Miracle-dz/UniFAR-FTUs huggingface dataset:
Empirical Operational Energy Analysis of Downtown High-Rise vs. Suburban Low-Rise Lifestyles: A Chicago Case Study

It is widely accepted that the concentration of people living in high-density city centers offers greater operational energy efficiency and lower greenhouse gas emissions than lower-density expanded suburbs. The prevailing assumption is that lower-density suburbs are dominated by larger low-rise buildings that have higher building energy use requirements and greater per-person automobile travel requirements than high-density city centers dominated by medium- and high-rise buildings located in close proximity to a variety of public transit systems. However, very few studies to date have utilized empirical data at an individual household scale to evaluate differences in the operational energy (OE) footprints for both building and transportation energy end-uses between high-rise urban and low-rise suburban households. Therefore, this work collects empirical data on building and transportation OE consumption by individuals and households living in two economically similar groups: existing high-rise residential buildings in downtown Chicago, IL, USA and existing low-rise residential buildings in suburban Oak Park, IL, USA. Data were collected from over 500 households via an online survey. We considered the following components of residential living: (1) building OE (BOE), which includes electricity and/or natural gas use for all building energy end-uses; and (2) transportation OE (TOE), which includes the OE for multiple modes of transportation ( i.e. , automobile, bus, subway, regional train, etc. ) based on average travel behavior in each location, as well as the OE for supporting transportation infrastructure. We estimate that downtown high-rise living in this sample of residences in Chicago, IL accounts for approximately 427 GJ of primary OE per household per year, on average, which was 14% lower than the average for suburban low-rise living in the Oak Park, IL homes (499 GJ per household per year). However, on a per-person basis, downtown high-rise living accounts for approximately 246 GJ of primary OE per person per year, which was approximately 61% higher than suburban low-rise living (153 GJ per person per year). In both building types, building OE was the single largest contributor to total OE use. This study accurately captured the energy requirements associated with realistic behaviors and lifestyles of occupants of both low-rise suburban and high-rise urban households, and found that building OE dominates the total OE, which suggests that efforts to reduce building OE should be given high priority in building design and management as well as urban planning.
Experimental Simulation of the Effects of an Initial Antibiotic Treatment on a Subsequent Treatment after Initial Therapy Failure

Therapy failure of empirical antibiotic treatments prescribed by primary care physicians occurs commonly. The effect of such a treatment on the susceptibility to second line antimicrobial drugs is unknown. Resistance to amoxicillin was rapidly induced or selected in E. coli at concentrations expected in the patient’s body. Strains with reduced susceptibility outcompeted the wild-type whenever antibiotics were present, even in low concentrations that did not affect the growth rates of both strains. Exposure of E. coli to amoxicillin caused moderate resistance to cefotaxime. The combined evidence suggests that initial treatment by amoxicillin has a negative effect on subsequent therapy with beta-lactam antibiotics.
Online Learning of Structured Predictors with Multiple Kernels

Training structured predictors often requires a considerable time selecting features or tweaking the kernel. Multiple kernel learning (MKL) sidesteps this issue by embedding the kernel learning into the training procedure. Despite the recent progress towards efficiency of MKL algorithms, the structured output case remains an open research front. We propose a family of online algorithms able to tackle variants of MKL and group-LASSO, for which we show regret, convergence, and generalization bounds. Experiments on handwriting recognition and dependency parsing attest the success of the approach.
A Classified Method of Human Hair for Hair Sketching

Human hair has significant effect on the life-likeness of human portrait and human recognition. In this paper, we present a classified method of human hair for hair sketching. We extract shape and appearance features from the training data of hair, including hair raw images and their corresponding sketching templates. Based on these features, we learn twenty-four hairstyles. Given a human hair raw image, we extract its shape and appearance features and find the best matched hair style and sketching template by Nearest Neighbor from twenty-four hairstyles. Taking the template as prototype, a new hair sketching corresponding to the raw image can be generalized by Thin Plate Spline. We test our algorithm to a large data set of hair images with diverse hair styles, experimental results demonstrate the effectiveness of our method.
XKaapi: A Runtime System for Data-Flow Task Programming on Heterogeneous Architectures

