Miracle-dz/UniFAR-FTUs huggingface dataset:
Speech: a privileged modality

Ever since the publication of Bolt’s ground-breaking “Put-That There” paper [1], providing multiple modalities as a means of easing the interaction between humans and computers has been a desirable attribute of user interface design. In Bolt’s early approach, the style of modality combination required the user to conform to a rigid order when entering spoken and gestural commands. In the early 1990s, the idea of synergistic multimodal combination began to emerge [4], although actual implemented systems (generally using keyboard and mouse) remained far from being synergistic. Nextgeneration approaches involved time-stamped events to reason about the fusion of multimodal input arriving in a given time window, but these systems were hindered by time-consuming matching algorithms. To overcome this limitation, we proposed [6] a truly synergistic application and a distributed architecture for flexible interaction that reduces the need for explicit time stamping. Our slotbased approach is command directed, making it suitable for applications using speech as a primary modality. In this article, we use our interaction model to demonstrate that during multimodal fusion, speech should be a privileged modality, driving the interpretation of a query, and that in certain cases, speech has even more power to override and modify the combination of other modalities than previously believed.
DoomDB: kill the query

Typically, fault-tolerance in parallel database systems is handled by restarting a query completely when a node failure happens. However, when deploying a parallel database on a cluster of commodity machines or on IaaS offerings such as Amazon's Spot Instances, node failures are a common case. This requires a more fine-granular fault-tolerance scheme. Therefore, most recent parallel data management platforms such as Hadoop or Shark use a fine-grained fault-tolerance scheme, which materializes all intermediate results in order to be able to recover from mid-query faults. While such a fine-grained fault-tolerance scheme is able to efficiently handle node failures for complex and long-running queries, it is not optimal for short-running latency-sensitive queries since the additional costs for materialization often outweigh the costs for actually executing the query. In this demo, we showcase our novel cost-based fault-tolerance scheme in XDB. It selects which intermediate results to materialize such that the overall query runtime is minimized in the presence of node failures. For the demonstration, we present a computer game called DoomDB. DoomDB is designed as an ego-shooter game with the goal of killing nodes in an XDB database cluster and thus prevent a given query to produce its final result in a given time frame. One interesting use-case of DoomDB is to use it for crowdsourcing the testing activities of XDB.
A modular approach to learning Dutch co-reference

This paper presents the first machine learning approach to the resolution of co-referential relations between nominal constituents in Dutch. Based on the hypothesis that different types of information sources contribute to a correct resolution of different types (pronominal, proper noun and common noun) of co-referential links, we propose a modular approach in which a separate module is trained per NP type. We present a thorough comparison of two machine learning techniques, a lazy learner and an eager learning approach, trained on the modular tasks as well as on the undecomposed task. In addition, we show that by postprocessing the resulting co-reference chains by means of a string-edit distance correction mechanism, we can avoid some unlikely local chainings and thereby improve precision. Lacking comparative results for Dutch, we also report results on the English MUC-6 and MUC-7 data sets, which are widely used for evaluation.
The fastclime Package for Linear Programming and Constrained l 1-Minimization Approach to Sparse Precision Matrix Estimation in R

We describe an R package called fastclime, which provides a generic linear programming solver and a parameterized linear programming solver. It also contains a solver for an important sparse precision matrix estimator named CLIME. Our package efficiently implements a method called the parametric simplex method. Compared with the existing package clime, fastclime has many extra advantages: (1) it gives a full piece-wise linear regularization solution path of the CLIME tuning parameter for each column; (2) it only requires a few iterations to recover each sparse precision matrix column; (3) the entire solver is written in C and is easy to modify. 1 The Parametric Simplex Method In this section, we will follow the normal linear programming convention and use x as the normal linear programming variable which has dimension n. Here we briefly describe the primal simplex method and introduces the parametric simplex methods. We give the standard linear programming in inequality form: max cx subject to: Ax ≤ b, x ≥ 0 x ∈ R (1) where A ∈ R, b ∈ R, c ∈ R are given. For the primal simplex method, we require that b ≥ 0. This property is called primal feasibility. We change the standard inequality form into equality form by adding slack variables w: max [ c 0 ] x w ] subject to: [ A I ] x w ] = b x, w ≥ 0 x ∈ R w ∈ R (2) For notation convenience, we rewrite the variable x as: x = [ x1 x2 · · · xn w1 · · · wm ]T = [ x1 x2 · · · xn x1+1 · · · xn+m ]T The new variables, xn+1, . , xn+m, are called slack variables. We separate the m + n variables into two parts: basic variables and nonbasic variables. Basics variables are used to represent the nonbasic variables. Initially, the slack variables are basic variables and the original variables are nonbasic variables. We separate the matrix [ A I ] into the basic part and the nonbasic part as well (with permutation). Initially the matrix A corresponds to nonbasic variables and the identity matrix part corresponds to basic variables. The vector [ c 0 ] is separated in the same fashion: [ A I ] = [ N B ] x w ] = [ xN xB ] [ c 0 ] = [ cN cB ] email: hpang@princeton.edu, Department of Electrical Engineering, Princeton University email: hanliu@princeton.edu, Department of Operational Research and Financial Engineering, Princeton University email: rvdb@princeton.edu, Department of Operational Research and Financial Engineering, Princeton University
Distributed Algorithm to Locate Critical Nodes to Network Robustness based on Spectral Analysis

