Miracle-dz/UniFAR-FTUs huggingface dataset:
Bilinear random projections for locality-sensitive binary codes

Locality-sensitive hashing (LSH) is a popular data-independent indexing method for approximate similarity search, where random projections followed by quantization hash the points from the database so as to ensure that the probability of collision is much higher for objects that are close to each other than for those that are far apart. Most of high-dimensional visual descriptors for images exhibit a natural matrix structure. When visual descriptors are represented by high-dimensional feature vectors and long binary codes are assigned, a random projection matrix requires expensive complexities in both space and time. In this paper we analyze a bilinear random projection method where feature matrices are transformed to binary codes by two smaller random projection matrices. We base our theoretical analysis on extending Raginsky and Lazebnik's result where random Fourier features are composed with random binary quantizers to form locality sensitive binary codes. To this end, we answer the following two questions: (1) whether a bilinear random projection also yields similarity-preserving binary codes; (2) whether a bilinear random projection yields performance gain or loss, compared to a large linear projection. Regarding the first question, we present upper and lower bounds on the expected Hamming distance between binary codes produced by bilinear random projections. In regards to the second question, we analyze the upper and lower bounds on covariance between two bits of binary codes, showing that the correlation between two bits is small. Numerical experiments on MNIST and Flickr45K datasets confirm the validity of our method.
Instance selection for model-based classifiers

Aspects of a classifier's training dataset can often make building a helpful and high accuracy classifier difficult. Instance selection addresses some of the issues in a dataset by selecting a subset of the data in such a way that learning from the reduced dataset leads to a better classifier. This work introduces an integer programming formulation of instance selection that relies on column generation techniques to obtain a good solution to the problem. Experimental results show that instance selection improves the usefulness of some classifiers by optimizing the training data so that that the training dataset has easier to learn boundaries between class values. Also included in this paper are two case studies from the Surveillance, Epidemiology, and End Results (SEER) database that further confirm the benefit of instance selection. Overall, results indicate that performing instance selection for a classifier is a competitive classification approach. However, it should be noted that instance selection might overfit classifiers that have already achieved a good fit to the dataset.
RFID Light Bulb

Radio-Frequency Identification (RFID) technology has the potential to provide inexpensive, wireless, battery-free connectivity and interactivity for objects that are traditionally not instrumented. However, these systems have not seen widespread deployment outside warehouses and supply chains, owing to the complexity of installing bulky RFID readers, antennas, and their supporting power and network infrastructure. In this work, we leverage advances in semiconductor optics, RF antenna design and system integration to create a hybrid RFID reader and smart LED lamp, in the form factor of a standard light bulb. This makes deploying RFID readers literally as easy as screwing in a light bulb. We explore the home-scale RFID interactions enabled by these smart bulbs, including infrastructure monitoring, localization and guided navigation, and standalone lighting effects.
Surface charge and calcium channel saturation in bullfrog sympathetic neurons

Currents carried by Ba2+ through calcium channels were recorded in the whole-cell configuration in isolated frog sympathetic neurons. The effect of surface charge on the apparent saturation of the channel with Ba2+ was examined by varying [Ba2+]o and ionic strength. The current increased with [Ba2+]o, and the I-V relation and the activation curve shifted to more positive voltages. The shift of activation could be described by Gouy-Chapman theory, with a surface charge density of 1 e- /140 A2, calculated from the Grahame equation. Changes in ionic strength (replacing N-methyl-D-glucamine with sucrose) shifted the activation curve as expected for a surface charge density of 1 e-/85 A2, in reasonable agreement with the value from changing [Ba2+]o. The instantaneous I-V for fully activated channels also changed with ionic strength, which could be described either by a low surface charge density (less than 1 e-/1,500 A2), or by block by NMG with Kd approximately 300 mM (assuming no surface charge). We conclude that the channel permeation mechanism sees much less surface charge than the gating mechanism. The peak inward current saturated with an apparent Kd = 11.6 mM for Ba2+, while the instantaneous I-V saturated with an apparent Kd = 23.5 mM at 0 mV. This discrepancy can be explained by a lower surface charge near the pore, compared to the voltage sensor. After correction for a surface charge near the pore of 1 e-/1,500 A2, the instantaneous I-V saturated as a function of local [Ba2+]o, with Kd = 65 mM. These results suggest that the channel pore does bind Ba2+ in a saturable manner, but the current-[Ba2+]o relationship may be significantly affected by surface charge.
Fibroblast Growth Factor 21 Suppresses Adipogenesis in Pig Intramuscular Fat Cells

