speech, can improve speech perception in cochlear implantees, particularly in noisy contexts. Cued Speech is a system that makes use of visual information from speechreading combined with hand shapes positioned in different places around the face in order to deliver completely unambiguous information about the syllables and the phonemes of spoken language. We support our view that exposure to Cued Speech before or after the implantation could be important in the aural rehabilitation process of cochlear implantees. We describe five lines of research that are converging to support the view that Cued Speech can enhance speech perception in individuals with cochlear implants.
Ranking commercial machines through data transposition

The performance numbers reported by benchmarking consortia and corporations provide little or no insight into the performance of applications of interest that are not part of the benchmark suite. This paper describes data transposition, a novel methodology for addressing this ubiquitous benchmarking problem. Data transposition predicts the performance for an application of interest on a target machine based on its performance similarities with the industry-standard benchmarks on a limited number of predictive machines. The key idea of data transposition is to exploit machine similarity rather than workload similarity as done in prior work, i.e., data transposition identifies a predictive machine that is most similar to the target machine of interest for predicting performance for the application of interest. We demonstrate the accuracy and effectiveness of data transposition using the SPEC CPU2006 benchmarks and a set of 117 commercial machines. We report that the machine ranking obtained through data transposition correlates well with the machine ranking obtained using measured performance numbers (average correlation coefficient of 0.93). Not only does data transposition improve average correlation, we also demonstrate that data transposition is more robust towards outlier benchmarks, i.e., the worst-case correlation coefficient improves from 0.59 by prior art to 0.71. More concretely, using data transposition to predict the top-1 machine for an application of interest leads to the best performing machine for most workloads (average deficiency of 1.2% and max deficiency of 24.8% for one benchmark), whereas prior work leads to deficiencies over 100% for some workloads.
Nosocomial spread of human immunodeficiency virus-related multidrug-resistant tuberculosis in Buenos Aires.

A steep upsurge of human immunodeficiency virus (HIV)-associated multidrug-resistant tuberculosis (MDR-TB) was recently observed at a referral treatment center in Buenos Aires City. Between January 1994 and June 1995, TB isolates resistant to at least five drugs were recovered from 101 of 272 HIV-infected inpatients. Highly resistant isolates from 77 patients underwent restriction fragment length polymorphism study with IS6110. After cross-contamination was eliminated, a single TB strain was found to have caused disease in 68 patients with a history of on-site exposure. The frequency of smear-positive pulmonary disease was higher among these patients than among non-MDR-TB HIV-infected patients (50/68 vs. 60/148, P < .001), and the 1-year survival was dramatically reduced (5/68 vs. 92/148). The strain involved in the outbreak was traced back to patients hospitalized in 1992. Institutional infection control policies were and may still be inadequate to contain the spread of TB among immunodepressed subjects, as is the case in other large urban hospitals in Argentina.
GA driven integrated exploration of loop unrolling factor and datapath for optimal scheduling of CDFGs during high level synthesis

A novel solution to the problem of integrated exploration of datapath resource configuration and loop unrolling factor (UF) for control data flow graphs (CDFGs) during high level synthesis (HLS) for optimal scheduling is presented in this paper. The proposed approach is fast as it is driven through an adaptive genetic algorithm (GA) process, capable of escaping local minima and an estimation model for single loops that determines the total delay without tediously unrolling CDFG. The contributions of the presented work are as follows: (a) novel encoding scheme for chromosomes comprising of `datapath string' (b) novel hybrid encoding scheme for `auxiliary string' that acts a priority resolver during scheduling (c) consideration of operation chaining in scheduling during delay evaluation as well as impact of loop unrolling on the configuration of multiplexer size during power evaluation (d) sensitivity analysis on the effect of `mutation probability (PM)' and `fraction of population mutated (FM)' on final cost and exploration time. Results of the proposed approach yielded reduced final cost (real optimal solution) and exploration speed for all tested benchmarks at PM = 0.5 and FM = lower 50% chromosome, when compared to previous GA approaches.
3D distance fields: a survey of techniques and applications

