Miracle-dz/UniFAR-FTUs huggingface dataset:
Building an Annotated Corpus for Text Summarization and Question Answering

We describe ongoing work in semi-automatic annotating corpus, with the goal to answer why-question in question answering system and give a construction of the coherent tree for text summarization. In this paper we present annotation schemas for identifying the discourse relations that hold between the parts of text as well as the particular textual of span that are related via the discourse relation. Furthermore, we address several tasks in building the annotated corpus in discourse level, namely creating annotated guidelines, ensuring annotation accuracy and evaluating.
On Binary Embedding using Circulant Matrices

Binary embeddings provide efficient and powerful ways to perform operations on large scale data. However binary embedding typically requires long codes in order to preserve the discriminative power of the input space. Thus binary coding methods traditionally suffer from high computation and storage costs in such a scenario. To address this problem, we propose Circulant Binary Embedding (CBE) which generates binary codes by projecting the data with a circulant matrix. The circulant structure allows us to use Fast Fourier Transform algorithms to speed up the computation. For obtaining $k$-bit binary codes from $d$-dimensional data, this improves the time complexity from $O(dk)$ to $O(d\log{d})$, and the space complexity from $O(dk)$ to $O(d)$. We study two settings, which differ in the way we choose the parameters of the circulant matrix. In the first, the parameters are chosen randomly and in the second, the parameters are learned using the data. For randomized CBE, we give a theoretical analysis comparing it with binary embedding using an unstructured random projection matrix. The challenge here is to show that the dependencies in the entries of the circulant matrix do not lead to a loss in performance. In the second setting, we design a novel time-frequency alternating optimization to learn data-dependent circulant projections, which alternatively minimizes the objective in original and Fourier domains. In both the settings, we show by extensive experiments that the CBE approach gives much better performance than the state-of-the-art approaches if we fix a running time, and provides much faster computation with negligible performance degradation if we fix the number of bits in the embedding.
Phytoestrogens in the environment, I: Occurrence and exposure effects on fathead minnows

Naturally occurring phytoestrogens may mimic biogenic estrogens and modulate endocrine action in vertebrates. Little is known, however, about their temporal and spatial variability in the environment and the biological effects associated with exposures. The present study assessed the environmental presence of phytoestrogens in human‐impacted and relatively pristine areas. The response in larval and sexually mature fathead minnows to environmentally relevant concentrations of 3 common phytoestrogens (genistein, daidzein, and formononetin), both singly and in mixture, was also quantified. Phytoestrogens were only present in the human‐impacted surface waters. When detected, mean concentrations were low (±standard deviation) in an urban lake: 1.4 ± 0.5 ng/L, 1.6 ± 0.7 ng/L, and 1.1 ± 0.2 ng/L for genistein, daidzein, and formononetin, respectively, and in treated wastewater effluent: 1.6 ± 0.4 ng/L, 1.8 ± 1.3 ng/L, and 2.0 ng/L. Biochanin A was detected twice, whereas zearalenone and coumestrol were never detected. No clear temporal trends of aqueous phytoestrogen concentration were evident. Larval survival was significantly reduced in genistein, formononetin, and mixture treatments, whereas adult male fish only exhibited subtle changes to their anatomy, physiology, and behavior. Daidzein‐exposed adult females produced greater quantities of eggs. The present study indicates that genistein, daidzein, and formononetin are likely attenuated rapidly and are unlikely to cause widespread ecological harm in the absence of other stressors. Environ Toxicol Chem 2014;33:553–559. © 2013 SETAC
Decompositions of Grammar Constraints

A wide range of constraints can be compactly specified using automata or formal languages. In a sequence of recent papers, we have shown that an effective means to reason with such specifications is to decompose them into primitive constraints (Quimper & Walsh 2006; 2007). We can then, for instance, use state of the art SAT solvers and profit from their advanced features like fast unit propagation, clause learning, and conflict-based search heuristics. This approach holds promise for solving combinatorial problems in scheduling, rostering, and configuration, as well as problems in more diverse areas like bioinformatics, software testing and natural language processing. In addition, decomposition may be an effective method to propagate other global constraints.
Experimental queueing analysis with long-range dependent packet traffic

Traffic measurement studies from a wide range of working packet networks have convincingly established the presence of significant statistical features that are characteristic of fractal traffic processes, in the sense that these features span many time scales. Of particular interest in packet traffic modeling is a property called long-range dependence (LRD), which is marked by the presence of correlations that can extend over many time scales. We demonstrate empirically that, beyond its statistical significance in traffic measurements, long-range dependence has considerable impact on queueing performance, and is a dominant characteristic for a number of packet traffic engineering problems. In addition, we give conditions under which the use of compact and simple traffic models that incorporate long-range dependence in a parsimonious manner (e.g., fractional Brownian motion) is justified and can lead to new insights into the traffic management of high speed networks.
Optimal training for block transmissions over doubly selective wireless fading channels

