Miracle-dz/UniFAR-FTUs huggingface dataset:
The role of the hippocampus in flexible cognition and social behavior

Successful behavior requires actively acquiring and representing information about the environment and people, and manipulating and using those acquired representations flexibly to optimally act in and on the world. The frontal lobes have figured prominently in most accounts of flexible or goal-directed behavior, as evidenced by often-reported behavioral inflexibility in individuals with frontal lobe dysfunction. Here, we propose that the hippocampus also plays a critical role by forming and reconstructing relational memory representations that underlie flexible cognition and social behavior. There is mounting evidence that damage to the hippocampus can produce inflexible and maladaptive behavior when such behavior places high demands on the generation, recombination, and flexible use of information. This is seen in abilities as diverse as memory, navigation, exploration, imagination, creativity, decision-making, character judgments, establishing and maintaining social bonds, empathy, social discourse, and language use. Thus, the hippocampus, together with its extensive interconnections with other neural systems, supports the flexible use of information in general. Further, we suggest that this understanding has important clinical implications. Hippocampal abnormalities can produce profound deficits in real-world situations, which typically place high demands on the flexible use of information, but are not always obvious on diagnostic tools tuned to frontal lobe function. This review documents the role of the hippocampus in supporting flexible representations and aims to expand our understanding of the dynamic networks that operate as we move through and create meaning of our world.
Evolution of Learning Rules for Supervised Tasks II: Hard Learning Problems

Recent experiments with a genetic based encoding schema are presented as a potentially powerful tool to discover learning rules by means of evolution. The representation used is similar to the one used in Genetic Programming (GP) but it employs only a (cid:12)xed set of functions to solve a variety of problems. In this paper three Monks' and parity problems are tested. The results indicate the usefulness of the encoding schema in discovering learning rules for hard learning problems. The problems and future research directions are discussed within the context of GP practices.
Modeling Energy Consumption of Wireless Sensor Networks by SystemC

Energy consumption is one of the most important metrics in wireless sensor networks because of the limited power supply in sensor nodes. Many efforts have been taken to reduce the energy consumption of the hardware, software, communication protocols and applications. Simulation is widely used to evaluate the performance of these new designs. Thus, it is necessary to accurately model the energy consumption during the simulation of WSN. In this paper, an energy model for WSN is proposed. It has been implemented in IDEA1, a simulator for WSN developed by SystemC and C++. It enables the energy estimation of both the hardware components of an individual node and the whole sensor network. It can be easily calibrated to different types of node if the electrical characteristics of the hardware components are available. An application based on IEEE 802.15. 4 and MICAz motes is studied to demonstrate the capability of the energy model.
Low-complexity quantization-aware belief-propagation (QA-BP) decoding for MLC NAND flash memory

State-of-the-art NAND flash chips experience significant channel noise impairments and raise several data reliability issues. To overcome these issues, low-density parity-check (LDPC) codes are becoming the main stream error-correcting-codes in flash memory controllers. Consequently, the long belief-propagation (BP) decoding latency starts to deteriorate the system performance. In this paper, a low-complexity quantization-aware belief-propagation (QA-BP) decoding scheme is introduced for meeting the requirement of faster BP convergence speed. Using the shuffled BP algorithm, the proposed QA-BP scheme takes advantage of highly unequal error probability of input log-likelihood-ratios (LLRs), transpired as a result of non-uniform channel quantization, and update only less reliable variable nodes that are initiated with higher error probability LLR values. For realizing the QA-BP scheme, neither additional runtime operations nor real-valued comparisons are required. Using simulation results, it is shown that the proposed QA-BP scheme can yield a noticeable improvement in BP convergence speed without compromising the error performance.
A Combined Model for Sustainable Competitive Advantage Based on knowledge Management, Organizational Innovation and Agility