Fibroblast growth factor 21 (FGF21) plays an important role in the treatment of disease associated with muscle insulin resistance which is characterized by various factors, such as intramuscular triglyceride (IMT) content. Studies have also shown that FGF21 inhibits triglyceride synthesis in vivo. However, the precise mechanism whereby FGF21 regulates triglyceride metabolism in intramuscular fat (IMF), which may influence the muscle insulin sensitivity, is not clearly understood. In order to understand the role of FGF21 in IMF deposition, we performed FGF21 overexpression in IMF cells by stable transfection. Our results showed that FGF21 inhibited the key adipogenesis gene mRNA expression of peroxisome proliferator-activated receptor gamma (PPARG), CCAAT/enhancer-binding protein (CEBP) family by reducing lysine-specific demethylase 1 (LSD1) expression which led to significant decline in lipid accumulation, and the result was confirmed by Western blot. Moreover, triggered by FGF21, parts of the adipokines—fatty acid-binding protein 4 (FABP4), glucose transporter 4 (GLUT4), adiponectin (ADIPOQ), and perilipin (PLIN1)—were also down-regulated. Furthermore, FGF21 gene expression was suppressed by transcription factor CEBP beta (CEBPB) which contributed strongly to triglyceride synthesis. Taken together, our study is the first to experimentally demonstrate FGF21 emerging as an efficient blockade of adipogenesis in IMF, thus also providing a new understanding of the mechanism whereby FGF21 improves insulin sensitivity.
MEANT at WMT 2013: A Tunable, Accurate yet Inexpensive Semantic Frame Based MT Evaluation Metric

The linguistically transparent MEANT and UMEANT metrics are tunable, simple yet highly effective, fully automatic approximation to the human HMEANT MT evaluation metric which measures semantic frame similarity between MT output and reference translations. In this paper, we describe HKUST’s submission to the WMT 2013 metrics evaluation task, MEANT and UMEANT. MEANT is optimized by tuning a small number of weights—one for each semantic role label—so as to maximize correlation with human adequacy judgment on a development set. UMEANT is an unsupervised version where weights for each semantic role label are estimated via an inexpensive unsupervised approach, as opposed to MEANT’s supervised method relying on more expensive grid search. In this paper, we present a battery of experiments for optimizing MEANT on different development sets to determine the set of weights that maximize MEANT’s accuracy and stability. Evaluated on test sets from the WMT 2012/2011 metrics evaluation, both MEANT and UMEANT achieve competitive correlations with human judgments using nothing more than a monolingual corpus and an automatic shallow semantic parser.
Competitive-Cooperative-Concurrent Reinforcement Learning with Importance Sampling

The speed and performance of learning depend on the complexity of the learner. A simple learner with few parameters and no internal states can quickly obtain a reactive policy, but its performance is limited. A learner with many parameters and internal states may finally achieve high performance, but it may take enormous time for learning. Therefore, it is difficult to decide in advance which architecture and algorithm should be used for a new task. In this paper, we propose a new framework for selecting an appropriate policy out of a set of heterogeneous reinforcement learning modules and for correctly improving the policies of all learning modules including those not selected, using the method of importance sampling. In this framework, multiple heterogeneous learning modules sharing the same sensory-motor system can compete to act and cooperate to learn, allowing the overall learning system to obtain a good performance faster. We show in a simulation of partially-observable pole balancing task and robotic experiments of battery-pack foraging and partially observable T-maze tasks that a complex learning module trained with the proposed method can actually learn faster than when it is trained alone, by exploiting task-relevant episodes generated by suboptimal, but fast-learning modules.
Angiostatin Inhibits Coronary Angiogenesis During Impaired Production of Nitric Oxide

