Miracle-dz/UniFAR-FTUs huggingface dataset:
The relationship between internalized homonegativity and body image concerns in sexual minority men: a meta-analysis

ABSTRACT It remains unclear whether internalised homonegativity, a recognised predictor of internalising mental health problems, is related to body dissatisfaction in sexual minority men. We conducted a meta-analysis to investigate the association between the two variables. A literature search and the application of the inclusion criteria enabled us to locate 14 studies. Two of these were excluded from the meta-analysis because needed data were missing; the remaining 12 studies were included. The results showed positive and statistically significant associations between internalised homonegativity and different elements of body image concerns, indicating that higher levels of internalised homonegativity were related to higher levels of body image dissatisfaction in sexual minority men. Specifically, the pooled effect sizes were internalised homonegativity and muscularity-oriented body dissatisfaction, r+ = . 19; internalised homonegativity and thinness-oriented body dissatisfaction, r+ = . 16; internalised homonegativity and body surveillance, r+ = . 17; and internalised homonegativity and body-related distress, r+ = . 28. Limitations and future research directions are discussed as well as implications for clinical practice.
Dynamical Bar Instability in Rotating Stars: Effect of General Relativity

We study the dynamical stability against bar-mode deformation of rapidly and differentially rotating stars in the first post-Newtonian approximation of general relativity. We vary the compaction of the star M/R (where M is the gravitational mass and R the equatorial circumferential radius) between 0.01 and 0.05 to isolate the influence of relativistic gravitation on the instability. For compactions in this moderate range, the critical value of β ≡ T/W for the onset of the dynamical instability (where T is the rotational kinetic energy and W the gravitational binding energy) slightly decreases from ~0.26 to ~0.25 with increasing compaction for our choice of the differential rotational law. Combined with our earlier findings based on simulations in full general relativity for stars with higher compaction, we conclude that relativistic gravitation enhances the dynamical bar-mode instability, i.e., the onset of instability occurs for smaller values of β in relativistic gravity than in Newtonian gravity. We also find that once a triaxial structure forms after the bar-mode perturbation saturates in dynamically unstable stars, the triaxial shape is maintained, at least for several rotational periods. To check the reliability of our numerical integrations, we verify that the general relativistic Kelvin-Helmholtz circulation is well conserved, in addition to rest-mass energy, total mass energy, and linear and angular momentum. Conservation of circulation indicates that our code is not seriously affected by numerical viscosity. We determine the amplitude and frequency of the quasi-periodic gravitational waves emitted during the bar formation process using the quadrupole formula.
The Invariant Approach to Source Separation I Introduction: Source Separation

The notion of equivariance is relevant to source separation because multiplication of mixed signals is equivalent to changing the unknown parameter (the mixing matrix) into another mixing matrix. Elaborating on this observation, a wide class of batch estimators of the mixing matrix is rst shown to ooer uniform performance: quality of separation does not depend on the hardness of the mixture. Equivariance is next extended to adaptive algorithms, by the device of`serial updating'. Adaptive separators based on such a learning rule also exhibit uniform performance in a strong sense. Source separation, blind array processing, signal copy, independent component analysis, waveform preserving estimation: : :: these keywords refer to a signal model which is receiving increasing attention in both signal processing and neural network literature since the sem-inal paper 1]. This model is that of n statistically independent signals whose m (possibly noisy) linear combinations are observed; the problem consists in recovering the original signals from their mixture. Thèblind' qualiication refers to the coeecients of the mixture: no a priori information is assumed to be available about them. This feature makes the blind approach extremely versatile because it does not rely on modeling the underlying physical phenomena. In this paper, we will consider the simplest case where any additive noise can be neglected and the number of mixtures is equal to the number of sources. The signal model then is that of a n-dimensional time series x t in the form : x t = As t t = 1; 2; where x t and s t are n 1 vectors. and A is a n n matrix. The components of s t are often termed`source signals' and matrix A is thèmixing matrix'. Adaptive source separation consists in updating an n n matrix B t such that its output y t : y t = B t x t is as close as possible to the source signals, i.e.the global system C t : C t def = B t A should be driven to the identity matrix. s t-Mixing matrix Ax t Separating matrix B t-y t B B B B B M Source signals Sensor output Estimated signals Batch source separation consists in computing an estimate b A of A from of a batch X T of T samples: X T def = x 1 ; : : :; x T ]: The estimate b A returned by any …
Conjoined Processor : A Fault Tolerant High Performance Microprocessor

