Miracle-dz/UniFAR-FTUs huggingface dataset:
Contrastively-reinforced Attention Convolutional Neural Network for Fine-grained Image Recognition

Fine-grained visual classiﬁcation is inherently challenging because of its inter-class similarity and intra-class variance. However, by contrasting the images with same/different labels, a human can instinctively notice that the key clues lie in certain objects while other objects are ignorable. Inspired by this, we propose Contrastively-reinforced Attention Convolutional Neural Network (CRA-CNN), which reinforces the attention awareness of deep activations. CRA-CNN mainly contains two parts: the classiﬁcation stream and attention regularization stream. The former classiﬁes the input image and simultaneously proposes to divide the input visual information into attention and redundancy. The latter evaluates the attention/redundancy proposal by classifying the attention and contrasting the attention/redundancy of various inputs. The evaluation information is backpropa-gated and forces the classiﬁcation stream to improve its awareness of visual attention, which helps classiﬁcation. Experimental results on CUB-Birds and Stanford Cars show that CRA-CNN distinctly outperforms the baselines and is comparable with state-of-art studies despite its simplicity.
Mesh Simplification by Curvature-Enhanced Quadratic Error Metrics

Polygonal meshes have a significant role in computer graphics, design and manufacturing technology for surface representation and it is often required to reduce their complexity to save memory. An efficient algorithm for detail retaining mesh simplification is proposed; in particular, the method presented is an iterative edge contraction algorithm based on the work of Garland and Heckberts. The original algorithm is improved by enhancing the quadratic error metrics with a penalizing factor based on discrete Gaussian curvature, which is estimated efficiently through the Gauss-Bonnet theorem, to account for the presence of fine details during the edge decimation process. Experimental results show that this new algorithm helps preserve the visually salient features of the model without compromising performance.
An introductory classroom exercise on protein molecular model visualization and detailed analysis of protein–ligand binding

A learning module for molecular level analysis of protein structure and ligand/drug interaction through the visualization of X‐ray diffraction is presented. Using DeepView as molecular model visualization software, students learn about the general concepts of protein structure. This Biochemistry classroom exercise is designed to be carried out by following the detailed instructions that make software handling straightforward. Students learn about protein structure and gain insight into the molecular level of the interaction of two active compounds with their receptor. © 2013 by The International Union of Biochemistry and Molecular Biology 41(2):118‐124, 2013.
Text Attribute Aggregation and Visual Feature Decomposition for Person Search

Person search is the task of retrieving a pedestrian image given a list of text attributes. We investigate a novel mechanism that operates in feature embedding space for matching data across visual and text modalities. We propose a framework (TAVD) with two complementary modules: Text attribute feature aggregation (TA) that aggregates multiple semantic attributes in a bimodal space for globally matching text descriptions with images and Visual feature decomposition (VD) which performs feature embedding for locally matching image regions with text attributes. The results and comparisons to the state of the art on three standard benchmarks demonstrate that our solution is an effective strategy for retrieving person images while retaining the semantic of each query text attribute.
CS 294 Partition Functions Fall 2020

We have seen that many natural counting problems, including their generalizations to computing partition functions, are #P-hard. Notwithstanding the two classical algorithms we saw in the last lecture, it turns out that almost all interesting problems of this type are hard, and this even applies to computing partition functions Z(λ) at any specific value of λ (except for one or two trivial values). For the rest of the course we will assume that we have no hope of finding polynomial time algorithms for the vast majority of problems of this kind.
Yeast dynamin and Ypt6 function in parallel for the endosome‐to‐Golgi retrieval of Snc1

Protein recycling is an important cellular process required for cell homeostasis. Results from prior studies have shown that vacuolar sorting protein‐1 (Vps1), a dynamin homolog in yeast, is implicated in protein recycling from the endosome to the trans‐Golgi Network (TGN). However, the function of Vps1 in relation to Ypt6, a master GTPase in the recycling pathway, remains unknown. The present study reveals that Vps1 physically interacts with Ypt6 if at least one of them is full‐length. We found that overexpression of full‐length Vps1, but not GTP hydrolysis‐defective Vps1 mutants, is sufficient to rescue abnormal phenotypes of Snc1 distribution provoked by the loss of Ypt6, and vice versa. This suggests that Vps1 and Ypt6 function in parallel pathways instead of in a sequential pathway and that GTP binding/hydrolysis of Vps1 is required for proper traffic of Snc1 toward the TGN. Additionally, we identified two novel Vps1‐binding partners, Vti1 and Snc2, which function for the endosome‐derived vesicle fusion at the TGN. Taken together, the present study demonstrates that Vps1 plays a role in later stages of the endosome‐to‐TGN traffic.
Variational Imitation Learning with Diverse-quality Demonstrations