We propose an algorithm to locate the most critical nodes to network robustness. Such critical nodes may be thought of as those most related to the notion of network centrality. Our proposal relies only on a localized spectral analysis of a limited subnetwork centered at each node in the network. We also present a procedure allowing the navigation from any node towards a critical node following only local information computed by the proposed algorithm. Experimental results confirm the effectiveness of our proposal considering networks of different scales and topological characteristics.
Ordinal Explanation of the Periodic System of Chemical Elements

Textbooks often claim that quantum mechanics explained the periodic system: namely, the actual configuration of electronic orbits that is responsible for the element's chemical properties can be described as the one that minimizes the total energy, and the energy of each configuration can be computed by using quantum mechanics. However, a careful analysis of this explanation reveals that, in addition to the basic equations of quantum mechanics, we need some heuristic rules that do not directly follow from quantum physics. One reason why additional heuristics are necessary is that the corresponding numerical equations are extremely difficult to solve, and as we move to atoms with larger and larger atomic numbers Z, they become even more difficult. Moreover, as Z grows, we must take relativistic effects into consideration, and this means going from partial differential equations to even more mathematically difficult operator equations. In this paper, we show that if instead of the (often impossible) numerical optimization, we consider the (available) ordinal information, we can then explain the observed periodic system.
Data integration by bi-directional schema transformation rules

We describe a new approach to data integration which subsumes the previous approaches of local as view (LAV) and global as view (GAV). Our method, which we term both as view (BAV), is based on the use of reversible schema transformation sequences. We show how LAV and GAV view definitions can be fully derived from BAV schema transformation sequences, and how BAV transformation sequences may be partially derived from LAV or GAV view definitions. We also show how BAV supports the evolution of both global and local schemas, and we discuss ongoing implementation of the BAV approach within the AutoMed project.
Prevalence of obesity and associated factors among secondary school students in Slemani City Kurdistan Region, Iraq

Obesity is a well-established risk factor for cardiovascular disease. The objective of the study was to determine the prevalence of obesity and factors associated among secondary school students aged between 13 to 17 years at Slemani City Centre, Iraq. Methodology: A cross sectional study design was used. The calculated sample size was 1656. Probability proportional to size sampling technique was used to select the sample. A validated pretested questionnaire was used to collect the data. Weight and height were also taken. Data were analyzed using SPSS version 21. Chi-square test was used to determine an association between two categorical variables. Independent t-test was used to compare two means. Multiple logistic regressions were used to determine the predictors for obesity. Results: The response rate was 92.54% and the overall mean age of the 1588 respondents was 15.42 (95% CI = 15.35, 15.49) years. The prevalence of overweight and obesity was 20.6% and 11.3% respectively. Variables significantly associated with overweight and obesity (age, monthly family income, education level of parents, body part satisfaction and body size perception) were entered into the logistic regression model. Multiple logistic regression analysis showed that the predictors for overweight and obesity were age, monthly family income, body part dissatisfaction and body size perception. Conclusion: The prevalence of overweight and obesity is high (20.6% and 11.3% respectively). The predictors of obesity among secondary school students aged between 13 to 17 years old at Slemani City Centre Kurdistan Region, Iraq were age, family income and body image dissatisfaction.
The Candida albicans Histone Acetyltransferase Hat1 Regulates Stress Resistance and Virulence via Distinct Chromatin Assembly Pathways

