Miracle-dz/UniFAR-FTUs huggingface dataset:
Serum perfluoroalkyl acids concentrations and memory impairment in a large cross-sectional study

Objectives To examine the cross-sectional association between serum perfluorooctanate (PFOA), perfuorooctane sulfonate (PFOS), perfluorononanoic acid (PFNA) and perfluorohexane sulfonate (PFHxS) concentrations with self-reported memory impairment in adults and the interaction of these associations with diabetes status. Design Cross-sectional study. Setting Population-based in Mid-Ohio Valley, West Virginia following contamination by a chemical plant. Participants The C8 Health Project collected data and measured the serum level of perfluoroalkyl acids (PFAAs) of 21 024 adults aged 50+ years. Primary outcome measure Self-reported memory impairment as defined by the question ‘have experienced short-term memory loss? ’ Results A total of 4057 participants self-reported short-term memory impairment. Inverse associations between PFOS and PFOA and memory impairment were highly statistically significant with fully adjusted OR=0.93 (95% CI 0.90 to 0.96) for doubling PFOS and OR=0.96 (95% CI 0.94 to 0.98) for doubling PFOA concentrations. Comparable inverse associations with PFNA and PFHxS were of borderline statistical significance. Inverse associations of PFAAs with memory impairment were weaker or non-existent in patients with diabetes than overall in patients without diabetes. Conclusions An inverse association between PFAA serum levels and self-reported memory impairment has been observed in this large population-based, cross-sectional study that is stronger and more statistically significant for PFOA and PFOS. The associations can be potentially explained by a preventive anti-inflammatory effect exerted by a peroxisome proliferator-activated receptor agonist effect of these PFAAs, but confounding or even reverse causation cannot be excluded as an alternative explanation.
Generation of primitive behaviors for non-linear hyperelastic octopus-inspired robotic arm

This work addresses open-loop control strategies for continuum robotic manipulators inspired by the octopus arm, which are based on solving numerically a detailed elasto-dynamic model. Octopus arms are muscular hydrostats, capable of performing a variety of dexterous movements, which can be of particular interest to the design of relevant robotic prototypes. Octopus arm muscles consist of fibers and surrounding tissues, and exhibit nearly incompressible hyperelastic behavior. In the present work, they are simulated by a three-dimensional non-linear finite element numerical procedure, using appropriate activation functions for the generation of primitive octopus arm behaviors involving large deformations, such as bending, reaching, fetching and grasping. Preliminary experimental evaluation is performed using an SMA-actuated robotic prototype.
A trip-based multicasting model for wormhole-routed networks with virtual channels

This paper considers the single-source and multi-source multicasting problem in wormhole-routed networks. A general trip-based model is proposed for any network having at least 2 virtual channels per physical channel. The underlying concept of this model is a node sequence called skirt, which always exists in graphs of any topology. The strength of this model is demonstrated by its capabilities: (a) the resulting routing scheme is simple, adaptive, distributed and deadlock-free; (b) the model is independent of the network topology, regular or irregular; (c) the minimum number of virtual channels required is constant as the network grows in size; and (d) it can tolerate faults easily. Using 2 virtual channels/physical channel, it is shown how to construct a single trip in faulty hypercubes and multiple trips in fault-free meshes. Simulation experimental results indicate the potential of the model to tolerate faults with very little performance degradation and to reduce multicast latency with multiple trips. <<ETX>>
Directional Preserving Gradient Vector Field for Active Contour Models

This paper presents a novel dynamic external force for snakes named directional preserving gradient vector field (DPGVF). Firstly, it proposes the concept of boundary direction preserved using the features of the Jacobian, which makes the segmentation results better. Secondly, it makes use of the gradients of gray level images and defines positive and negative boundaries in horizontal and vertical directions respectively.The experimental results show that the method largely prevents interference from the initial contour to the boundary closely and weak boundary, leakage of the boundary clearance and other issues. Additionally, the DPGVF Snake has larger capture ranges and better initialization. It has excellent segmentation results for complex medical images.
An Algorithm of Routing and Spectrum Assignment Based on Revenue-Maximization with Resource-Constrained in Elastic Optical Networks

The routing and spectrum assignment (RSA) is one of the most important issues in elastic optical networks, we compare the total revenue of different sorting strategies of services for static traffics. To maximum the total revenue of the traffic matrix with the limited spectrum, an improved techno-economic RSA algorithm is proposed. We evaluate the performance of the proposed algorithm, and the results show that the proposed RSA algorithm obtains higher revenue compared to existing RSA algorithms.
An anti-let-7 sponge decoys and decays endogenous let-7 functions

