Miracle-dz/UniFAR-FTUs huggingface dataset:
Linagliptin Improves Insulin Sensitivity and Hepatic Steatosis in Diet-Induced Obesity

Linagliptin (tradjenta™) is a selective dipeptidyl peptidase-4 (DPP-4) inhibitor. DPP-4 inhibition attenuates insulin resistance and improves peripheral glucose utilization in humans. However, the effects of chronic DPP-4 inhibition on insulin sensitivity are not known. The effects of long-term treatment (3–4 weeks) with 3 mg/kg/day or 30 mg/kg/day linagliptin on insulin sensitivity and liver fat content were determined in diet-induced obese C57BL/6 mice. Chow-fed animals served as controls. DPP-4 activity was significantly inhibited (67–89%) by linagliptin (P<0.001). Following an oral glucose tolerance test, blood glucose concentrations (measured as area under the curve) were significantly suppressed after treatment with 3 mg/kg/day (–16.5% to –20.3%; P<0.01) or 30 mg/kg/day (–14.5% to –26.4%; P<0.05) linagliptin (both P<0.01). Liver fat content was significantly reduced by linagliptin in a dose-dependent manner (both doses P<0.001). Diet-induced obese mice treated for 4 weeks with 3 mg/kg/day or 30 mg/kg/day linagliptin had significantly improved glycated hemoglobin compared with vehicle (both P<0.001). Significant dose-dependent improvements in glucose disposal rates were observed during the steady state of the euglycemic–hyperinsulinemic clamp: 27.3 mg/kg/minute and 32.2 mg/kg/minute in the 3 mg/kg/day and 30 mg/kg/day linagliptin groups, respectively; compared with 20.9 mg/kg/minute with vehicle (P<0.001). Hepatic glucose production was significantly suppressed during the clamp: 4.7 mg/kg/minute and 2.1 mg/kg/minute in the 3 mg/kg/day and 30 mg/kg/day linagliptin groups, respectively; compared with 12.5 mg/kg/minute with vehicle (P<0.001). In addition, 30 mg/kg/day linagliptin treatment resulted in a significantly reduced number of macrophages infiltrating adipose tissue (P<0.05). Linagliptin treatment also decreased liver expression of PTP1B, SOCS3, SREBP1c, SCD-1 and FAS (P<0.05). Other tissues like muscle, heart and kidney were not significantly affected by the insulin sensitizing effect of linagliptin. Long-term linagliptin treatment reduced liver fat content in animals with diet-induced hepatic steatosis and insulin resistance, and may account for improved insulin sensitivity.
Effects of Optical Layer Protection Granularity in Survivable Hierarchical Optical Path Network

SUMMARY This study compares the performances of waveband protection and wavelength path protection in survivable hierarchical optical path networks. Network costs and the number of switching operations necessary are evaluated for different ratios of protected demand. Numerical results demonstrate that waveband protection can drastically decrease the number of switching operations in the case of failure, while both waveband and wavelength path protection effectively reduce the network resources
Joint Source-Channel Cooperative Transmission over Relay-Broadcast Networks

Reliable transmission of a discrete memoryless source over a multiple-relay relay-broadcast network is considered. Motivated by sensor network applications, it is assumed that the relays and the destinations all have access to side information correlated with the underlying source signal. Joint source-channel cooperative transmission is studied in which the relays help the transmission of the source signal to the destinations by using both their overheard signals, as in th e classical channel cooperation scenario, as well as the available correlated side information. Decode-and-forward (DF) based cooperative transmission is considered in a network of multiple relay terminals and two different achievability schemes are proposed: i) a regular encoding and sliding-window decoding scheme without explicit source binning at the encoder, and ii) a semi-regular encoding and backward decoding scheme with binning based on the side information statistics. It is shown that both of these s chemes lead to the same source-channel code rate, which is shown to be the source-channel capacity in the case of i) a physically degraded relay network in which the side information signals are also degraded in the same order as the channel; and ii) a relay-broadcast network in which all the terminals want to reconstruct the source reliably, while at most one of them can act as a relay.
Multi-layer supervised dictionary learning for visual classification

For the task of visual categorization, the learning model is expected to be endowed with discriminative visual feature representation and flexibilities in processing multi-layer categories structure. Many existing approaches are designed based on a flat category structure, or rely on a restricted category structure, hence may not be appreciated for dealing with complex category structure and large numbers of categories. In this paper, we propose a novel dictionary learning method by taking advantage of the hierarchical category structure. A shared discriminative dictionary and a discriminative classification model are learnt for visual categorization. An optimization framework for learning all the components of the proposed model is presented. In the process of optimization, the hierarchical semantic structure among categories is preserved in the dictionary. Experiments on Caltech256 and ImageNet object data subset demonstrate that our approach achieves promising performance on data with large numbers of classes compared with some state-of-the-art methods.
Forming test collections with no system pooling

