Miracle-dz/UniFAR-FTUs huggingface dataset:
Upper Limb Neurodynamic Test 1 on Healthy Individuals: Intra- and Intersession Reliability of the Angle between Pain Onset and Submaximal Pain

Assessment of nerve trunk mechanosensitivity using the upper limb neurodynamic test 1 (ULNT1) often includes measurement of the angle of occurrence in the range of pain onset (PO) and submaximal pain (SP). A measurement that better fits the idea of mechanosensitivity could be the angle between PO and SP (AbOS). This study investigated the intra- and intersession reliability of AbOS, PO, and SP during the ULNT1. Forty-four healthy volunteers underwent three ULNT1 to the point of PO and SP, twice in the first session and once in the second. AbOS, PO, and SP angles of occurrence reliability were examined using the Intraclass Correlation Coefficient (ICC 3,1) and Bland-Altman plots. The intra- and intersession ICC values for AbOS were 0.71 (95% CI: 0.47; 0.85) and 0.79 (95% CI: 0.60; 0.89), respectively. The intra- and intersession mean difference and 95% limits of agreement (±1.96 SD) in the Bland-Altman plots were 2.3° (−18.3°; 23.1°) and 2.8° (−14.7°; 20.4°), respectively. The intra- and intersession reliability of the AbOS during the ULNT1 in healthy individuals is high and higher than the reliability of PO and SP angles of occurrence. The AbOS could be a preferable variable in the assessment of neural mechanosensitivity.
InDReS: An Intrusion Detection and response system for Internet of Things with 6LoWPAN

Internet of Things (IoT), an emerging Internet based technical architecture where heterogeneous sensors collaborate for ubiquitous computing based on several technologies and standard communication protocols. With, such an amount of scaling and diverse technologies involved, IoT is susceptible to various threats. Hence, devising an IoT system, providing security through resistance against attacks is a de facto requirement to make IoT secure and operational. The existing works for detecting adversaries like SVELTE and INTI consume too much resource. Further, packet dropping ratio is high and number of nodes taken for evaluation is low with some critical metrics overlooked. A novel detection technique with constraint based specification is proposed in this paper which significantly improves the shortcomings of SVELTE and INTI. The effectiveness of proposed scheme is valuated through comparative analysis using NS-2 simulation tool.
Using Genetic Algorithm Image Retrieval Based on MultiFeature Similarity Score Fusion

This paper proposes an image retrieval method based on multi-feature similarity score fusion using genetic algorithm. Single feature describes image content only from one point of view, which has a certain one-sided. Fusing multifeature similarity score is expected to improve the system's retrieval performance. In this paper, the retrieval results from color feature and texture feature are analyzed, and the method of fusing multi-feature similarity score is described. For the purpose of assigning the fusion weights of multi-feature similarity scores reasonably, the genetic algorithm is applied. For comparison, other three methods are implemented. They are image retrieval based on color feature, texture feature and fusion of colortexture feature similarity score with equal weights. The experimental results show that the proposed method is superior to other methods.
Inhibition of leptin-induced vascular extracellular matrix remodelling by adiponectin

Vascular extracellular matrix (ECM) remodelling, which is the result of disruption in the balance of ECM synthesis and degradation, induces vessel fibrosis and thereby leads to hypertension. Leptin is known to promote tissue fibrosis, while adiponectin has recently been demonstrated to be anti-fibrogenic in tissue fibrosis. In this study, we aimed to evaluate the leptin-antagonist function of adiponectin and to further elucidate the mechanisms through which adiponectin dampens leptin signalling in vascular smooth muscle cells, thus preventing excess ECM production, in our already established 3D co-culture vessel models. Our 3D co-culture vessel model, which mimics true blood vessels, is composed of vascular endothelial cells, vascular smooth muscle cells and collagen type I. We validated the profibrogenic effects of leptin and analysed matrix metalloproteinase 2 (MMP2), MMP9, tissue inhibitor of metalloproteinase 1 (TIMP1) and collagen types II/IV secretion in 3D vessel models. The protective/inhibitory effects of adiponectin were re-analysed by inhibiting adiponectin receptor 1 (AdipoR) and AdipoR2 expression in endothelial cells using RNAi technology. In the 3D vessel models, adiponectin blocked the leptin-stimulated secretion of collagen types II/IV and TIMP1 while significantly increasing MMP2/9 activity. In endothelial cells, adiponectin induced phosphorylation of AMPK, thereby suppressing leptin-mediated STAT3 phosphorylation through induction of SOCS3 in smooth muscle cells. Our findings indicate that adiponectin disrupted the leptin-induced vascular ECM remodelling via AdipoR1 and enhanced AMPK signalling in endothelial cells, which, in turn, promoted SOCS3 up-regulation in smooth muscle cells to repress leptin-stimulated phosphorylation of STAT3.
Optimum Power Control at Finite Blocklength