Background—The in vivo mechanism by which inhibition of NO synthase impairs ischemia-induced coronary vascular growth is unknown. We hypothesized that production of the growth inhibitor angiostatin increases during decreased NO production, blunting angiogenesis and collateral growth. Methods and Results—Measurements were made in myocardial tissue or interstitial fluid (MIF) from dogs undergoing repetitive coronary occlusions under control conditions or during antagonism of NO synthase (NG-nitro-l-arginine methyl ester [L-NAME]) for 7, 14, or 21 days. A sham group was instrumented identically but received no occlusions. In controls, capillary density in the ischemic zone increased initially but returned to baseline at the later times. In the L-NAME group, capillary density was lower at 7 days compared with that of controls. MIF from control dogs induced in vitro endothelial tube formation and cell proliferation, significantly greater than that from the L-NAME group. MIF from shams did not stimulate tube formation. In controls or shams, tube formation or cell proliferation did not change after administration of antiangiostatin, but this antibody restored the responses to control levels in the L-NAME group. Angiostatin expression in MIF was increased in the L-NAME group compared with controls and shams. The activities of tissue matrix metalloproteinases (MMPs) MMP-2 and MMP-9, which generate angiostatin, were increased in the L-NAME group. Conclusions—Inhibition of NO synthase increased expression of angiostatin and activities of MMP-2 and MMP-9. Our findings indicate that angiostatin inhibits coronary angiogenesis during compromised NO production and may underscore the impairment of coronary angiogenesis during endothelial dysfunction.
A systematic formulation of the continuous adjoint method applied to viscous aerodynamic design

A continuous adjoint approach to aerodynamic design for viscous compressible flows on unstructured grids is developed, and three important problems raised in the continuous adjoint literature are solved: using tools of shape deformation of boundary integrals a generic adjoint formulation is developed with independence of the kind of mesh used; a systematic way of reducing the 2 nd order derivative terms which arise is presented which avoids the need of using higher order numerical solvers to obtain accurate approximations of the 2 nd order derivatives; and finally, the class of admissible optimization functionals is clarified. Several remarks are made concerning the longstanding discrete vs. continuous adjoint dichotomy, with the emphasis not on the advantages or disadvantages of each method, but rather on the well-posedness of the approaches. The accuracy of the sensitivity derivatives is assessed by comparison with finite-difference computations, and the validity of the overall methodology is illustrated with design examples under demanding subsonic conditions.
Inhibited and Enhanced Spontaneous Emission Using Silicon-Based on Finite Thickness Photonic Crystal Waveguides

Inhibited and enhanced spontaneous emission of light is essential to quantum optics in design and development of high efficiency optical devices which are useful to security optical communication system. Thus, we performed to develop an efficient single photon source by controlling inhibited or enhanced spontaneous emission of the photon using silicon-based honeycomb lattice patterned finite thickness photonic crystal waveguide. A quantum dot embedded in planar photonic crystal membrane waveguide is the light source. The honeycomb lattice of circular air holes on silicon plate is simulated to obtain large completely photonic band gaps. This significant property shows the potential applied guide modes of photonic crystal membrane for controlling inhibited or enhanced spontaneous emission between the quantum dots and the photonic crystal waveguide. Significantly, this work is oriented to produce the novel single photon sources which can emit one photon at a time for the quantum optical security network with single photon state. In addition to the honeycomb lattice can easily be made on a Si on insulator (SOI) wafer.
Study of Carbon Nano-Tubes Effects on the Chondrogenesis of Human Adipose Derived Stem Cells in Alginate Scaffold

