Miracle-dz/UniFAR-FTUs huggingface dataset:
Locally Synchronized MAC Protocols in Wireless Sensor Networks: A Survey

Wireless Sensor Networks (WSNs) consist of large number of sensor nodes. These nodes are distributed in a particular area for the purpose of collecting data/queries etc. WSNs have played an important role in applications such as industrial automation, smart buildings and intrusion detection so on. On the basis of energy efficiency, overhead and data delivery performance, WSN MAC is divided into four categories: locally synchronized, nonsynchronized, globally synchronized and multichannel. This paper surveys the latest progresses in locally synchronized MAC protocols.
Remote network labs: an on-demand network cloud for configuration testing

Network equipment is difficult to configure correctly. To minimize configuration errors, network administrators typically build a smaller scale test lab replicating the production network and test out their configuration changes before rolling out the changes to production. Unfortunately, building a test lab is expensive and the test equipment is rarely utilized. In this paper, we present Remote Network Labs, which is aimed at leveraging the expensive network equipment more efficiently and reducing the cost of building a test lab. Similar to a server cloud such as Amazon EC2, a user could request network equipment remotely and connect them through a GUI or web services interface. The network equipment is geographically distributed, allowing us to reuse test equipment anywhere. Beyond saving costs, Remote Network Labs brings about many additional benefits, including the ability to fully automate network configuration testing.
Multiview Geometry for Texture Mapping 2D Images Onto 3D Range Data

The photorealistic modeling of large-scale scenes, such as urban structures, requires a fusion of range sensing technology and traditional digital photography. This paper presents a system that integrates multiview geometry and automated 3D registration techniques for texture mapping 2D images onto 3D range data. The 3D range scans and the 2D photographs are respectively used to generate a pair of 3D models of the scene. The first model consists of a dense 3D point cloud, produced by using a 3D-to-3D registration method that matches 3D lines in the range images. The second model consists of a sparse 3D point cloud, produced by applying a multiview geometry (structure-from-motion) algorithm directly on a sequence of 2D photographs. This paper introduces a novel algorithm for automatically recovering the rotation, scale, and translation that best aligns the dense and sparse models. This alignment is necessary to enable the photographs to be optimally texture mapped onto the dense model. The contribution of this work is that it merges the benefits of multiview geometry with automated registration of 3D range scans to produce photorealistic models with minimal human interaction. We present results from experiments in large-scale urban scenes.
CURA

Organic Light-Emitting Diode (OLED) technology is regarded as a promising alternative to mobile displays. In this article, we introduce the design, algorithm, and implementation of a novel framework called CURA for quality-retaining power saving on mobile OLED displays. First, we link human visual attention to OLED power saving and model the OLED image scaling optimization problem. The objective is to minimize the power required to display an image without adversely impacting the user’s visual experience. Then, we present the algorithm used to solve the modeled problem, and prove its optimality even without an accurate power model. Finally, based on the framework, we implement two practical applications on a commercial OLED mobile tablet. The results of experiments conducted on the tablet with real images demonstrate that CURA can reduce significant OLED power consumption while retaining the visual quality of images.
Multiplanes: Assisted Freehand VR Drawing

Multiplanes is a virtual reality (VR) drawing system that provides users with the flexibility of freehand drawing and the ability to draw perfect shapes. Through the combination of both beautified and 2D drawing, Multiplanes addresses challenges in creating 3D VR drawings. To achieve this, the system beautifies user's strokes based on the most probable, intended shapes while the user is drawing them. It also automatically generates snapping planes and beautification trigger points based on previous and current strokes and the current controller pose. Based on geometrical relationships to previous strokes, beautification trigger points act as guides inside the virtual environment. Users can hit these points to (explicitly) trigger a stroke beautification. In contrast to other systems, when using Multiplanes, users do not need to manually set or do any kind of special gesture to activate, such guides allowing the user to focus on the creative process.
Implementation of an Affine-Covariant Feature D etector in Field-Programmable Gate Arrays

