Miracle-dz/UniFAR-FTUs huggingface dataset:
Burden of illness associated with painful diabetic peripheral neuropathy among adults seeking treatment in the US: results from a retrospective chart review and cross-sectional survey

Background The purpose of this study was to characterize the burden of illness among adult subjects with painful diabetic peripheral neuropathy (pDPN) seeking treatment in the US. Methods This observational study recruited 112 subjects with pDPN during routine visits from general practitioner and specialist sites. Subjects completed a one-time questionnaire, which included demographics, symptom duration, health care resource use, out-of-pocket costs, employment status, and validated measures that assessed pain, functioning, sleep, anxiety and depression, health status, and productivity. Investigators completed a case report form based on a 6-month retrospective chart review to capture clinical information, pDPN-related treatments, and other pDPN-related health care resource use over the past 6 months. Annualized costs were extrapolated based on reported 6-month health care resource use. Results The mean age of the subjects was 61.1 years, 52.7% were female, and 17.9% were in paid employment. The most common comorbid conditions were sleep disturbance/insomnia (43.8%), depressive symptoms (41.1%), and anxiety (35.7%). The mean pain severity score was 5.2 (0–10 scale), and 79.5% reported moderate or severe pain. The mean pain interference with function score was 5.0 (0–10 scale) overall, with 2.0 among mild, 5.1 among moderate, and 7.0 among severe. The mean Medical Outcomes Study sleep problems index score was 48.5 (0–100 scale). The mean health state utility score was 0.61. Among subjects employed for pay, mean overall work impairment was 43.6%. Across all subjects, mean overall activity impairment was 52.3%. In total, 81.3% were prescribed at least one medication for their pDPN; 50.9% reported taking at least one nonprescription medication. Adjusted mean annualized total direct and indirect costs per subject were $4841 and $9730, respectively. Outcomes related to pain interference with function, sleep, health status, activity impairment, prescription medication use, and direct and indirect costs were significantly worse among subjects with more severe pain (P < 0.0020). Conclusion Subjects with pDPN exhibited high pain levels, which were associated with poor sleep, function, and productivity. Health care resource utilization in pDPN was prevalent and costs increased with greater pain severity. The burden of pDPN was greater among subjects with greater pain severity.
Using Maximum Entropy Modeling for Optimal Selection of Sampling Sites for Monitoring Networks

Environmental monitoring programs must efficiently describe state shifts. We propose using maximum entropy modeling to select dissimilar sampling sites to capture environmental variability at low cost, and demonstrate a specific application: sample site selection for the Central Plains domain (453,490 km2) of the National Ecological Observatory Network (NEON). We relied on four environmental factors: mean annual temperature and precipitation, elevation, and vegetation type. A “sample site” was defined as a 20 km × 20 km area (equal to NEON’s airborne observation platform [AOP] footprint), within which each 1 km2 cell was evaluated for each environmental factor. After each model run, the most environmentally dissimilar site was selected from all potential sample sites. The iterative selection of eight sites captured approximately 80% of the environmental envelope of the domain, an improvement over stratified random sampling and simple random designs for sample site selection. This approach can be widely used for cost-efficient selection of survey and monitoring sites.
Deftpack: A Robust Piece-Picking Algorithm for Scalable Video Coding in P2P Systems

The volume of Internet video is growing, and is expected to exceed 57 percent of global consumer Internet traffic by 2014. Peer-to-Peer technology can help delivering this massive volume of traffic in a cost-efficient, scalable, and reliable manner. However, single bit rate streaming is not sufficient given today's device and network connection diversity. A possible solution to this problem is provided by layered coding techniques, such as Scalable Video Coding, which allow addressing this diversity by providing content in various qualities within a single bit stream. In this paper we propose a new self-adapting piece-picking algorithm for downloading layered video streams, called Deft pack. Our algorithm significantly reduces the number of stalls, minimises the frequency of quality changes during playback, and maximizes the effective usage of the available bandwidth. Deft pack is the first algorithm that is specifically crafted to take all these three quality dimensions into account simultaneously, thus increasing the overall quality of experience. Additionally, Deft pack can be integrated into Bit torrent-based P2P systems and so has the chance of large-scale deployment. Our results from realistic swarm simulations show that Deft pack significantly outperforms previously proposed algorithms for retrieving layered content when all three quality dimensions are taken into account.
Human-Data Interaction

We have moved from a world where computing is siloed and specialised, to a world where computing is ubiquitous and everyday. In many, if not most, parts of the world, networked computing is now mundane as both foreground (e.g., smartphones, tablets) and background (e.g., road tra c management, financial systems) technologies. This has permitted, and continues to permit, new gloss on existing interactions (e.g., online banking) as well as distinctively new interactions (e.g., massively scalable distributed real-time mobile gaming). An e ect of this increasing pervasiveness of networked computation in our environments and our lives is that data are also now ubiquitous: in many places, much of society is rapidly becoming “data driven”.
Web-Based Handwriting Education with Animated Virtual Teacher

