Miracle-dz/UniFAR-FTUs huggingface dataset:
The Unified Accumulator Architecture

Applications accelerated by field-programmable gate arrays (FPGAs) often require pipelined floating-point accumulators with a variety of different trade-offs. Although previous work has introduced numerous floating-point accumulation architectures, few cores are available for public use, which forces designers to use fixed-point implementations or vendor-provided cores that are not portable and are often not optimized for the desired set of trade-offs. In this article, we combine and extend previous floating-point accumulator architectures into a configurable, open-source core, referred to as the unified accumulator architecture (UAA), which enables designers to choose between different trade-offs for different applications. UAA is portable across FPGAs and allows designers to specialize the underlying adder core to take advantage of device-specific optimizations. By providing an extensible, open-source implementation, we hope for the research community to extend the provided core with new architectures and optimizations.
Predicting Personality Traits with Instagram Pictures

Instagram is a popular social networking application, which allows photo-sharing and applying different photo filters to adjust the appearance of a picture. By applying photo filters, users are able to create a style that they want to express to their audience. In this study we tried to infer personality traits from the way users take pictures and apply filters to them. To investigate this relationship, we conducted an online survey where we asked participants to fill in a personality questionnaire, and grant us access to their Instagram account through the Instagram API. Among 113 participants and 22,398 extracted Instagram pictures, we found distinct picture features (e.g., hue, brightness, saturation) that are related to personality traits. Our findings suggest a relationship between personality traits and the way users want to make their pictures look. This allow for new ways to extract personality traits from social media trails, and new ways to facilitate personalized systems.
Results with non-operative treatment in dorsolumbar fractures with no neurological deficit: A functional assessment -

Background: The purpose of this prospective study was to assess the functional outcome of conservative treatment with early ambulation of dorso-lumbar spine fractures with no neurological deficit. Aims & Objective: The purpose of this prospective study was to identify the risk factors for dorso-lumbar spine injuries, and to assess the efficacy of non-operative treatment with early ambulation, and functional outcome of the patients. Material and Methods: From October 2008 to June 2010, 48 consecutive patients with single- level thoracolumbar spinal injury, with no neurological deficit were managed non-operatively. A custom-made thoracolumbosacral orthosis was worn by all patients for six months, and early ambulation was recommended. Patients were evaluated as per TLICS score, and if score was
Retailers' Multichannel and Price Advertising Strategies

Online retailing boasts two major advantages: convenience of home shopping and easy access to information. In this paper, I argue that these two features have important implications for retailers' channel and advertising decisions. Two major questions are addressed: When should a conventional bricks-and-mortar retailer adopt a multichannel strategy? When should a multichannel retailer use its website to advertise offline prices? Analysis shows that the answers hinge on the nature of the product, the retailer's costs, and the competitors' strategies as well as the competitiveness of the market. Multichannel retailing is not necessarily the best strategy for all retailers; no adoption, asymmetric adoption, and symmetric adoption of the strategy are all possible equilibria. Advertising the in-store prices online is not always optimal. Price advertising in multichannel retailing has a different effect when compared with conventional single-channel retailing. It helps coordinate the channels by shifting the sales from online to offline, which is particularly useful when margins online are relatively low. The finding that multichannel retailers can benefit from drawing consumers back to physical stores highlights the risk of boosting online sales without considering the adverse effect on the offline channel and indicates a shifting role of the Web in retailers' businesses.
ALPS: A Bluetooth and Ultrasound Platform for Mapping and Localization

The proliferation of Bluetooth Low-Energy (BLE) chipsets on mobile devices has lead to a wide variety of user-installable tags and beacons designed for location-aware applications. In this paper, we present the Acoustic Location Processing System (ALPS), a platform that augments BLE transmitters with ultrasound in a manner that improves ranging accuracy and can help users configure indoor localization systems with minimal effort. A user places three or more beacons in an environment and then walks through a calibration sequence with their mobile device where they touch key points in the environment like the floor and the corners of the room. This process automatically computes the room geometry as well as the precise beacon locations without needing auxiliary measurements. Once configured, the system can track a user's location referenced to a map. The platform consists of time-synchronized ultrasonic transmitters that utilize the bandwidth just above the human hearing limit, where mobile devices are still sensitive and can detect ranging signals. To aid in the mapping process, the beacons perform inter-beacon ranging during setup. Each beacon includes a BLE radio that can identify and trigger the ultrasonic signals. By using differences in propagation characteristics between ultrasound and radio, the system can classify if beacons are within Line-Of-Sight (LOS) to the mobile phone. In cases where beacons are blocked, we show how the phone's inertial measurement sensors can be used to supplement localization data. We experimentally evaluate that our system can estimate three-dimensional beacon location with a Euclidean distance error of 16.1cm, and can generate maps with room measurements with a two-dimensional Euclidean distance error of 19.8cm. When tested in six different environments, we saw that the system can identify Non-Line-Of-Sight (NLOS) signals with over 80% accuracy and track a user's location to within less than 100cm.
R-Apriori: An Efficient Apriori based Algorithm on Spark