Background: Osteoarthritis is one of the most common diseases in middle-aged populations in the World and could become the fourth principal cause of disability by the year 2020. One of the critical properties for cartilage tissue engineering (TE) is the ability of scaffolds to closely mimic the extracellular matrix and bond to the host tissue. Therefore, TE has been presented as a technique to introduce the best combination of cells and biomaterial scaffold and to stimulate growth factors to produce a cartilage tissue resembling natural articular cartilage. The aim of study is to improve differentiation of adipose derived stem cells (ADSCs) into chondrocytes in order to provide a safe and modern treatment for patients suffering from cartilage damages. Methods: After functionalization, dispersions and sterilizing carbon nano-tubes (CNTs), a new type of nanocomposite gel was prepared from water-soluble CNTs and alginate. ADSCs seeded in 1.5% alginate scaffold and cultured in chondrogenic media with and without transforming growth factor-β1 (TGF-β1) for 7 and 14 days. The genes expression of sex determining region Y-box 9 (SOX9), types II and X collagens was assessed by real-time polymerase chain reaction and the amount of aggrecan (AGC) and type I collagen was measured by ELISA. Results: Our findings showed that the expression of essential cartilage markers, SOX9, type II collagen and AGC, in differentiated ADSCs at the concentration of 1 μg/ml CNTs in the presence of TGF-β1 were significantly increased in comparison with the control group (P < 0.001). Meanwhile, type X collagen expression and also type I collagen production were significantly decreased (P < 0.001). Conclusions: The results showed that utilized three-dimensional scaffold had a brilliant effect in promoting gene expression of chondrogenesis.
Drivers of Internet shopping

The two distinct forms of e-commerce—business-to-business (B2B) and business-toconsumer (B2C)—have emerged as an important way of doing business that will surely grow in years to come. According to some recent forecasts, total Web sales will reach 1.4 trillion dollars in 2004. Most of the growth, however, is expected to be in B2B, with projected B2C barely constituting 21% of Web sales in 2004 [3]. With the exception of software, hardware, travel services, and a few other niche areas, shopping on the Internet is far from universal, even among people who spend long hours online. While B2C has not yet attained widespread acceptance in the overall scheme of e-commerce, the potential is definitely there. Indeed, there is substantial room for the growth of B2C once the major obstacles are overcome. Although some of the hurdles to the growth of B2C e-commerce have been discussed in the literature, we still lack a good understanding of consumer behavior on the Internet and how new technologies challenge the traditional assumptions underlying conventional theories and models. Butler and Peppard [1], for example, explain the failure of IBM-sponsored Web shopping malls on a lack of understanding of the true nature of consumer behavior on the Internet. A critical understanding of this behavior in cyberspace, as in the physical world, cannot be achieved without a good appreciation of the factors affecting the purchase decision. If cybermarketers know how consumers make these decisions, they can adjust their marketing strategies to fit this new way of selling so that they can convert potential customers to real ones and retain them. Similarly, Web site designers, who are faced with the difficult question of how to design pages to make them not only popular but also effective in increasing sales, can benefit from such an understanding. In this research we applied well-established behavioral theories to explain Internet consumer behavior. We then conducted a longitudinal survey study to identify key factors influencing purchasing on the Web and to examine their relative importance. The results of this study enhance our understanding of consumer behavior on the Web and lead to valuable implications for marketers and managers on how to
Evaluation of Gas Permeability in Microfluidic Device by Confocal Micro-PIV Combined With LIF Technique