The let-7 family contains 12 members, which share identical seed regions, suggesting that they may target the same mRNAs. It is essential to develop a means that can regulate the functions of all members. Using a DNA synthesis technique, we have generated an anti-let-7 sponge aiming to modulate the function of all members. We found that products of the anti-let-7 construct could bind and inactivate all members of the let-7 family, producing decoy and decay effects. To test the role of the anti-let-7 sponge, we stably expressed the anti-let-7 construct in two types of cells, the breast carcinoma cells MT-1 and the oldest and most commonly used human cervical cancer cell line, HeLa cells. We found that expression of anti-let-7 increased cell survival, invasion and adhesion, which corroborate with known functions of let-7 family members. We further identified a novel target site across all species of the let-7 family in hyaluronan synthase 2 (HAS2). HAS2 overexpression produced similar effects as the anti-let-7 sponge. Silencing HAS2 expression by siRNAs produced opposite effects to anti-let-7 on cell survival and invasion. The ability of anti-let-7 to regulate multiple members of the let-7 family allows us to observe their multiple functions using a single reagent. This approach can be applied to other family members with conserved sequences.
A Framework of Stochastic Power Management Using Hidden Markov Model

The effectiveness of stochastic power management relies on the accurate system and workload model and effective policy optimization. Workload modeling is a machine learning procedure that finds the intrinsic pattern of the incoming tasks based on the observed workload attributes. Markov Decision Process (MDP) based model has been widely adopted for stochastic power management because it delivers provable optimal policy. Given a sequence of observed workload attributes, the hidden Markov model (HMM) of the workload is trained. If the observed workload attributes and states in the workload model do not have one-to-one correspondence, the MDP becomes a Partially Observable Markov Decision Process (POMDP). This paper presents a framework of modeling and optimization for stochastic power management using HMM and POMDP. The proposed technique discovers the HMM of the workload by maximizing the likelihood of the observed attribute sequence. The POMDP optimization is formulated and solved as a quadraticly constrained linear programming (QCLP). Compared with traditional optimization technique, which is based on value iteration, the QCLP based optimization provides superior policy by enabling stochastic control.
An Intrusion Tolerant Identity Management Infrastructure for Cloud Computing Services

Identity management is a central point to the security of large applications such cloud services. The identity providers (IdPs) offer services that handle critical information of users. Usually, this kind of information is stored with special care in these providers and intrusions do not necessarily result in security violations. But intrusions may implant malicious behaviors which modify the action of these authentication services. In this way, unauthorized accesses may be achieved for denying accesses to legitimate users of the system. In this paper we introduce an approach for intrusion tolerance to ensure the correct behavior in authentication of large systems, even in presence of possible intrusions.
Distance Exponent: A New Concept for Selectivity Estimation in Metric Trees

This paper discusses the problem of selectivity estimation for range queries in metric datasets, which include vector, or dimensional, datasets as a special case. The main contribution of this paper is that, surprisingly, many different real datasets follow a "power law". From this observation we derive an analysis for the distance distribution of metric datasets. This is the first analysis of distance distributions for real metric datasets.We called the exponent of our power law as "distance exponent". We show that it plays a relevant role for the analysis of real, metric datasets. Specifically, we show (a) how to exploit the distance exponent to derive formulas for selectivity estimation of range queries and (b) how to compute it quickly from a metric index tree.We performed several experiments on many real datasets (road intersections of U.S. counties, vectors characteristics extracted from face matching systems, sets of words, distance matrixes) and synthetic datasets (Sierpinsky triangle, a 2-dimensional uniform distribution and a 2-dimensional line). Our selectivity estimation formulas are accurate, within relative error from 4% to 17%, and always within one standard deviation from the analytical results. Moreover, we present also a quick algorithm to estimate the "distance exponent", which gives good accuracy and saves orders of magnitude in computation time.
A fast, robust and low bit-rate representation for SIFT and SURF features

SIFT features have become extremely popular in computer vision because of their reliable matching qualities under changing lighting conditions. In Robotics they are ubiquitious in self localisation and mapping (SLAM), object tracking and recognition. However, the length of the descriptor is a major obstacle for real-time applications and mobile platforms where the computation time and storage capacity is limited. It has also been shown that the high-dimensional SIFT descriptors suffer from a numerical instability known as the curse of dimensionality. Therefore, we investigate low bit-rate representations based on a binarisation of SIFT and SURF features. Our method is able to reduce the descriptor length by a factor of 8–32 and lifts the curse of dimensionality. The new descriptor is tested in a series of robotics related experiments, namely stereo analysis and object tracking under outdoor conditions, as well as image recognition. Our experiments show that the new representation is not only extremely compact, but also yields faster, more accurate and more robust results.
Combining frontend-based memory with MFCC features for Bandwidth Extension of narrowband speech

