Miracle-dz/UniFAR-FTUs huggingface dataset:
A novel CNN-based match kernel for image retrieval

The recent decade has witnessed remarkable developments of SIFT-based approaches for image retrieval. However, such approaches are inherently insufficient in handling the semantic gap and large viewpoint changes, leading to inferior performance. To address these limitations, this paper extends SIFT-based match kernels by integrating the match functions for SIFT and CNN features. Specifically, a thresholded exponential match kernel for CNN features is proposed to calculate their semantic similarity of images. Thus, images having semantic similarity lower than the threshold will be filtered out, and the remaining images will be assigned semantic weights to adjust the similarity scores measured by SIFT. Enhanced discriminability is achieved in the proposed method by effectively encoding the complementary cues from SIFT and CNN features into the match kernels. Extensive evaluations on benchmark datasets demonstrate the effectiveness of our method, which outperforms existing methods by a considerable margin.
Online Bayesian Inference of Diffusion Networks

Understanding the process by which a contagion disseminates throughout a network is of great importance in many real-world applications. The required sophistication of the inference approach depends on the type of information we want to extract as well as the number of observations that are available to us. We analyze scenarios in which not only the underlying network structure (parental relationships and link strengths) needs to be detected, but also the infection times must be estimated. We assume that our only observation of the diffusion process is a set of time series, one for each node of the network, which exhibit changepoints when an infection occurs. After formulating a model to describe the contagion, and selecting appropriate prior distributions, we seek to find the set of model parameters that best explains our observations. Modeling the problem in a Bayesian framework, we exploit Monte Carlo Markov Chain, sequential Monte Carlo, and time series analysis techniques to develop batch and online inference algorithms. We evaluate the performance of our proposed algorithms via numerical simulations of synthetic network contagions and analysis of real-world datasets.
Understanding the role of schools in the Asian-white gap: a seasonal comparison approach

Abstract Asian Americans are the most highly educated racial group in the United States and are commonly heralded as the model minority for their high academic success. Nevertheless, previous research suggests that Asian Americans may face certain disadvantages in school settings. For example, Asian Americans’ academic advantage over non-Hispanic white students diminishes between kindergarten entry and the next several years of schooling. This study provides a closer examination of the educational progress of Asian American students compared to white students through a seasonal comparison approach. Using the Northwest Evaluation Association, we analyze reading and math scores for over 130,000 Asian American and white students in grades K-7 in approximately 675 public schools across the US. We find that Asian Americans have higher academic achievement than white students in general, but that these advantages are maintained primarily through faster rates of learning during the summer months. When school is in session, the Asian advantage either remains unchanged or shrinks, consistent with the view that some school processes undermine the educational progress of Asian American students relative to white students.
Chemical Characterization and Water Holding Capacity of Fibre-rich Feedstuffs Used for Pigs in Vietnam

During two years, four samples per year were collected in Vietnam from rice bran, cassava residue, brewer’s grain, tofu residue, soybean meal, coconut cake, sweet potato vines and water spinach for chemical analysis and assessment of water holding capacity (WHC). The selected feedstuffs represent fibre-rich plant sources and agro-industry co-products commonly used in pig feeding in Vietnam. The content (g/kg DM) of crude protein (CP), ether extract (EE) and non-starch polysaccharides (NSP) varied between feedstuffs and ranged from 21 to 506 for CP, from 14 to 118 for EE and from 197 to 572 for NSP. Cassava residue had a high starch content of 563 g/kg DM, while sweet potato vines, water spinach, coconut cake and soybean meal had a high content of sugars (63–71 g/kg DM). The content of individual neutral sugars varied between feed ingredients, with the highest content of arabinose, galactose and glucose in tofu residue, the highest content of xylose in brewer’s grain and the highest content of mannose in coconut cake. The content of uronic acid was high for cassava residue, tofu residue, sweet potato vines and water spinach (57–88 g/kg DM). The content of soluble non-cellulosic polysaccharides (S-NCP) was positively correlated (r2 = 0.82) to the WHC. The content (g/kg DM) of CP, NDF, neutral sugars, total NSP, total NCP, S-NCP and total dietary fibre in tofu residue, water spinach and coconut cake varied (p<0.05) between years. In conclusion, diet formulation to pigs can be improved if the variation in chemical composition of the fibre fraction and in WHC between potential feed ingredients is taken into account.
Classification for Alzheimer's disease based on SVM using a spatial texture feature of cortical thickness