High data rates give rise to frequency-selective propagation, whereas carrier frequency-offsets and mobility-induced Doppler shifts introduce time-selectivity in wireless links. To mitigate the resulting time- and frequency-selective (or doubly selective) channels, optimal training sequences have been designed only for special cases: pilot symbol assisted modulation (PSAM) for time-selective channels and pilot tone-assisted orthogonal frequency division multiplexing (OFDM) for frequency-selective channels. Relying on a basis expansion channel model, we design low-complexity optimal PSAM for block transmissions over doubly selective channels. The optimality in designing our PSAM parameters consists of maximizing a tight lower bound on the average channel capacity that is shown to be equivalent to the minimization of the minimum mean-square channel estimation error. Numerical results corroborate our theoretical designs.
Mixing WordNet, VerbNet and PropBank for studying verb relations

In this paper we present a novel resource for studying the semantics of verb relations. The resource is created by mixing sense relational knowledge enclosed in WordNet, frame knowledge enclosed in VerbNet and corpus knowledge enclosed in PropBank. As a result, a set of about 1000 frame pairs is made available. A frame pair represents a pair of verbs in a peculiar semantic relation accompanied with specific information, such as: the syntactic-semantic frames of the two verbs, the mapping among their thematic roles and a set of textual examples extracted from the PennTreeBank. We specifically focus on four relations: Troponymy, Causation, Entailment and Antonymy. The different steps required for the mapping are described in detail and statistics on resource mutual coverage are reported. We also propose a practical use of the resource for the task of Textual Entailment acquisition and for Question Answering. A first attempt for automate the mapping among verb arguments is also presented: early experiments show that simple techniques can achieve good results, up to 85% F-Measure.
The Structural Form in Image Categorization

In this paper we show an unsupervised approach how to find the most natural organization of images. Previous methods which have been proposed to discover the underlying categories or topics of visual objects create no structure or at least the structure, usually tree-shaped, is defined in advance. This causes a problem since the most relevant structure of the data is not always known. It is worthwhile to consider a generic way to find the most suitable structure of images. For this, we apply the model of finding the structural form (among eight natural forms) to automatically discover the best organization of objects in visual domain. The model simultaneously finds the structural form and an instance of that form that best explains the data. In addition, we present a generic structural form, so called meta structure, which can result in even more natural connections between clusters of images. We show that the categorization results are competitive with the state-of-the-art methods while giving more generic insight to the connections between different categories.
Analysis of false starts in spontaneous speech

A primary difference between spontaneous speech and read speech concerns the use of false starts, where a speaker interrupts the flow of speech to restart his or her utterance. A study examined the acoustic aspects of such restarts in a widely-used speech database, examining approximately 1000 utterances, about 10% of which contained a restart. Identifying the type of restart in such cases could improve the performance of an automatic speech recognizer, by eliminating from consideration some hypotheses based on spectral analysis. Results showed that simple restarts (those without inserted or substituted words) could be distinguished acoustically, via an analysis of duration, FO (fundamental frequency) and spectral detail in the neighborhood of a pause. This would allow automatic identification of such restarts with an accuracy exceeding 80%, while keeping false alarms to below 10%. (Author/SR) *********************************************************************** Reproductions supplied by EDRS are the best that can be made from the original document. *********************************************************************** PERMISSION TO REPRODUCE THIS IATERIAL HAS BEEN GRANTED BY
Parallel iterative solution of sparse linear systems using orderings from graph coloring heuristics

The efficiency of a parallel implementation of the conjugate gradient method preconditioned by an incomplete Cholesky factorization can vary dramatically depending on the column ordering chosen. One method to minimize the number of major parallel steps is to choose an ordering based on a coloring of the symmetric graph representing the nonzero adjacency structure of the matrix. In this paper, we compare the performance of the preconditioned conjugate gradient method using these coloring orderings with a number of standard orderings on matrices arising from applications in structural engineering. Because optimal colorings for these systems may not be a priori known: we employ several graph coloring heuristics to obtain consistent colorings. Based on lower bounds obtained from the local structure of these systems, we find that the colorings determined by these heuristics are nearly optimal. For these problems, we find that the increase in parallelism afforded by the coloring-based orderings more than offsets any increase in the number of iterations required for the convergence of the conjugate gradient algorithm.
Analyzing the Electronic Coupling in Molecular Crystals—The Instructive Case of α‐Quinacridone