Traditionally, in teaching handwriting, the teacher writes a character on the blackboard, and then students try to follow the teacher in order to write the same character. The teacher can check whether a student’s handwriting is spatially correct by looking at its shape but he/she is not able to verify if the student is writing in the correct stroke order with the correct number of strokes. As a result, we design a web-based handwriting education system. With our proposed system, students can stay at home to do the exercises for learning the handwriting of characters. A student’s handwriting can be captured in digital format with the information in both temporal and spatial domain and the results can be sent to the teacher for evaluation. In addition, we propose to animate a virtual teacher on the student’s terminal for demonstrating the handwriting of the characters to give students a sense of presence.
Sim-to-real Transfer of Visuo-motor Policies for Reaching in Clutter: Domain Randomization and Adaptation with Modular Networks

A modular method is proposed to learn and transfer visuo-motor policies from simulation to the real world in an efficient manner by combining domain randomization and adaptation. The feasibility of the approach is demonstrated in a table-top object reaching task where a 7 DoF arm is controlled in velocity mode to reach a blue cuboid in clutter through visual observations. The learned visuo-motor policies are robust to novel (not seen in training) objects in clutter and even a moving target, achieving a 93.3% success rate and 2.2 cm control accuracy.
Natural Killer Cell Regulation by MicroRNAs in Health and Disease

Natural killer (NK) cells are innate immune lymphocytes that are critical for normal host defense against infections and mediate antitumor immune responses. MicroRNAs (miRNAs) are a family of small, noncoding RNAs that posttranscriptionally regulate the majority of cellular processes and pathways. Our understanding of how miRNAs regulate NK cells biology is limited, but recent studies have provided novel insight into their expression by NK cells, and how they contribute to the regulation of NK cell development, maturation, survival, and effector function. Here, we review the expression of miRNAs by NK cells, their contribution to cell intrinsic and extrinsic control of NK cell development and effector response, and their dysregulation in NK cell malignancies.
Error-Rate Performance Analysis of Wireless Sensor Networks over Fading Channels

In this paper, we analyze the bit-error-rate (BER) performance of wireless sensor networks. A wireless sensor node with a single transmitter antenna and multiple receiver antennas is considered here. We consider M (M greater or equal ro 1) receiver antennas to mitigate channel fading effects. BER performance is analyzed in the presence of a co-channel interference source. Wireless channel is assumed to follow independently Nakagami fading for the wireless sensor network and Rayleigh fading for the interference signal. Based on the derived analytical expressions, the effects of different fading channel and co-channel interference parameters on the BER performance of wireless sensor networks are assessed.
Maximizing Acceptance in Rejection-Aware Spatial Crowdsourcing

With the rapid development of mobile networks and the widespread usage of mobile devices, spatial crowdsourcing, which refers to assigning location-based tasks to moving workers, has drawn increasing attention. One of the important issues in spatial crowdsourcing is task assignment, which allocates tasks to appropriate workers. However, existing works generally assume that no rejection would happen after the task assignment is completed by the server. Ignorance of such an operation can lead to low system throughput. Thus, in this paper, we take workers’ rejection into consideration and try to maximize workers’ acceptance in order to improve the system throughput. Specifically, we first formally define the problem of maximizing workers’ acceptance in rejection-aware spatial crowdsourcing. Unfortunately, the problem is NP-hard. We propose two exact solutions to obtain the optimal assignment, but they are not efficient enough and not scalable for large inputs. Then, we present four approximation approaches for improving the efficiency. Finally, we show the effectiveness of the proposed pruning strategy for the exact solutions and the superiority of the proposed Greedy algorithm over other approximation methods through extensive experiments.
Adversarial Task Transfer from Preference

Task transfer is extremely important for reinforcement learning, since it provides possibility for generalizing to new tasks. One main goal of task transfer in reinforcement learning is to transfer the action policy of an agent from the original basic task to specific target task. Existing work to address this challenging problem usually requires accurate hand-coded cost functions or rich demonstrations on the target task. This strong requirement is difficult, if not impossible, to be satisfied in many practical scenarios. In this work, we develop a novel task transfer framework which effectively performs the policy transfer using preference only. The hidden cost model for preference and adversarial training are elegantly combined to perform the task transfer. We give the theoretical analysis on the convergence about the proposed algorithm, and perform extensive simulations on some well-known examples to validate the theoretical results.
AdHoc routing performance study using OPNET Modeler

