Miracle-dz/UniFAR-FTUs huggingface dataset:
The climate of the Last Glacial Maximum: Results from a coupled atmosphere-ocean general circulation model

Results from a coupled atmosphere-ocean general circulation model simulation of the Last Glacial Maximum reveal annual mean continental cooling between 48 and 78C over tropical landmasses, up to 268 of cooling over the Laurentide ice sheet, and a global mean temperature depression of 4.38C. The simulation incorporates glacial ice sheets, glacial land surface, reduced sea level, 21 ka orbital parameters, and decreased atmospheric CO2. Glacial winds, in addition to exhibiting anticyclonic circulations over the ice sheets themselves, show a strong cyclonic circulation over the northwest Atlantic basin, enhanced easterly flow over the tropical Pacific, and enhanced westerly flow over the Indian Ocean. Changes in equatorial winds are congruous with a westward shift in tropical convection, which leaves the western Pacific much drier than today but the Indonesian archipelago much wetter. Global mean specific humidity in the glacial climate is 10% less than today. Stronger Pacific easterlies increase the tilt of the tropical thermocline, increase the speed of the Equatorial Undercurrent, and increase the westward extent of the cold tongue, thereby depressing glacial sea surface temperatures in the western tropical Pacific by ;58 -6 8C.
Genetic mouse models of Huntington’s disease: focus on electrophysiological mechanisms

The discovery of the HD (Huntington’s disease) gene in 1993 led to the creation of genetic mouse models of the disease and opened the doors for mechanistic studies. In particular, the early changes and progression of the disease could be followed and examined systematically. The present review focuses on the contribution of these genetic mouse models to the understanding of functional changes in neurons as the HD phenotype progresses, and concentrates on two brain areas: the striatum, the site of most conspicuous pathology in HD, and the cortex, a site that is becoming increasingly important in understanding the widespread behavioural abnormalities. Mounting evidence points to synaptic abnormalities in communication between the cortex and striatum and cell–cell interactions as major determinants of HD symptoms, even in the absence of severe neuronal degeneration and death.
QCD string as vortex string in Seiberg-dual theory

We construct a classical vortex string solution in a Seiberg-dual theory of = 1 supersymmetric SO(Nc) QCD which flows to a confining phase. We claim that this vortex string is a QCD string, as previouly argued by M. Strassler. In SO(Nc) QCD, it is known that stable QCD strings exist even in the presence of dynamical quarks. We show that our vortex strings are stable in the Seiberg-dual theory.
A Retransmission Based Collision Solution for Cognitive Radio Networks

In cognitive radio (CR) networks, the secondary users are allowed to use the licensed frequency bands of the primary users (PUs) when these bands are vacant. However, once the PUs come back, they may collide with the transmitting secondary users. Therefore, the secondary users are generally required to sense the channel periodically to ensure that the primary users are absent during their data transmission in order to avoid the collisions with primary users. In this paper, we first analyze the performance of the periodic sensing scheme. Then retransmission scheme based on the cooperation of the primary base station(PBS) and secondary access point (AP) associated with Independent Component Analysis (ICA) algorithm is proposed, which relieves the impact of packet loss for primary users, while improving the transmission efficiency for secondary users. The computer simulation shows that under the limited spectrum access chances, the proposed scheme outperforms periodical sensing schemes.
Comparison of loss-based overload control mechanisms in signaling system with SIP protocol

In this paper we compare two approaches for loss-based overload control mechanisms in signaling system with SIP protocol, hop-by-hop and end-to-end. Both discussed mechanisms are based on using a simple non-preemptive priority queueing scheme in SIP server. The effectiveness of both approaches is compared with the system with no overload control. The effectiveness of the solution is measured by average call setup time, goodput and basic fairness.
Acquired prior knowledge modulates audiovisual integration

