Miracle-dz/UniFAR-FTUs huggingface dataset:
Assessing the detailed time course of perceptual sensitivity change in perceptual learning

The learning curve in perceptual learning is typically sampled in blocks of trials, which could result in imprecise and possibly biased estimates, especially when learning is rapid. Recently, Zhao, Lesmes, and Lu (2017, 2019) developed a Bayesian adaptive quick Change Detection (qCD) method to accurately, precisely, and efficiently assess the time course of perceptual sensitivity change. In this study, we implemented and tested the qCD method in assessing the learning curve in a four-alternative forced-choice global motion direction identification task in both simulations and a psychophysical experiment. The stimulus intensity in each trial was determined by the qCD, staircase or random stimulus selection (RSS) methods. Simulations showed that the accuracy (bias) and precision (standard deviation or confidence bounds) of the estimated learning curves from the qCD were much better than those obtained by the staircase and RSS method; this is true for both trial-by-trial and post hoc segment-by-segment qCD analyses. In the psychophysical experiment, the average half widths of the 68.2% credible interval of the estimated thresholds from the trial-by-trial and post hoc segment-by-segment qCD analyses were both quite small. Additionally, the overall estimates from the qCD and staircase methods matched extremely well in this task where the behavioral rate of learning is relatively slow. Our results suggest that the qCD method can precisely and accurately assess the trial-by-trial time course of perceptual learning.
SHORT GEODESICS AND END INVARIANTS

This expository article discusses some connections between the geometry of a hyperbolic 3-manifold homotopy-equivalent to a surface, and the combinatorial properties of its end invariants. In particular a necessary and sufficient condition is stated for the manifold to have arbitrarily short geodesics, in terms of a sequence of coefficients called subsurface projection distances, which are analogous in some ways to continued-fraction coefficients. (The proof of sufficiency appeared in math.GT/9907070)
Harmonic retrieval in non-circular complex-valued multiplicative noise: Cramer-Rao bound

We address the problem of harmonic retrieval in the presence of multiplicative and additive noise. We derive the finite-sample Cramer Rao bound (CRB) as well as the asymptotic (large sample) CRB when the multiplicative noise is complex-valued and noncircular. These bounds are then analyzed with respect to the signal parameters. Finally, we prove that the square-power based frequency estimate, which is equivalent to the so-called nonlinear least square estimate, is asymptotically efficient when the multiplicative noise is white.
ON A FINITE DIFFERENCE SCHEME FOR A BEELER-REUTER BASED MODEL OF CARDIAC ELECTRICAL ACTIVITY

We investigate an explicit finite dierence scheme for a Beeler-Reuter based model of cardiac electrical activity. As our main result, we prove that the finite dierence solutions are bounded in the L 1 -norm. We also prove the existence of a weak solution by showing convergence to the solutions of the underlying model as the discretization parameters tend to zero. The convergence proof is based on the compactness method.
Blockade of Lysosomal Acid Ceramidase Induces GluN2B-Dependent Tau Phosphorylation in Rat Hippocampal Slices

The lysosomal acid ceramidase, an enzyme known to limit intracellular ceramide accumulation, has been reported to be defective in neurodegenerative disorders. We show here that rat hippocampal slices, preincubated with the acid ceramidase inhibitor (ACI) d-NMAPPD, exhibit increased N-methyl-D-aspartate (NMDA) receptor-mediated field excitatory postsynaptic potentials (fEPSPs) in CA1 synapses. The ACI by itself did not interfere with either paired pulse facilitation or alpha-amino-3-hydroxy-5-methylisoxazole-4-propionate (AMPA) receptor-mediated fEPSPs, indicating that its influence on synaptic transmission is postsynaptic in origin and specific to the NMDA subtype of glutamate receptors. From a biochemical perspective, we observed that Tau phosphorylation at the Ser262 epitope was highly increased in hippocampal slices preincubated with the ACI, an effect totally prevented by the global NMDA receptor antagonist D/L(−)-2-amino-5-phosphonovaleric acid (AP-5), the calcium chelator 1,2-bis(o-aminophenoxy)ethane-N,N,N′,N′-tetraacetic acid (BAPTA), and the GluN2B (but not the GluN2A) receptor antagonist RO25-6981. On the other hand, preincubation of hippocampal slices with the compound KN-62, an inhibitor known to interfere with calcium/calmodulin-dependent protein kinase II (CaMKII), totally abolished the effect of ACI on Tau phosphorylation at Ser262 epitopes. Collectively, these results provide experimental evidence that ceramides play an important role in regulating Tau phosphorylation in the hippocampus via a mechanism dependent on GluN2B receptor subunits and CaMKII activation.
Do Ultraluminous X-Ray Sources Really Contain Intermediate-Mass Black Holes?