A wireless Ad Hoc network is formed by a collection of nodes disposed in a dynamic way, without the need for a central node to coordinate them. This is one of the reasons why a great number of routing protocols for Ad Hoc networks are being developed. After the study of a set of them, this paper has subjected four different protocols to different scenario situations, in order to evaluate their performance and, from the detailed analysis of simulation results using OPNET Modeler, create a choice guide of a routing protocol for a given network scenario.
Geometry compression

This paper introduces the concept of Geometry Compression, allowing 3D triangle data to be represented with a factor of 6 to 10 times fewer bits than conventional techniques, with only slight losses in object quality. The technique is amenable to rapid decompression in both software and hardware implementations; if 3D rendering hardware contains a geometry decompression unit, application geometry can be stored in memory in compressed format. Geometry is ﬁrst represented as a generalized triangle mesh, a data structure that allows each instance of a vertex in a linear stream to specify an average of two triangles. Then a variable length compression is applied to individual positions, colors, and normals. Delta compression followed by a modiﬁed Huffman compression is used for positions and colors; a novel table-based approach is used for normals. The table allows any useful normal to be represented by an 18-bit index, many normals can be represented with index deltas of 8 bits or less. Geometry compression is a general space-time trade-off, and offers advantages at every level of the memory/interconnect hierarchy: less storage space is needed on disk, less transmission time is needed on networks.
Influence of weather conditions on fly abundance and its implications for transmission of rabbit haemorrhagic disease virus in the North Island of New Zealand

Abstract. Blowflies (Diptera: Calliphoridae) and flesh flies (Diptera: Sarcophagidae) are potential vectors of rabbit haemorrhagic disease virus (RHDV) in New Zealand. The associations between habitat and weather factors on the abundance of these flies were investigated. Between October 1999 and June 2001, flies were trapped on open pasture and in dense vegetation patches on farmland in the Himatangi area of the North Island. Five calliphorid species were trapped commonly at scrub edges and the most abundant sarcophagid, Oxysarcodexia varia Walker, was trapped mainly on open pasture. An abundance peak of O. varia was probably associated with the occurrence of a rabbit haemorrhagic disease (RHD) outbreak in the study area. Overall abundance of flies varied according to habitat and species, and species numbers differed between seasons and years. The all‐day minimum temperature 3 weeks before trapping was a significant variable in all models of fly abundance, whereas average rainfall did not affect fly abundance. The all‐day temperature range was significant only for O. varia. The influence of other climatic factors varied between fly species. Climate dependent variations in fly abundance may contribute to the risk of transmission of RHD, which occurred intermittently on the site during the study period.
Progressive Large Scale-Invariant Image Matching in Scale Space

The power of modern image matching approaches is still fundamentally limited by the abrupt scale changes in images. In this paper, we propose a scale-invariant image matching approach to tackling the very large scale variation of views. Drawing inspiration from the scale space theory, we start with encoding the image’s scale space into a compact multi-scale representation. Then, rather than trying to find the exact feature matches all in one step, we propose a progressive two-stage approach. First, we determine the related scale levels in scale space, enclosing the inlier feature correspondences, based on an optimal and exhaustive matching in a limited scale space. Second, we produce both the image similarity measurement and feature correspondences simultaneously after restricting matching between the related scale levels in a robust way. The matching performance has been intensively evaluated on vision tasks including image retrieval, feature matching and Structurefrom- Motion (SfM). The successful integration of the challenging fusion of high aerial and low ground-level views with significant scale differences manifests the superiority of the proposed approach.
Adenosine and hyaluronan promote lung fibrosis and pulmonary hypertension in combined pulmonary fibrosis and emphysema

ABSTRACT Combined pulmonary fibrosis and emphysema (CPFE) is a syndrome that predominantly affects male smokers or ex-smokers and it has a mortality rate of 55% and a median survival of 5 years. Pulmonary hypertension (PH) is a frequently fatal complication of CPFE. Despite this dismal prognosis, no curative therapies exist for patients with CPFE outside of lung transplantation and no therapies are recommended to treat PH. This highlights the need to develop novel treatment approaches for CPFE. Studies from our group have demonstrated that both adenosine and its receptor ADORA2B are elevated in chronic lung diseases. Activation of ADORA2B leads to elevated levels of hyaluronan synthases (HAS) and increased hyaluronan, a glycosaminoglycan that contributes to chronic lung injury. We hypothesize that ADORA2B and hyaluronan contribute to CPFE. Using isolated CPFE lung tissue, we characterized expression levels of ADORA2B and HAS. Next, using a unique mouse model of experimental lung injury that replicates features of CPFE, namely airspace enlargement, PH and fibrotic deposition, we investigated whether 4MU, a HAS inhibitor, was able to inhibit features of CPFE. Increased protein levels of ADORA2B and HAS3 were detected in CPFE and in our experimental model of CPFE. Treatment with 4MU was able to attenuate PH and fibrosis but not airspace enlargement. This was accompanied by a reduction of HAS3-positive macrophages. We have generated pre-clinical data demonstrating the capacity of 4MU, an FDA-approved drug, to attenuate features of CPFE in an experimental model of chronic lung injury. This article has an associated First Person interview with the first author of the paper. Summary: Fibrotic deposition and PH are inhibited by the FDA-approved drug hymecromone, suggesting hyaluronan synthesis inhibition as a potential therapy for CPFE and highlighting a novel mechanism through HAS3-positive macrophages.
In defense of definitions