This article describes an FPGA-based implementation of the Harris-Affine feature detector introduced by Mikolajczyk and Schmid (1, 2). The system is implemented on the Transmogrifier-4, a prototyping platform that includes four Altera Stratix S80 FPGAs and NTSC/VGA video interfaces. The system achieves a speed of 90-9000 times the speed of an equivalent software implementation, allowing it to process standard video (640 × 480 pixels) at 30 frames per second.
Autonomous mobile robot navigation algorithm for planning collision-free path designed in dynamic environments

An autonomous mobile robot, which is able to achieve not only collision-free but also reaching the goal, is a system which possesses a sufficient intelligence to ensure global and local navigation in new environment with static or dynamic obstacles. In an unknown environment, the difficult issue in mobile robot navigation or path planning is to find the path from the starting point to the target and at the same time avoid collisions. In this paper, we present a developed algorithm for solving the problem of robot path planning in dynamic environment. By the proposed strategy, the mobile robot can travel smoothly to the designed target without collision. Simulation results are introduced to show performances and the effectiveness of this approach.
Classification of familiarity based on cross-correlation features between EEG and music

An approach to recognize the familiarity of a listener with music using both the electroencephalogram (EEG) signals and the music signal is proposed in this paper. Eight participants listened to melodies produced by piano sounds as simple natural stimuli. We classified the familiarity of each participant using cross-correlation values between EEG and the envelope of the music signal as features of the support vector machine (SVM) or neural network used. Here, we report that the maximum classification accuracy was 100% obtained by the SVM. These results suggest that the familiarity of music can be classified by cross-correlation values. The proposed approach can be used to recognize high-level brain states such as familiarity, preference, and emotion.
Segmentation of the Solid Bed in Infrared Image Sequences of Rotary Kilns

This paper presents two novel methods for segmenting the sol id bed in infrared image sequences of metalrecycling rotary kilns. Exploiting the different dynamics and temperatures of gas phase, solid bed and kiln wall, we developed filter chains for an image segmentation of the solid bed. For the image acquisition we employed infrared cameras with a spectral filter. Two image p rocessing algorithms were realized according to the two most common camera positions (frontal and top-left v i w on the solid bed at the rear-end of the kiln). Results show that both algorithms are capable to segment the solid bed in the image sequences accurately and reliably. The work presented here provides a basis for the ex traction of characteristic process state variables, that can help to improve the process control with regard to pr oduct quality, energy consumption and emission
Occupational exposure to particles and incidence of stroke.

OBJECTIVES This paper aims to investigate the relation between occupational exposure to particles, particle size, and the incidence of ischemic and hemorrhagic stroke. METHODS The cohort included all manual workers identified from the Swedish National Census in 1980, who were alive as of 1 January 1987. First time events of ischemic or hemorrhagic stroke during the period 1987-2005 were identified through linkage to the Hospital Discharge Register and the National Cause of Death Register. A job-exposure matrix for exposure to small (<1 µm) and large (>1 µm) particles was developed and applied. Hazard ratios (HR) were estimated by Cox regression with adjustment for age, socioeconomic group, and residential area. RESULTS Increased HR of ischemic stroke were found among both women and men occupationally exposed to small as well as large particles for ≥5 years. The risks were higher for workers exposed for ≥5 years compared to "ever exposed" participants indicating a dose-response relationship, but no trend with exposure intensity was observed. A tentative association between particle exposure and hemorrhagic stroke was also found. CONCLUSIONS Occupational exposure to small and large particles was associated with increased risks of ischemic stroke. Further studies are needed to explore the relationships between exposure to different types of particles and various doses and the occurrence of stroke among women as well as men.
A Pathway-Specific Function for Different AMPA Receptor Subunits in Amygdala Long-Term Potentiation and Fear Conditioning