Most recent HPC platforms have heterogeneous nodes composed of multi-core CPUs and accelerators, like GPUs. Programming such nodes is typically based on a combination of OpenMP and CUDA/OpenCL codes; scheduling relies on a static partitioning and cost model. We present the XKaapi runtime system for data-flow task programming on multi-CPU and multi-GPU architectures, which supports a data-flow task model and a locality-aware work stealing scheduler. XKaapi enables task multi-implementation on CPU or GPU and multi-level parallelism with different grain sizes. We show performance results on two dense linear algebra kernels, matrix product (GEMM) and Cholesky factorization (POTRF), to evaluate XKaapi on a heterogeneous architecture composed of two hexa-core CPUs and eight NVIDIA Fermi GPUs. Our conclusion is two-fold. First, fine grained parallelism and online scheduling achieve performance results as good as static strategies, and in most cases outperform them. This is due to an improved work stealing strategy that includes locality information; a very light implementation of the tasks in XKaapi; and an optimized search for ready tasks. Next, the multi-level parallelism on multiple CPUs and GPUs enabled by XKaapi led to a highly efficient Cholesky factorization. Using eight NVIDIA Fermi GPUs and four CPUs, we measure up to 2.43 TFlop/s on double precision matrix product and 1.79 TFlop/s on Cholesky factorization; and respectively 5.09 TFlop/s and 3.92 TFlop/s in single precision.
Distributed Data Storage in Support for Context-Aware Applications

Context-aware computing is a new paradigm that relies on large amounts of data collected from a variety of sources, ranging from smartphones to sensors, to automatically take smart decisions. This usually leads to large volumes of data, that need to be further processed to derive higher-level context information. Clouds have recently emerged as interesting candidates to support the storage and aggregation of such data for large-scale context-aware applications. However, specific extensions to support context-aware data need to be designed in order to be able to fully exploit the clouds' potential. In this paper we introduce such a cloud-based system, designed to support real-time processing and persistent storage of context data. Context Aware Framework is designed as an extension of the BlobSeer storage system, building a context-aware layer on top of it to enable scalable high-throughput under high-concurrency for big context data. Our experimental evaluation validates the transparency, mobility and real-time guarantees provided by our approach to context-aware applications.
Loca: a location-oblivious co-location attack in crowds

Recent studies have introduced co-location attacks as a powerful way to extract social information from location traces. However, these attacks all rely by some means on the position of targeted users. This requires the attacker to be able to locate either the user or the sensors detecting the user. Implicitly, it also forbids the use of these attacks on devices whose location is unknown. In this paper, we consider attack scenarios where the attacker has no position information on users and devices sensing users. Such attack scenarios typically fit Internet of Things use-cases, where low-end devices are scattered in an environment that is unknown to the attacker: the sole source of information is a set of timestamped user/sensor proximity logs. To exploit proximity logs, we describe Loca, a location-oblivious co-location attack. Our approach exploits location-oblivious logs in two steps: i) we exploit users' flows between sensors to construct a virtual map of the sensors, and ii) we conduct a co-location attack based on that virtual map. Our tests on both synthetic and real datasets match up to 90% of the targeted social network with a surprisingly low number of sensors. These results greatly extend the scope of such co-location attacks, and hopefully awareness about their threat.
Generating and sharing of market intelligence in sales teams: an economic social network perspective

Abstract In this research, we take a multimethod approach to shed light on the potential costs to sales teams that generate and share market intelligence (MI). First, we introduce an analytical model to propose the respective levels of effort that sales managers, experts, and team members spend generating and sharing MI. To test our propositions, we utilize social network data from 40 independent, business-to-business (B2B) sales teams, representing 287 salespeople. Interestingly, our results support the premise that team members become dependent (reduce MI efforts) when their sales manager or team expert shares MI among the team. We term this a “sharing tax” that sales managers and team experts pay when they share MI. Consequently, sales managers demonstrate greater MI-generation efforts the more they share MI. We also find that experts who share more (less) also show greater (lesser) MI-generation efforts, but only for teams where sales managers share low (high) levels of MI. In summary, our research innovatively conducts an empirical test of the Nash Equilibrium pattern of sales team effort to show that two critical team members, the sales manager and expert, are at a disadvantage when they share valuable MI.
Throughput of Infrastructure-Based Cooperative Vehicular Networks

In this paper, we provide the detailed analysis of the achievable throughput of infrastructure-based vehicular network with a finite traffic density under a cooperative communication strategy, which explores the combined use of vehicle-to-infrastructure (V2I) communications, vehicle-to-vehicle (V2V) communications, the mobility of vehicles, and cooperations among vehicles and infrastructure to facilitate the data transmission. A closed form expression of the achievable throughput is obtained, which reveals the relationship between the achievable throughput and its major performance-impacting parameters, such as distance between adjacent infrastructure points, the radio ranges of infrastructure and vehicles, the transmission rates of V2I and V2V communications, and vehicular density. Numerical and simulation results show that the proposed cooperative communication strategy significantly increases the throughput of vehicular networks, compared with its non-cooperative counterpart, even when the traffic density is low. Our results shed insight on the optimum deployment of vehicular network infrastructure and the optimum design of cooperative communication strategies in vehicular networks to maximize the throughput.
Synthesizable HDL generation method for configurable VLIW processors