This study aims to providing a conceptual model for gaining sustainable competitive advantage based on knowledge management, organizational innovation and agility. For this scientific purpose, sustainable competitive advantage approaches reviewed and the newest and also the most complete one that eliminates previous approaches disadvantages, dynamic capabilities view, identified. Based on selected SCA approach, knowledge management, organizational innovation and agility dynamic capabilities and their inter-relationships recognized from the literature. Finally after a deep literature reviewing, primary model suggested and validated in two stages. In first stage five experts of strategic management who were very familiar with KM, innovation and agility concepts revised the model. In second stage based on proposed model a questionnaire suggested for model validation. Questionnaire validity confirmed according to experts views and considered in interview and responding sessions with 30 experts and its reliability gained 0.708 in Cronbach's alpha method. Collected answered questionnaires analyzed with SPSS software. Results indicated the model approximately high validity.
Towards Automated Yield Estimation in Viticulture

Forecasting the grape yield of vineyards is of critical importance in the wine industry, as it allows grape growers to more accurately and condently invests in capital equipment, negotiate pricing, schedule labour and develop marketing strategies. Currently, industry standard forecasts are generated by manual sampling of bunch weights, grape size, grape numbers and seasonal predictions which takes signicant time and eort and thus can sample a very small proportion of the vines. The rst step in automating this procedure is to accurately estimate the weight of fruit on the vine and thus this paper presents a survey of the automated image processing methods which have been applied to this problem. Using manually harvested bunches photographed in a laboratory environment, the contribution of various bunch parameters to the weight of a bunch was examined to provide a baseline for the accuracy of weight calculation methods. Then, several recent colour classication methods were compared using images of grapes in vivo. The results showed that a linear regression approach to bunch weight estimation using berry number, bunch perimeter, bunch area and estimated bunch volume was accurate to within 5.3%. Results from in vivo colour classication showed a weight prediction accuracy of 4.5% on a much smaller dataset, demonstrating the promise of this approach in achieving grape grower yield estimation targets.
More Constraints, Smaller Coresets: Constrained Matrix Approximation of Sparse Big Data

We suggest a generic data reduction technique with provable guarantees for computing the low rank approximation of a matrix under some $ellz error, and constrained factorizations, such as the Non-negative Matrix Factorization (NMF). Our main algorithm reduces a given n x d matrix into a small, ε-dependent, weighted subset C of its rows (known as a coreset), whose size is independent of both n and d. We then prove that applying existing algorithms on the resulting coreset can be turned into (1+ε)-approximations for the original (large) input matrix. In particular, we provide the first linear time approximation scheme (LTAS) for the rank-one NMF. The coreset C can be computed in parallel and using only one pass over a possibly unbounded stream of row vectors. In this sense we improve the result in [4] (Best paper of STOC 2013). Moreover, since C is a subset of these rows, its construction time, as well as its sparsity (number of non-zeroes entries) and the sparsity of the resulting low rank approximation depend on the maximum sparsity of an input row, and not on the actual dimension d. In this sense, we improve the result of Libery [21](Best paper of KDD 2013) and answer affirmably, and in a more general setting, his open question of computing such a coreset. Source code is provided for reproducing the experiments and integration with existing and future algorithms.
A Scene-based Model of Word Prediction

This paper proposes a semantico-statistical model of word prediction which uses local context of each scene in a text. Scenes are text segments, each of which displays local context. The occurrence of a word inside a scene is predicted by its local context. On the other hand, a text (de ned as a sequence of scenes) is less constrained by word occurrence, so word prediction becomes more di cult. The proposed model reads a given text word by word and predicts the succeeding word at each step according to the local context of the current scene. The prediction procedure consists of (1) detecting scene boundaries, (2) extracting local context from the current scene, and (3) predicting the succeeding word according to the extracted context. An experiment on a short story showed that the proposed method gives (a) a lower prediction error than do other models and (b) scene boundaries correlated with those identi ed by human subjects.
Emulation of GMPLS-controlled Ethernet Label Switching