Orienting responses to audiovisual events in the environment can benefit markedly by the integration of visual and auditory spatial information. However, logically, audiovisual integration would only be considered successful for stimuli that are spatially and temporally aligned, as these would be emitted by a single object in space–time. As humans do not have prior knowledge about whether novel auditory and visual events do indeed emanate from the same object, such information needs to be extracted from a variety of sources. For example, expectation about alignment or misalignment could modulate the strength of multisensory integration. If evidence from previous trials would repeatedly favour aligned audiovisual inputs, the internal state might also assume alignment for the next trial, and hence react to a new audiovisual event as if it were aligned. To test for such a strategy, subjects oriented a head‐fixed pointer as fast as possible to a visual flash that was consistently paired, though not always spatially aligned, with a co‐occurring broadband sound. We varied the probability of audiovisual alignment between experiments. Reaction times were consistently lower in blocks containing only aligned audiovisual stimuli than in blocks also containing pseudorandomly presented spatially disparate stimuli. Results demonstrate dynamic updating of the subject’s prior expectation of audiovisual congruency. We discuss a model of prior probability estimation to explain the results.
Mémoire autobiographique et self dans la maladie d'Alzheimer : étude neuropsychologique et en neuro-imagerie

L’objectif de ce travail etait d’etudier la memoire autobiographique (MAb) aux stades debutants de la maladie d’Alzheimer et d’analyser le lien avec le Self defini selon le modele de Prebble et collaborateurs. (2013) et en investiguant les substrats neuro-anatomiques. Notre etude a confirme qu’il existait une alteration de la MAb chez les patients atteints de maladie d’Alzheimer, dans sa composante episodique et emotionnelle, quelle que soit l’anciennete des souvenirs. Nous avons pu rattacher ce deficit episodique a l’atrophie des regions temporales internes et en suggerer l’implication de l’hippocampe gauche dans le contexte temporel des souvenirs et de l’amygdale droite dans la composante emotionnelle. En revanche, il existait une relative preservation du niveau des details des souvenirs emotionnels residuels, et surtout, des souvenirs semantises, ces derniers etant supportes par le neocortex temporal. Concernant le Self de facon plus generale, les resultats mettent en evidence un lien entre Self-conceptuel et MAb, par le biais des processus de semantisation et d’integration des souvenirs qui permettent de former des representations abstraites a partir des experiences vecues. Par ailleurs, nous avons egalement montre que le sens subjectif de soi est inherent a toutes les autres composantes du Self. Par l’etude d’un cas unique et d’imagerie volumetrique de groupe, le cortex prefrontal median a ete mis en evidence comme substrat commun a toutes les composantes du Self, suggerant un role cle de cette structure pour supporter le sens subjectif de soi. Ces resultats ouvrent des pistes de remediation par la reminiscence basee sur les mecanismes de semantisation, d’integration et sur les aspects emotionnels, au centre desquels se trouvent les souvenirs definissant-le-soi. Aussi notre etude engage-t-elle a analyser ces composantes du Self au sein de reseaux, en connectivite fonctionnelle et anatomique.
Two-stage data envelopment analysis based on interval data

Measuring the performance of a production system has been an important task in management for purposes of control, planning, etc. One technique widely applied to measure the relative efficiency of a set of production systems, or decision making units (DMUs), which utilize the same inputs to produce the same outputs, is data envelopment analysis (DEA). Conventionally, the system is treated as a black box, in which only inputs and outputs of the black box are considered in measuring the efficiency. The performance of the component processes interacting with each other in the system are not considered. In some situations (in real applications) of DEA applications, there are Decision Making Units (DMUs) which have two-stage structure. In the previous two-stage models have been assumed that all inputs and outputs of DMUs are exactly known and this assumption may not be true (i.e. some or all of inputs and outputs may be imprecise). This paper proposes a method to determine efficiency of two-stage DMUs with interval inputs and outputs. Then illustrates the proposed method using an example.
Collecting Spontaneously Spoken Queries for Information Retrieval