An open question remains whether Ultraluminous X-ray Sources (ULXs) really contain intermediate-mass black holes (IMBHs). We carefully investigated the XMM-Newton EPIC spectra of four ULXs that were claimed to be strong candidates of IMBHs by several authors. Wefirst triedfitting by the standard spectral model of disk blackbody (DBB) + power-law (PL), finding good fits to all of the data, in agreement with others. We, however, found that the PL component dominates the DBB component at ∼ 0.3 to 10keV. Thus, the black hole parameters derived solely from the minor DBB component are questionable. Next, we tried to fit the same data by the “p-free disk model” without the PL component, assuming an effective temperature profile of Teff ∝ r −p ,w herer is the disk radius. Interestingly, in spite of one less free-model parameter, we obtained similarly good fits with much higher innermost disk temperatures, 1.8 <k Tin < 3.2keV. More importantly, we obtained p ∼ 0.5, just the value predicted by the slim (super-critical) disk theory, rather than p =0 . 75 that is expected from the standard disk model. The estimated black hole masses from the p-free disk model are much smaller; M 40 M� . Furthermore, we applied a more sophisticated slim-disk model by Kawaguchi (2003, ApJ, 593, 69), and obtained good fits with roughly consistent black hole masses. We thus conclude that the central engines of these ULXs are super-critical accretion flows to stellar-mass black holes.
A Blueprint for an Educational Trajectory: The Power of Discourse in Constructing “Naughty” and “Adorable” Kindergarten Students

Discursive practices enacted by educators in kindergarten create a blueprint for how the educational trajectories of students with disabilities are constructed. This two-year ethnographic case study critically examines educators’ relationships with students considered to present challenging behaviors in one kindergarten classroom located in a predominantly White middle class school district in the Northeast of the United States. Focusing on the language and practices used by one special education teacher and three teaching assistants, this paper analyzes how teacher responses to students’ behaviors constructs and positions students over one year of kindergarten education. Using a critical discourse analysis it shows that educators understand students’ behaviors as deficit and needing consequences. This study highlights how educators’ responses reflect students' individual characteristics including family background, socioeconomics and ability status. This paper offers in depth analysis of two students’ stories, which evidenced that the language used by educators amplifies the social positioning of students within the classroom and creates a foundation for who they are constructed to be. Through exploring routine language and practices, this paper demonstrates that educators outlined a blueprint of kindergartners, which positioned students as learners in ways that became the ground for either a limited or a promising educational pathway for them. Keywords—Behavior, early education, special education, critical discourse analysis.
Open-vocabulary speech indexing for voice and video mail retrieval

This paper presents recent work on a multimedia retrieval project at Cambridge University and Olivetti Research Limited (ORL). We present novel techniques that allow extremely rapid audio indexing, at rates approaching several thousand times real time. Unlike other methods, these techniques do not depend on a fixed vocabulary recognition system or on keywords that must be known well in advance. Using statistical methods developed for text, these indexing techniques allow rapid and efficient retrieval and browsing of audio and video documents. This paper presents the project background, the indexing and retrieval techniques, and a video mail retrieval application incorporating content-based audio indexing, retrieval, and browsing.
Adaptive Fuzzy Finite-Time Control of Nonlinear Systems With Actuator Faults

This paper addresses the trajectory tracking control problem of a class of nonstrict-feedback nonlinear systems with the actuator faults. The functional relationship in the affine form between the nonlinear functions with whole state and error variables is established by using the structure consistency of intermediate control signals and the variable-partition technique. The fuzzy control and adaptive backstepping schemes are applied to construct an improved fault-tolerant controller without requiring the specific knowledge of control gains and actuator faults, including both stuck constant value and loss of effectiveness. The proposed fault-tolerant controller ensures that all signals in the closed-loop system are semiglobally practically finite-time stable and the tracking error remains in a small neighborhood of the origin after a finite period of time. The developed control method is verified through two numerical examples.
I Know (What) You Are, But What Am I? The Effect of Recategorization Threat and Perceived Immutability on Prejudice

Learning one is similar to a stigmatized group can threaten one’s identity and prompt disassociation from the group. What are the consequences of learning of a similarity to a stigmatized group when that similarity implies possible recategorization into the group? We investigated how learning of an immutable, recategorization implying similarity with an outgroup affects implicitly and explicitly assessed prejudice. In Study 1, White participants who believed they had above average genetic overlap with African Americans showed decreased prejudice on implicit but not explicit measures. In Study 2, straight/heterosexual participants who were led to believe they exhibited some same-sex attraction showed reduced implicitly assessed prejudice, but only if they believed sexual orientation was biologically determined. Thus, learning of an identity-implying similarity with an outgroup can reduce implicit prejudice if that group membership is believed to be immutable. Theoretical and practical implications are discussed.
Instance-Based Natural Language Generation