Last decades bridged Ethernet has been the de facto standard layer 2 technology for LAN environments. This is mainly because it is easy to deploy, it has low cost and is relatively robust. These attractive properties together with highly increasing PHY speeds result in several initiatives of research projects and standardization bodies to extend Ethernet such as to use the technology in carrier environments. However these Carrier Ethernet solutions are difficult to test with given simulation tools. This paper presents an emulation platform based on open-source software that allows to evaluate a Carrier Ethernet variant called Ethernet Label Switching (ELS). The platform allows various topology setups and has the possibility to perform time and performance benchmarking and traffic generation using standard Linux software. We will present the architecture of the emulation framework and show promising results in base setups.
Unifying Software Similarity Patterns with a Meta-level Generative Technique : A Case Study and Evaluation 1

In a previous study, we analyzed similarity patterns in the Java Buffer library, JDK 1.5. We observed many similar classes, methods and yet smaller fragments elements of class design. We argued that, given the design goals, it was difficult to avoid those repetitions with conventional design techniques. We also argued that the reasons why the problem arises and its symptoms are common. In this paper, we describe a possible solution to the problem: We apply a meta-level method of XVCL on top of Java code, to unify differences among similar buffer classes. In a meta-level Java-XVCL solution, we represent each of the important similarity patterns in a unique generic, but adaptable, form, along with information necessary to obtain its instances – specific classes or class methods. We believe such explication of similarity patterns reduces program complexity as perceived by developers. Non-redundancy achieved in that way also reduces the risk of update anomalies which helps in maintenance. As the meta-level solution does not come for free, we evaluate its strengths and weaknesses in quantitative and qualitative way, and also by conducting a controlled experiment. The presented method is based on synergistic use of a programming language (e.g., Java) and meta-level parameterization and manipulation supported by XVCL, to achieve a non-redundancy of the Java-XVCL meta-level solution. The idea of the solution and the method itself is general, can be applied to any program, independently of an application domain or a programming language. We believe some of the observations from this case study apply to other class libraries, as well as application programs.
Dietary Approaches that Delay Age-Related Diseases

Reducing food intake in lower animals such as the rat decreases body weight, retards many aging processes, delays the onset of most diseases of old age, and prolongs life. A number of clinical trials of food restriction in healthy adult human subjects running over 2–15 years show significant reductions in body weight, blood cholesterol, blood glucose, and blood pressure, which are risk factors for the development of cardiovascular disease and diabetes. Lifestyle interventions that lower energy balance by reducing body weight such as physical exercise can also delay the development of diabetes and cardiovascular disease. In general, clinical trials are suggesting that diets high in calories or fat along with overweight are associated with increased risk for cardiovascular disease, type 2 diabetes, some cancers, and dementia. There is a growing literature indicating that specific dietary constituents are able to influence the development of age-related diseases, including certain fats (trans fatty acids, saturated, and polyunsaturated fats) and cholesterol for cardiovascular disease, glycemic index and fiber for diabetes, fruits and vegetables for cardiovascular disease, and calcium and vitamin D for osteoporosis and bone fracture. In addition, there are dietary compounds from different functional foods, herbs, and neutraceuticals such as ginseng, nuts, grains, and polyphenols that may affect the development of age-related diseases. Long-term prospective clinical trials will be needed to confirm these diet—disease relationships. On the basis of current research, the best diet to delay age-related disease onset is one low in calories and saturated fat and high in wholegrain cereals, legumes, fruits and vegetables, and which maintains a lean body weight. Such a diet should become a key component of healthy aging, delaying age-related diseases and perhaps intervening in the aging process itself. Furthermore, there are studies suggesting that nutrition in childhood and even in the fetus may influence the later development of aging diseases and lifespan.
Arabidopsis resistance protein SNC1 activates immune responses through association with a transcriptional corepressor

