Miracle-dz/UniFAR-FTUs huggingface dataset:
Language and Cognitive Functions in a Neurological Tumour Population: A Long-Term Follow-Up Study

Complex cognitive capacities such as language and “executive function” are difficult to evaluate in neuropsychological populations due to their multifactorial nature. The current study takes a cognitively-motivated core-skills approach to their assessment. Across four investigations, these various capacities are decomposed into simpler core skills based on current cognitive theory. An undifferentiated sample of 28 neurological tumour patients is then assessed on these skills. In the first study, we assessed the “core skills” underpinning language function at three time points: pre-operative (one day prior to surgery), post-operative (within three days of surgery), and at long-term follow-up (at least three months post-surgery). This approach was sensitive at detecting impairment; indeed, almost half of the patients showed persistent long-term language deficits even at long-term follow-up. The decompositional approach also proved effective at predicting long-term outcomes. Overall, these results suggest that the subtle language deficits may be more common and more persistent than previously estimated in tumour populations. The second study examined the relationship between “core” language skills and sentence-level language processing at long-term follow up. Whilst there were few significant correlations, the results nonetheless suggest that “core skills” measures may be useful predictors of some aspects of sentence-level processing. The third study isolated and identified “core” skills that are essential for complex cognitive control more generally, and assessed these in our patient sample at long-term follow-up. Results were broadly supportive of this decompositional approach, and again, our assessments proved highly sensitive at detecting deficits in this patient sample. The fourth study examined the relationship between language processing and complex cognitive control. Specifically, we examined whether there are systems specially dedicated to the control of language, or whether control functions operate across all domains. Overall, our results were broadly consistent with the domain-specific view - that there may be functionally distinct control systems operating on verbal and nonverbal material. The results, taken together suggest that a core skills approach to neuropsychological assessment has considerable promise, and is worth exploring further in a large patient sample. This approach may also help extend our understanding of the functional organisation of language, and the broader cognitive skills necessary for linguistic operations.
Reusable Off-line Electronic Cash Using Secret Splitting

The idea of electronic cash, as a payment instrument is appealing, but has yet to be widely deployed commercially. We outline the properties of two major approaches to the provision of electronic cash, and discuss their strengths and weaknesses. We then propose a new scheme based on secret splitting that allows for off-line spending of coins with a tailored level of anonymity and allowing reuse of the same coin many times. We then discuss a number of practical issues such as the trade off between coin size and the ability to detect double spending.
Multitemporal/multiband SAR classification of urban areas using spatial analysis: statistical versus neural kernel-based approach

In this paper, we derive two techniques for the classification of multifrequency/multitemporal polarimetric SAR images, based respectively on a statistical and on a neural approach. Both techniques are especially designed to exploit the spatial structure of the observed scene, thus allowing more stable classification results. Such techniques are useful when looking at medium- to large-scale features, like the boundaries between urban and nonurban areas. They are applied to a set of SIR-C images of a urban area, to test their effectiveness in the identification of the different classes that compose the observed scene. A lower and an upper bound to the classification performance are introduced to characterize their limits. They correspond respectively to pixel-by-pixel classification and to the joint classification of the pixels belonging to the different classes identified in the ground truth. The results achieved with the two approaches are quantitatively analyzed by comparing them to the ground truth. Moreover, a hybrid approach is presented, where the homogeneous regions identified through statistical segmentation are classified using a neurofuzzy technique. Finally, a quantitative analysis of the results achieved with all the proposed techniques is carried out, showing that their classification performance is much higher than the lower bound and reasonably close to the upper bound. This is a consequence of their effectiveness in the exploitation of the spatial information.
Stress and Diabetes Mellitus

Stress is a potential contributor to chronic hyperglycemia in diabetes. Stress has long been shown to have major effects on metabolic activity. Energy mobilization is a primary result of the fight or flight response. Stress stimulates the release of various hormones, which can result in elevated blood glucose levels. Although this is of adaptive importance in a healthy organism, in diabetes, as a result of the relative or absolute lack of insulin, stress-induced increases in glucose cannot be metabolized properly. Furthermore, regulation of these stress hormones may be abnormal in diabetes. However, evidence characterizing the effects of stress in type I diabetes is contradictory. Although some retrospective human studies have suggested that stress can precipitate type I diabetes, animal studies have shown that stressors of various kinds can precipitate—or prevent—various experimental models of the disease. Human studies have shown that stress can stimulate hyperglycemia, hypoglycemia, or have no affect at all on glycemic status in established diabetes. Much of this confusion may be attributable to the presence of autonomic neuropathy, common in type I diabetes. In contrast, more consistent evidence supports the role of stress in type II diabetes. Although human studies on the role of stress in the onset and course of type II diabetes are few, a large body of animal study supports the notion that stress reliably produces hyperglycemia in this form of the disease. Furthermore, there is mounting evidence of autonomic contributions to the pathophysiology of this condition in both animals and humans.
Beyond The Gene List : Using Bioinformatics To Make Sense Out Of Array Data

