Miracle-dz/UniFAR-FTUs huggingface dataset:
Competitive Trace Theory: A Role for the Hippocampus in Contextual Interference during Retrieval

Much controversy exists regarding the role of the hippocampus in retrieval. The two dominant and competing accounts have been the Standard Model of Systems Consolidation (SMSC) and Multiple Trace Theory (MTT), which specifically make opposing predictions as to the necessity of the hippocampus for retrieval of remote memories. Under SMSC, memories eventually become independent of the hippocampus as they become more reliant on cortical connectivity, and thus the hippocampus is not required for retrieval of remote memories, only recent ones. MTT on the other hand claims that the hippocampus is always required no matter the age of the memory. We argue that this dissociation may be too simplistic, and a continuum model may be better suited to address the role of the hippocampus in retrieval of remote memories. Such a model is presented here with the main function of the hippocampus during retrieval being “recontextualization,” or the reconstruction of memory using overlapping traces. As memories get older, they are decontextualized due to competition among partially overlapping traces and become more semantic and reliant on neocortical storage. In this framework dubbed the Competitive Trace Theory (CTT), consolidation events that lead to the strengthening of memories enhance conceptual knowledge (semantic memory) at the expense of contextual details (episodic memory). As a result, remote memories are more likely to have a stronger semantic representation. At the same time, remote memories are also more likely to include illusory details. The CTT is a novel candidate model that may provide some resolution to the memory consolidation debate.
A unified context model for web image retrieval

Content-based web image retrieval based on the query-by-example (QBE) principle remains a challenging problem due to the semantic gap as well as the gap between a user's intent and the representativeness of a typical image query. In this article, we propose to address this problem by integrating query-related contextual information into an advanced query model to improve the performance of QBE-based web image retrieval. We consider both the local and global context of the query image. The local context can be inferred from the web pages and the click-through log associated with the query image, while the global context is derived from the entire corpus comprising all web images and the associated web pages. To effectively incorporate the local query context we propose a language modeling based approach to deal with the combined structured query representation from the contextual and visual information. The global query context is integrated by the multi-modal relevance model to “reconstruct” the query from the document models indexed in the corpus. In this way, the global query context is employed to address the noise or missing information in the query and its local context, so that a comprehensive and robust query model can be obtained. We evaluated the proposed approach on a representative product image dataset collected from the web and demonstrated that the inclusion of the local and global query contexts significantly improves the performance of QBE-based web image retrieval.
Modelling security and trust with Secure Tropos

Although the concepts of security and trust play an important issue in the development of information systems, they have been mainly neglected by software engineering methodologies. In this chapter, we present an approach that considers security and trust throughout the software development process. Our approach integrates two prominent software engineering approaches, one that provides a security-oriented process and one that provides a trust management process. The result is the development of a methodology that considers security and trust issues as part of its development process. Such integration represents an advance over the current state of the art by providing the ﬁrst effort to consider security and trust issues under a single software engineering methodology. A case study from the health domain is employed to illustrate our approach.
“A high price paid”: Migration-related loss and distress among undocumented Mexican immigrants.

El objetivo de este estudio transversal fue identificar la prevalencia de pérdidas relacionadas con la migración, así como su asociación con el estrés entre inmigrantes Mexicanos indocumentados. Respondent Driven Sampling (RDS) fué utilizado para recopilar y analizar datos de entrevistas clínicas con 248 inmigrantes Mexicanos indocumentados que residen cerca de la frontera entre California y México. Todos los participantes reportaron un historial de pérdidas relacionadas con la migración con un promedio de 13 diferentes tipos de pérdidas (SD 4.59, rango 2 a 25). Se observaron diferencias significativas en el tipo de pérdidas relacionadas con la migración en cuanto a características sociodemográficas y de inmigración. Después de controlar las covariables relevantes, la pérdida de interdependencia, específicamente el ser tratado/a de manera diferente por no tener una visa, fue el predictor más fuerte de estrés (OR 4,97, 95% CI [2.18, 11.34], p .001). Dada la actual situación anti-inmigrante en los E.U. que margina aún más a los inmigrantes indocumentados, es necesario aumentar esfuerzos de abogacía y desarrollar nuevas alternativas que faciliten el acceso a servicios de salud mental destinados a disminuir el estrés asociado con la discriminación entre estos inmigrantes en riesgo.
An improved Monte Carlo POMDPs online planning algorithm combined with RAVE heuristic