Features defined on the cortical surface derived from magnetic resonance imaging provide important information to diagnosis the Alzheimer's disease (AD) and its premonitory symptoms Mild Cognitive Impairment (MCI). In general, the methods based on region-wise features poorly reflect the detailed spatial variation of cortical thickness, and those based on vertex-wise features are sensitive to noise. In this paper, we propose an innovative method to describe the spatial texture information of cortical thickness named Spatial Multi-scale Block Local Binary Patterns. And combine this information with the region-wise characters of cortical thickness as the final classification features. The performance based on the proposed method is significantly higher for the AD/NC, AD/MCI and sMCI/cMCI groups than using PCA or taking the average of cortical thickness zones as classification features.
Semantic Features for Food Image Recognition with Geo-Constraints

Food-related photos have become increasingly very popular, due to social networks, food recommendation and dietary assessment systems. Reliable annotation is essential in those systems, but user-contributed tags are often non-informative and inconsistent, and unconstrained automatic food recognition still has relatively low accuracy. Most works focus on exploiting only the visual content while ignoring the context. In this paper, we improve the food image recognition with using two novel components two kinds of context. Firstly, different from the conventional approach representing image in a visual feature space the visual features, we try to represent the images in the a semantic space (also called semantic simplex), where we model aiming at modeling more context information between each categories. Secondly, we explore leveraging leverage the geographic context of the user and information about geolocation and information about restaurants to simplify the classification problem. Thus, We propose a food recognition framework for the food recognition based on these two kinds of context, based on including semantic features learning and location-adaptive classification. We collected a restaurant-oriented food dataset with food images, dish tags and restaurant-level information, such as the menu and geographic location. Experiments on this dataset show that exploiting geolocation improves around 30% the recognition performance, and the semantic feature has a gain of 3%-10% to the other visual features.
More a Molehill than a Mountain: The Effects of the Blanket Primary on Elected Officials’ Behavior from California

Scholars, politicians, parties, and the U.S. Supreme Court argue that restricting the set of eligible voters for a primary election affects the nature of representation voters receive from elected officials. Evidence supporting this argument is elusive because the same elected officials are often responsible for determining the type of primary election used. Using a quasi-natural experiment resulting from a 1996 constitutional referendum in California, we examine whether expanding the eligible primary electorate from only registered partisans to any registered voter affects who is elected and the positions elected representatives take. We show that the blanket primary appears to favor the election of more moderate representatives and that incumbents take more moderate positions in the U.S. House of Representatives and California Assembly. This is true only in less partisan districts, and there are no effects in districts dominated by a single party. These differential effects suggest that concerns about r.
Application of iterative learning control methods for a service robot with multi-body kinematics

This paper describes the investigation of linear and parametric iterative learning control (ILC) methods with an integrated computed torque control approach and the application in a mobile multi-body service robot. In addition to the controller structure, the development of the nonlinear dynamic model and the trajectory generation for the robot-configuration control are briefly explained.
A study of multichannel noise reduction linear filters in the time domain

We propose a new study of multichannel time-domain noise reduction filters where we decompose the noise-free microphone array observations into two components: the first is correlated with the target signal and perfectly coherent across the sensors while the second consists of residual interference. Then, alternative formulations of well known time-domain filters, namely, the minimum variance distortionless response (MVDR), the linearly constrained minimum variance (LCMV), the multichannel tradeoff, and Wiener filters are given. Besides, the performance of these time-domain filters are analytically investigated and new insights are presented. Numerical results are finally provided to corroborate our study.
The Local Hubble flow: A Manifestation of dark energy

Our local environment at $r<10$ Mpc expands linearly and smoothly, as if ruled by a uniform matter distribution, while observations show the very clumpy local universe. This is a long standing enigma in cosmology. We argue that the recently discovered vacuum or quintessence (dark energy (DE) component with the equation of state $p_Q = w \rho_Q c^2$, $w \in [-1,0)$) from observations of the high-redshift universe may also manifest itself in the properties of the very local Hubble flow. We introduce the concept of the critical distance $r_Q$ where the repulsive force of dark energy starts to dominate over the gravity of a mass concentration. For the Local Group $r_Q$ is about 1.5 Mpc. Intriguingly, at the same distance 1.5 Mpc the linear and very "cold" Hubble flow emerges, with about the global Hubble constant. We also consider the critical epoch $t_Q$, when the DE antigravity began to dominate over the local matter gravity for a galaxy which at the present epoch is in the local DE dominated region. Our main result is that the homogeneous dark energy component, revealed by SNIa observations, resolves the old confrontation between the local Hubble flow and local highly non-uniform, fractal matter distribution. It explains why the Hubble law starts on the outskirts of the Local Group, with the same Hubble constant as globally and with a remarkably small velocity dispersion.
Generalized Privacy Ampli cation

This paper provides a general treatment of privacy ampliication by public discussion, a concept introduced by Bennett, Brassard and Robert for a special scenario. Privacy ampliication is a process that allows two parties to distill a secret key from a common random variable about which an eavesdropper has partial information. The two parties generally know nothing about the eavesdropper's information except that it satisses a certain constraint. The results have applications to unconditionally-secure secret-key agreement protocols and quantum cryptography, and they yield results on wire-tap and broadcast channels for a considerably strengthened deenition of secrecy capacity.
Focal Pharmacological Modulation of Atrioventricular Nodal Conduction via Implantable Catheter: A Novel Therapy for Atrial Fibrillation?