Association rule mining remains a very popular and effective method to extract meaningful information from large datasets. It tries to find possible associations between items in large transaction based datasets. In order to create these associations, frequent patterns have to be generated. The "Apriori" algorithm along with its set of improved variants, which were one of the earliest proposed frequent pattern generation algorithms still remain a preferred choice due to their ease of implementation and natural tendency to be parallelized. While many efficient single-machine methods for Apriori exist, the massive amount of data available these days is far beyond the capacity of a single machine. Hence, there is a need to scale across multiple machines to meet the demands of this ever-growing data. MapReduce is a popular fault-tolerant framework for distributed applications. Nevertheless, heavy disk I/O at each MapReduce operation hinders the implementation of efficient iterative data mining algorithms, such as Apriori, on MapReduce platforms. A newly proposed in-memory distributed dataflow platform called Spark overcomes the disk I/O bottlenecks in MapReduce. Therefore, Spark presents an ideal platform for distributed Apriori. However, in the implementation of Apriori, the most computationally expensive task is the generation of candidate sets having all possible pairs for singleton frequent items and comparing each pair with every transaction record. Here, we propose a new approach which dramatically reduces this computational complexity by eliminating the candidate generation step and avoiding costly comparisons. We conduct in-depth experiments to gain insight into the effectiveness, efficiency and scalability of our approach. Our studies show that our approach outperforms the classical Apriori and state-of-the-art on Spark by many times for different datasets.
An investigation into fatigue post-stroke and its multidimensional nature

Abstract There is increasing anecdotal and scientific evidence of the presence of fatigue following stroke. This study examines this fatigue and evaluates the relationship between fatigue and other factors post-stroke, such as physical function, depression and reintegration to normal living. A cross-sectional case–control methodology was used. People with stroke were recruited from two day hospitals, Volunteer Stroke Scheme meetings and active retirement groups. Control subjects were recruited from active retirement groups in the same catchment area. One hundred people with stroke and 100 age- and gender-matched controls completed four standardized outcome measures: the Multidimensional Fatigue Inventory, Geriatric Depression Scale, Barthel Index and Reintegration to Normal Living Index. Demographic details were also recorded. Fatigue levels are significantly higher in people who have had a stroke. No relationship was demonstrated between fatigue and physical function or reintegration to normal living. Fatigue and depression co-existed in many respondents; however, fatigue was also seen to be independent of depression. Depression did not have an effect on fatigue in people with stroke. Fatigue needs to be considered in the rehabilitation process post-stroke. It is independent of depression and may persist up to 3 years post-stroke.
Sexual Violence Victimization of Women

Sexual violence is a serious public health problem with devastating health-related consequences. In this article, the authors review the prevalence, characteristics, vulnerability factors for, and impacts of sexual violence victimization for women. Some key factors are reviewed that have been shown to increase vulnerability for victimization, including certain demographic characteristics, previous victimization, and use of drugs or alcohol. The impacts of rape and other sexual violence are described, including physical and sexual health; psychological, social, and societal impacts; as well as impact on risky health behaviors. The authors conclude with a discussion of the public health approach to sexual violence, primary prevention, the relevance of sexual violence research for health care practitioners, and recommendations for health care practice.
Inhibition of Nuclear Factor-κB in T Cells Suppresses Lung Fibrosis

Rationale: Cytokines secreted by T cells play a pivotal role in the pathogenesis of lung injury and fibrosis, and the transcription factors nuclear factor (NF)-κB and activator protein (AP)-1 are involved in the expression of cytokines from T cells during lung injury.Objectives: We assessed the potential therapeutic effect of SP100030, a specific inhibitor of T-cell NF-κB and AP-1 in lung fibrosis.Methods: The effect of SP100030 was evaluated using a mouse model of chronic lung fibrosis.Measurements and Main Results: Mice treated with SP100030, as compared with untreated mice, had significantly less cachexia and less lung injury and had decreased levels of inflammatory cytokines and growth factors, decreased activation of coagulation activation, and decreased collagen deposition in the lung. The inhibitory activity of SP100030 was dose dependent and was effective in acute and chronic phases of lung fibrosis. SP100030 inhibited the activation of the protein kinase Cθ-isoform in T-cell lines and suppressed .
MH4 : multiple-supply-voltages aware high-level synthesis for high-integrated and high-frequency circuits for HDR architectures