Motivated to realize the speech-driven information retrieval systems that accept spontaneously spoken queries, we developed a method to collect such speech data derived from the pre-defined search topics that had been systematically constructed for IR research. In order to evaluate both our method and the performance of the document retrieval by using the spontaneously spoken queries, we took place two experiments of collecting the speech data by our method using publicly available test collections of evaluating document retrieval. The first preliminary experiment took place with relatively small number of search topics selected from the NTCIR-3Web retrieval collection, which had been constructed for the TREC-style evaluation workshop, in order to test our method. The second experiment took place with all of the search topics released from the NTCIR-4 Web task to participate the formal run of the evaluation. The information about the collected data and the result of the evaluation with respect to both the speech recognition accuracy and the precision of document retrieval by using the collected data are presented in this paper.
SHADE: Technology for Knowledge-based Collaborative Engineering

Effective information sharing and decision coordination are vital to collaborative product development and integrated manufacturing. However, typical special-purpose CAE systems tend to isolate information at tool boundaries, and typical integrated CAE systems tend to limit flexibility and process innovation. The SHADE (SHAred Dependency Engineering) project strikes a balance between these undesirable extremes by supporting reconfigurable exchange of engineering knowledge among special-purpose CAE systems. SHADE's approach has three main components: a shared knowledge representation (language and domain-specific vocabulary), protocols supporting information exchange for change notification and subscription, and facilitation services for content-directed routing and intelligent matching of information consumers and producers.
Environmental Sniffing: Noise Knowledge Estimation for Robust Speech Systems

Automatic speech recognition systems work reasonably well under clean conditions but become fragile in practical applications involving real-world environments. To date, most approaches dealing with environmental noise in speech systems are based on assumptions concerning the noise, or differences in collecting and training on a specific noise condition, rather than exploring the nature of the noise. As such, speech recognition, speaker ID, or coding systems are typically retrained when new acoustic conditions are to be encountered. In this paper, we propose a new framework entitled Environmental Sniffing to detect, classify, and track acoustic environmental conditions. The first goal of the framework is to seek out detailed information about the environmental characteristics instead of just detecting environmental changes. The second goal is to organize this knowledge in an effective manner to allow smart decisions to direct subsequent speech processing systems. Our current framework uses a number of speech processing modules including a hybrid algorithm with T2-BIC segmentation, Gaussian mixture model/hidden Markov model (GMM/HMM)-based classification and noise language modeling to achieve effective noise knowledge estimation. We define a new information criterion that incorporates the impact of noise into Environmental Sniffing performance. We use an in-vehicle speech and noise environment as a test platform for our evaluations and investigate the integration of Environmental Sniffing for automatic speech recognition (ASR) in this environment. Noise sniffing experiments show that our proposed hybrid algorithm achieves a classification error rate of 25.51%, outperforming our baseline system by 7.08%. The sniffing framework is compared to a ROVER solution for automatic speech recognition (ASR) using different noise conditioned recognizers in terms of word error rate (WER) and CPU usage. Results show that the model matching scheme using the knowledge extracted from the audio stream by Environmental Sniffing achieves better performance than a ROVER solution both in accuracy and computation. A relative 11.1% WER improvement is achieved with a relative 75% reduction in CPU resources
Decision Making in an Uncertain Environment: the Scenario based Optimization Approach

A central issue arising in financial, engineering and, more generally, in many applicative endeavors is to make a decision in spite of an uncertain environment. Along a robust approach, the decision should be guaranteed to work well in all possible realizations of the uncertainty. A less restrictive approach consists instead of requiring that the risk of failure associated to the decision should be small in some – possibly probabilistic – sense. From a mathematical viewpoint, the latter formulation leads to a chance-constrained optimization program, i.e. to an optimization program subject to constraints in probability. Unfortunately, however, both the robust approach as well as the chance-constrained approach are computationally intractable in general. In this paper, we present a computationally efficient methodology for dealing with uncertainty in optimization based on sampling a finite number of instances (or scenarios) of the uncertainty. In particular, we consider uncertain programs with convexity structure, and show that the scenario-based solution is, with high confidence, a feasible solution for the chance-constrained problem. The proposed approach represents a viable way to address general convex decision making problems in a risk-adjusted sense.
Crowdsourcing