This paper investigates the maximal channel coding rate achievable at a given blocklength n and error probability ϵ, when the codewords are subjected to a long-term (i.e., averaged-over-all-codeword) power constraint. The second-order term in the large-n expansion of the maximal channel coding rate is characterized both for additive white Gaussian noise (AWGN) channels and for quasi-static fading channels with perfect channel state information available at both the transmitter and the receiver. It is shown that in both the cases, the second-order term is proportional to (n-1 ln n)1/2. For the quasi-static fading case, this second-order term is achieved by truncated channel inversion, namely, by concatenating a dispersion-optimal code for an AWGN channel subject to a short-term power constraint, with a power controller that inverts the channel whenever the fading gain is above a certain threshold. Easy-to-evaluate approximations of the maximal channel coding rate are developed for both the AWGN and the quasi-static fading case.
Regularization Methods for Radio Tomographic Imaging

Radio Tomographic Imaging (RTI) is an emerging technology that uses received signal strength measurements to image the attenuation of objects within a wireless network area. RTI is by nature an ill-posed inverse problem, therefore, regularization techniques must be utilized to obtain accurate images. This paper discusses some common regularization techniques, including Tikhonov, truncated singular value decomposition, and total variation, and presents the results of applying them to RTI.
The Flexible Preconditions Model for Macro-Dataflow Execution

In this paper, we propose the flexible preconditions model for macro-dataflow execution. Our approach unifies two current approaches for managing task dependences, eager execution vs. Strict preconditions. When one of the two outperforms the other, flexible preconditions can always attain, and possibly surpass, the performance of the better approach. This work focuses on the performance of parallel programming models based on macro-dataflow, in which applications are composed of tasks and inter-task dependences. Data-flow models usually make a choice between specifying the task dependences before task creation (as strict preconditions), or during task execution, when they are actually needed (eager execution). This paper shows how the choice between eager execution and strict preconditions affects the performance, memory consumption and expressiveness of macro-dataflow applications. The flexible preconditions model is sufficiently flexible to support both eager execution and strict preconditions, as well as hybrid combinations thereof. This capability enables programmers and future auto-tuning systems to pick the precondition combination that yields the best performance for a given application. The experimental evaluation was performed on a 32-core SMP, and is based on a new macro-dataflow implementation, QtCnC, that supports eager execution, strict preconditions and flexible preconditions in a single framework. (QtCnC is an implementation of the CnC model on the QThreads library.) For applications where all dependences are known ahead of time, flexible and strict preconditions execute up to 56% faster than eager execution (for the benchmarks and platform used in our study). On the other hand, for applications where the complete set of per-task dependences is determined after the tasks are spawned, flexible preconditions and eager execution perform up to 38% better than strict preconditions.
Shared and distributed memory implementations for parallel simulations of a quantum search algorithm to solve the 3SAT problem

This article presents two parallel implementations for simulations of a new quantum search algorithm to solve the 3-SAT problem, by using shared-memory and distributedmemory approaches. Very large computing time are needed to perform the simulations of quantum computing algorithms to solve realistic problems, thus high performance computing approaches are the only viable option to compute results in reasonable times. The experimental analysis demonstrates the advantage of using both shared-memory and distributed-memory parallel computing techniques in order to take advantage of available computing resources in a distributed computing environment to perform higly complex quantum simulations. Speedup values up to 5× for the shared-memory implementation and up to 6× for the distributed-memory implementation are reported.
When Love is Not Enough: Parenting an Adopted Child with Mental Illness

This article presents the results of a qualitative study designed to explore the experiences of adoptive parents who placed an adopted child with mental illness in a residential treatment center (RTC). Twenty-four adoptive families from across the United States who placed an adopted child in residential treatment were interviewed. The adopted children represented various types of adoption including public child welfare, domestic infant, and intercountry adoption. Parents reported feeling victimized by their child and by the very systems designed to help them, including child welfare, mental health, health care, and education. The findings reveal signs of trauma in the adoptive parents as a result of their experiences. The article concludes with recommendations from adoptive parents for adoption, mental health, and residential treatment professionals who work with adopted children and their families.
Overexpression of Apolipoprotein F Reduces HDL Cholesterol Levels In Vivo