Microfluidic devices with the gas permeability through polymer membranes were developed for further high-efficiency of gas-liquid chemical reactions and high-accuracy of environmental diagnosis. The devices were composed of a cover glass and a polydimethylsiloxane (PDMS) chip which has the ability to permeate various gases, because PDMS is made of the elastomeric material. In the chip, microchannels with a width ranging from a few micrometers to a few hundred micrometers were manufactured by using the cryogenic micro machining. The gas permeation phenomena in microchannels are dominated by several factors, such as the gas and liquid flow rates, the membrane thickness between gas and liquid flow, and the surface area of membranes. The advantage of the present work is to realize the simple control of the gas permeability by changing the surface roughness of PDMS, because the cryogenic micro machining enables to control the surface roughness of microchannels and the increase in its roughness yields that in the surface area of membranes. For the evaluation of the gas permeability, the velocity and dissolved gas concentration distribution in the liquid flow field were measured by utilizing micron-resolution particle image velocimetry combined with laser induced fluorescence, and the measurement system was based on the confocal microscope to improve the depth resolution drastically. The experiments were performed under the several conditions with a change in the gas flow rate, the PDMS membrane thickness and the surface roughness, which affect the gas permeation phenomena. The results indicate that the velocity-vector distributions in the liquid flow have a similar pattern and the magnitudes of the velocity are approximately the same values under all conditions, while the dissolved gas concentration distributions have different patterns. It was quantitatively clear that the gas permeability through PDMS membranes was increased with an increase in the surface roughness and has the linearity to the surface area of membranes. The important conclusion is that the proposed device achieves to control the gas permeability by using the elastomeric material and changing the surface roughness. © 2011 ASME
Micromechanics prediction of the effective elastic moduli of graphene sheet-reinforced polymer nanocomposites

We investigate the stiffening effect of graphene sheets dispersed in polymer nanocomposites using the Mori–Tanaka micromechanics method. The effective elastic moduli of graphene sheet-reinforced composites are first predicted by assuming that all the graphene sheets are either aligned or randomly oriented in the polymer matrix while maintaining their platelet-like shape. It is shown that a very low content of graphene sheets can considerably enhance the effective stiffness of the composite. The superiority of graphene sheets as a kind of reinforcement is further verified by a comparison with carbon nanotubes, another promising nanofiller in polymer composites. In addition, we analyze several critical physical mechanisms that may affect the reinforcing effects, including the agglomeration, stacking-up and rolling-up of graphene sheets. The results reveal the extent to which these factors will negatively influence the elastic moduli of graphene sheet-reinforced nanocomposites. This theoretical study may help to understand the relevant experimental results and facilitate the mechanical characterization and optimal synthesis of these kinds of novel and highly promising nanocomposites.
Person Authentication Using Color Face Recognition

Face is a complex multidimensional structure and needs good computing techniques for recognition. Our approach consists of two feature extraction algorithms that are Gabor wavelet and local binary pattern for the purpose of color face recognition. These methods provide excellent recognition rates for face images taken under severe variation in pose, illumination as well as for small resolution face images. Face recognition is done by Principal Component Analysis. This work shows the performance of color face recognition with YCbCr color space using GW and LBP. The experiments are performed on FEI color database. Result includes recognition rate (in percent) for different method such as YCbCr and Gray using GW and LBP for different pixels resolution such as 54x36, 81x54, and 108x72.
Relearning of metric relations in a familiar environment

The ability of spatial memory to adapt to changes in an environment was investigated in an experiment, where participants navigated in a virtual reality reconstruction of a city they were familiar with. In the learning phase, participants used a special bike to pedal through a reconstruction of the inner city of Tubingen, Germany. Their task was to memorize the locations of the buildings in the city. In the experimental conditions, the city was scaled nonuniformly compared to the actual city, that is the north-south axis was streched or compressed relative to the east-west axis. In the control condition, the proportions of the virtual city were not distorted. In the test phase participants were asked to judge distances between buildings from memory by both, verbal magnitude estimation and navigation. They had to estimate all possible distances between 8 pairs of houses. Distance estimates differed between scaling conditions, but they reflected the scaling of the virtual environment only in one condition. Therefore, it could be shown, that modifications of a familiar environment on a purely metrical level affect spatial representations, but there is only weak evidence that distance estimates mirror the distortions of the virtual environments.