In the present article, an evaluation of different approaches for estimating the electronic coupling and charge‐transport parameters in organic semiconductors is provided. As a testbed for that comparison, the α‐polymorph of quinacridone is chosen. This system is particularly well suited for the purpose, as α‐quinacridone intermolecular interactions in distinct crystallographic directions are dominated by the three mechanisms most relevant in organic semiconductors: π‐stacking, H‐bonding, and van der Waals stacking. Density‐functional theory‐based simulations yield a comparably complex band structure, which provides the means for demonstrating shortcomings of commonly applied approaches. These include the estimation of transport properties based on bandwidths and the calculation of electronic transfer integrals considering molecular dimers. As a particularly promising alternative, the fitting of suitably complex tight‐binding models to the DFT‐calculated bands in the entire Brillouin zone is proposed. These fits bear the advantage of directly producing intermolecular coupling constants for all relevant neighboring molecules as input parameters for hopping and dynamic disorder models. They also yield an analytic expression for the electronic bands. These allow the extraction of parameters relevant for band‐transport models (like group velocities and effective masses) in the entire Brillouin zone.
Time-dependent parametric and harmonic templates in non-negative matrix factorization

This paper presents a new method to decompose musical spectrograms derived from Non-negative Matrix Factorization (NMF). This method uses time-varying harmonic templates (atoms) which are parametric: these atoms correspond to musical notes. Templates are synthesized from the values of the parameters which are learnt in an NMF framework. This parameterization permits to accurately model some musical effects (such as vibrato) which are inaccurately modeled by NMF.
Human Frontal Lobes and AI Planning Systems An interdisciplinary view of integrated planning and reaction

This paper presents an AI model of integrated planning and reaction based on neuropsychological theories of similar human behavior. The model forges a link between frontal lobe models and AI planning systems that can improve our understanding of situated planning through a combined perspective. This paper rst sets the frontal lobes in context, and then summarizes the frontal lobe theories upon which the model is based. Those theories are then synthesized into a single computational model for an arti cial frontal lobe, and the components are discussed from both neuropsychological and AI perspectives. The paper concludes with a discussion of evaluation issues.
Effect of tillage on biological nitrogen fixation and yield of soybean (Glycine max L. Merril) varieties

Soil properties, plant characteristics, agronomic practices and environmental factors often influence biological nitrogen fixation of legumes. Tillage method used in a specific agro-ecological zone is among these factors thus, the aim of this study was to determine the effect of tillage and no tillage on biological nitrogen fixation and grain yields of three soybean varieties. The study was conducted at four agro-ecological zones of Western Kenya where treatments were replicated thrice using randomized complete block design in split plot arrangement. No tillage and conventional tillage were the main plots whereas, soybean varieties: Nyala, SB19 and SB20 were the sub-plots. Amount of N fixed was determined using 15N abundance method and quantity of nitrogen fixed under no tillage practice exceeded conventional tillage at all sites however, soybean varieties fixed same amount of N. Interaction effect of tillage method and variety on amount of N fixed was varying at every agro-ecological zone though, interaction between no tillage and Nyala fixed more nitrogen at LM 3 than at other agro-ecological zones. Soybean grain yield was similar between tillage methods and also among varieties in a combination of all sites. Practicing no tillage encourages biological nitrogen fixation and its longtime operation leads to yield increase as it improves most soil properties.
Spatial light modulator and method

The present invention provides a spatial light modulator method of resetting pixels. In a kind of "soft landing" of the spatial light modulator electrodes, there may deflect sheet adhered on the landing electrodes. The method of the present invention comprises (1) applying a voltage to store energy in the hinges of the deflection plate, and then (2) the voltage is removed to release the stored energy.
Representations of the texture of food in the primate orbitofrontal cortex: neurons responding to viscosity, grittiness, and capsaicin.