(19) Since ftpφ,ωq “ Ftpφ,ω,ψq “ maxψ Ftpφ,ω,ψq, we have that fpφ,ωq “ maxψ Fpφ,ω,ψq. A.2. Lower-bound G Next, we derive the lower-bound G of gpφ,ωq “ logZφ,ω . We first derive a trivial lower-bound using a “general” variational distribution over trajectories and discuss its issue. Then, we derive a lower-bound presented in the paper by using a structured variational distribution. Recall that the normalization term Zφ,ω of the model pφ,ω is given by Zφ,ω “ K
WAMDA: Weighted Alignment of Sources for Multi-source Domain Adaptation

Unsupervised Domain Adaptation aims to learn a model for an unlabelled target domain, given access to a single labelled but differently distributed source domain. However, often multiple labelled sources which share complementary information are present, resulting in the more practical problem of multi-source domain adaptation (MSDA). Recent works in MSDA learn a domain-invariant space from the sources and target. However, they treat each source to be equally relevant to the target and are not sensitive towards the intrinsic statistical similarities amongst domains. In this work, we propose a novel method for MSDA, termed WAMDA , which utilizes the multiple sources based on their relative importance to the target. Our aim is to explore the relevance of each source-target alignment and source-source alignment, and then perform weighted alignment of domains by using the relevance scores. We experimentally validate the performance of our proposed method on multiple datasets, and achieve either state-of-the-art results or competitive performances across all these datasets.
FDI on the Move: the Facilitation of Greenfield FDI and Cross-border M&A by Migrant Networks

This paper empirically investigates the heterogeneous roles of migrant networks in facilitating foreign direct investment (FDI). The impact of migrant networks varies depending on the entry mode of FDI, either greenfield or merger and acquisition (M&A), as well as the barriers associated with each. Using deal-level data on Chinese outward FDI and Chinese migrant stocks in 135 countries from 2003-2014, we find that migrant networks have a positive and significant impact on FDI, which is larger for M&A than greenfield FDI. Additional findings provide strong evidence of migrants’ role in overcoming informal and formal informational barriers. The positive relationship is more pronounced for investment from non-listed companies and privately-owned enterprises, for cross-industry FDI, in service industries, and in host countries with tighter regulations and culturally more distant from China. We also provide evidence consistent with learning-by-investing, where the impact of migrant networks declines with the investors’ experience. Our results are robust to various specifications and estimation methods, including an instrumental variables approach that addresses potential endogeneity concerns. JEL: F21, F22, F23
Price and Energy Demand Optimization in the Microgrids-Manager System

Microgrid has become one of the most potential research field because of its sustainability, flexibility and economic superiority. One of the characters of Microgrid is that new energy like photovoltaic(PV) or wind turbines can be used in the system. A user in one Microgrid loaded with solar panels may no longer be only one consumer but also can store and gain energy used for selling to other users who need it. In this research, a transaction model which contains both Microgrids and the utility grid is formulated and researched. In every Microgrid, there are users which loads PV or wind turbines which can generate energy every day. A profit organization in the system, which is named Manager is set to do the optimization so that the system is divided into two sides. A game is designed between the two sides and both of them want to achieve its optimization based on their own evaluation method. The evaluation method of Microgrids is estimate utility function which is designed for every user and the one for Manager is the income function which is designed for it. Both sides will make their decisions based on the thought that the decision other side makes is the best. The paper proves the existence and uniqueness of the optimal decision, enumerates a detailed case of the system model we formulated and then designs an algorithm to obtain the equilibrium in every time slot in one day.
The Balmer Decrement of Low Redshift Active Galactic Nuclei