Background— Pharmacological ventricular rate control is an acceptable atrial fibrillation (AF) therapy limited by systemic toxicity. We postulate that focal catheter-based drug delivery into the atrioventricular nodal (AVN) region may effectively control ventricular rate during AF without systemic toxicity. This study evaluated the effects of focally administered acetylcholine on AVN conduction and refractoriness during sinus rhythm and AF. Methods and Results— Canines (n=7) were anesthetized and instrumented to assess cardiac electrophysiology and blood pressure. A custom drug delivery catheter was implanted in the AVN region. Incremental doses of acetylcholine starting at 10 &mgr;g/min were infused until complete AV block was achieved. Acetylcholine induced dose-dependent AV block. AF induction and electrophysiology measurements were performed during baseline and acetylcholine-induced first-degree and third-degree AV block. During AF, infusion of acetylcholine decreased ventricular rates from 182±32 to 77±28 and 28±8 bpm (first-degree and third-degree AV block, respectively; P<0.05). At the first-degree AV block dose, AVN effective refractory period increased from 186±37 to 282±33 ms, and Wenckebach cycle length increased from 271±29 to 378±58 ms (P<0.05). The first-degree AV block dose prolonged AV and AH intervals by 26% and 23% (P<0.05), whereas AA intervals and blood pressure remained unchanged, demonstrating a local effect. All effects were reversed 20 minutes after infusion was stopped. Conclusions— Focal acetylcholine delivery into the AVN increased AVN refractoriness and significantly decreased ventricular rate response during induced AF in a dose-related, reversible manner without systemic side effects. This may represent a novel therapy for AF whereby ventricular rate is controlled with the use of an implantable drug delivery system.
In silico candidates miRNA prediction From genome Dictyostelium discoideum

The discovery of microRNAs, almost 10 years ago, changed dramatically our perspective on eukaryotic gene expression regulation. However, the broad and important functions of these regulators are only now becoming apparent. Dictyostelium discoideum is one of the simplest studied eukaryote that possesses true multicellularity and provides unique advantages for studying fundamental cellular processes with powerful molecular genetic, biochemical, and cell biological tools. Small RNAs play crucial roles in regulation of gene expression in many eukaryotes. Different methods for elucidation of microRNA from the genome are described briefly. However, in absence of availability of microRNA sequence data from closely related species, the identification of microRNA in D. discoideum is challenging. Here we report the probable microRNA candidates from the social amoeba D. discoideum. We have used two approaches for prediction of microRNA from the social amoeba D. discoideum. Using comparative genomics approach, we identified 171 putative candidate microRNA and its precursor from D. discoideum genome sequence. In ab initio approach we have identified 79 putative candidate microRNA and its 116 precursor using neural network method.
A power aware system level interconnect design methodology for latency-insensitive systems

Latency-insensitive interconnects require first-in-first-out buffers (FIFO) for flow-control and storage. Interconnect delays are not scaling in proportion to the clock period and hence multiple stages of FIFOs will be needed for high performance interconnects. FIFOs in the interconnect are a significant contributor to the total power consumption. In this work, we propose a design methodology to synthesize a low power interconnect channel containing series connected FIFOs for latency-insensitive systems. Our approach is the first to consider and simultaneously optimize the channel clock frequency, voltage and the FIFO sizes to minimize the power consumption. For small problem size, we show that our approach finds solutions which are close to optimal. The power aware interconnect channel synthesis is affected by the system parameters like the data production rate and data consumption rate. The choice of optimal channel clock frequency, voltage and FIFO sizes can lead to power savings as high as 77.7%, 83.6% and 87% for a 3 stage, 4 stage and a 5 stage channel respectively.
‘(Dis)empowered by what we see … ’: exploring the use of video-stimulated reflection in physical education pedagogy and practice