Forming test collection relevance judgments from the pooled output of multiple retrieval systems has become the standard process for creating resources such as the TREC, CLEF, and NTCIR test collections. This paper presents a series of experiments examining three different ways of building test collections where no system pooling is used. First, a collection formation technique combining manual feedback and multiple systems is adapted to work with a single retrieval system. Second, an existing method based on pooling the output of multiple manual searches is re-examined: testing a wider range of searchers and retrieval systems than has been examined before. Third, a new approach is explored where the ranked output of a single automatic search on a single retrieval system is assessed for relevance: no pooling whatsoever. Using established techniques for evaluating the quality of relevance judgments, in all three cases, test collections are formed that are as good as TREC.
The Type Ia Supernova 1998bu in M96 and the Hubble Constant

We present optical and near-infrared photometry and spectroscopy of the Type Ia SN 1998bu in the Leo I Group galaxy M96 (NGC 3368). The data set consists of 356 photometric measurements and 29 spectra of SN 1998bu between UT 1998 May 11 and July 15. The well-sampled light curve indicates the supernova reached maximum light in B on UT 1998 May 19.3 (JD 2450952.8 ± 0.8) with B = 12.22 ± 0.03 and V = 11.88 ± 0.02. Application of a revised version of the Multicolor Light Curve Shape (MLCS) method yields an extinction toward the supernova of AV = 0.94 ± 0.15 mag, and indicates the supernova was of average luminosity compared to other normal Type Ia supernovae. Using the HST Cepheid distance modulus to M96 and the MLCS fitted parameters for the supernova, we derive an extinction-corrected absolute magnitude for SN 1998bu at maximum, MV = -19.42 ± 0.22. Our independent results for this supernova are consistent with those of Suntzeff et al. Combining SN 1998bu with three other well-observed local calibrators and 42 supernovae in the Hubble flow yields a Hubble constant, H0 = 64img1.gif km s-1 Mpc-1, where the error estimate incorporates possible sources of systematic uncertainty including the calibration of the Cepheid period-luminosity relation, the metallicity dependence of the Cepheid distance scale, and the distance to the LMC.
Ubiquitous Computing and Communication Journal 1 OPERATING SYSTEMS FOR WIRELESS SENSOR NETWORKS : AN OVERVIEW

The technological trend in the recent years has led to the emergence of complete systems on a single chip with integrated low power communication and transducer capabilities. This has opened the way for wireless sensor networks: a paradigm of hundreds or even thousands of tiny, smart sensors with transducer and communication capabilities. Manage such a complex network that has to work unattended for months or years, being aware of the limited power resouces of battery-supplied nodes is a challenging task. Attending that task requires an adequate software platform, in other words an operating system specifically suited for wireless sensor networks. This paper presents a brief overview of the most known operating systems, highlighting the key challenges that have driven their design.
Automated Essay Scoring.

The impacts of computers on writing have been widely studied for three decades. Even basic computers functions, i.e. word processing, have been of great assistance to writers in modifying their essays. The research on Automated Essay Scoring (AES) has revealed that computers have the capacity to function as a more effective cognitive tool (Attali, 2004). AES is defined as the computer technology that evaluates and scores the written prose (Shermis & Barrera, 2002; Shermis & Burstein, 2003; Shermis, Raymat, & Barrera, 2003). Revision and feedback are essential aspects of the writing process. Students need to receive feedback in order to increase their writing quality. However, responding to student papers can be a burden for teachers. Particularly if they have large number of students and if they assign frequent writing assignments, providing individual feedback to student essays might be quite time consuming. AES systems can be very useful because they can provide the student with a score as well as feedback within seconds (Page, 2003). Four types of AES systems, which are widely used by testing companies, universities, and public schools: Project Essay Grader (PEG), Intelligent Essay Assessor (IEA), E-rater, and IntelliMetric. AES is a developing technology. Many AES systems are used to overcome time, cost, and generalizability issues in writing assessment. The accuracy and reliability of these systems have been proven to be high. The search for excellence in machine scoring of essays is continuing and numerous studies are being conducted to improve the effectiveness of the AES systems.
A search for brown dwarf like secondaries in cataclysmic variables – II

We present VTL/ISAAC infrared spectroscopy of a sample of short-orbital-period cataclysmic variables that are candidates for harbouring substellar companions. We have detected the K I and Na I absorption lines of the companion star in VY Aqr. The overall spectral distribution in this system is best fitted with an M9.5 type dwarf spectrum, implying a distance of 100 ′ 10 pc. VY Aqr seems to fall far from the theoretical distribution of secondary star temperatures around the orbital period minimum. Fitting of the IR spectral energy distribution (SED) was performed by comparing the observed spectrum with late-type templates. The application of such a spectral fitting procedure suggests that the continuum shape in the 1.1-2.5 μm spectral region in short-orbital-period cataclysmic variables may be a useful indicator of the companion's spectral type. SED fitting for RZ Leo and CU Vel suggests M5-type dwarf companions, and distances of 340 ′ 110 and 150 ′ 50 pc, respectively. These systems may be placed in the upper evolution branch for short-period cataclysmic variables.
Combining Privileged Information to Improve Context-Aware Recommender Systems