In this paper, we propose multiple-supply-voltages aware high-level synthesis algorithm for HDR architectures which realizes high-speed and high-efficient circuits. We propose three new techniques: virtual area estimation, virtual area adaptation, and floorplanning-directed huddling, and integrate them into our HDR architecture synthesis algorithm. Virtual area estimation/adaptation effectively estimates a huddle area by gradually reducing it during iterations, which improves the convergence of our algorithm. Floorplanningdirected huddling determines huddle composition very effectively by performing floorplanning and functional unit assignment inside huddles simultaneously. Experimental results show that our algorithm achieves about 29% run-time-saving compared with the conventional algorithms, and obtains a solution which cannot be obtained by our original algorithm even if a very tight clock constraint is given.
Crumbs of the Cookie: User Profiling in Customer-Base Analysis and Behavioral Targeting

User profile is a summary of a consumer's interests and preferences revealed through the consumer's online activity. It is a fundamental component of numerous applications in digital marketing. McKinsey & Company view online user profiling as one of the promising opportunities companies should take advantage of to unlock "big data's" potential. This paper proposes a modeling approach that uncovers individual user profiles from online surfing data and allows online businesses to make profile predictions when limited information is available. The approach is easily parallelized and scales well for processing massive records of user online activity. We demonstrate application of our approach to customer-base analysis and display advertising. Our empirical analysis uncovers easy-to-interpret behavior profiles and describes the distribution of such profiles. Furthermore, it reveals that even for information-rich online firms profile inference that is based solely on their internal data may produce biased results. We find that although search engines cover smaller portions of consumer Web visits than major advertising networks, their data is of higher quality. Thus, even with the smaller information set, search engines can effectively recover consumer behavioral profiles. We also show that temporal limitations imposed on individual-level tracking abilities are likely to have a differential impact across major online businesses, and that our approach is particularly effective for temporally limited data. Using economic simulation we demonstrate potential gains the proposed model may offer a firm if used in individual-level targeting of display ads. Data, as supplemental material, are available at http://dx.doi. org/10.1287/mksc.2015. 0956 .
New and emerging treatments of Guillain–Barré syndrome

Introduction: Guillain-Barré syndrome (GBS) is an acute inflammatory monophasic peripheral neuropathy of autoimmune origin. Although there are effective immunomodulatory treatments available for GBS, a substantial number of patients are left with residual neurological deficits. Nowadays, it is accepted that autoantibodies against gangliosides are pathogenic effectors in subtypes of GBS. It has been demonstrated that these autoantibodies can not only induce damage to intact nerve fibers but also inhibit axon regeneration after nerve injury, which might potentially account for the slow/poor recovery and prognosis in some GBS patients. Areas covered: Recent insights into the pathogenesis of GBS led to the recognition of promising targets for future development of novel therapies. This review discusses potentially new and emerging treatments that stemmed from preclinical studies. These emerging treatments can be grouped into immunomodulatory/anti-inflammatory and neuroprotective categories including inhibition of complement activation, enhanced clearance of autoantibodies against gangliosides by blocking neonatal Fc receptor, modulation of Fc γ receptors-mediated inflammation by sialylated intravenous Ig (IVIg) or IVIg-mimics and strategies for neuroprotection/enhancing regeneration by erythropoietin. Expert opinion: New and emerging treatments offer promise to optimize therapy for GBS patients, especially those who are refractory to currently used treatment modalities in the short term and those who are at higher risk of developing permanent sequelae (neurological deficits) in the long run.
Respiratory medication use in Australia 2003–2013: treatment of asthma and COPD

This report describes patterns of dispensing of respiratory medications in Australia through detailed analyses of Pharmaceutical Benefits Scheme data, as well as other sources, to draw inferences about respiratory medication use among patients with asthma and chronic obstructive pulmonary disease. Summary This report focuses on medications dispensed for asthma (a chronic airways disease affecting children and adults) and chronic obstructive pulmonary disease, which primarily affects older adults who have been smokers). In 2013, one or more respiratory medications were dispensed to 2,042,104 people in Australia (9.1% of the population).
Reliability and validity of center of pressure measures for balance assessment in older adults

[Purpose] This study was conducted to assess the reliability and validity of center of pressure-based parameters for balance assessment. [Subjects and Methods] Two hundred and forty older adults were evaluated using a force platform and the Berg Balance Scale at 1-week intervals. The intra-class correlation coefficient and the Pearson correlation coefficient were used to test reliability and validity respectively. [Results] The reliability of the 12 selected center of pressure measures was satisfactory (intra-class correlation coefficient = 0.75–0.99) and the validity between the parameters and the Berg Balance Scale was moderate to good (r = −0.62 to −0.88). [Conclusion] Center of pressure-based parameters are reliable and valid measures in older adults.
Human cytochrome P450 mono-oxygenase system is suppressed by propofol