This paper proposes a synthesizable HDL code generation method using a processor specification description. The proposed approach can change the number of slots and pipeline stages, and dispatching rule to assign operations to resources. In addition, designers can specify each instruction behavior using the specification language. A control logic, a decode logic, and a data path of VLIW processor are generated from the processor specification. Designers can explore ASIP design space using the proposed a p proach effectively, because the amount of description and the modification cost are small. Using this approach, it took about eight hours to design 36 VLIW processors. Moreover, this approach provides a 82% reduction on the average compared to the description of the HDL code.
Whether age of menarche is influenced by body mass index and lipoproteins profile? a retrospective study

Background: Menarche, a milestone in the reproductive life span of a woman, is influenced by several genetics and environmental factors. There is no consensus regarding the impact of body mass index (BMI) and lipid profiles on the age of menarche, as the results of various studies demonstrate. Objective: To investigate the correlation between age of menarche and BMI/lipoprotein profile in a community sample of Iranian girls. Materials and Methods: In the study, 370 girls, aged 10-16 years, who began their menarche within six months prior to the study, were recruited from the Tehran Lipid and Glucose Study (TLGS) population. Information was documented regarding their body composition, including height, weight, BMI, waist and hip circumference were collected and their lipid profiles were assessed after a 12-hour fast. Results: In this study, the mean±SD of age of menarche and BMI were 12.6±1.1 years and 21.7±3.9 kg/m2, respectively. There were statistically significant relationships between age of menarche and height, BMI, waist circumference, and the maternal educational level. The relationship between age of menarche and the weight and lipid profiles of subjects was not statistically significant. Conclusion: Age at menarche is not influenced by lipid profiles but it is influenced by BMI.
Oxidant Exposure Induces Cysteine-Rich Protein 61 (CCN1) via c-Jun/AP-1 to Reduce Collagen Expression in Human Dermal Fibroblasts

Human skin is a primary target of oxidative stress from reactive oxygen species (ROS) generated from both extrinsic and intrinsic sources. Oxidative stress inhibits the production of collagen, the most abundant protein in skin, and thus contributes to connective tissue aging. Here we report that cysteine-rich protein 61 (CCN1), a negative regulator of collagen production, is markedly induced by ROS and mediates loss of type I collagen in human dermal fibroblasts. Conversely, antioxidant N-acetyl-L-cysteine significantly reduced CCN1 expression and prevented ROS-induced loss of type I collagen in both human dermal fibroblasts and human skin in vivo. ROS increased c-Jun, a critical member of transcription factor AP-1 complex, and increased c-Jun binding to the AP-1 site of the CCN1 promoter. Functional blocking of c-Jun significantly reduced CCN1 promoter and gene expression and thus prevented ROS-induced loss of type I collagen. Targeting the c-Jun/CCN1 axis may provide clinical benefit for connective tissue aging in human skin.
BMPs Regulate msx Gene Expression in the Dorsal Neuroectoderm of Drosophila and Vertebrates by Distinct Mechanisms

In a broad variety of bilaterian species the trunk central nervous system (CNS) derives from three primary rows of neuroblasts. The fates of these neural progenitor cells are determined in part by three conserved transcription factors: vnd/nkx2.2, ind/gsh and msh/msx in Drosophila melanogaster/vertebrates, which are expressed in corresponding non-overlapping patterns along the dorsal-ventral axis. While this conserved suite of “neural identity” gene expression strongly suggests a common ancestral origin for the patterning systems, it is unclear whether the original regulatory mechanisms establishing these patterns have been similarly conserved during evolution. In Drosophila, genetic evidence suggests that Bone Morphogenetic Proteins (BMPs) act in a dosage-dependent fashion to repress expression of neural identity genes. BMPs also play a dose-dependent role in patterning the dorsal and lateral regions of the vertebrate CNS, however, the mechanism by which they achieve such patterning has not yet been clearly established. In this report, we examine the mechanisms by which BMPs act on cis-regulatory modules (CRMs) that control localized expression of the Drosophila msh and zebrafish (Danio rerio) msxB in the dorsal central nervous system (CNS). Our analysis suggests that BMPs act differently in these organisms to regulate similar patterns of gene expression in the neuroectoderm: repressing msh expression in Drosophila, while activating msxB expression in the zebrafish. These findings suggest that the mechanisms by which the BMP gradient patterns the dorsal neuroectoderm have reversed since the divergence of these two ancient lineages.
Design and Implementation of Artificially Intelligent Microcontroller based Chess Opponent

This project is an attempt to design an Artificially Intelligent Microcontroller based Chess Opponent (MAICO) which can compete against the user and calculate its moves. In this work, a chess playing system has been designed which implements an interface allowing a computer to control chess movements using PC’s parallel port and AT89C51. It is a fully automated system integrated with microcontrollers, camera, and Artificially Intelligent agent. Various algorithms are implemented for interfacing hardware in C and MATLAB. In this paper we have aimed to design a cost effective robot that consumes minimal amount of power.
A Systems Approach to Scalable Transportation Network Modeling