A recommender system is an information filtering technology which can be used to predict preference ratings of items (products, services, movies, etc) and/or to output a ranking of items that are likely to be of interest to the user. Context-aware recommender systems (CARS) learn and predict the tastes and preferences of users by incorporating available contextual information in the recommendation process. One of the major challenges in context-aware recommender systems research is the lack of automatic methods to obtain contextual information for these systems. Considering this scenario, in this paper, we propose to use contextual information from topic hierarchies of the items (web pages) to improve the performance of context-aware recommender systems. The topic hierarchies are constructed by an extension of the LUPI-based Incremental Hierarchical Clustering method that considers three types of information: traditional bag-of-words (technical information), and the combination of named entities (privileged information I) with domain terms (privileged information II). We evaluated the contextual information in four context-aware recommender systems. Different weights were assigned to each type of information. The empirical results demonstrated that topic hierarchies with the combination of the two kinds of privileged information can provide better recommendations.
GPS Map Tracking of Fuzzy Logic Based Lateral Control

The robotic control of the upper and the maneuvering of a vehicle are two of the primary stairs in society to develop autonomous intelligent vehicles. In this paper, a development of steering control for automated cars based on fuzzy logic and its related subject area tests are given. Artificial intelligence techniques are employed for controlling a wide range of systems, trying to emulate the human behavior when classical control models are too much complex and take a great deal of design time. Particularly, fuzzy logic control techniques are well proven successful methods of managing systems where there seem to be limitations of classical control. Our control scheme has been set up in two Citroen Berlin go test bed vans whose steering wheel has been automated and can be manipulated from a computer. The main sensorial input is an RTK DGPS that gives us positioning with one-centimeter precision. The answers of the realized experiments show a human like system performance with adaptation capability of any kind of track
High performance genetic programming on GPU

The availability of low cost powerful parallel graphics cards has stimulated the port of Genetic Programming (GP) on Graphics Processing Units (GPUs). Our work focuses on the possibilities offered by Nvidia G80 GPUs when programmed in the CUDA language. We compare two parallelization schemes that evaluate several GP programs in parallel. We show that the fine grain distribution of computations over the elementary processors greatly impacts performances. We also present memory and representation optimizations that further enhance computation speed, up to 2.8 billion GP operations per second. The code has been developed with the well known ECJ library.
Efficient aggregation of encrypted data in wireless sensor networks

Wireless sensor networks (WSNs) are ad-hoc networks composed of tiny devices with limited computation and energy capacities. For such devices, data transmission is a very energy-consuming operation. It thus becomes essential to the lifetime of a WSN to minimize the number of bits sent by each device. One well-known approach is to aggregate sensor data (e.g., by adding) along the path from sensors to the sink. Aggregation becomes especially challenging if end-to-end privacy between sensors and the sink is required. In this paper, we propose a simple and provably secure additively homomorphic stream cipher that allows efficient aggregation of encrypted data. The new cipher only uses modular additions (with very small moduli) and is therefore very well suited for CPU-constrained devices. We show that aggregation based on this cipher can be used to efficiently compute statistical values such as mean, variance and standard deviation of sensed data, while achieving significant bandwidth gain.
Behavioral context of call production by eastern North Pacific blue whales

We assessed the behavioral context of calls produced by blue whales Balaenoptera mus- culus off the California coast based on acoustic, behavioral, and dive data obtained through acoustic recording tags, sex determination from tissue sampling, and coordinated visual and acoustic obser- vations. Approximately one-third of 38 monitored blue whales vocalized, with sounds categorized into 3 types: (1) low-frequency pulsed A and tonal B calls, in either rhythmic repetitive song sequences or as intermittent, singular calls; (2) downswept D calls; and (3) highly variable amplitude- or frequency-modulated calls. Clear patterns of behavior, sex, and group size are evident for some call types. Only males were documented producing AB calls, with song produced by lone, traveling blue whales, and singular AB calls were more typically produced by whales in pairs; D calls were heard from both sexes during foraging, commonly from individuals within groups. The sex bias evi- dent in AB callers suggests that these calls probably play a role in reproduction, even though the calls are produced year-round. All calls are produced at shallow depth, and calling whales spend more time at shallow depths than non-calling whales, suggesting that a cost may be incurred during D call- ing, as less time is spent feeding at deeper depths. This relationship between calling and depth may predict the traveling behavior of singing blue whales, as traveling whales do not typically dive to deep depths and therefore would experience little extra energetic cost related to the production of long repetitive song bouts while moving between foraging areas.
3D Pose Estimation and Normalization for Face Recognition