Abstract We investigate the use of instance-based ranking methods for surface realization in natural language generation. Our approach to instance-based natural language generation (IBNLG) employs two components: a rule system that ‘overgenerates’ a number of realization candidates from a meaning representation and an instance-based ranker that scores the candidates according to their similarity to examples taken from a training corpus. We develop an efficient search technique for identifying the optimal candidate based on a novel extension of the A* algorithm. The rule system is produced automatically from a semantically annotated fragment of the Penn Treebank II containing management succession texts. We detail the annotation scheme and grammar induction algorithm and evaluate the efficiency and output of the generator. We also discuss issues such as input coverage (completeness) and fluency that are relevant to surface generation in general.
When relevance is not Enough: Promoting Visual Attractiveness for Fashion E-commerce

Fashion, and especially apparel, is the fastest-growing category in online shopping. As consumers requires sensory experience especially for apparel goods for which their appearance matters most, images play a key role not only in conveying crucial information that is hard to express in text, but also in affecting consumer's attitude and emotion towards the product. However, research related to e-commerce product image has mostly focused on quality at perceptual level, but not the quality of content, and the way of presenting. This study aims to address the effectiveness of types of image in showcasing fashion apparel in terms of its attractiveness, i.e. the ability to draw consumer's attention, interest, and in return their engagement. We apply advanced vision technique to quantize attractiveness using three common display types in fashion filed, i.e. human model, mannequin, and flat. We perform two-stage study by starting with large scale behavior data from real online market, then moving to well designed user experiment to further deepen our understandings on consumer's reasoning logic behind the action. We propose a Fisher noncentral hypergeometric distribution based user choice model to quantitatively evaluate user's preference. Further, we investigate the potentials to leverage visual impact for a better search that caters to user's preference. A visual attractiveness based re-ranking model that incorporates both presentation efficacy and user preference is proposed. We show quantitative improvement by promoting visual attractiveness into search on top of relevance.
Performance Evaluation of Tools and Techniques for Representing Cost-Based Decision Criteria for On-Road Autonomous Navigation

Abstract : The purpose of the work described in this paper is to analyze rule-based and functional tools to determine which are best suited for cost generation in an autonomous vehicular system. In this context, costs are numeric values that represent a penalty the vehicle incurs by taking a certain action or occupying a state. Based on the requirements for this application, the results showed that Tool A was the most promising for future research.
A geometric correction method using stereo vision for projected images

This paper proposes a geometric correction method using stereo camera for projected images. When we use a projector in the various places, a projected image is deformed due to the shape of a display surface, the positional relationship between a projector and a display surface, etc. Addressing this problem, we propose a geometric correction method for projected images, which employs 3D measurement technique with the stereo camera. The proposed method enables us to automatically correct the deformation of projected images during the projection of ordinary images. In our method, we employ the correspondence search technique based on Phase-Only Correlation (POC) to obtain dense 3D shape of the display surface. A set of experiments demonstrates that the proposed method is effective for correcting deformation of projected images.
Performance Evolution of Positive and Negative Association and Confined Rules

Positive and negative association rules are important to find useful information hided in massive datasets, especially negative association rules can reflect mutually exclusive correlation among items. Despite a great deal of research, a number of challenges still exist in mining positive and negative association rules. In order to solve the problem of " difficult to determine frequent item sets " and " how to delete contradictive positive and negative association rules " , the paper presents a new algorithm for mining positive and negative association rules. The algorithm applies a new measurement framework of support and confidence to solve the problems existing. The performance study shows that the method is highly efficient and accurate in comparison with other reported mining methods. We propose an algorithm that extends the support-confidence framework With a sliding correlation coefficient threshold. In addition to finding confident positive rules that have a strong correlation, the algorithm discovers negative association rules with strong negative correlation between the antecedents and consequents. And we implement the performance evaluated on the basis of time and space complexity of positive and negative association rule and confiding rules implemented. I. Introduction Association rule mining is a data mining task that discovers relationships among items in a transactional database. Association rules have been extensively studied in the literature for their usefulness in many application domains such as recommender systems, diagnosis decisions support, telecommunication, intrusion detection, etc. The efficient discovery of such rules has been a major focus in the data mining research community. From the original Apriori algorithm [1] there have been a remarkable number of variants and improvements of association rule mining algorithms [2]. Association rule analysis is the task of discovering association rules that occur frequently in a given data set. A typical example of association rule mining application is the market basket analysis. In this process, the behavior of the customers is studied when buying different products in a shopping store. The discovery of interesting patterns in this collection of data can lead to important marketing and management strategic decisions. All the traditional association rule mining algorithms were developed to find Positive associations between items. By positive associations we refer to associations between items existing in transactions (i.e. items bought). What about Associations of the type: " customers that buy Coke do not buy Pepsi " or " customers that buy juice do not buy bottled water " ? In …
A Basic Study of Photodynamic Therapy with Glucose-Conjugated Chlorin e6 Using Mammary Carcinoma Xenografts