The primate orbitofrontal cortex (OFC) is a site of convergence from taste, olfactory, and somatosensory cortical areas. We describe a population of single neurons in the macaque OFC that responds to the texture of food in the mouth. Use of oral viscosity stimuli consisting of carboxymethylcellulose (CMC) in the range 1-10,000 centipoise showed that the responses of one subset of these neurons were related to stimulus viscosity. Some of the neurons had increasing responses to increasing viscosity, some had decreasing responses, and some neurons were tuned to a range of viscosities. These neurons are a different population to oral fat-sensitive neurons, in that their responses to fats (e.g., safflower oil), to silicone oil [(Si(CH3)2O)n], and to mineral oil (hydrocarbon) depended on the viscosity of these oils. Thus there is a dissociation between texture channels used to sense viscosity and fat. Some of these viscosity-sensitive single neurons were unimodal (somatosensory; 25%) and some received convergent taste inputs (75%). A second subpopulation of neurons responded to gritty texture (produced by microspheres suspended in CMC). A third subpopulation of neurons responded to capsaicin. These results provide evidence about the information channels used to represent the texture and flavor of food in a part of the brain important in appetitive responses to food and are relevant to understanding the physiological and pathophysiological processes related to food intake, food selection, and the effects of variety of food texture in combination with taste and other inputs that affect food intake.
Hypertension: Pathophysiology and Treatment

Hypertension is a significant risk factor for heart disease, stroke and other cardiovascular diseases and an estimated 970 million people worldwide suffer from the disease resulting in significant morbidity, mortality and financial burden globally. Despite significant advances in pharmaceutical treatment only 53% achieve targeted blood pressure goals largely due to poor patient compliance compelling a structured and flexible yet, individually tailored approach for treatment of HTN.1, 2This review addresses the pathophysiology, diagnosis and current management for the disease.
Descripteurs locaux pour l'imagerie radar et applications. (Local features for SAR images and applications)

Nous etudions ici l’interet des descripteurs locaux pour les images satellites optiques et radar. Ces descripteurs, par leurs invariances et leur representation compacte, offrent un interet pour la comparaison d’images acquises dans des conditions differentes. Facilement applicables aux images optiques, ils offrent des performances limitees sur les images radar, en raison de leur fort bruit multiplicatif. Nous proposons ici un descripteur original pour la comparaison d’images radar. Cet algorithme, appele SAR-SIFT, repose sur la meme structure que l’algorithme SIFT (detection de points-cles et extraction de descripteurs) et offre des performances superieures pour les images radar. Pour adapter ces etapes au bruit multiplicatif, nous avons developpe un operateur differentiel, le Gradient par Ratio, permettant de calculer une norme et une orientation du gradient robustes a ce type de bruit. Cet operateur nous a permis de modifier les etapes de l’algorithme SIFT. Nous presentons aussi deux applications pour la teledetection basees sur les descripteurs. En premier, nous estimons une transformation globale entre deux images radar a l’aide de SAR-SIFT. L’estimation est realisee a l’aide d’un algorithme RANSAC et en utilisant comme points homologues les points-cles mis en correspondance. Enfin nous avons mene une etude prospective sur l’utilisation des descripteurs pour la detection de changements en teledetection. La methode proposee compare les densites de points-cles mis en correspondance aux densites de points-cles detectes pour mettre en evidence les zones de changement.
Bayesian reasoning and machine learning

Machine learning methods extract value from vast data sets quickly and with modest resources. They are established tools in a wide range of industrial applications, including search engines, DNA sequencing, stock market analysis, and robot locomotion, and their use is spreading rapidly. People who know the methods have their choice of rewarding jobs. This hands-on text opens these opportunities to computer science students with modest mathematical backgrounds. It is designed for final-year undergraduates and master's students with limited background in linear algebra and calculus. Comprehensive and coherent, it develops everything from basic reasoning to advanced techniques within the framework of graphical models. Students learn more than a menu of techniques, they develop analytical and problem-solving skills that equip them for the real world. Numerous examples and exercises, both computer based and theoretical, are included in every chapter. Resources for students and instructors, including a MATLAB toolbox, are available online.
Current Challenges and Perspectives for Governing Forest Restoration

Negotiation, reconciliation of multiple scales through both ecological and social dimensions and minimization of power imbalances are considered critical challenges to overcome for effective governance of forest restoration. Finding the right mix of "command and control" in forest restoration vs. "environmental governance", which includes non-state actors, regulatory flexibility, and market based instruments is at the heart of these challenges. This Special Issue attempts at shedding light on these challenges with case studies from South and Central America, Africa, and Asia. Some provide within-country as well as cross-country comparisons. A few others present case studies at the household level. Both policy and legal constraints towards implementing forest restoration are also discussed as a function of top down vs. bottom up approaches. The effectiveness of payments for environmental services is examined as catalyzers of forest restoration initiatives. Finally, two papers deal with the legal and policy constraints in making restoration through natural regeneration a viable and cost-effective tool. In the face of renewed perspectives for expanding forest restoration programs globally, governance issues will likely play a key role in eventually determining success. As many of the papers in this Special Issue suggest, the fate of forest restoration outcomes is, more often than not, associated with overall governance challenges, some of which are often overlooked particularly across multiple scales.