Abstract Automatic face recognition has been a di cult problem in the eld of computer vision for many years. Robust face recognition requires the ability to recognize identity despite many variations in appearance the face can have in a scene. We propose preceding recognition with a hierarchical detection system capable of searching images for human faces e ciently and with invariance to position, deformation, illumination, scale, and 3D pose. The detection and localization of a face are used to perform 3D normalization to the face prior to 2D linear recognition. Biologically motivated, low-level attentional mechanisms are applied at multiple scales to identify possible face regions. The facial contour is then estimated by computing symmetric enclosure and is used to guide the search for feature points within the face. Symmetric blob detection, limb extraction and signature analysis are used to locate the eyes, mouth and nose of each individual. A database of 3D range data of human heads allows us to align a 3D model to the coordinates of the detected feature points in the input image. The intensity image's textural representation of the face is mapped onto the 3D range data, thereby segmenting the face from the image. The 3D model is then rotated into a frontal view to synthesize a frontal \mug-shot" of the individual. Lighting and shading variations are corrected by histogram tting. Once fully normalized, the image is projected into a low-dimensional subspace via Karhunen-Loeve Decomposition to compress the data and to verify detection. The resulting low-dimensional vector description is matched against a database using simple distance measures to determine the face's identity as one of the previously identi ed training examples. Due to the computational e ciency of the hierarchical detection scheme and the initial step of applying simple attentional mechanisms, tracking faces from a video source could be achieved. ii ACKNOWLEDGEMENTS Acknowledgements Whether you think you can or think you can't { you are right. -Henry Ford Thank you, Professor Martin Levine for making me think that I can, for sharing your extensive knowledge of the eld, and for patiently reminding me each time I got ahead of myself to slow down and re ect. Thank you, Thierry Baron, Marc Bolduc, Stephen Benoit, Don Bui, Franco Callari, Michael Daum, James Elder, Michael Kelly, Gal Sela, Gilbert Soucy, Kenong Wu and John Zelek for your advice, insight and technical expertise. Thanks to Ornella Cavaliere for invaluable advice and support. I also extend my thanks to Professor Peter Caines and Professor George Zames for their help. Mom, Dad, and Carine, this is for you. iii TABLE OF CONTENTS TABLE OF CONTENTS Abstract : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : ii Acknowledgements : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : iii LIST OF FIGURES : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : vii LIST OF TABLES : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : xii CHAPTER
Characteristics of Parent Center Assistance From the Federation for Children With Special Needs

To assist parents of children with disabilities with navigating the special education system, the Individuals With Disabilities in Education Act (IDEA) established Parent Training and Information (PTI) Centers and Community Parent Resource Centers (CPRC) in each state. However, little is known about the 103 total Parent Centers currently operating across the country and whom they serve. We address this research gap by analyzing administrative data on 29,194 parents served over a 13-year period through the only PTI in Massachusetts, the Federation for Children With Special Needs (FCSN). Results show White parents and parents of children with autism spectrum disorders were more likely to contact the FCSN, while parents from rural and economically disadvantaged backgrounds were less likely to do so. Consistent across all backgrounds and disability categories, parents came to the FCSN most frequently for assistance related to individualized education programs (IEPs). Implications related to how Parent Centers can improve family outreach efforts and work with schools and community-based organizations are provided.
Maximum Sustainable throughput Prediction for Data Stream Processing over Public Clouds

In cloud-based stream processing services, the maximum sustainable throughput (MST) is defined as the maximum throughput that a system composed of a fixed number of virtual machines (VMs) can ingest indefinitely. If the incoming data rate exceeds the system's MST, unprocessed data accumulates, eventually making the system inoperable. Thus, it is important for the service provider to keep the MST always larger than the incoming data rate by dynamically changing the number of VMs used by the system. In this paper, we identify a common data processing environment used by modern data stream processing systems, and we propose MST prediction models for this environment. We train the models using linear regression with samples obtained from a few VMs and predict MST for a larger number of VMs. To minimize the time and cost for model training, we statistically determine a set of training samples using Intel's Storm benchmarks with representative resource usage patterns. Using typical use-case benchmarks on Amazon's EC2 public cloud, our experiments show that, training with up to 8 VMs, we can predict MST for streaming applications with less than 4% average prediction error for 12 VMs, 9% for 16 VMs, and 32% for 24 VMs. Further, we evaluate our prediction models with simulation based elastic VM scheduling on a realistic workload. These simulation results show that with 10% over provisioning, our proposed models' cost efficiency is on par with the cost of an optimal scaling policy without incurring any service level agreement violations.