The AMPA receptor subunit glutamate receptor 1 (GluR1 or GluR-A) contributes to amygdala-dependent emotional learning. It remains unclear, however, to what extent different amygdala pathways depend on GluR1, or other AMPA receptor subunits, for proper synaptic transmission and plasticity, and whether GluR1-dependent long-term potentiation (LTP) is necessary for auditory and contextual fear conditioning. Here, we dissected the role of GluR1 and GluR3 (GluR-C) subunits in AMPA receptor-dependent amygdala LTP and fear conditioning using knock-out mice (GluR1−/− and GluR3−/−). We found that, whereas LTP at thalamic inputs to lateral amygdala (LA) projection neurons and at glutamatergic synapses in the basal amygdala was completely absent in GluR1−/− mice, both GluR1 and GluR3 contributed to LTP in the cortico-LA pathway. Because both auditory and contextual fear conditioning were selectively impaired in GluR1−/− but not GluR3−/− mice, we conclude that GluR1-dependent synaptic plasticity is the dominant form of LTP underlying the acquisition of auditory and contextual fear conditioning, and that plasticity in distinct amygdala pathways differentially contributes to aversive conditioning.
Investigating translation of Parliament speeches

This paper reports on recent experiments for speech to text (STT) translation of European Parliamentary speeches. A Spanish speech to English text translation system has been built using data from the TC-STAR European project. The speech recognizer is a state-of-the-art multipass system trained for the Spanish EPPS task and the statistical translation system relies on the IBM-4 model. First, MT results are compared using manual transcriptions and 1-best ASR hypotheses with different word error rates. Then, a n-best interface between the ASR and MT components is investigated to improve the STT process. Derivation of the fundamental equation for machine translation suggests that the source language model is not necessary for STT. This was investigated by using weak source language models and by n-best rescoring adding the acoustic model score only. A significant loss in the BLEU score was observed suggesting that the source language model is needed given the insufficiencies of the translation model. Adding the source language model score in the n-best rescoring process recovers the loss and slightly improves the BLEU score over the 1-best ASR hypothesis. The system achieves a BLEU score of 37.3 with an ASR word error rate of 10% and a BLEU score of 40.5 using the manual transcripts
Weak data secrecy via obfuscation in network coding based content distribution

Content distribution systems benefit from network coding in terms of error/loss protection and faster dissemination at the cost of exposing the data to intermediate nodes that are not the intended recipients. In these systems data secrecy is at risk and it can be guaranteed by using encryption as a straightforward method. However, this inevitably increases the communication costs and decreases the willingness to cooperate of intermediate nodes, which have no expectation of recovering the file. In this paper we propose an incentive secrecy preserving scheme based on obfuscation for time-sensitive data. The source intentionally obscures the original file and informs only the intended recipients which file blocks are corrupted. This scheme, combined with network coding, preserves the data secrecy and, in the meantime, fosters the cooperation of intermediate nodes. The evaluation of our solution shows that the proposed scheme is more efficient than encryption based secrecy preserving methods. In particular, the distribution of content in terms of downloading speed is greatly enhanced.
An Integrated Approach to Parallel Scheduling Using Gang-Scheduling, Backfilling, and Migration

Effective scheduling strategies to improve response times, throughput, and utilization are an important consideration in large supercomputing environments. Such machines have traditionally used space-sharing strategies to accommodate multiple jobs at the same time. This approach, however, can result in low system utilization and large job wait times. This paper discusses three techniques that can be used beyond simple space-sharing to greatly improve the performance figures of large parallel systems. The first technique we analyze is backfilling, the second is gang-scheduling, and the third is migration. The main contribution of this paper is an evaluation of the benefits from combining the above techniques. We demonstrate that, under certain conditions, a strategy that combines backfilling, gang-scheduling, and migration is always better than the individual strategies for all quality of service parameters that we consider.
A Handoff Report Card for General Nursing Orientation