By using the Warburg effect—a phenomenon where tumors consume higher glucose levels than normal cells—on cancer cells to enhance the effect of photodynamic therapy (PDT), we developed a new photosensitizer, glucose-conjugated chlorin e6 (G-Ce6). We analyzed the efficacy of PDT with G-Ce6 against canine mammary carcinoma (CMC) in vitro and in vivo. The pharmacokinetics of G-Ce6 at 2, 5, and 20 mg/kg was examined in normal dogs, whereas its intracellular localization, concentration, and photodynamic effects were investigated in vitro using CMC cells (SNP cells). G-Ce6 (10 mg/kg) was administered in vivo at 5 min or 3 h before laser irradiation to SNP tumor-bearing murine models. The in vitro study revealed that G-Ce6 was mainly localized to the lysosomes. Cell viability decreased in a G-Ce6 concentration- and light intensity-dependent manner in the PDT group. Cell death induced by PDT with G-Ce6 was not inhibited by an apoptosis inhibitor. In the in vivo study, 5-min-interval PDT exhibited greater effects than 3-h-interval PDT. The mean maximum blood concentration and half-life of G-Ce6 (2 mg/kg) were 15.19 ± 4.44 μg/mL and 3.02 ± 0.58 h, respectively. Thus, 5-min-interval PDT with G-Ce6 was considered effective against CMC.
Automatic recovery of root causes from bug-fixing changes

What is the root cause of this failure? This question is often among the first few asked by software debuggers when they try to address issues raised by a bug report. Root cause is the erroneous lines of code that cause a chain of erroneous program states eventually leading to the failure. Bug tracking and source control systems only record the symptoms (e.g., bug reports) and treatments of a bug (e.g., committed changes that fix the bug), but not its root cause. Many treatments contain non-essential changes, which are intermingled with root causes. Reverse engineering the root cause of a bug can help to understand why the bug is introduced and help to detect and prevent other bugs of similar causes. The recovered root causes are also better ground truth for bug detection and localization studies. In this work, we propose a combination of machine learning and code analysis techniques to identify root causes from the changes made to fix bugs. We evaluate the effectiveness of our approach based on a golden set (i.e., ground truth data) of manually recovered root causes of 200 bug reports from three open source projects. Our approach is able to achieve a precision, recall, and F-measure (i.e., the harmonic mean of precision and recall) of 76.42%, 71.88%, and 74.08% respectively. Compared with the work by Kawrykow and Robillard, our approach achieves a 60.83% improvement in F-measure.
The Griffiths singularity random field

We consider a spin system on sites of a d-dimensional cubic lattice (d ≥ 2), with the values 0, 1 or -1. It is built over the Bernoulli site percolation model, with spins taking the value 0 on empty sites, and taking values ±1 on occupied sites according to the ferromagnetic Ising model distribution on the occupied clusters. The Hamiltonian corresponds to the nearest neighbor interaction under external field h, at inverse temperature β, and the boundary conditions for clusters are free. When the probability p for a site to be occupied is small enough, so that a.s. all the clusters of non-0 spins are finite, this description gives rise to a unique random field. We show that it is non-Markovian, and when p is small, β is large and h = 0, it is even non-Gibbsian, but only almost Gibbsian. This provides another example of a non-Gibbsian, but almost Gibbsian, random field which emerges naturally in a Gibbsian context. Our random field is directly related to, and motivated by, the model studied by Griffiths in connection to what became known as the phenomenon of Griffiths' singularities.
Extracting Strong Sentiment Trends from Twitter

Twitter is a popular real-time microblogging service that allows its users to share short pieces of information known as “tweets” (limited to 140 characters). Users write tweets to express their opinions about various topics pertaining to their daily lives. With a total 175 million users and 95 million tweets published per day (as of September 2010), Twitter serves as an ideal platform for the analysis and extraction of general public sentiment regarding specific issues. The measurement of presidential performance is one domain where the analysis and extraction of general public sentiment is a large component. Currently, presidential approval polls are hand-measured by random telephone sampling of a small population. This technique is both timeconsuming and costly. Therefore, an automated way of measuring these polls from easily accessible public data would be immensely useful in reducing the required time and costs. This project explores an approach to automatically extract large-scale trends in the perception of presidential performance among the general public by analyzing tweets published on Twitter. Specifically, macro trends in strong approval and strong disapproval of presidential performance are extracted from tweets using a simple lexiconbased approach. The extracted sentiments are compared against a hand-measured presidential performance poll to measure correlation and determine whether strong political sentiments regarding presidential performance can be extracted from Twitter. From a natural language processing perspective, this problem is interesting because .