The arguments of Fodor, Garret, Walker and Parkes [(1980) Against definitions, Cognition, 8, 263-367] are the source of widespread skepticism in cognitive science about lexical semantic structure. Whereas the thesis that lexical items, and the concepts they express, have decompositional structure (i.e. have significant constituents) was at one time "one of those ideas that hardly anybody [in the cognitive sciences] ever considers giving up" (p. 264), most researchers now believe that "[a]ll the evidence suggests that the classical [(decompositional)] view is wrong as a general theory of concepts" [Smith, Medin & Rips (1984) A psychological approach to concepts: comments on Rey, Cognition, 17, 272], and cite Fodor et al. (1980) as "sounding the death knell for decompositional theories" [MacNamara & Miller (1989) Attributes of theories of meaning, Psychological Bulletin, 106, 360]. I argue that the prevailing skepticism is unmotivated by the arguments in Fodor et al. Fodor et al. misrepresent the form, func.
Simple Pleasures, Small Annoyances, and Goal Progress in Daily Life

Despite the explosion of research on goal pursuit, relatively little is known about the shaping of goal progress by the simple experiences that characterize everyday life. Two literatures furnish competing predictions about the relationship between pleasant daily experiences (simple pleasures), unpleasant daily experiences (small annoyances), and day-to-day goal progress. A 6-day experience-sampling study revealed support for the favored integrative account. On a given day, a relatively high number of simple pleasures offset the negative relationship between the number of small annoyances and goal progress through a restoration of daily happiness rather than a reduction of daily stress. This study highlights the bright side of pleasurable experiences, indicating that goal progress can flourish in a life punctuated with frequent simple pleasures because they help offset daily irritations. As natural precursors to positive and negative affect, simple pleasures and small annoyances could be powerful predictors of important consumer outcomes.
Interactions of Lipid Genetic Risk Scores With Estimates of Metabolic Health in a Danish Population

Background—There are several well-established lifestyle factors influencing dyslipidemia and currently; 157 genetic susceptibility loci have been reported to be associated with serum lipid levels at genome-wide statistical significance. However, the interplay between lifestyle risk factors and these susceptibility loci has not been fully elucidated. We tested whether genetic risk scores (GRS) of lipid-associated single nucleotide polymorphisms associate with fasting serum lipid traits and whether the effects are modulated by lifestyle factors or estimates of metabolic health. Methods and Results—The single nucleotide polymorphisms were genotyped in 2 Danish cohorts: inter99 (n=5961) for discovery analyses and Health2006 (n=2565) for replication. On the basis of published effect sizes of single nucleotide polymorphisms associated with circulating fasting levels of total cholesterol, low-density lipoprotein-cholesterol, high-density lipoprotein-cholesterol, or triglyceride, 4 weighted GRS were constructed. In a cross-sectional design, we investigated whether the effect of these weighted GRSs on lipid levels were modulated by diet, alcohol consumption, physical activity, and smoking or the individual metabolic health status as estimated from body mass index, waist circumference, and insulin resistance assessed using homeostasis model assessment of insulin resistance. All 4 lipid weighted GRSs associated strongly with their respective trait (from P=3.3×10–69 to P=1.1×10–123). We found interactions between the triglyceride weighted GRS and body mass index and waist circumference on fasting triglyceride levels in Inter99 and replicated these findings in Health2006 (Pinteraction=9.8×10–5 and 2.0×10–5, respectively, in combined analysis). Conclusions—Our findings suggest that individuals who are obese may be more susceptible to the cumulative genetic burden of triglyceride single nucleotide polymorphisms. Therefore, it is suggested that especially these genetically at-risk individuals may benefit more from targeted interventions aiming at obesity prevention.
Algebraic decoder specification: coupling formal-language theory and statistical machine translation

The specification of a decoder, i.e., a program that translates sentences from one natural language into another, is an intricate process, driven by the application and lacking a canonical methodology. The practical nature of decoder development inhibits the transfer of knowledge between theory and application, which is unfortunate because many contemporary decoders are in fact related to formal-language theory. This thesis proposes an algebraic framework where a decoder is specified by an expression built from a fixed set of operations. As yet, this framework accommodates contemporary syntax-based decoders, it spans two levels of abstraction, and, primarily, it encourages mutual stimulation between the theory of weighted tree automata and the application.