In this paper, we continue our previous work on improving Bandwidth Extension (BWE) of narrowband speech. We have shown that including memory into the parametrization frontend (through delta features) results in higher highband certainty irrespective of feature type, with MFCCs exhibiting higher correlation, in general, between both bands, reaching twice that using LSFs. By incorporating memory into the frontend of a conventional LP-based BWE system, we were able to translate the higher correlation due to memory into BWE performance improvement. Using high-resolution inverse DCT, we also achieved high quality speech reconstruction from MFCCs, thus enabling MFCC-based BWE with improved performance compared to conventional static LP-based BWE. We continue this work by incorporating the superior correlation properties of frontend memory into our MFCC-based BWE system. Log-Spectral Distortion as well as the more perceptually-correlated Itakura-based measures show that incorporating memory into our MFCC-based BWE system results in BWE performance superior to that of our dynamic LP-based BWE system.
Assistance of Web browsing by indicating the future Web pages

This paper describes a novel method to assist a user in gathering interested Web pages on the WWW by narrowing a search space using sequences of the user’s current browsed pages. Various methods of gathering interested Web pages for a user on the WWW have been proposed. Those approaches can’t make an agreeable response to user’s interest shift dynamically. We think those approaches don’t try to understand which links will be selected and what kind of pages will be interested by a user in the past and the future. To cope with this problem, rules to select a link which a user wants see are introduced in our system, and the rules called “Navigation Rule”. Each rule has a class of Web page types in the condition part and a link-type in the action part. Also they are weighted for indicating the preference. Every time user selects a link to see the next page, the weights of matched rules are increased. The rule having the highest weight value was applied and some matched Web pages are fetched by Web robots again and again. We called the search strategy “Navigation Search”. Web robots don’t gather some useless Web pages and the search space will be narrow using the Navigation Search. Also we describe the way to provide an interface which display Web pages gathered by Web robots. A user can understand that a user will be reach what kind of Web pages with this interface. Also using the interface, a user also be able to select some links to interest Web pages directly without crawl all path.
Refactoring Java Concurrent Programs Based on Synchronization Requirement Analysis

Writing high quality concurrent programs is challenging. A concurrent program that is not well-written may suffer from coarse synchronization problems, e.g., overly-large critical sections, overly-coarse locks, and etc. These coarse synchronizations may introduce unnecessary lock contention and thereby affect the parallel execution of running threads. To optimize them, people suggest use refactorings, e.g., Split Lock refactoring and Split Critical Section refactoring, to gradually evolve the synchronization code for better parallelism. However, manually identifying the refactoring opportunities is difficult and by-hand code transformations are error-prone. To reduce the manual efforts, this paper proposes an automated refactoring approach for Java concurrent programs based on synchronization requirement analysis. It can automatically analyze the existing synchronization code to identify synchronization requirements. Bases on these requirements, we can find Split Lock, Split Critical Section, and Convert to Atomic refactoring opportunities and then make proper code transformation for each of them. Our experiment shows that the approach does find effective refactoring opportunities in real projects and can transform the refactorable code correctly. This indicates the approach could be helpful for concurrent program evolution.
NP=P: From Polynomial Time Approximation Bounded Solutions of TSP

The question of whether all problems in NP class are also in P class is generally considered one of the most important open questions in mathematics and theoretical computer science as it has far-reaching consequences to other problems in mathematics, computer science, biology, philosophy and cryptography. There are intensive research on proving `NP not equal to P' and `NP equals to P'. However, none of the `proved' results is commonly accepted by the research community up to date. In this paper, motived by approximability of traveling salesman problem (TSP) in polynomial time, we aim to provide a new perspective: showing that NP=P from polynomial time approximation-bounded solutions of TSP in Euclidean space.
UMAPRM: Uniformly sampling the medial axis

Maintaining clearance, or distance from obstacles, is a vital component of successful motion planning algorithms. Maintaining high clearance often creates safer paths for robots. Contemporary sampling-based planning algorithms that utilize the medial axis, or the set of all points equidistant to two or more obstacles, produce higher clearance paths. However, they are biased heavily toward certain portions of the medial axis, sometimes ignoring parts critical to planning, e.g., specific types of narrow passages. We introduce Uniform Medial Axis Probabilistic RoadMap (UMAPRM), a novel planning variant that generates samples uniformly on the medial axis of the free portion of Cspace. We theoretically analyze the distribution generated by UMAPRM and show its uniformity. Our results show that UMAPRM's distribution of samples along the medial axis is not only uniform but also preferable to other medial axis samplers in certain planning problems. We demonstrate that UMAPRM has negligible computational overhead over other sampling techniques and can solve problems the others could not, e.g., a bug trap. Finally, we demonstrate UMAPRM successfully generates higher clearance paths in the examples.
A single-phase transformer-less inverter with active decoupling