Partially observable Markov decision processes (POMDPs) provide a kind of general model that can deal with problems in uncertain environment efficiently. There are many different planning methods for POMDPs model. Partially Observable Monte Carlo planning (POMCP) method, using Monte Carlo Tree Search (MCTS) method, which can help break the curse of dimensionality and the curse of history. However, the method has strong dependence on the count of simulations. The POMCP algorithm was improved in this paper by combining Rapid Action Value Estimate (RAVE) method and MCTS. There's less dependence on the count of simulations and higher efficiency in the improved algorithm, which is a promising online planning algorithm. Experimental results on the benchmark problems indicate that efficiency of the improved algorithm is higher than the basic POMCP algorithm.
Cashing in on hints for better prefetching and caching in PVFS and MPI-IO

In this work, we propose, implement and test a novel approach to the management of parallel I/O in high-performance computing. Our proposed approach is built upon three complementary ideas: (i) allowing users to place hints into the application code indicating high-level data access patterns, (ii) enabling an optimizing compiler to process these hints and develop I/O optimization strategies, and (iii) enhancing the I/O stack to accept these optimizations and process them across the different layers in the stack. We describe a general hint processing framework that accommodates this approach and demonstrate its potential by applying it to two sample problems: (i) shared storage cache management and (ii) I/O prefetching. In the former, our approach decides, at each program point of interest, the ideal set of data blocks to keep in shared storage caches in the I/O stack, and in the latter, the high-level data access pattern is propagated from application layer to the parallel file system layer for prefetching data from the storage subsystem. Our approach is designed to complement and work synergistically with the MPI-IO and PVFS frameworks and exploits the characteristics of applications written using these software. We tested our approach using both synthetic data access patterns and disk I/O intensive application programs. The results collected indicate that the proposed approach improves over existing storage caching and I/O prefetching schemes by 28% and 66%, respectively.
T-PICE: Twitter Personality Based Influential Communities Extraction System

The identification of influential users in social media communities has been recently of major concern, since these users can contribute to viral marketing campaigns. In our approach we extend the notion of influence from users to networks and consider personality as a key characteristic for identifying influential networks. We describe the Twitter Personality based Influential Communities Extraction (T-PICE) system that creates the best influential communities in a Twitter network graph considering users' personality. We then expand existing approaches in users' personality extraction by aggregating data that represent several aspects of user behavior using machine learning techniques. We use an existing modularity based community detection algorithm and we extend it by inserting a pre-processing step that eliminates graph edges based on users' personality. The effectiveness of our approach is demonstrated by sampling the twitter graph and comparing the influence of the created communities with and without considering the personality factor. We define several metrics to count the influence of communities. Our results show that the T-PICE system creates the most influential communities.
Edge-assistant visual objects decoding using sparse representation

Visual objects decoding with functional magnetic resonance imaging (fMRI) often merely depends on the brain activity, using pattern classification to decode information about visual stimuli from patterns of activity. However, the spatial resolution of fMRI is still limited to the > mm range. Limited by its spatial resolution, fMRI voxels lack high-spatial-frequency information of visual stimuli. The overcomplete sparse representation can effectively match the sparse coding strategy in the primary visual cortex of human. Thus, it provides a probable way to represent the high-spatial-frequency information. Aiming to improve classification performance of images which were visual stimuli presented to participants, this paper proposed an edge-assistant approach for visual stimuli decoding, using sparse representation to supplement the ensemble of early visual voxels with high-spatial-frequency information. We supplemented early visual voxels with the sparse representation coefficients of edge patches and significantly improved the classification performance in a four-category (car, face, building, and animal) object classification analysis, which has valuable reference for practical fMRI-based image retrieval system.
Rich Variety of Bifurcation and Chaos in a Simple Non-Source Free Electronic Circuit with a Diode *

A very simple nonlinear parallel forced LCR circuit is proposed as Balamurugan-Sengodan-Daniel (BSD) circuit includes linear inductors, capacitor, locally active resistor and a diode driven by a sinusoidal voltage source. By varying the amplitude of the external signal, we identify the familiar quasiperiodic routes to chaos as well as period doubling sequences, windows, and so on. The performance of the proposed system is demonstrated experimentally and proved with Poincaré map results.
Automated Interpretations of Unsupervised Learning-Derived Clusters for Activity Recognition