1) Introduction 2) What is a microarray? a) Experimental steps in a typical cDNA microarray experiment b) If you don? t see it, it doesn? t mean that it isn? t there. 3) Experimental Design a) Comparing independent ? replicate? arrays b) ? A vs. M? plot. 4) Informatic Annotation of Array Data 5) Initial annotation of gene list based on published literature a) Linking array data to the literature b) GenMAPP c) PubGene 6) Databases— The need for public databases for gene expression annotation and analysis. 7) Data Mining And Annotation By Chromosome a) Data reduction. b) Assessing chromosomal location using genome databases. c) Using databases of other array experiments to annotate gene functions. d) Validate informatic hypotheses prospectively 8) Using Microarrays to study underlying regulatory mechanisms a) Identify co-regulated genes. b) Collect 5kb upstream of transcription start site. c) Perform similarity search to identify shared sequences. d) Promotor/Enhancer Motif Search e) Follow-up shared sequences to identify putative TF binding sites. f) Pitfalls/Suggestions 9) Summary and Acknowlegements 10) Resources Nervous system development and function in part depends on the careful orchestration of gene expression in the CNS. The magnitude of the problem of understanding this system from a molecular standpoint is underscored by the estimate that more than half of all genes are expressed in the nervous system and many of them are relatively specific to the brain. Currently, there are many different high throughput methods to examine gene expression in different cells, tissues or even pathological specimens under various conditions, including serial analysis of gene expression (SAGE)(Velculescu, Zhang et al. 1995) large-scale cDNA sequencing (Okubo, Hori et al. 1992), and DNA microarrays (Schena, Shalon et al. 1995). All of these approaches rely heavily on use of informatics for interpretation. In addition, there are now pure informatic approaches available now, which include expressed sequence tag (EST) database comparison (O'Dowd, Nguyen et al. 1998), and mining of SAGE or other databases. This syllabus focuses on DNA microarrays. Oligonucleotide and cDNA arrays enable the monitoring of thousands of known and unknown genes in parallel in many samples in an efficient Due to the high throughput nature of this approach, large amounts of data are generated in a single experiment. Formerly, cost and availability of microarrays were limitations to performing experiments, but as these issues are resolved, the new limitation becomes interpretation and analysis. The ability to study gene expression on a genomic level using microarrays raises …
170-GHz transceiver with on-chip antennas in SiGe technology

A single-chip transceiver with on-die transmit and receive antennas, Rx and Tx amplifiers, 165-GHz oscillator and static frequency divider is reported in a SiGe HBT process with fT/fMAX of 270 GHz/340 GHz. This marks the highest frequency transceiver in silicon and the highest level of functional integration above 100 GHz in any semiconductor technology. The downconversion gain peaks at -5 dB and the transmit power is -5 dBm when measured at the transceiver pads. Both degrade by approximately 25 dB when measured above the antennas of the transceiver with on-die antennas. The experimental performance of dipole (with and without floating metal strips) and patch antennas is also investigated. The measured 15-dB gain of a standalone amplifier is centered at 170 GHz and remains higher than 10 dB from 160 GHz to 180 GHz while the saturated output power is 0 dBm at 165 GHz.
Insight: In-situ Online Service Failure Path Inference in Production Computing Infrastructures

Online service failures in production computing environments are notoriously difficult to debug. When those failures occur, the software developer often has little information for debugging. In this paper, we present Insight, a system that reproduces the execution path of a failed service request onsite immediately after a failure is detected. Upon a request failure is detected, Insight dynamically creates a shadow copy of the production server and performs guided binary execution exploration in the shadow node to gain useful knowledge on how the failure occurs. Insight leverages both environment data (e.g., input logs, configuration files, states of interacting components) and runtime outputs (e.g., console logs, system calls) to guide the failure path finding. Insight does not require source code access or any special system recording during normal production run. We have implemented Insight and evaluated it using 13 failures from a production cloud management system and 8 open source software systems. The experimental results show that Insight can successfully find high fidelity failure paths within a few minutes. Insight is light-weight and unobtrusive, making it practical for online service failure inference in the production computing environment.
Multiclass Semisupervised Learning Based Upon Kernel Spectral Clustering