Early iterations of the crowdsourcing typology presented here were developed through a 2009 white paper with Noah Friedland for the Friedland Group, and presentations at the 2010 Stakeholder Engagement online conference and the 2010 American Planning Association conference in New Orleans. Portions of this chapter were drawn from the author's dissertation at the University of Utah, directed by Professor Joy Pierce. As our understanding of participatory cultures advances, there is a growing interest among practitioners and scholars in how best to take charge of the creative, productive capabilities of Internet users for specific purposes. A number of online businesses in the past decade have actively recruited individuals in online communities to design products and solve problems for them, often motivating an online community's creative output or harnessing their creative input through the format of an open challenge with various rewards. Organizations that issue specific tasks to online communities in an open call format engage in the practice of " crowdsourcing. " Crowdsourcing is a model for problem solving, not merely a model for doing crowdsourcing model is also well suited to organizations' marketing and public relations goals, as the process of managing an online community allows organizations to forge close relationships with publics and allows consumers to participate in the making of brands (Phillips and Brabham, 2011). Thus, it is important to understand how crowdsourcing works so that the collective intelligence of online communities can be leveraged in future participatory media applications for the public good. In this chapter, I further define the crowdsourcing model by putting forth a typology of crowdsourcing. Ultimately, these types may inform the design of future participatory media applications for governments, non-profits, and activists hoping to solve pressing political, social, and environmental problems. The Basics of Crowdsourcing Jeff Howe, a contributing editor for Wired magazine, coined the term " crowdsourcing " in a June 2006 article (Howe, 2006c). In a companion blog, Howe (2006a) offered the following definition of crowdsourcing: Crowdsourcing 3 Simply defined, crowdsourcing represents the act of a company or institution taking a function once performed by employees and outsourcing it to an undefined (and generally large) network of people in the form of an open call. The crucial prerequisite is the use of the open call format and the large network of potential laborers. (para. 5). It is important to emphasize that this process is one that is sponsored by an …
Tradeoff between network energy consumption and terminal energy consumption via small cell power control

In this paper, we propose a novel power control scheme for small cells deployed within macro cells. Our aim is to find the optimal power level for each small cell according to the energy consumption tradeoff between network and User Equipments (UEs). Two different small cell deployment scenarios are considered: the non-dense scenario and the dense scenario. The multiagent decentralized Reinforcement Learning (RL) technique is applied to deal with the dense deployment scenario where the coverage of different small cells are overlapped. In the proposed multiagent RL algorithm, each small cell is modeled as an agent to learn the optimal policy from interaction with environment to dynamically change its transmit power. Simulation results are presented to validate the proposed method and show that the RL based algorithm could provide a satisfactory performance.
A Scanned Document Image Processing Model for Information System

Document Image Processing (DIP) is important to the document image information system. The scanned image needs DIP to satisfy the national standards and industrial rules. Based on the analysis of related works, a specific DIP model was proposed. It was designed for official documents used in government departments. The years running demonstrates that the DIP model could perform skew correction, enhancement and compression coherently and perfectly.
The Peculiar Type II Supernova 1997D: A Case for a Very Low 56Ni Mass