In both plants and animals, nucleotide-binding (NB) domain and leucine-rich repeat (LRR)-containing proteins (NLR) function as sensors of pathogen-derived molecules and trigger immune responses. Although NLR resistance (R) proteins were first reported as plant immune receptors more than 15 years ago, how these proteins activate downstream defense responses is still unclear. Here we report that the Toll-like/interleukin-1 receptor (TIR)-NB-LRR R protein, suppressor of npr1-1, constitutive 1 (SNC1) functions through its associated protein, Topless-related 1 (TPR1). Knocking out TPR1 and its close homologs compromises immunity mediated by SNC1 and several other TIR-NB-LRR–type R proteins, whereas overexpression of TPR1 constitutively activates SNC1-mediated immune responses. TPR1 functions as a transcriptional corepressor and associates with histone deacetylase 19 in vivo. Among the target genes of TPR1 are Defense no Death 1 (DND1) and Defense no Death 2 (DND2), two known negative regulators of immunity that are repressed during pathogen infection, suggesting that TPR1 activates R protein-mediated immune responses through repression of negative regulators.
A Metaplan Model for Problem-Solving Discourse

The structure of problem-solving discourse in the expert advising setting can be modeled by adding a layer of metaplans to a plan-based model of the task domain. Classes of metaplans are introduced to model both the agent's gradual refinement and instatiation of a domain plan for a task and the space of possible queries about preconditions or fillers for open variable slots that can be motivated by the exploration of particular classes of domain plans. This metaplan structure can be used to track an agent's problem-solving progress and to predict at each point likely follow-on queries based on related domain plans. The model is implemented in the Pragma system where it is used to suggest corrections for ill-formed input.
Conditional access in mobile systems: securing the application

This paper describes two protocols for the secure download of content protection software to mobile devices. The protocols apply concepts from trusted computing to demonstrate that a platform is in a sufficiently trustworthy state before any application or associated keys are securely downloaded. The protocols are designed to allow mobile devices to receive broadcast content protected by proprietary conditional access applications. They may also be applied in the general case where demonstration of a secure execution environment is required before an application is downloaded.
Motion planning for a dynamically-coupled hyper-dynamic manipulator by reinforcement learning

This paper proposes a new motion planning method for a hyper-dynamic robot which aims at realizing the motion control skills exhibited by professional golfers by a dexterous robot. The robot has similar distribution of actuators' capability to that of human, which is compact but requires dynamically-coupled driving for high speed motion. For such a robot with strong coupling and nonlinearity, to realize a motion including multi-boundary conditions, motion planning is very difficult. As a new method for the motion planning, the motion planning method using reinforcement learning is proposed in this paper. Simulation results show the effectiveness of the proposed motion planning method.
Expansion of Th17 Cells and Functional Defects in T Regulatory Cells Are Key Features of the Pancreatic Lymph Nodes in Patients With Type 1 Diabetes

OBJECTIVE Autoimmune diseases, including type 1 diabetes, are thought to have a Th17-cell bias and/or a T-regulatory cell (Treg) defect. Understanding whether this is a hallmark of patients with type 1 diabetes is a crucial question that is still unsolved, largely due to the difficulties of accessing tissues targeted by the disease. RESEARCH DESIGN AND METHODS We phenotypically and functionally characterized Th17 cells and Tregs residing in the pancreatic-draining lymph nodes (PLNs) of 19 patients with type 1 diabetes and 63 nondiabetic donors and those circulating in the peripheral blood of 14 type 1 diabetic patients and 11 healthy subjects. RESULTS We found upregulation of Th17 immunity and functional defects in CD4+CD25bright Tregs in the PLNs of type 1 diabetic subjects but not in their peripheral blood. In addition, the proinsulin-specific Treg-mediated control was altered in the PLNs of diabetic patients. The dysfunctional Tregs isolated from diabetic subjects did not contain contaminant effector T cells and were all epigenetically imprinted to be suppressive, as defined by analysis of the Treg-specific demethylated region within the forkhead box P3 (FOXP3) locus. CONCLUSIONS These data provide evidence for an unbalanced immune status in the PLNs of type 1 diabetic subjects, and treatments restoring the immune homeostasis in the target organ of these patients represent a potential therapeutic strategy.
Killer Cell Inhibitory Receptor Recognition of Human Leukocyte Antigen (HLA) Class I Blocks Formation of a pp36/PLC-γ Signaling Complex in Human Natural Killer (NK) Cells