This paper proposes a multiclass semisupervised learning algorithm by using kernel spectral clustering (KSC) as a core model. A regularized KSC is formulated to estimate the class memberships of data points in a semisupervised setting using the one-versus-all strategy while both labeled and unlabeled data points are present in the learning process. The propagation of the labels to a large amount of unlabeled data points is achieved by adding the regularization terms to the cost function of the KSC formulation. In other words, imposing the regularization term enforces certain desired memberships. The model is then obtained by solving a linear system in the dual. Furthermore, the optimal embedding dimension is designed for semisupervised clustering. This plays a key role when one deals with a large number of clusters.
Indexing the Distance: An Efficient Method to KNN Processing

In this paper, we present an eAEcient method, called iDistance, for K-nearest neighbor (KNN) search in a high-dimensional space. iDistance partitions the data and selects a reference point for each partition. The data in each cluster are transformed into a single dimensional space based on their similarity with respect to a reference point. This allows the points to be indexed using a B-tree structure and KNN search be performed using onedimensional range search. The choice of partition and reference point provides the iDistance technique with degrees of freedom most other techniques do not have. We describe how appropriate choices here can e ectively adapt the index structure to the data distribution. We conducted extensive experiments to evaluate the iDistance technique, and report results demonstrating its e ectiveness.
The developmental trajectory of children's statistical learning abilities

Infants, children and adults are capable of implicitly extracting regularities from their environment through statistical learning (SL). SL is present from early infancy and found across tasks and modalities, raising questions about the domain generality of SL. However, little is known about its’ developmental trajectory: Is SL fully developed capacity in infancy, or does it improve with age, like other cognitive skills? While SL is well established in infants and adults, only few studies have looked at SL across development with conflicting results: some find age-related improvements while others do not. Importantly, despite its postulated role in language learning, no study has examined the developmental trajectory of auditory SL throughout childhood. Here, we conduct a large-scale study of children's auditory SL across a wide age-range (5-12y, N=115). Results show that auditory SL does not change much across development. We discuss implications for modality-based differences in SL and for its role in language acquisition.
Learning user's intent using user tags: intelligent interactive image search system

The contents of the image files can be completely different per se, if we consider the descriptors such as color histograms, textures, edge histograms, etc., however they might have the same semantic metadata, such as user tags, at the same time. The exploitation of user tags in image search tasks results in lower computational costs considering much smaller number of features that are necessary to compute the similarity between the objects, as well as it helps to overcome the semantic gap (this kind of similarity is, of course, different from the one computed in content-based methods). Thanks to these two properties it becomes very easy to create an interactive image search system that is based on online learning from interaction between the user and the system. We propose a simple algorithm for system training, that uses dwell-time data as input parameters for relevance recalculation, which we implemented in the prototype of our experimental search system.
Evaluation of corner extraction schemes using invariance methods

We describe a new method to evaluate corner extraction schemes using invariance methods. Since the locations of centers in an image depend both on the intrinsic parameters of the camera and the relative position and orientation of the object with respect to the camera, the exact positions of corners in an image are generally not known. To circumvent the need for this knowledge, we use sets of points (instead of individual points) extracted from images of polyhedral objects and projective invariants to calculate a manifold of constraints on the coordinates of the corners. We then estimate the variance of the detected corners from the distance of the coordinate vector to this manifold. This is independent of the camera parameters and the relative position and orientation between the camera and the object. Five different kinds of corner extraction schemes are investigated. The purpose of the paper is to show that invariance methods can effectively be used to make this comparison rather than to make a thorough comparison of different corner extraction schemes.
Utility, usability and acceptability: an ergonomic approach to the evaluation of external representations for learning