Emerging needs in transportation network modeling and simulation are raising new challenges with respect to scalability of network size and vehicular traffic intensity, speed of simulation for simulation-based optimization, and fidelity of vehicular behavior for accurate capture of event phenomena. Parallel execution is warranted to sustain the required detail, size and speed. However, few parallel simulators exist for such applications, partly due to the challenges underlying their development. Moreover, many simulators are based on time-stepped models, which can be computationally inefficient for the purposes of modeling evacuation traffic. Here an approach is presented to designing a simulator with memory and speed efficiency as the goals from the outset, and, specifically, scalability via parallel execution. The design makes use of discrete event modeling techniques as well as parallel simulation methods. Our simulator, called SCATTER, is being developed, incorporating such design considerations. Preliminary performance results are presented on benchmark road networks, showing scalability to one million vehicles simulated on one processor
Engineered telomere degradation models dyskeratosis congenita data

object identifier (DOIs) and date of initial publication. by PubMed from initial publication. Citations to Advance online articles must include the digital publication). Advance online articles are citable and establish publication priority; they are indexed appeared in the paper journal (edited, typeset versions may be posted when available prior to final Advance online articles have been peer reviewed and accepted for publication but have not yet
Levaldi: An improved network distance prediction algorithm based on network coordinate system

The Vivaldi Algorithm is a simple, adaptive and distributed algorithm for computing and predicting the hosts' coordinates and their network distance in a network coordinate system. However, coordinate calculation of the convergence of the Vivaldi algorithm is rather slow with high noise ratio samples. A new efficient algorithm (Levaldi Algorithm) is proposed, which uses a method in which we take the adapt-step problem into consideration and modify the size of the step in Vivaldi Algorithm that reduces the affection on other nodes by a certain node. Experiments indicate that the Levaldi Algorithm significantly reduces the time of convergence in coordinate prediction. Its accuracy and the speed of the convergence increase compared to the Vivaldi Algorithm.
The Floating Column Algorithm for Shaded, Parallel Display of Function Surfaces without Patches

The floating column algorithm is a new method for the shaded rendering of function surfaces. Derived from the monochromatic floating horizon algorithm, it uses the partial derivatives of the function to compute surface normals, thus enabling intensity or normal-interpolation shading. Current rendering methods require tiling the surface with patches, so higher-resolution patching is required for zoom-in views, interactive modification or time-varying surfaces. The new algorithm requires no patching and uses only constant space, so it can be implemented on graphics cards and hand-held devices. Each pixel column is displayed independently of the others, and this "independent column mode" makes the algorithm inherently parallel in the image space, so it is suitable for multiprocessor workstations and clusters and it is scalable in the resolution size. Furthermore, the sampling frequency of the surface can be controlled locally, matching local surface features, distance or artifact elimination requirements. Space-efficient super-sampling for anti-aliasing is also possible. The new algorithm, which allows orthogonal and perspective projections, produces pixel-wide strips which can be displayed in software or hardware. Various extensions are described, including shadows and texture mapping. These properties, together with the algorithm's parallelism, make it potentially useful for the real-time display of functionally-defined textured terrains and the animated display of time-varying surfaces.
Towards the performance measurements of private cloud

Performance measurement has become a vital factor especially in the area of cloud computing. Performance of private cloud becomes a matter of concern when it comes to maintaining the performance parameters at desired and controlled levels. To address this issue, in this paper, we analyze and compare the performance parameters like CPU usage, average load, memory usage and read/writes per second with the help of system commands as well as benchmarking tool `monit'. These parameters were analyzed 1) when only the Eucalyptus cloud was running, 2) when one Virtual Machine running on the cloud, 3) when two Virtual Machines running on the cloud and 4) when mathematical workload running on the cloud with the help of Hadoop MapReduce.
Ubiquitous Computing and Communication Journal 1 An overview of Geographic Restriction Mobility Models

In this paper, we present and visit the limitation of Random mobility model, the unconstraint motion of mobile node. Mobile nodes, in the Random Waypoint and Random walk mobility models, are allowed to move freely and randomly anywhere in the simulation field. However, in most real life applications, we observe that a node’s movement is subject to the environment. In particular, the motions of vehicles are bounded to the freeways or local streets in the urban area, and on campus the pedestrians may be blocked by the buildings and other obstacles. Therefore, the nodes may move in a pseudo-random way on predefined pathways in the simulation field. Some recent works address this characteristic and integrate the paths and obstacles into mobility models. We call this kind of mobility model a mobility model with geographic restriction.