Reliability has become a serious concern as systems embrace nanometer technologies. Current reliability enhancement techniques cause slowdown in processor operation. In this work, we propose a novel approach that organizes redundancy in a special way to provide high degree of fault tolerance. Our approach improves performance by reliably adapting the system clock frequency during run time, based on the current running application and environmental conditions. The organization of redundancy in the proposed conjoined processor supports overclocking, provides concurrent error detection and recovery capability for soft errors, timing errors, intermittent faults and detects silicon defects. The fast recovery process requires no checkpointing and takes three cycles. Post-layout timing annotated gate level simulations of a conjoined two stage arithmetic pipeline shows that our approach achieves near 100% fault coverage, and a performance improvement of 21%. A five stage in-order conjoined pipeline processor was designed and implemented to verify correctness of the proposed architecture.
Smart objects: Challenges for Semantic Web research

The increased availability and robustness of sensors, the wide-spread use of the internet as a communication environment and the intensified adoption of semantic technologies foster the vision of embedding intelligence in physical objects. The race of realizing this vision is pervasive to a variety of research fields, most notably ambient intelligence and semantic web, and leads to the proliferation of several overlapping definitions and terminologies: smart products, semantic devices, semantic gadgets - to which we collectively refer to as smart objects. What exactly are smart objects? And what are the research challenges in realizing them? We hereby explore the answers to these questions. Part of the work reported in this paper has been performed by the author in the context of the FP7 SmartProducts project (231204) while working at the Knowledge Media Institute(KMi), The Open University, UK.
Tackling engagement in computing with computational music remixing

In this paper, we describe EarSketch, an integrated curriculum, software toolset, and social media website, grounded in constructionist principles, that targets introductory high school computing education. We hypothesize that the use of collaborative computational music composition and remixing may avoid some of the engagement and culture-specific issues that other approaches, both in music and other media, have had. We discuss the design of EarSketch, its use in a pilot summer camp, and the evaluation results from that pilot.
Deploying Static Application Security Testing on a Large Scale

Static Code Analysis (SCA), if used for finding vulnerabilities also called Static Application Security Testing (SAST), is an important technique for detecting software vulnerabilities already at an early stage in the software development life- cycle. As such, SCA is adopted by an increasing number of software vendors. The wide-spread introduction of SCA at a large software vendor, such as SAP, cre- ates both technical as well as non-technical challenges. Technical challenges include high false positive and false negative rates. Examples of non-technical challenges are the insufficient security awareness among the developers and managers or the integra- tion of SCA into a software development life-cycle that facilitates agile development. Moreover, software is not developed following a greenfield approach: SAP's security standards need to be passed to suppliers and partners in the same manner as SAP's customers begin to pass their security standards to SAP. In this paper, we briefly present how the SAP's Central Code Analysis Team intro- duced SCA at SAP and discuss open problems in using SCA both inside SAP as well as across the complete software production line, i. e. , including suppliers and partners.
A new method for selecting gradient weight in incremental eigen-background modeling

Background modeling is an important approach used in motion detection. The background model should adapt to dynamic change of the environment and update quickly. In this paper, some improvements on incremental eigen-background modeling are made. A new method of selecting weight according to reconstruction error is proposed. Compared with existing means, this method can not only model background effectively but also more efficiently.
Optimization of Green Synthesis of Silver Nanoparticles from Leaf Extracts of Pimenta dioica (Allspice)

Production of silver nanoparticles from the leaf extracts of Pimenta dioica is reported for the first time in this paper. Three different sets of leaves were utilized for the synthesis of nanoparticles—fresh, hot-air oven dried, and sun-dried. These nanoparticles were characterized using UV-Vis spectroscopy and AFM. The results were diverse in that different sizes were seen for different leaf conditions. Nanoparticles synthesized using sun-dried leaves (produced using a particular ratio (1 : 0.5) of the leaf extract sample and silver nitrate (1 mM), resp. ) possessed the smallest sizes. We believe that further optimization of the current green-synthesis method would help in the production of monodispersed silver nanoparticles having great potential in treating several diseases.
RolX : Extracting Predictive Roles from Graphs

A research challenge faced in graph analysis is: how can we automatically discover the roles that nodes play in a graph? Roles can express structural graph properties (e.g., active node, isolated node, bridge between communities) as well as relationships to other roles (e.g., managers may communicate with subordinates and vice presidents while subordinates communicate with other subordinates and their managers). Role discovery can inform graph analysis and support prediction tasks. For example, given two large graphs from the same domain (e.g., IP communications from two enterprise networks), we can transfer information discovered about roles in one graph to the second graph, effectively performing across-network classification or network transfer learning. We propose the RolX (Role eXtraction) framework for addressing the aforementioned problem related to role discovery in graphs. RolX has two components: (1) feature extraction and (2) role discovery. Feature extraction in RolX recursively combines local (node-based) features, like degree, with neighborhood (egonet-based) features, like count of bridge-like neighbors, to capture “behavioral” information. The role discovery in RolX clusters these features using a carefully designed pipeline that includes Non-Negative Matrix factorization (NMF) and Minimum Description Length (MDL). We demonstrate how these powerful predictive roles can be used in across-network classification tasks without relying on homophily or the availability of class labels. The contributions of our work are as follows: (a) RolX is scalable, requiring time linear on the number of edges and (b) it is effective, capturing roles that agree with intuition and are predictive in classification tasks. We report experiments on real graphs from various domains with over 1M edges, where RolX outperforms its competitors on typical graph mining tasks like network classification.
‘Whole Earth?’ Using an Exhibition to Raise Sustainability Awareness at a UK University