Since the beginning of information and communication technology, many external representations were designed and distributed to schools and universities. But sadly, many of them are not used. Consequently, it is both interesting and useful to evaluate and to improve these external representations before trying to distribute them. Ergonomic approaches attempt to fulfil this type of goal: evaluation in order to improve. But these approaches are very often confused with usability evaluation. Confusing usability approaches and ergonomic approaches can lead to a deadlock. We conducted different studies in external representations’ evaluation. These studies show that an external representations can be usable but not used, and vice versa. That is the reason why we propose an ergonomic framework where the quality of an external representation is defined by its utility, usability and acceptability. We present data, from different empirical studies, which allow us to say that these three dimensions are equally important and interrelate. We also found that these relationships are not always similar. Then, we defend an inductive approach to the analysis of the relationships between utility, usability and acceptability. Extended abstract Since the beginning of information and communication technology, many external representations were designed and distributed to schools and universities. But many of them are not used. Consequently, it is both interesting and useful to evaluate and to improve these external representations before trying to distribute them. Ergonomic approaches attempt to fulful this type of goal: evaluation in order to improve. But these approaches are very often confused with usability evaluation. Confusing usability approaches with ergonomics approaches can lead to a deadlock.
Automatic Acquisition of Script Knowledge from a Text Collection

In this paper, we describe a method for automatic acquisition of script knowledge from a Japanese text collection. Script knowledge represents a typical sequence of actions that occur in a particular situation. We extracted sequences (pairs) of actions occurring in time order from a Japanese text collection and then chose those that were typical of certain situations by ranking these sequences (pairs) in terms of the frequency of their occurrence. To extract sequences of actions occurring in time order, we constructed a text collection in which texts describing facts relating to a similar situation were clustered together and arranged in time order.We also describe a preliminary experiment with our acquisition system and discuss the results.
Quasi-Fuchsian Surfaces In Hyperbolic Link Complements

We show that every hyperbolic link complement contains closed quasi-Fuchsian surfaces. As a consequence, we obtain the result that on a hyperbolic link complement, if we remove from each cusp of the manifold a certain finite set of slopes, then all remaining Dehn fillings on the link complement yield manifolds with closed immersed incompressible surfaces.
Why Visualization is an AI-complete Problem (and Why That Matters)

Artificial Intelligence (AI) has infiltrated almost every scientific and social endeavour, including everything from medical research to the sociology of crowd control. But the foundation of AI continues to be based on digital representations of knowledge, and computational reasoning therewith. Because so much of modern knowledge infrastructure and social behaviour is connected to AI, understanding the role of AI in each such endeavour not only helps accelerate progress in those fields in which it applies, but also creates the challenges to extend the foundation for modern AI methods. The simple hypothesis herein is that so-called AI-complete problems have a role in helping to articulate the appropriate integration of AI within other disciplines. With the current growth of interest in "big data" and visualization, we argue that relatively simple formal structures provide a basis for the claim that visualization is an AI-complete problem. The value of confirming this claim is largely to encourage stronger formalizations of the visualization process in terms of the AI foundations of representation and reasoning. This connection will help ensure that relevant components of AI are appropriately applied and integrated, to provide value for a basis of a theory of visualization. The sketch of this claim here is based on the simple idea that visualization is an abstraction process, and that abstractions from partial information, however voluminous, directly confronts the non monotonic reasoning challenge, thus the need for caution in engineering visualization systems without carefully considering the consequences of visual abstraction. This is particularly important with interactive visualization, which has recently formed the basis for such fields as visual analytics.
System Design Considerations for Undersea Networks: Link and Multiple Access Protocols

We address several inter-related aspects of underwater network design within the context of a cross-layer approach. We first highlight the impact of key characteristics of the acoustic propagation medium on the choice of link layer parameters; in turn, the consequences of these choices on design of a suitable MAC protocol and its performance are investigated. Specifically, the paper makes contributions on the following fronts: a) Based on accepted acoustic channel models, the pointto- point (link) capacity is numerically calculated, quantifying sensitivities to factors such as the sound speed profile, power spectral density of the (colored) additive background noise and the impact of boundary (surface) conditions for the acoustic channel; b) It provides an analysis of the Micromodem-like linklayer based on FH-FSK modulation; and finally c) it undertakes performance evaluation of a simple MAC protocol based on ALOHA with Random Backoff, that is shown to be particularly suitable for small underwater networks.
An Overview of Independent Component Analysis and Its Applications

Independent Component Analysis (ICA), a computationally efficient blind source separation technique, has been an area of interest for researchers for many practical applications in various fields of science and engineering. This paper attempts to cover the fundamental concepts involved in ICA techniques and review its applications. A thorough discussion of the applications and ambiguities problems of ICA has been carried out.Different ICA methods and their applications in various disciplines of science and engineering have been reviewed. In this paper, we present ICA methods from the basics to their potential applications to serve as a comprehensive single source for an inquisitive researcher to carry out research in this field.