Human fungal pathogens like Candida albicans respond to host immune surveillance by rapidly adapting their transcriptional programs. Chromatin assembly factors are involved in the regulation of stress genes by modulating the histone density at these loci. Here, we report a novel role for the chromatin assembly-associated histone acetyltransferase complex NuB4 in regulating oxidative stress resistance, antifungal drug tolerance and virulence in C. albicans. Strikingly, depletion of the NuB4 catalytic subunit, the histone acetyltransferase Hat1, markedly increases resistance to oxidative stress and tolerance to azole antifungals. Hydrogen peroxide resistance in cells lacking Hat1 results from higher induction rates of oxidative stress gene expression, accompanied by reduced histone density as well as subsequent increased RNA polymerase recruitment. Furthermore, hat1Δ/Δ cells, despite showing growth defects in vitro, display reduced susceptibility to reactive oxygen-mediated killing by innate immune cells. Thus, clearance from infected mice is delayed although cells lacking Hat1 are severely compromised in killing the host. Interestingly, increased oxidative stress resistance and azole tolerance are phenocopied by the loss of histone chaperone complexes CAF-1 and HIR, respectively, suggesting a central role for NuB4 in the delivery of histones destined for chromatin assembly via distinct pathways. Remarkably, the oxidative stress phenotype of hat1Δ/Δ cells is a species-specific trait only found in C. albicans and members of the CTG clade. The reduced azole susceptibility appears to be conserved in a wider range of fungi. Thus, our work demonstrates how highly conserved chromatin assembly pathways can acquire new functions in pathogenic fungi during coevolution with the host.
DETECTION OF PERSONS IN MLS POINT CLOUDS

Abstract. In this paper we present an approach for the detection of persons in point clouds gathered by mobile laser scanning (MLS) systems. The approach consists of a preprocessing and the actual detection. The main task of the preprocessing is to reduce the amount of data which has to be processed by the detection. To fulfill this task, the preprocessing consists of ground removal, segmentation and several filters. The detection is based on an implicit shape models (ISM) approach which is an extension to bag-of-words approaches. For this detection method, it is sufficient to work with a small amount of training data. Although in this paper we focus on the detection of persons, our approach is able to detect multiple classes of objects in point clouds. Using a parameterization of the approach which offers a good compromise between detection and runtime performance, we are able to achieve a precision of 0.68 and a recall of 0.76 while having a average runtime of 370 ms per single scan rotation of the rotating head of a typical MLS sensor.
A Chinese Herbal Medicine, Tokishakuyakusan, Reduces the Worsening of Impairments and Independence after Stroke: A 1-Year Randomized, Controlled Trial

In post-stroke patients, the recurrence of stroke and progression of impairments lead to a bedridden state and dementia. As for their treatments, only anti-hypertension and anti-coagulation therapies to prevent the recurrence of stroke are available. In Asia, post-stroke patients with impairments are often treated with herbal medicine. The present study evaluated the effectiveness of tokishakuyakusan (TS) in improving the impairment and independence in post-stroke patients. Thirty-one post-stroke patients (mean age = 81.4 years) were recruited and enrolled. Participants were randomly assigned to the TS group (n = 16) or non-treatment (control) group (n = 15) and treated for 12 months. Impairments were assessed using the Stroke Impairment Assessment Set (SIAS). Independence was evaluated using the functional independence measure (FIM). For each outcome measure, mean change was calculated every 3 months. The results were that impairments according to SIAS did not significantly change in the TS group. In contrast, SIAS significantly worsened in the control group. There was a significant difference between the two groups. In each term of SIAS, affected lower extremity scores, abdominal muscle strength, function of visuospatial perception, and so forth. in the TS group were better than those in the control group. Independence according to FIM did not change significantly in the TS group. In contrast, FIM significantly worsened in the control group. There was also a significant difference between the two groups. In conclusion, TS was considered to suppress the impairments of lower limbs and to exert a favorable effect on cerebral function for post-stroke patients.
Long-term efficacy of gamma knife radiosurgery in mesial temporal lobe epilepsy

Background: Gamma knife (GK) radiosurgery has been proposed as an alternative to classic microsurgery in mesial temporal lobe epilepsy (MTLE). Short-term follow-up studies have reported encouraging results, but long-term efficacy is not known. Objective: To report the efficacy and tolerance of GK radiosurgery in MTLE after a follow-up > 5 years. Methods: Patients with a follow-up > 5 years presenting with MTLE and treated with a marginal dose of 24 Gy were included in the study. Results: Fifteen patients were included. Eight were treated on the left side, and 7 were treated on the right. The mean follow-up was 8 years (range 6–10 years). At the last follow-up, 9 of 16 patients (60%) were considered seizure free (Engel Class I) (4/16 in Class IA, 5/16 in Class IB). Seizure cessation occurred with a mean delay of 12 months (± 3) after GK radiosurgery, often preceded by a period of increasing aura or seizure occurrence (6/15 patients). The mean delay of appearance of the first neuroradiologic changes was 12 months (± 4). Nine patients (60%) experienced mild headache and were placed on corticosteroid treatment for a short period. All patients who were initially seizure free experienced a relapse of isolated aura (10/15, 66%) or complex partial seizures (10/15, 66%) during antiepileptic drug tapering. Restoration of treatment resulted in good control of seizures. Conclusion: Gamma knife radiosurgery is an effective and safe treatment for mesial temporal lobe epilepsy. Results are maintained over time with no additional side effects. Long-term results compare well with those of conventional surgery.
Toward smart metering application exploiting IPv6 over wM-bus