We have studied the effect of propofol on the cytochrome P450-dependent mono-oxygenase system in human liver microsomes by assaying mono-oxygenase activities toward specific cytochrome P450 isoform test substrates, aniline, 7-ethoxycoumarin, benzphetamine and benzo(a) pyrene. Propofol inhibited benzo(a)pyrene hydroxylation to a greater extent than the oxidative metabolism of the other test substrates, even at 0.05 mmol litre−1 The degrees of inhibition of benzphetamine N-demethylation and 7-ethoxy-coumarin O-de-ethylation were similar, while aniline hydroxylation was least affected by propofol. Spectral analysis showed that propofol competed with carbon monoxide for binding to the haem moiety of haemoprotein in the P450 enzyme. The variable inhibition observed may be caused by the differential binding of propofol to P450 isoforms. Propofol 0.05–1.0 mmol litre−1 exhibited a concentration-dependent inhibitory effect on human cytochrome P450 2E1, 2B1 and 1A1. These inhibitory actions of propofol on human liver microsomal enzymes in vitro suggest that potential drug interactions may exist between propofol and other drugs administered clinically. (Br. J. Anaesth. 1995; 74: 558–562)
Data-driven fluid simulations using regression forests

Traditional fluid simulations require large computational resources even for an average sized scene with the main bottleneck being a very small time step size, required to guarantee the stability of the solution. Despite a large progress in parallel computing and efficient algorithms for pressure computation in the recent years, realtime fluid simulations have been possible only under very restricted conditions. In this paper we propose a novel machine learning based approach, that formulates physics-based fluid simulation as a regression problem, estimating the acceleration of every particle for each frame. We designed a feature vector, directly modelling individual forces and constraints from the Navier-Stokes equations, giving the method strong generalization properties to reliably predict positions and velocities of particles in a large time step setting on yet unseen test videos. We used a regression forest to approximate the behaviour of particles observed in the large training set of simulations obtained using a traditional solver. Our GPU implementation led to a speed-up of one to three orders of magnitude compared to the state-of-the-art position-based fluid solver and runs in real-time for systems with up to 2 million particles.
Leadership and Competition in Network Supply Chains

This paper considers network supply chains with price dependent demand by modelling them as large acyclic networks. Such large networks are common in the automobile and apparel industries. We develop a model to analyze the effect of these large-scale problems involving long sequences of contracts, and show that contract leadership, as well as leader position in the network, affect the performance of the entire supply chain. We generalize Spengler (Spengler, J. 1950. Vertical integration and anti-trust policy. J. Political Econom.58 347--352) to a game on a “contract tree” for a particular supply chain and extend the concept of double marginalization so that it can be applied in the form of a transformation to each contract that is offered by one member to another in the “contract tree. ” We construct an algorithm to find the equilibrium solution, and derive the optimal location of the leader (“optimal” being the leader location that maximizes total supply chain profits). Our work formalizes many intuitive insights; for example, member profits are determined by systemwide rather than individual costs. Finally, we model Cournot competition between competing supply chains (both two heterogeneous trees and multiple identical trees) and show the effect of changes in leader position as well as cost structure on the equilibrium.
Shared Spectrum for Mobile-Cell's Backhaul and Access Link

Offloading cellular hotspot regions to small-cells has been the main theme for the fifth generation of cellular network. One such hotspot is the public transport which carries a large number of cellular users who frequently receive low quality of service (QoS) due to vehicular penetration effect (VPE). Hence installation of mobile-cell (MC) within public transport is seen as a potential enabler to enhance QoS for commuting users. However, unlike fixed cells, MC requires wireless backhaul (BH) connectivity along with in- vehicle Access-Link (AL) communication. These additional wireless links for MC communication will pose an excessive burden on an already scarce frequency spectrum. Hence, in this research, we exploit VPE and line-of-sight (LOS) communication to allow the downlink backhaul (DL-BH) sub- channels to be shared by in-vehicle downlink access-link (DL-AL) transmission. Our analysis and simulations show that using the above-mentioned technique, both links maintain high success probability, especially in regions with low signal to interference ratios.
How Blind People Interact with Visual Content on Social Networking Services

In this paper, we explore blind people's motivations, challenges, interactions, and experiences with visual content on Social Networking Services (SNSs). We present findings from an interview study of 11 individuals and a survey study of 60 individuals, all with little to no functional vision. Compared to sighted SNS users, our blind participants faced profound accessibility challenges, including the prevalence of photos without sufficient text descriptions. To overcome the challenges, they developed creative strategies, including using a variety of methods to access SNS features (e.g., opening the mobile site on a desktop browser), and inferring photo content from textual cues and social interactions. When strategies failed, participants reached out for help from trusted friends, or avoided certain features. We discuss our findings in the context of CSCW research and SNS accessibility as a design value. We highlight the social significance of photo interactions for blind people and suggest design practices.