A distance field is a representation where, at each point within the field, we know the distance from that point to the closest point on any object within the domain. In addition to distance, other properties may be derived from the distance field, such as the direction to the surface, and when the distance field is signed, we may also determine if the point is internal or external to objects within the domain. The distance field has been found to be a useful construction within the areas of computer vision, physics, and computer graphics. This paper serves as an exposition of methods for the production of distance fields, and a review of alternative representations and applications of distance fields. In the course of this paper, we present various methods from all three of the above areas, and we answer pertinent questions such as How accurate are these methods compared to each other? How simple are they to implement? , and What is the complexity and runtime of such methods?
Interaction Between Munc13-1 and RIM Is Critical for Glucagon-Like Peptide-1–Mediated Rescue of Exocytotic Defects in Munc13-1–Deficient Pancreatic β-Cells

OBJECTIVE—Glucagon-like peptide-1 (GLP-1) rescues insulin secretory deficiency in type 2 diabetes partly via cAMP actions on exchange protein directly activated by cAMP (Epac2) and protein kinase A (PKA)-activated Rab3A-interacting molecule 2 (Rim2). We had reported that haplodeficient Munc13-1+/− mouse islet β-cells exhibited reduced insulin secretion, causing glucose intolerance. Munc13-1 binds Epac2 and Rim2, but their functional interactions remain unclear. RESEARCH DESIGN AND METHODS—We used Munc13-1+/− islet β-cells to examine the functional interactions between Munc13-1 and Epac2 and PKA. GLP-1 stimulation of Munc13-1+/− islets normalized the reduced biphasic insulin secretion by its actions on intact islet cAMP production and normal Epac2 and Rim2 levels. RESULTS—To determine which exocytotic steps caused by Munc13-1 deficiency are rescued by Epac2 and PKA, we used patch-clamp capacitance measurements, showing that 1) cAMP restored the reduced readily releasable pool (RRP) and partially restored refilling of a releasable pool of vesicles in Munc13-1+/− β-cells, 2) Epac-selective agonist [8-(4-chloro-phenylthio)-2′-O-methyladenosine-3′,5′-cyclic monophosphate] partially restored the reduced RRP and refilling of a releasable pool of vesicles, and 3) PKA blockade by H89 (leaving Epac intact) impaired cAMP ability to restore the RRP and refilling of a releasable pool of vesicles. Conversely, PKA-selective agonist (N6-benzoyladenosine-cAMP) completely restored RRP and partially restored refilling of a releasable pool of vesicles. To determine specific contributions within Epac-Rim2–Munc13-1 interaction sites accounting for cAMP rescue of exocytosis caused by Munc13-1 deficiency, we found that blockade of Rim2–Munc13-1 interaction with Rim-Munc13-1–binding domain peptide abolished cAMP rescue, whereas blockade of Epac-Rim2 interaction with Rim2-PDZ peptide only moderately reduced refilling with little effect on RRP. CONCLUSIONS—cAMP rescue of priming defects caused by Munc13-1 deficiency via Epac and PKA signaling pathways requires downstream Munc13-1–Rim2 interaction.
Scale‐invariant superiority of foveal vision in perceptual categorization

The recognition of objects is exceedingly difficult in indirect view. This complication cannot be explained in terms of retino‐cortical magnification, as size scaling fails to establish position invariance both for character recognition [Strasburger, H. & Rentschler, I. (1996) Eur. J. Neurosci. , 8 1787–1791] and pattern classification [Jüttner, M. & Rentschler, I. (1996) Vision Res., 36, 1007–1021]. Thus we compared, for two tasks of discrimination learning and category learning with respect to a common set of grey‐level patterns, how humans perform in foveal and extrafoveal vision. Observers learnt to discriminate (size‐scaled) images equally well in foveal and extrafoveal view, whereas they displayed profound deficiencies in extrafoveal category learning for the same patterns. From the behavioural learning data, internal representations of the learning signals were reconstructed by means of computer simulations. For foveal view, these representations were found to be veridical to their physical counterparts for both learning tasks. For extrafoveal view, they were severely distorted for category learning but not for discrimination learning. A variance reduction of the pattern classes by a factor of 100 reduced the dissociation between extrafoveal categorization and discrimination but did not remove it. These observations suggest a scale‐invariant superiority of foveal vision for learning object categories. This implies a high degree of space variance of visual cognition which is vastly underestimated by classical measures of visual performance, e.g. acuity, visual field and contrast sensitivity.
Genetic relationships of pathogenic Typhula species assessed by RAPD, ITS-RFLP and ITS sequencing