Objective—Apolipoprotein F (ApoF) is a protein component of several lipoprotein classes including HDL. It is also known as lipid transfer inhibitor protein (LTIP) based on its ability to inhibit lipid transfer between lipoproteins ex vivo. We sought to investigate the role of ApoF in HDL metabolism. Methods and Results—Adeno-associated viruses (AAV) based on serotype 8, were used to overexpress either murine or human ApoF in mice. Overexpression of murine ApoF significantly reduced total cholesterol levels by 28% (P<0.001), HDL by 27% (P<0.001), and phospholipid levels by 19% (P<0.001). Overexpression of human ApoF had similar effects. Human ApoF was nearly exclusively HDL-associated in mice. In agreement with this finding, greater than 90% of the ApoF in human plasma was found on HDL3, with only a small amount on LDL. Overexpression of mouse ApoF accelerated the plasma clearance of [3H]-cholesteryl ether labeled HDL. Plasma from mice overexpressing ApoF showed improved macrophage cholesterol efflux on a per HDL-C basis. Conclusions—ApoF overexpression reduces HDL cholesterol levels in mice by increasing clearance of HDL-CE. ApoF may be an important determinant of HDL metabolism and reverse cholesterol transport.
Analysis of opinionated text for opinion mining

In sentiment analysis, the polarities of the opinions expressed on an object/feature are determined to assess the sentiment of a sentence or document whether it is positive/negative/neutral. Naturally, the object/feature is a noun representation which refers to a product or a component of a product, let’s say, the "lens" in a camera and opinions emanating on it are captured in adjectives, verbs, adverbs and noun words themselves. Apart from such words, other meta-information and diverse effective features are also going to play an important role in influencing the sentiment polarity and contribute significantly to the performance of the system. In this paper, some of the associated information/meta-data are explored and investigated in the sentiment text. Based on the analysis results presented here, there is scope for further assessment and utilization of the meta-information as features in text categorization, ranking text document, identification of spam documents and polarity classification problems.
Level set methods for geometric inverse problems in linear elasticity

In this paper, we investigate the regularization and numerical solution of geometric inverse problems related to linear elasticity with minimal assumptions on the geometry of the solution. In particular, we consider the probably severely ill-posed reconstruction problem of a two-dimensional inclusion from a single boundary measurement. In order to avoid parametrizations, which would introduce a priori assumptions on the geometric structure of the solution, we employ the level set method for the numerical solution of the reconstruction problem. With this approach, we construct an evolution of shapes with a normal velocity chosen depending on the shape derivative of the corresponding least-squares functional in order to guarantee its descent. Moreover, we analyse penalization by perimeter as a regularization method, based on recent results on the convergence of Neumann problems and a generalization of Golab's theorem. The behaviour of the level set method and of the regularization procedure in the presence of noise are tested in several numerical examples. It turns out that reconstructions of good quality can be obtained only for simple shapes or for unreasonably low noise levels. However, it seems reasonable that the quality of reconstructions improves by using more than a single boundary measurement, which is an interesting topic for future research.
Side Channel: Bits over Interference

Interference is a critical issue in wireless communications. In a typical multiple-user environment, different users may severely interfere with each other. Coordination among users therefore is an indispensable part for interference management in wireless networks. It is known that coordination among multiple nodes is a costly operation taking a significant amount of valuable communication resource. In this paper, we have an interesting observation that by generating intended patterns, some simultaneous transmissions, i.e., "interference,” can be successfully decoded without degrading the effective throughput in original transmission. As such, an extra and "free” coordination channel can be built. Based on this idea, we propose a DC-MAC to leverage this "free” channel for efficient medium access in a multiple-user wireless network. We theoretically analyze the capacity of this channel under different environments with various modulation schemes. USRP2-based implementation experiments show that compared with the widely adopted CSMA, DC-MAC can improve the channel utilization efficiency by up to 250 percent.
A Framework to Eliminate Backdoors from Response-Computable Authentication

Response-computable authentication (RCA) is a two-party authentication model widely adopted by authentication systems, where an authentication system independently computes the expected user response and authenticates a user if the actual user response matches the expected value. Such authentication systems have long been threatened by malicious developers who can plant backdoors to bypass normal authentication, which is often seen in insider-related incidents. A malicious developer can plant backdoors by hiding logic in source code, by planting delicate vulnerabilities, or even by using weak cryptographic algorithms. Because of the common usage of cryptographic techniques and code protection in authentication modules, it is very difficult to detect and eliminate backdoors from login systems. In this paper, we propose a framework for RCA systems to ensure that the authentication process is not affected by backdoors. Our approach decomposes the authentication module into components. Components with simple logic are verified by code analysis for correctness, components with cryptographic/ obfuscated logic are sand boxed and verified through testing. The key component of our approach is NaPu, a native sandbox to ensure pure functions, which protects the complex and backdoor-prone part of a login module. We also use a testing-based process to either detect backdoors in the sand boxed component or verify that the component has no backdoors that can be used practically. We demonstrated the effectiveness of our approach in real-world applications by porting and verifying several popular login modules into this framework.
Correcting Popularity Bias by Enhancing Recommendation Neutrality