ABSTRACT Reflexive accounts of physical education (PE) pedagogy and practice offer potential to reveal much about the intended and unintended learning, for both students and teachers, that can result from certain pedagogic encounters. Despite the promotion of reflective practice/s as a possible ‘panacea’ for improved teaching and learning, there is widespread concern that such approaches have relatively little influence in ‘shaking or stirring’ the deep-seated notions of effective pedagogy that are held by many physical educators [Mordal-Moen, K. , & Green, K. (2014). Neither shaking nor stirring: A case study of reflexivity in Norwegian PE teacher education. Sport, Education and Society, 19(4), 415–434]. Inherent here are concerns that an increase in critical awareness alone does not necessarily lead to pedagogic translation [Hickey, C. (2001). “I feel enlightened now, but … ”: The limits to the pedagogic translation of critical social discourses in PE. Journal of Teaching in Physical Education, 20(3), 227–246]. Against that backdrop, we continue to support the use of reflective practice techniques as tools through which to interrogate the socio-cultural conditions and power relations that install enduring notions of effective pedagogy and dominant professional identities [Coia, L. , & Taylor, M. (2017). Let’s stay in the swamp: Poststructural feminist reflective practice. In R. Brandenburg, K. Glasswell, M. Jones, & J. Ryan (Eds.), Reflective theory and practice in teacher education (pp. 49–62). Singapore: Springer Singapore]. In this paper, we explore PE pedagogy and practice through reflective practice with two female teachers in all-boys’ schools. Specifically, we critique the use of video-stimulated reflections from a feminist poststructuralist perspective to make visible the possibilities, challenges and tensions inherent in this methodological approach.
NCFS: On the Practicality and Extensibility of a Network-Coding-Based Distributed File System

An emerging application of network coding is to improve the robustness of distributed storage. Recent theoretical work has shown that a class of regenerating codes, which are based on the concept of network coding, can improve the data repair performance over traditional storage schemes such as erasure coding. However, there remain open issues regarding the feasibility of deploying regenerating codes in practical storage systems. We present NCFS, a distributed file system that realizes regenerating codes under real network settings. NCFS transparently stripes data across multiple storage nodes, without requiring the storage nodes to coordinate among themselves. It adopts a layered design that allows extensibility, such that different storage schemes can be readily included into NCFS. We deploy and evaluate our NCFS prototype in different real network settings. In particular, we use NCFS to conduct an empirical study of different storage schemes, including the traditional erasure codes RAID-5 and RAID-6, and a special family of regenerating codes that are based on E-MBR [16]. Our work provides a practical and extensible platform for realizing theories of regenerating codes in distributed file systems.
Depth estimation from a single image in pedestrian candidate generation

Depth estimation, which is mostly performed by stereo vision, is a remarkable task in vision and scene understanding. In this paper, depth map estimation from a single image is investigated and applied in pedestrian candidate generation. To recover accurate depth map from a single image, a Markov Random Field (MRF) model that incorporates both image depth cues and the relationships between different parts of the image is employed. The MRF model can be trained via supervised learning. Then a method is proposed to generate pedestrian candidates using both our estimated depth information and geometric information achieved from the image. Both representations of the scene are fused to limit the region of interest to objects standing vertically on the ground and having certain height. The proposed algorithm is tested using a public database and a considerable reduction in the number of candidate windows is achieved, which translates into a significant time-saving.
Efficient and Scalable Purification of Cardiomyocytes from Human Embryonic and Induced Pluripotent Stem Cells by VCAM1 Surface Expression

Rationale Human embryonic and induced pluripotent stem cells (hESCs/hiPSCs) are promising cell sources for cardiac regenerative medicine. To realize hESC/hiPSC-based cardiac cell therapy, efficient induction, purification, and transplantation methods for cardiomyocytes are required. Though marker gene transduction or fluorescent-based purification methods have been reported, fast, efficient and scalable purification methods with no genetic modification are essential for clinical purpose but have not yet been established. In this study, we attempted to identify cell surface markers for cardiomyocytes derived from hESC/hiPSCs. Method and Result We adopted a previously reported differentiation protocol for hESCs based on high density monolayer culture to hiPSCs with some modification. Cardiac troponin-T (TNNT2)-positive cardiomyocytes appeared robustly with 30–70% efficiency. Using this differentiation method, we screened 242 antibodies for human cell surface molecules to isolate cardiomyocytes derived from hiPSCs and identified anti-VCAM1 (Vascular cell adhesion molecule 1) antibody specifically marked cardiomyocytes. TNNT2-positive cells were detected at day 7–8 after induction and 80% of them became VCAM1-positive by day 11. Approximately 95–98% of VCAM1-positive cells at day 11 were positive for TNNT2. VCAM1 was exclusive with CD144 (endothelium), CD140b (pericytes) and TRA-1-60 (undifferentiated hESCs/hiPSCs). 95% of MACS-purified cells were positive for TNNT2. MACS purification yielded 5−10×105 VCAM1-positive cells from a single well of a six-well culture plate. Purified VCAM1-positive cells displayed molecular and functional features of cardiomyocytes. VCAM1 also specifically marked cardiomyocytes derived from other hESC or hiPSC lines. Conclusion We succeeded in efficiently inducing cardiomyocytes from hESCs/hiPSCs and identifying VCAM1 as a potent cell surface marker for robust, efficient and scalable purification of cardiomyocytes from hESC/hiPSCs. These findings would offer a valuable technological basis for hESC/hiPSC-based cell therapy.