While redesigning nursing orientation, a gap in the transmission of information from general hospital to unit orientation was identified: Unit managers were unaware of the strengths and weaknesses identified in the nursing orientation of newly hired experienced nurses and, therefore, could not tailor the unit orientation to meet the specific needs of these orientees. The authors discuss the development and implementation of a nursing orientation report card, consisting of a 100-point score containing a summary of skills, knowledge, and Benner level measuring clinical performance and critical thinking, to facilitate better exchange of performance data.
Fully-Dynamic Bin Packing with Limited Repacking

We study the classic Bin Packing problem in a fully-dynamic setting, where new items can arrive and old items may depart. We want algorithms with low asymptotic competitive ratio \emph{while repacking items sparingly} between updates. Formally, each item $i$ has a \emph{movement cost} $c_i\geq 0$, and we want to use $\alpha \cdot OPT$ bins and incur a movement cost $\gamma\cdot c_i$, either in the worst case, or in an amortized sense, for $\alpha, \gamma$ as small as possible. We call $\gamma$ the \emph{recourse} of the algorithm. This is motivated by cloud storage applications, where fully-dynamic Bin Packing models the problem of data backup to minimize the number of disks used, as well as communication incurred in moving file backups between disks. Since the set of files changes over time, we could recompute a solution periodically from scratch, but this would give a high number of disk rewrites, incurring a high energy cost and possible wear and tear of the disks. In this work, we present optimal tradeoffs between number of bins used and number of items repacked, as well as natural extensions of the latter measure.
Impact of antimicrobials with or without the addition of phytase on phosphorus digestibility and utilization in swine and broiler chickens

McCormick, Katherine. M. S. , Purdue University, May 2014. Impact of antimicrobials with or without the addition of phytase on phosphorus digestibility and utilization in swine and broiler chickens. Major Professor: Dr. Olayiwola Adeola. Three studies were conducted for this thesis. In study 1, effects of antimicrobials on nutrient digestibility and retention of 18 to 20-kg pigs was investigated. The study used a randomized complete block design (RCBD) with 4 treatments and 6 replicate pigs in individual metabolism crates per treatment. The four treatments consisted of, negative control (NC) devoid of antimicrobials with 0.41% total P and 0.71% Ca, NC with 55 mg/kg carbadox (Mecadox 10, Phibro Animal Health), 44 mg/kg tylosin (Tylan 40, Elanco Animal Health), and 11 mg/kg virginiamycin (Stafac 20, Phibro Animal Health). A 5-d total collection period with chromic oxide used as an initiation and termination marker for feces followed a 5-d adaptation period. Supplementation with carbadox increased (P < 0.05) energy (E) digestibility and retention relative to pigs fed the NC diet. Overall, supplementation of an antimicrobial did not significantly influence DM, P, and Ca utilization; however, specific antimicrobial supplementation influenced N digestibility and E utilization.
Predicting the Internet ’ s Evolution with Decision Trees and Lasso Logistic Regression Models

The Internet self-evolves rapidly and its dynamic structure poses many interesting questions for researchers in network analysis. In this paper I show how we can simplify the entire Internet as a mathematical graph and then extract its structural characteristics; these characteristics in turn help us build statistical models that can predict how the Internet will evolve. The data describing the Internet structure are both clustered and unbalanced. I hence test various models, including lasso logistic regression, gradient-boosted decision trees and random forest decision trees, to see how well they cope with unbalanced and clustered data. The best performing model was created through a gradient-boosted decision tree that balances flexibility in fitting with robustness in prediction. I show that we can achieve good predicting power using fairly simple explanatory variables, but I also discuss how we can extract more sophisticated variables to improve the models’ performance.
An 80-Fold Speedup, 15.0 TFlops Full GPU Acceleration of Non-Hydrostatic Weather Model ASUCA Production Code