In this paper, we attempt to correct a popularity bias, which is the tendency for popular items to be recommended more frequently, by enhancing recommendation neutrality. Recommendation neutrality involves excluding specified information from the prediction process of recommendation. This neutrality was formalized as the statistical independence between a recommendation result and the specified information, and we developed a recommendation algorithm that satisfies this independence constraint. We correct the popularity bias by enhancing neutrality with respect to information regarding whether candidate items are popular or not. We empirically show that a popularity bias in the predicted preference scores can be corrected.
Auction-based Agent Negotiation via Programmable Reactive Tuple Spaces

Auctions are proposed as a distributed negotiation mean, particularly useful in multiagent systems where self-interested agents compete for resources. The Internet is the most known example of environment where agents belonging to different applications exploit the available resources and services. The aim of this paper is to show how auction mechanisms can be easily implemented by using programmable reactive tuple spaces. Tuple spaces are shared repositories of messages that follow the Linda model; the addition of programmable reactivity permits to adapt the behaviour of tuple space to the application-specific requirements. In the implementation of auctions, programmable reactivity is exploited to uncouple the auction mechanisms from selling and bidding policies of the participant agents. Keyword: Internet, Auctions, Coordination, Tuple Space.
CS 838 : Advanced Graphics Project 1 : Stylized

The goal of this project was the creation of simple, recognizable and easily editable vectorized representations of real-world image inputs. Towards this goal I implemented a novel vectorization system using the combination of two preexisting, but relatively new, algorithms one for image abstraction and simplification, one for segmentation and vectorization of abstract or cartoon images. I also experimented with methods of detecting occlusion and filling holes in the vectorized result, with a qualitative goal of simplifying the steps necessary to modify or delete regions within the image and produce a visually consistent result.
Off-diagonal correlations in one-dimensional anyonic models: a replica approach

We propose a generalization of the replica trick that allows us to calculate the large distance asymptotics of off-diagonal correlation functions in anyonic models with a proper factorizable ground-state wavefunction. We apply this new method to the exact determination of all the harmonic terms of the correlations of a gas of impenetrable anyons and to the Calogero–Sutherland model. Our findings are checked against available analytic and numerical results.
Load Prediction and Hot Spot Detection Models for Autonomic Cloud Computing

Cloud computing is leading to transformational changes with stringent requirements on usability, performance and security over very heterogeneous workloads. Their run-time management requires realistic algorithms and techniques for sampling, measurement and characterization for load prediction. Due to the expectation of elasticity, large swings in their demand are common, which cannot be modeled accurately based on raw measures such as the number of session requests, which show very large variability and poor auto-correlation. We demonstrate the use of load prediction algorithms for cloud platforms, using a two-step approach of load trend tracking followed by load prediction, using cubic spline Interpolation, and hotspot detection algorithm for sudden spikes. Such algorithms integrated into the autonomic management framework of a cloud platform can be used to ensure that the SaaS sessions, virtual desktops or VM pools are autonomically provisioned on demand, in an elastic manner. Results indicate that the algorithms are able to match representative SaaS load trends accurately. This approach is suitable to support different load decision systems on cloud platforms with highly variable trends in demand, and is characterized by a moderate computational complexity compatible to run-time decisions.
The Effects of Experience on Preference Uncertainty: Theory and Empirics for Public and Quasi-Public Environmental Goods

This paper develop and estimates a model of demand estimation for environmental public goods which allows for consumers to learn about their preferences through consumption experiences. We develop a theoretical model of Bayesian updating, perform comparative statics over the model, and show how the theoretical model can be consistently incorporated into a reduced form econometric model. We then estimate the model using data collected for two environmental goods. We find that the predictions of the theoretical exercise that additional experience makes consumers more certain over their preferences in both mean and variance are supported in each cas e.
The Performance of Short-Term Heart Rate Variability in the Detection of Congestive Heart Failure

Congestive heart failure (CHF) is a cardiac disease associated with the decreasing capacity of the cardiac output. It has been shown that the CHF is the main cause of the cardiac death around the world. Some works proposed to discriminate CHF subjects from healthy subjects using either electrocardiogram (ECG) or heart rate variability (HRV) from long-term recordings. In this work, we propose an alternative framework to discriminate CHF from healthy subjects by using HRV short-term intervals based on 256 RR continuous samples. Our framework uses a matching pursuit algorithm based on Gabor functions. From the selected Gabor functions, we derived a set of features that are inputted into a hybrid framework which uses a genetic algorithm and k-nearest neighbour classifier to select a subset of features that has the best classification performance. The performance of the framework is analyzed using both Fantasia and CHF database from Physionet archives which are, respectively, composed of 40 healthy volunteers and 29 subjects. From a set of nonstandard 16 features, the proposed framework reaches an overall accuracy of 100% with five features. Our results suggest that the application of hybrid frameworks whose classifier algorithms are based on genetic algorithms has outperformed well-known classifier methods.