Despite mounting evidence of global environmental stress, many educationalists appear to be discounting warnings of ecological collapse from scientists, futurists and community leaders. One way of promoting sustainability awareness may be to combine cognitive reasoning with emotional awareness. This article considers the complex dynamics relating to attitudinal and behavioural institutional change by exploring the impact of a large-scale exhibition called ‘Whole Earth? ’ on the staff and students at a UK university over a 15-month period. The exhibition contained a wide range of powerful visual images and drew on a famous protest song to frame its wider message. Although there were a variety of responses, the exhibition had the overall effect of raising the profile of sustainability across the university. Could initiatives of this kind, which are open-ended in character and which harness the arts to engender an emotional response, offer a model which could be used more widely?
Dynamic Filter Merging for Publish/Subscribe

Filter-based publish/subscribe and content-based routing have been proposed for flexible information dissemination in distributed environments. A content-based router is part of an overlay structure, in which each router forwards events to neighbouring routers and local clients based on their interests. Filter merging or summarization has been proposed as an optimization strategy in this environment. These techniques combine filters to reduce the number of propagated filters and thus the size of distributed state. In this paper, we present the algorithms for generic dynamic filter merging and discuss integration with routing tables. The algorithms are based on a formal framework of merging rules. Experimental results are examined and analyzed for both desktop systems and small devices. The results indicate that dynamic filter merging is feasible given that the workload is mergeable.
Marital Enqing and Depression in Taiwanese Couples: The Mediating Role of Active and Passive Sacrifice

ABSTRACT Using social exchange and identity theory, the authors examined the association between marital enqing and depression in 372 Taiwanese couples, and the mediating effects of active and passive sacrifice on that association. Marital enqing is a unique form of affection toward the partner in Taiwanese couples. Data was analyzed by using the actor-partner interdependence model to test for actor and partner effects among study variables. Results indicated that husbands, who reported more marital enqing, tended to have lower levels of depression. Partner effects revealed that couples’ marital enqing decreased their spouse’s depressive symptoms. Husbands’ sacrifice behaviors mediated the association between his marital enqing and depression. However, Taiwanese wives’ sacrifice behavior did not mediate the association between her marital enqing and depression. Chinese cultural principles indeed impacted how couples in Taiwan conceptualized marital affection differently from couples in western cultures, and it provided insights into its relationships with couples’ sacrifices and relational and individual wellbeing.
Assessing Students' Self-Regulatory Skills.

Students’ ability to use self-regulatory learning skills is becoming increasingly important with the advent of web-based learning. Online courses delivered through the Web require students to take more ownership over how and when learning takes place, rather than tutors and lecturers making these decisions. This comes at a time when higher education institutions have increasing pressure to develop students’ life long learning and generic skills from both employers and funding authorities. In this paper we will investigate a conceptual framework for identifying students’ self-regulatory skills and consider a testing instrument to identify students strengths and weaknesses. The instrument can be administered online, from which a full analysis of the results is immediately returned to both student and tutor. From the 77 items used, a subset of these are selected and mapped to the six dimensions defined in the conceptual framework. The conceptual framework and associated questions related to each of the defined dimensions, provides a quick and convenient tool for assessing students’ self-regulatory skills. This mapping can help educators provide assessment and timely feedback to students at a time when there is a pronounced emphasis in higher education to provide skills and competencies that can be transferred to the workplace.
CS 497yyz Project Report: LLVA-emu