Regional weather forecasting demands fast simulation over fine-grained grids, resulting in extremely memory- bottlenecked computation, a difficult problem on conventional supercomputers. Early work on accelerating mainstream weather code WRF using GPUs with their high memory performance, however, resulted in only minor speedup due to partial GPU porting of the huge code. Our full CUDA porting of the high- resolution weather prediction model ASUCA is the first such one we know to date; ASUCA is a next-generation, production weather code developed by the Japan Meteorological Agency, similar to WRF in the underlying physics (non-hydrostatic model). Benchmark on the 528 (NVIDIA GT200 Tesla) GPU TSUBAME Supercomputer at the Tokyo Institute of Technology demonstrated over 80-fold speedup and good weak scaling achieving 15.0 TFlops in single precision for 6956 x 6052 x 48 mesh. Further benchmarks on TSUBAME 2.0, which will embody over 4000 NVIDIA Fermi GPUs and deployed in October 2010, will be presented.
Exploiting locality in maintaining potential causality

In distributed systems it is often important to be able to determine the temporal relationships between events generated by different processes. An algorithm to determiue such relationships is presented in [3] and [5]. This algorithm has many favorable attributes such as it allows for any kind of interprocess communication, and it requires no extra synchronization messages, additional communication links or cent ral t imestamping authority. The algorithm, however, requires O(n) space for each process (where n is the number of processes). i.e., it requires an overall space of 0(n2 ). This can be a large overhead especially when there are a very large number of processes. By cutting down on this generality, we can significantly decrease the amount of space required to determine temporal relationships. In this paper, we show how one may reduce the space requirements by assuming that the communication links between processes is static and known ahead oj time; and also that one is interested only in determining the temporal ordering between messages arriving at the same process. We argue that these assumptions are reasonable to make for a large class of problems.
Selected Topics in the Evaluation of Pediatric Gastrointestinal Mucosal Biopsies

The practice of pediatric gastrointestinal pathology provides specific challenges to the pathologist. Often the clinical information accompanying a biopsy specimen will read simply, “failure to thrive. ” Other situations, such as the evaluation of a neonate's rectal suction biopsy for Hirschsprung disease, are notorious for providing diagnostic challenges in high-pressure situations. In this review, we will discuss several commonly encountered situations in pediatric pathology: the evaluation of pediatric esophageal eosinophilia, a child who has swallowed a non food item, caustic substance, or hot liquid, the evaluation of a pediatric colorectal biopsy with focal active colitis, and the evaluation of rectal suction biopsies for Hirschsprung disease. With each topic, we will discuss a general approach to the case, diagnostic tips, and how to avoid commonly encountered pitfalls. Finally, we highlight key references pertinent to these issues that can he used to help convey pathologic findings to pediatric gastroenterologists.
A Generalized K-Means Algorithm with Semi-Supervised Weight Coefficients

A new classification algorithm corresponding to a generalization of the k-means algorithm is proposed, whose algorithm is named as a weighted k-means algorithm. Weight coefficients, which provide weighted distortions between data and cluster centers, are incorporated into the algorithm to realize reliable classification. A method determining the appropriate values of the weight coefficients from class labeled data is introduced. Under the situations where statistical distributions of data are changing gradually with time, the weighted k-means algorithm for semi-supervised data composed from initial labeled data and succeeding unlabeled data is investigated
Analysis of Efficient Message Relay Methods in Intervehicle Networks

The aim of this paper is to analyze efficient message relay methods in vehicular ad hoc networks (VANETs). Safety applications installed in vehicles aim to send a warning message to all reachable network vehicles as soon as possible. There are several broadcast methods for disseminating messages. We implement each broadcast method and compare methods. We evaluate the reliability and performance of our distance based priority broadcast method for VANETs environment on a highway simulation. This analysis allows us to study the impact of vehicle density and of multi hop forwarding. The proposed method can stably spread the messages in high density vehicular networks. Furthermore, we can confirm only 1.8%, the percentage of forwarded packets by the simulation results.