Several species of Typhula cause diseases of cereals and grasses at low temperatures. Taxonomic separation of T. phacorrhiza, T. incarnata and T. ishikariensis was investigated using RAPD, ITS-RFLP and nucleotide sequencing of the ITS region. Using 10 RAPD primers on genomic DNA or eight restriction enzymes on PCR-amplified ITS regions, banding patterns were generated for each of 19 isolates from five species or varieties. Polymorphisms were found between species and varieties, with fewer among isolates within species or varieties. RAPD analysis suggested that T. ishikariensis var. idahoensis may be distinct from the other varieties (var. ishikariensis and var. canadensis), although this was not supported by ITS-RFLP. To confirm relationships, ITS nucleotides were obtained from single isolates of each of the five Typhula taxa, and sequence alignments were subjected to distance and parsimony analysis. Although there were differences in branching between dendrograms derived from sequencing vs. RAPD or ITS-RFLP, sequence data supported the distinction of the three main Typhula species, with the T. ishikariensis varieties grouping together and showing differences at an infraspecific level. ITS sequence data also indicated the closer taxonomic affinity of Typhula species to the agaricoid Tricholoma sejunctum compared to the clavarioid Thelephora terrestris.
Distributed Kalman filtering using Broadcast Gossip

For many processes it is required to have a reliable view of an environment of interest. One way to achieve this is by performing a distributed Kalman filter. In this thesis, three distribution methods from different research backgrounds are implemented and evaluated using multiple metrics for use in a Gossip based wireless sensor network: consensus, weighted consensus and covariance intersection. The modular solution makes it possible to easily switch between the different implemented distribution methods and Gossip algorithms. From the evaluation based on metrics like the correctness of the estimate and the agreement among the different nodes, it follows that the naive consensus algorithm does not perform well. The weighted consensus and covariance intersection perform both with errors smaller than two degrees Celsius. However, the weighted consensus does require assumptions that covariance intersection does not.
Separate Neural Processing of Timbre Dimensions in Auditory Sensory Memory

Timbre is a multidimensional perceptual attribute of complex tones that characterizes the identity of a sound source. Our study explores the representation in auditory sensory memory of three timbre dimensions (acoustically related to attack time, spectral centroid, and spectrum fine structure), using the mismatch negativity (MMN) component of the auditory event-related potential. MMN is elicited by a discriminable change in a sound sequence and reflects the detection of the discrepancy between the current stimulus and traces in auditory sensory memory. The stimuli used in the present study were carefully controlled synthetic tones. MMNs were recorded after changes along each of the three timbre dimensions and their combinations. Additivity of unidimensional MMNs and dipole modeling results suggest partially separate MMN generators for different timbre dimensions, reflecting their mainly separate processing in auditory sensory memory. The results expand to timbre dimensions a property of separation of the representation in sensory memory that has already been reported between basic perceptual attributes (pitch, loudness, duration, and location) of sound sources.
An Accurate 3D UWB Hyperbolic Localization in Indoor Multipath Environment Using Iterative Taylor-Series Estimation

An accurate approach in locating a source based on the intersections of hyperboloids defined by the time difference of arrival (TDOA) of a signal received at a number of reference nodes is proposed and investigated. The demonstrated system consists of a UWB transmitter, irradiating a UWB Gaussian pulse, and eight coherent receivers where positions are known. Techniques like optimal template-based estimator used for time delay estimation are discussed. The effect of errors in range measurement on the accuracy of positioning is reduced by adopting Taylor-series-based minimization procedures. Furthermore, we evaluate the performance of the proposed method by comparing with traditional LSE minimization procedure in both simulation and experimental results. The experimental results show that the target positioning error is only about (0.7198±0.271)cm (mean±standard deviation), which is accurate for potential applications such as robot navigation, personal tracking and monitoring.
Heterogeneous Clustered VLIW Microarchitectures