The LLVM compiler infrastructure [11] employs an intermediate representation which is similar to a typed assembly language. LLVM’s representation is fully typed, RISC-like, has data-flow information in Static Single-Assignment form built-in, and is powerful enough to express any C or C++ program, type-safe or otherwise. It is superior to typical bytecode and machine-code formats because it retains sophisticated control-flow and type information about the values used in each and every instruction of every function, thus making useful compiler analyses and optimizations cheaper to perform. The LLVM system includes static and dynamic (just-in-time) compilers, as well as a small and growing suite of program analysis tools and optimizations. Traditional architectures use the hardware instruction set for dual purposes: first, as a language in which to express the semantics of software programs, and second, as a means for controlling the hardware. The thesis of the LowLevel Virtual Architecture (LLVA) [1] project is to decouple these two uses from one another, allowing software to be expressed in a semantically richer, more easily-manipulated format, and allowing for more powerful optimizations and whole-program analyses directly on compiled code. The semantically rich format we use in LLVA, which is based on the LLVM compiler infrastructure’s intermediate representation, can best be understood as a “virtual instruction set”. This means that while its instructions are closely matched to those available in the underlying hardware, they may not correspond exactly to the instructions understood by the underlying hardware. These underlying instructions we call the “implementation instruction set. ” Between the two layers lives the translation layer, typically implemented in software. The structure of this two-layer architecture can be seen in Figure 1. In this project, we have taken our next logical steps in this effort by (1) porting the entire Linux kernel to LLVA, and (2) engineering an environment in which a kernel can be run directly from its LLVM bytecode representation — essentially, a minimal, but complete, emulated computer system with LLVA as its native instruction set. The emulator we have invented, llva-emu, executes
Impact of extended alliance portfolio configuration on firm innovation

Abstract In the past, alliance portfolio configuration (APC) studies concentrated mostly on the direct alliances or partners of a focal firm. However, a focal firm is also influenced by indirect alliances or partners. This study endeavors to focus on this aspect of APC. It contributes to APC research by extending the scope to three degrees from a focal firm. To assess the effects of extended APCs, 186 3-year window snapshots were created of the extended APCs of 31 Korean bio-pharmaceutical firms. These snapshots range from 2007 to 2014. The effects of structure (density), size (number of alliances and partners), and relationships to firm innovation were measured using the two-step generalized method of moments estimates. The results show that structural sparseness and larger-sized extended APCs are more favorable conditions for innovation, and that structural sparseness and size have a positive relationship to innovation performance.
A new code construction for polar codes using min-sum density

Polar codes are capacity achieving codes for binary-input symmetric memoryless channels (B-SMCs) if the code word length tends to infinity. Its encoding and decoding complexity is low. However, low complexity in code construction holds only for some special B-SMCs like the binary erasure channel (BEC). For all other B-SMCs, the code construction becomes a trade off between low complexity and construction accuracy. This paper gives an overview on the most popular code construction methods including a new approach yielding to near optimum code construction while reducing the complexity and increasing robustness against numerical problems. In addition, we compare the performance of the presented methods in terms of frame error rates (FERs) for codes of length 2048.
Expectation-Maximization Method for EEG-Based Continuous Cursor Control

To develop effective learning algorithms for continuous prediction of cursor movement using EEG signals is a challenging research issue in brain-computer interface (BCI). In this paper, we propose a novel statistical approach based on expectation-maximization (EM) method to learn the parameters of a classifier for EEG-based cursor control. To train a classifier for continuous prediction, trials in training data-set are first divided into segments. The difficulty is that the actual intention (label) at each time interval (segment) is unknown. To handle the uncertainty of the segment label, we treat the unknown labels as the hidden variables in the lower bound on the log posterior and maximize this lower bound via an EM-like algorithm. Experimental results have shown that the averaged accuracy of the proposed method is among the best.
Spontaneous Tumor Lysis Syndrome

Tumor lysis syndrome (TLS) is a known complication of malignancy and its treatment. The incidence varies on malignancy type, but is most common with hematologic neoplasms during cytotoxic treatment. Spontaneous TLS is thought to be rare. This case study is of a 62-year-old female admitted with multisystem organ failure, with subsequent diagnosis of aggressive B cell lymphoma. On admission, laboratory abnormalities included renal failure, elevated uric acid (20.7 mg/dL), and 3+ amorphous urates on urinalysis. Oliguric renal failure persisted despite aggressive hydration and diuretic use, requiring initiation of hemodialysis prior to chemotherapy. Antihyperuricemic therapy and hemodialysis were used to resolve hyperuricemia. However, due to multisystem organ dysfunction syndrome with extremely poor prognosis, the patient ultimately expired in the setting of a terminal ventilator wean. Although our patient did not meet current TLS criteria, she required hemodialysis due to uric acid nephropathy, a complication of TLS. This poses the clinical question of whether adequate diagnostic criteria exist for spontaneous TLS and if the lack of currently accepted guidelines has resulted in the underestimation of its incidence. Allopurinol and rasburicase are commonly used for prevention and treatment of TLS. Although both drugs decrease uric acid levels, allopurinol mechanistically prevents formation of the substrate rasburicase acts to solubilize. These drugs were administered together in our patient, although no established guidelines recommend combined use. This raises the clinical question of whether combined therapy is truly beneficial or, conversely, detrimental to patient outcomes.