I. INTRODUCTION For robots to properly interact with humans, it is important that they are able to recognize users' plans and activities so that they may respond accordingly. Many activity recognition (AR) algorithms involve signal processing [1] or supervised learning [2], [3] to label raw sensor data with a human-defined action, but these methods restrict the generalizability of the learned activity models. In particular, sensor inputs that drastically differ from the training data cannot be labeled correctly, making it hard to build robots that respond appropriately to novel actions. This is evident in the codebook method used by Wang and Mori [2] to cluster the set of all sensor readings into k clusters via k-means clustering of the training data. The signal input at the center of each cluster is used to represent all sensor readings that fall within the cluster. This means that any novel signal input is aliased with one from the training data so that a misclassified action/activity is certain. Such a phenomenon was observed by Zhang and Parker [4] when they derived a codebook for compressed vector representations of spatial-temporal input signals, but they used an unsupervised topic model for AR. Unsupervised learning methods such as the latent Dirichlet allocation topic model (LDA) [5] were recently proposed for AR tasks which enable machines to derive their own activity clusters [6]. The primary challenge with such methods is the inability for humans to clearly interpret these machine-defined actions, which can lead to difficulty in verification and determining response behaviors. The original work by Hù ynh et al. [6] provided interpretive evidence by aligning the learned topics with an annotated timeline of activities, but few other applications have had access to such annotations. Furthermore, it is evident that machine learning of practical activity clusters can be difficult when displaying sensor readings since numbers are not always easy to relate. Freedman et al. [7] represented activity clusters learned using LDA on red, green, blue, depth (RGB-D) sensor data as collections of stick figures in an attempt to resemble the topic modeling literature where collections of words are presented for each topic. As snapshots of an activity in progress, stick figures still cannot reveal the underlying trend(s) between each other like actual words can because words have semantic definitions. We thus propose the use of feature vectors for signal data such as postures read by a RGB-D sensor in order …
Public sector enterprise resource planning

The management of the US Department of Defense (DoD) enterprise must change. Years of under‐funding have led to a wide gap between enterprise support requirements and resources. Private sector firms have faced similar choices. This paper shows how the public enterprise can be changed. Our hypothesis is that private sector implementations of standard software will lead to increased effectiveness and efficiency in public sector organizations. Sufficient detail is provided on how to transition to a modern integrated public sector enterprise, and the steps for implementing such a project are outlined, following standard private sector implementation practices. To explain the problem and solution, the DoD installation management enterprise is used as an example.
Paid and unpaid graduate internships: prevalence, quality and motivations at six months after graduation

ABSTRACT Secondary analysis of the UK’s 2011/12 Destinations of Leavers from Higher Education survey reveals that many existing assumptions about graduate internships are questionable. This article proposes a reliable way of estimating the true extent of internships including those reported as ‘voluntary’ jobs: hidden internships. In doing so the article finds: 1) At six months after graduation, internships are a small feature of the UK graduate labour market, but significant in certain sectors; 2) Unpaid internships are much more prevalent than previously estimated, especially in these same sectors; and 3) Contrary to some public policy debate, unpaid internships appear, on balance, to be a residual option more likely to lead to underemployment and less favourable career development outcomes. Post-graduation internships appear less likely than pre-graduation work experiences to confer long-term employability advantages.
S-1 plus cisplatin with concurrent radiotherapy versus cisplatin alone with concurrent radiotherapy in Chinese patients with nonsmall-cell lung cancer

Background:The aim of this study was to investigate the efficacy and safety of S-1 plus cisplatin combined with concurrent radiotherapy (SCCCR) versus cisplatin alone combined with concurrent radiotherapy (CCCR) in Chinese patients with unresectable stage III nonsmall-cell lung cancer (NSCLC). Methods:Between January 2012 and December 2014, 72 eligible Chinese patients with NSCLC were included and randomly divided into 2 groups, each having 36 patients. Patients in the SCCCR group received S-1 plus cisplatin with concurrent, radiotherapy. The other 36 patients in the CCCR group were administered cisplatin with concurrent radiotherapy. The primary outcome was the overall response rate. The secondary outcomes were overall survival (OS), progression-free survival (PFS), and adverse events. Results:The 3-year overall response rates for the SCCCR and CCCR groups were 60.1% and 53.3%, respectively (P = 0.041). The median OS was 35.1 (range, 6.5–47.2) months and 24.6 (range, 2.8–24.3) months for the SCCCR and CCCR groups, respectively (P = 0.016). The median PFS for the SCCCR and CCCR groups was 31.4 (range, 5.6–39.3) months and 22.3 (range, 2.4–36.5) months, respectively (P = 0.023). The toxicity profiles were similar for both groups. Conclusion:The efficacy and safety of SCCCR was more encouraging compared to those of CCCR in Chinese NSCLC patients. In addition, the toxicities in both groups were tolerable.
On physical-aware synthesis of vertically integrated 3D systems

Vertical integration of active device layers in the third dimension makes room for significant reduction in interconnect lengths leading to decrease in both delay and power consumption associated with interconnects. This characteristic can be well leveraged only by judicious layer assignment of active devices. To address layer assignment as a part of behavioral synthesis, we proposed a 0-1 linear program formulation to simultaneously schedule, bind and perform layer assignment for synthesis of vertically integrated 3D systems. In these systems, inter-stratal communication occurs through inter-layer vias that have higher resistivity and capacitance. In this paper, we propose a trade-off between reducing the total interconnect delay in critical paths while reducing the inter-stratal communication. Three different objective models are proposed and their combinations examined to find the most suitable methodology to achieve these goals. A power gradient is proposed between layers to address the thermal issues associated with these systems that have been shown to be of concern. Results shows a significant reduction in total interconnect lengths compared to a traditional two-dimensional implementation. The examination of the various proposed optimization objectives indicate that a combination of communication minimization and critical path optimization leads to the best synthesis results for a range of benchmarks.
User Demand Aware Soft-Association Control in Ultra-Dense Small Cell Networks