Increasing performance, while at the same time reducing power consumption, is a major design tradeoff in current microprocessors. In this paper, we investigate the potential of using a heterogeneous clustered VLIW microarchitecture. In the proposed microarchitecture, each cluster, the interconnection network and the supporting memory hierarchy can run at different frequencies and voltages. Some of the clusters can then be configured to be performance-oriented and run at high frequency, while the other clusters can be configured to be low-power-oriented and run at lower frequencies, thus reducing overall consumption. For this heterogeneous design to be effective, we need to select the most suitable frequencies and voltages for each component. We propose a scheme to choose these parameters based on a model that estimates the energy consumption and the execution time of floating-point codes at compile time. Finally, we present a modulo scheduling technique based on graph partitioning that exploits the opportunities presented on heterogeneous clustered microarchitectures. Results show that the Energy-Delay product (ED2) can be significantly reduced by 15% on average for a microarchitecture with 4-clusters and by as much as 35% for selected programs
The Relative Efficiency of Propositional Proof Systems

We are interested in studying the length of the shortest proof of a propositional tautology in various proof systems as a function of the length of the tautology. The smallest upper bound known for this function is exponential, no matter what the proof system. A question we would like to answer (but have not been able to) is whether this function has a polynomial bound for some proof system. (This question is motivated below.) Our results here are relative results. In §§2 and 3 we indicate that all standard Hilbert type systems (or Frege systems, as we call them) and natural deduction systems are equivalent, up to application of a polynomial, as far as minimum proof length goes. In §4 we introduce extended Frege systems, which allow introduction of abbreviations for formulas. Since these abbreviations can be iterated, they eliminate the need for a possible exponential growth in formula length in a proof, as is illustrated by an example (the pigeonhole principle). In fact, Theorem 4.6 (which is a variation of a theorem of Statman) states that with a penalty of at most a linear increase in the number of lines of a proof in an extended Frege system, no line in the proof need be more than a constant times the length of the formula proved.
Vision based robotic gesture recognition

Vision in complex environments is a big scientific challenge for two reasons. First, in complex environments it is not possible to segment a scene into the constituent objects on the basis of simple cues. Second, unpredictable changes in the environment must be tolerated. A proper domain for investigating these problems is robotic gesture recognition, since the two problems arise there naturally. Furthermore, gesture recognition holds the promise of making man-machine interaction more natural and intuitive. The principal idea for tackling the first problem is the integration of information stemming from different cues. In the first part of this thesis, methods for tracking human hands, finding fingertips and recognizing hand postures despite complex backgrounds are presented, which owe their robustness to the integration of different complementary cues. The components have been integrated into a user-independent gesture interface implemented on an anthropomorphic robot. The second part is concerned with the adaptive integration of different cues, aimed at addressing the second problem. A model of adaptive sensory integration in the brain is proposed, which relates the psychophysical phenomena of suppression and recalibration of discordant sensory information to a self-organized adaptation employing fast synaptic plasticity mechanisms. Finally, the idea of self-organized adaptation is applied to the tracking of human faces in a scene. To this end, an adaptive tracking scheme is proposed which combines different cues in a “democratic” manner.
Learning Deep ResNet Blocks Sequentially using Boosting Theory

Deep neural networks are known to be difficult to train due to the instability of back-propagation. A deep \emph{residual network} (ResNet) with identity loops remedies this by stabilizing gradient computations. We prove a boosting theory for the ResNet architecture. We construct $T$ weak module classifiers, each contains two of the $T$ layers, such that the combined strong learner is a ResNet. Therefore, we introduce an alternative Deep ResNet training algorithm, \emph{BoostResNet}, which is particularly suitable in non-differentiable architectures. Our proposed algorithm merely requires a sequential training of $T$ "shallow ResNets" which are inexpensive. We prove that the training error decays exponentially with the depth $T$ if the \emph{weak module classifiers} that we train perform slightly better than some weak baseline. In other words, we propose a weak learning condition and prove a boosting theory for ResNet under the weak learning condition. Our results apply to general multi-class ResNets. A generalization error bound based on margin theory is proved and suggests ResNet's resistant to overfitting under network with $l_1$ norm bounded weights.
Stability of nonconstant stationary solutions in a reaction-diffusion equation coupled to the system of ordinary differential equations

In this paper we study pattern formation arising in a system of a single reaction-diffusion equation coupled with subsystem of ordinary differential equations, describing spatially-distributed growth of clonal populations of precancerous cells, whose proliferation is controlled by growth factors diffusing in the extracellular medium and binding to the cell surface. We extend the results on the existence of nonhomogenous stationary solutions obtained in [9] to a general Hill-type production function and full parameter set. Using spectral analysis and perturbation theory we derive conditions for the linearized stability of such spatial patterns.