The killer cell inhibitory receptors (KIR) of human natural killer (NK) cells recognize human leukocyte antigen class I molecules and inhibit NK cell cytotoxicity through their interaction with protein tyrosine phosphatases (PTP). Here, we report that KIR recognition of class I ligands inhibits distal signaling events and ultimately NK cell cytotoxicity by blocking the association of an adaptor protein (pp36) with phospholipase C-γ in NK cells. In addition, we demonstrate that pp36 can serve as a substrate in vitro for the KIR-associated PTP, PTP-1C (also called SHP-1), and that recognition of class I partially disrupts tyrosine phosphorylation of NK cell proteins, providing evidence for KIR-induced phosphatase activity.
Recognition Driven Page Orientation Detection

In document image recognition, orientation detection of the scanned page is necessary for the following procedures to work correctly as they assume that the text is well oriented. Several methods have been proposed, but most of them rely on heuristics of the script such as the graphical asymmetry between ascenders and descenders for Roman script. The literature shows that as soon as this assumption is not fulfilled, e.g. plain capital text, noisy or degraded characters, etc. they fail. For a large-scale digitalization process, a low error and rejection rate are expected in order to reduce the amount of human intervention. We propose a Recognition Driven Page Orientation Detection (RD-POD) which does not depend on external criteria or assumption on the shape of the script. It uses the OCR engine for estimating the right orientation with a few lines of the document image. The RD-POD is highly robust and accurate, and is able to detect multiple orientations. Experimental evaluation shows that our method outperforms the current state-of-the-art on UW-1 dataset with an accuracy of 99.7%. Further tests on other three large and public datasets (MARG, ICDAR07, Google 1000 books) show accuracies of above 99% on each of them.
Tax Revenue Volatility

State governments experienced unprecedented fiscal crises in the 2000s precipitated by an extraordinary increase in the volatility of their tax revenues. Between 1970-2001 and 2001-2014 tax revenue volatility increased from 1.23 percent of revenues to 10.36 percent. To understand the factors that led to the increase in tax revenue volatility, I develop a novel application of decomposition methods. I find that tax policy differences explain 58.9 percent of the increase in tax revenue volatility and differences in economic uncertainty and tax base changes explain 22.3 and 18.8 percent respectively. To understand how governments should tax different revenue sources in the face of uncertainty, I derive an updated Ramsey rule that characterizes the optimal tax policy. I use the updated Ramsey rule to assess how well state governments are setting tax policy and find that the number of states that exposed themselves to excessive risk increased from 26 in 1975 to 36 in 2005.
Twitter Sentiment Analysis -- A More Enhanced Way of Classification and Scoring

In this paper we present a novel approach to Twitter Sentiment Analysis. The approach adopted is to analyse the lexicon features of the tweets for classifying its sentiment (positive, negative and neutral). The training data is made more exhaustive by including various manually labelled tweets, in addition to the existing word stock to keep up with the changing micro logging trends. For Data Preprocessing, a novel spell checking algorithm is introduced, an operation for disjoining compound words such as "high hopes" is implemented and emoticons are replaced by suitable emotion words like happy or sad. After this initial preprocessing, the machine learning algorithms are (Support vector machines and Maximum entropy) are applied. We also propose an avant-garde sentiment scoring mechanism to estimate the degree of the sentiment. Our approach is able to assign sentiments to tweets with an accuracy of 80%.