In the last years, the increasing focus on energy savings is the key factor in the transformation of the legacy distribution grid toward the so called smart grid. One of the most evident, it is the wide diffusion, in some countries, of smart metering technologies for monitoring of customer electrical consumption. The proven benefits of this technology suggested to the Italian regulative agency the adoption of similar smart solutions for water and gas meters. Currently, the transmission technology which seems to satisfy the strict constraints of power consumption and wide range is the wM-bus operating in different frequency bands, including the recently standardized portion of the spectrum around 169 MHz. This technology provides a low-power and reliable communication channel, but it is not integrated with the IP-based protocols. This may represent a limit since all the ongoing standardization efforts consider the IP suite the unifying framework for the very different network tiers of a Smart Grid. In this paper, the mapping of IPv6 protocol, over wM-bus has been proposed and evaluated in a dedicated simulation environment. The results confirm the feasibility of the proposed approach: the transmission of a typical DLMS/COSEM packet takes only few additional resources compared to wM-bus solution, although the medium access technique of wM-bus decreases the communication performance.
Toward Adaptive Unsupervised Dialogue Act Classification in Tutoring by Gender and Self-Efficacy

For tutorial dialogue systems, classifying the dialogue act (such as questions, requests for feedback, and statements) of student natural language utterances is a central challenge. Recently, unsupervised machine learning approaches are showing great promise; however, these models still have much room for improvement in terms of accuracy. To address this challenge, this paper presents a new unsupervised dialogue act modeling approach that leverages non-cognitive factors of gender and selfefficacy to better model students’ utterances during tutorial dialogue. The experimental findings show that for females, leveraging learner characteristics within dialogue act classification significantly improves performance of the models, producing better accuracy. This line of investigation will inform the design of next-generation tutorial dialogue systems, which leverage machine-learned models to adapt to their users with the help of non-cognitive factors.
Genome-Wide Meta-Analysis of Longitudinal Alcohol Consumption Across Youth and Early Adulthood

The public health burden of alcohol is unevenly distributed across the life course, with levels of use, abuse, and dependence increasing across adolescence and peaking in early adulthood. Here, we leverage this temporal patterning to search for common genetic variants predicting developmental trajectories of alcohol consumption. Comparable psychiatric evaluations measuring alcohol consumption were collected in three longitudinal community samples (N = 2,126, obs = 12,166). Consumption-repeated measurements spanning adolescence and early adulthood were analyzed using linear mixed models, estimating individual consumption trajectories, which were then tested for association with Illumina 660W-Quad genotype data (866,099 SNPs after imputation and QC). Association results were combined across samples using standard meta-analysis methods. Four meta-analysis associations satisfied our pre-determined genome-wide significance criterion (FDR < 0.1) and six others met our ‘suggestive’ criterion (FDR <0.2). Genome-wide significant associations were highly biological plausible, including associations within GABA transporter 1, SLC6A1 (solute carrier family 6, member 1), and exonic hits in LOC100129340 (mitofusin-1-like). Pathway analyses elaborated single marker results, indicating significant enriched associations to intuitive biological mechanisms, including neurotransmission, xenobiotic pharmacodynamics, and nuclear hormone receptors (NHR). These findings underscore the value of combining longitudinal behavioral data and genome-wide genotype information in order to study developmental patterns and improve statistical power in genomic studies.
Adaptive Service Performance Control using Cooperative Fuzzy Reinforcement Learning in Virtualized Environments

Designing efficient control mechanisms to meet strict performance requirements with respect to changing workload demands without sacrificing resource efficiency remains a challenge in cloud infrastructures. A popular approach is fine-grained resource provisioning via auto-scaling mechanisms that rely on either threshold-based adaptation rules or sophisticated queuing/control-theoretic models. While it is difficult at design time to specify optimal threshold rules, it is even more challenging inferring precise performance models for the multitude of services. Recently, reinforcement learning have been applied to address this challenge. However, such approaches require many learning trials to stabilize at the beginning and when operational conditions vary thereby limiting their application under dynamic workloads. To this end, we extend the standard reinforcement learning approach in two ways: a) we formulate the system state as a fuzzy space and b) exploit a set of cooperative agents to explore multiple fuzzy states in parallel to speed up learning. Through multiple experiments on a real virtualized testbed, we demonstrate that our approach converges quickly, meets performance targets at high efficiency without explicit service models.
Comparative analysis of the expression of ERBIN and Erb‐B2 in normal human skin and cutaneous carcinomas

Background ERBIN is a binding partner of Erb‐B2, an orphan receptor within the Erb‐B family critically involved in the regulation of cell growth and differentiation. Although its function remains unclear, ERBIN is thought to affect the polarity of epithelial cells and cell growth via the Ras signalling pathway.