SN 1997D in NGC 1536 is possibly the least luminous and energetic Type II supernova discovered to date. The entire light curve is subluminous, never reaching MV=-14.65. The radioactive tail follows the 56Co decay slope. In the case of a nearly complete trapping of the γ-rays, the 56Ni mass derived from the tail brightness is extremely small, ~0.002 M☉. At discovery, the spectra showed a red continuum and line velocities on the order of 1000 km s−1. The luminosity and the photospheric expansion velocity suggest that the explosion occurred about 50 days before discovery and that a plateau probably followed. Model light curves and spectra of the explosion of a 26 M☉ star successfully fitted the observations. Low-mass models are inconsistent with the observations. The radius of the progenitor, constrained by the prediscovery upper limits, is R0 ≲ 300 R☉. A low explosion energy of ~4 × 1050 ergs is then required in the modeling. The strong Ba II lines in the photospheric spectra are reproduced with a solar abundance and low Teff. A scenario in which the low 56Ni mass observed in SN 1997D is due to fallback of material onto the collapsed remnant of the explosion of a 25-40 M☉ star appears to be favored over the case of the explosion of an 8-10 M☉ star with low 56Ni production.
A Scalable MMR Approach to Sentence Scoring for Multi-Document Update Summarization

We present SMMR, a scalable sentence scoring method for query-oriented update summarization. Sentences are scored thanks to a criterion combining query relevance and dissimilarity with already read documents (history). As the amount of data in history increases, non-redundancy is prioritized over query-relevance. We show that SMMR achieves promising results on the DUC 2007 update corpus.
On the round-off error analysis of the Gram-Schmidt algorithm with reorthogonalization

In this paper we analyse the numerical behavior of the Gram-Schmidt orthogonalization process with reorthogonalization. Assuming numerical nonsingularity of the matrix we prove that two steps of iterative Gram-Schmidt process are enough for preserving the orthogonality of computed vectors close to the machine precision level. We give a rounding error analysis of classical reorthogonalization and modified Gram-Schmidt algorithm with (exactly one) reorthogonalization and relate our results to the approach used in the Kahan-Parlett “twice is enough” algorithm as well as to results shown by Abdelmalek, Daniel et al, Hoffmann and others.
Rarest of the rare: advances in combining evolutionary distinctiveness and scarcity to inform conservation at biogeographical scales

Aim In an era of global habitat loss and species extinction, conservation biology is increasingly becoming a science of triage. A key approach has been the designation of global biodiversity hotspots – areas of high species richness and endemism – prioritizing regions that are disproportionately valuable. However, traditional hotspot approaches leave absent information on species evolutionary histories. We argue that prioritizing the preservation of evolutionary diversity is one way to maximize genotypic and functional diversity, providing ecosystems with the greatest number of options for dealing with an uncertain future.
Enabling Performance as a Service for a Cloud Storage System

One of the main contributions of the paper is that we introduce "performance as a service" as a key component for future cloud storage environments. This is achieved through demonstration of the design and implementation of a multi-tier cloud storage system (CACSS), and the illustration of a linear programming model that helps to predict future data access patterns for efficient data caching management. The proposed caching algorithm aims to leverage the cloud economy by incorporating both potential performance improvement and revenue-gain into the storage systems.
Face Recognition With Pose Variations and Misalignment via Orthogonal Procrustes Regression

A linear regression-based method is a hot topic in face recognition community. Recently, sparse representation and collaborative representation-based classifiers for face recognition have been proposed and attracted great attention. However, most of the existing regression analysis-based methods are sensitive to pose variations. In this paper, we introduce the orthogonal Procrustes problem (OPP) as a model to handle pose variations existed in 2D face images. OPP seeks an optimal linear transformation between two images with different poses so as to make the transformed image best fits the other one. We integrate OPP into the regression model and propose the orthogonal Procrustes regression (OPR) model. To address the problem that the linear transformation is not suitable for handling highly non-linear pose variation, we further adopt a progressive strategy and propose the stacked OPR. As a practical framework, OPR can handle face alignment, pose correction, and face representation simultaneously. We optimize the proposed model via an efficient alternating iterative algorithm, and experimental results on three popular face databases, such as CMU PIE database, CMU Multi-PIE database, and LFW database, demonstrate the effectiveness of our proposed method.