To address the challenge of unprecedented growth in mobile data traffic, ultra-dense network deployment is a cost efficient solution to offload the traffic over some small cells. The overlapped coverage areas of small cells create more than one candidate access points for one mobile station. Signal strength based user association in IEEE 802.11 results in a significantly unbalanced load distribution among access points. However, the bandwidth demand of each user actually differs vastly due to their different preferences on mobile applications. In this paper, we formulate a non-linear integer programming model for joint user association and user bandwidth demand guarantee problem. In this model, we try to maximize the system capacity and guarantee the effective bandwidth demand for each user by soft-association control. Finally, we evaluate the proposed algorithm performance for the edge users with dynamic and heterogeneous bandwidth demands. Simulation results show that the proposed soft-association control performs better than the distributed ones and improves the individual quality of user experience with a little price on system throughput.
Imaging of cerebrovascular pathology in animal models of Alzheimer's disease

In Alzheimer's disease (AD), vascular pathology may interact with neurodegeneration and thus aggravate cognitive decline. As the relationship between these two processes is poorly understood, research has been increasingly focused on understanding the link between cerebrovascular alterations and AD. This has at last been spurred by the engineering of transgenic animals, which display pathological features of AD and develop cerebral amyloid angiopathy to various degrees. Transgenic models are versatile for investigating the role of amyloid deposition and vascular dysfunction, and for evaluating novel therapeutic concepts. In addition, research has benefited from the development of novel imaging techniques, which are capable of characterizing vascular pathology in vivo. They provide vascular structural read-outs and have the ability to assess the functional consequences of vascular dysfunction as well as to visualize and monitor the molecular processes underlying these pathological alterations. This article focusses on recent in vivo small animal imaging studies addressing vascular aspects related to AD. With the technical advances of imaging modalities such as magnetic resonance, nuclear and microscopic imaging, molecular, functional and structural information related to vascular pathology can now be visualized in vivo in small rodents. Imaging vascular and parenchymal amyloid-β (Aβ) deposition as well as Aβ transport pathways have been shown to be useful to characterize their dynamics and to elucidate their role in the development of cerebral amyloid angiopathy and AD. Structural and functional imaging read-outs have been employed to describe the deleterious affects of Aβ on vessel morphology, hemodynamics and vascular integrity. More recent imaging studies have also addressed how inflammatory processes partake in the pathogenesis of the disease. Moreover, imaging can be pivotal in the search for novel therapies targeting the vasculature.
Sputum induction as a research tool for sampling the airways of subjects with cystic fibrosis

BACKGROUND Sputum induction (SI) has proved to be a reliable non-invasive tool for sampling inflammatory airway contents in asthma, with distinct advantages over collection of expectorated sputum (ES) and bronchoalveolar lavage (BAL). A study was undertaken to evaluate the safety of SI and to assess if it might be an equally valuable outcome tool in patients with cystic fibrosis (CF). METHODS The safety of the procedure was examined and sample volume, cell counts, cytokine concentrations, and bacterial culture results obtained by SI, spontaneous ES, and fibreoptic bronchoscopy were compared in 10 adults with CF. RESULTS SI was well tolerated and was preferred to BAL by all subjects. The mean (SE) sample volume obtained by SI was significantly greater than ES (6.74 (1.46) ml v 1.85 (0.33) ml, p = 0.005). There was no significant difference in the number of cells per ml of sample collected. There was a difference in the mean (SD) percentage of non-epithelial, non-squamous cells collected (67 (28)%, 86 (21)%, and 99 (1)% for ES, SI, and BAL, respectively). These percentage counts were different between ES and both SI and BAL (p=0.03 and p=0.006, respectively). Cell differential counts (excluding squamous cells) from all collection methods were similar (mean (SD) 84 (9)%, 87 (7)%, and 88 (11)% polymorphonuclear cells for ES, SI, and BAL, respectively). The concentrations of interleukin (IL)-8 and tumour necrosis factor (TNF)-α were the same in all three samples when corrected for dilution using urea concentration. The test specific detection rate for recovery of bacteriological pathogens was 79% for SI, 76% for ES, and 73% for BAL. CONCLUSION SI offers safety advantages over BAL and may be a more representative airway outcome measurement in patients with CF.