This paper presents a novel inverter topology that is applied to single-phase grid-connected photovoltaic systems. The proposed topology has an active decoupling function, which not only eliminates the double line frequency ripple power in the dc-link but also accepts large voltage ripple across the decoupling capacitor in order to reduce the requirement of the decoupling capacitance. Traditionally, bulky electrolytic capacitors are employed in passive decoupling of power, which limits the system lifespan. The reduction of the decoupling capacitance makes it possible for using long life film capacitors in photovoltaic systems. The simulation results verify the operation of the proposed topology and its control strategy.
2016 Ieee International Conference on Big Data (big Data) Universal Data Discovery Using Atypicality

—With the enormous amount of data generated through the internet and sensors, Internet of Things, it becomes too overwhelming for humans to examine it all. One solution is to reduce the data to a set of statistics. The perspective in this paper is the opposite, namely that most of this data is just background noise, and the interesting parts are those that deviate from background noise, the parts that are atypical. In order to find such " interesting " parts of data, universal approaches are required, since it is not known in advance what we are looking for. The paper develops new algorithms for detecting atypical data based on information theory concepts. These are applied to a number of real-world data.
Practical Implementation of Identity Based Encryption for Secure E-mail Communication

Identity Based Encryption (IBE) constitutes an alternative cryptographic method to the conventional public key systems. The main feature of IBE is that public keys are not transmitted over the network, but they are efficiently computed from the receivers’ unique identity information; thus, the system is certificateless. This paper presents a complete implementation of a practical IBE infrastructure for secure e-mail communication. As far as we know, this is the first open-source attempt to simultaneously provide a fully functional and user-friendly IBE system together with a trustworthy Private Key Generator.
with plasma cell leukemia Murine anti-interleukin-6 monoclonal antibody therapy for a patient

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“They are Us—We are Them”

Stigmatization towards persons with mental illnesses is a major quality of care concern. Unfortunately, nurses are no less implicated than other health professions. This article reports the results of an evaluation of a learning intervention for psychiatric nursing students in partnership between Brandon University and the Mental Health Commission of Canada. We describe this intervention as an example of transformational learning through nursing education leadership and suggest its use as a promising model for cultural change in healthcare practice. Leaders and managers are encouraged to explore how such a model may be adaptable or implementable for their own organizations and departments.
Securing Sensor Networks Using A Novel Multi-Channel Architecture

In many applications of sensor networks, security is a very important issue. To be resistant against the various attacks, nodes in a sensor network can establish pairwise secret keys, authenticate all communications with cryptographic functions, and also apply secure information aggregation schemes or hop-by-hop filtering methods. However, these security measures can take considerable overhead in terms of storage, communication and computation, which are scarce resources in sensor nodes. Previously proposed security measures can only resist against a limited number of compromised nodes, which we define as the resistance level. In this paper, we propose a separate solution to any security measure. This technique either significantly reduces the overhead, or increases the resistance level without increasing overhead. The solution is based on a new "mixed multi-channel" (MMC) architecture. In this design, each node can only use one fixed channel. The whole network is thus divided into multiple "planes" by the different planes. Exploiting the characteristics of multi-channel communication, a series of methods are proposed, such as MMC-1, MMC-k and MMC-r. We then present designs to integrate the methods with current security measures, and analyze their resistance level and energy conservation.
The Neuroinflammation Perspective of Depression: Reuniting the Outstanding Mechanisms of the Pathophysiology

ABSTRACTMajor Depressive Disorder (MDD) is a serious mental health problem that leads to patients' disability and has huge impact on social and economic burden to society. The current available medications for the treatment of depression are mainly targeted on enhancing monoamine neurotransmission. However, antidepressant treatments are still lacking high efficacy in many cases which is associated with low treatment response and remission rates. However, the latest knowledge regarding the pathophysiology of depression indicates that depression is developed by highly complex and integrated mechanisms in which monoaminergic deficiency could only be part of. The paradigm is now shifting from monoaminergic hypothesis to significance of other novel mechanisms that could possibly play substantial role for the development of depression in a highly inter-related manner. In fact, neuroinflammation, amongst other mechanisms does seem to be a key pathological component by having impact on certain pathway pathologies.