Observations indicate that emission from the Narrow Line Region (NLR) of Active Galactic Nuclei (AGN) is reddened by dust. This dust is generally assumed to be located in the NLR itself. The Broad Line Region (BLR) is assumed to be dust-free, as the temperatures and photon fluxes in the region are too extreme for dust to survive. The electron density in the NLR is ∼ 103 cm−3. The electron density of the BLR is not well known, but is believed to be higher than that of the NLR to within an order of magnitude of 108 − 1010. I have investigated the Balmer decrement (the ratio of Hα/Hβ emission line fluxes) for a sample of 873 AGN selected from the Sloan Digital Sky Survey, Data Release 7. Using two different methods to determine the narrow and broad components of the Hα and Hβ emission lines, I calculated the NLR Balmer decrement and the BLR Balmer decrement before any correction for dust extinction. I then used the NLR Balmer decrement to determine the color excess, E(B-V), and the total extinction, A(V). These results indicate an average of 1.5− 2.5 magnitudes of dust in the NLR of my sample objects. I corrected the spectra for extinction due to dust, after which I calculated the Balmer decrements of the corrected spectra. By correcting the AGN spectra for the internal reddening caused by dust in the NLR, I assumed to have accounted for any internal reddening of the AGN. Based on this assumption, I used The BLR Balmer decrements to examine the gas density in the BLR. For this sample, I find a mean BLR Balmer decrement of 1.7− 1.8 after correction for the effects of dust. This result indicates an electron density in the BLR of ∼ 1013 cm−3.
What Do Divided Cities Have in Common? An International Comparison of Income Segregation

This paper provides a comparative assessment of income segregation within cities in 12 countries. We use spatial entropy indexes based on small-scale gridded income data and consistent definition of city boundaries to ensure international comparability of our segregation measures. Results show considerable variation in the levels of income segregation across cities, even within countries, reflecting the diversity of cities within urban systems. Larger, more affluent, productive, and more unequal cities tend to be more segregated. Urban form, demographic, and economic factors explain additional variation in segregation levels through the influence of high-income households, who tend to be the most segregated. The positive association between productivity and segregation is mitigated in polycentric cities.
Luminescence properties and dating of proglacial sediments from northern Switzerland

Abstract. Luminescence dating has become a pillar of the understanding of Pleistocene glacial advances in the northern foreland of the Swiss Alps. However, both quartz and feldspar from the region are equally challenging as dosimeters with anomalous fading, partial bleaching and unstable components being some of the obstacles to overcome. In this study, luminescence properties of coarse- and fine-grained quartz, feldspar and polymineral fractions of eight samples from a palaeovalley, Rinikerfeld, in northern Switzerland are assessed and found appropriate for dating. While anomalous fading of the IRSL signal of the feldspar and polymineral fraction is observed, fading corrected coarse-grained feldspar ages are consistent with those derived from quartz. In general, coarse-grained quartz and feldspar as well as the fine-grained polymineral fractions are in agreement and present negligible evidence for partial bleaching. However, ages derived from fine-grained quartz are found to underestimate those of the coarse-grained quartz fractions. Impact of total dose rate on finite ages was assessed but age underestimation is likely due to grain size dependent luminescence properties. The top six samples indicate sedimentation of at least 16.6 m during Marine Isotope Stage 6 with a rapid transition from a lacustrine environment to a landscape dominated by colluvial deposits. For the two lowest samples, no finite ages are derived.
Low-Wage Countries Competition and the Quality of French Exports

We ask how the quality composition of French exports is affected by competitive pressures in foreign markets. We show that, over the 1995-2005 period, the mean quality of French manufacturing exports has increased by more than 10%. The quality upgrading explains by a reallocation of demand in favor of higher quality producers, at the intensive and the extensive margins. Quality growth is affected by changes in the competitive pressures French firms face in foreign markets. It is more pronounced in markets where the penetration of developing countries increases. This suggests lowwage countries competition has a heterogeneous impact on French exporters, with lower quality producers being more strongly hurt. This is consistent with a world of within-product specialization in different cones of diversification.
The Impact of Audit Technology Usage and Corporate Governance on Financial Reporting Timeliness

This study examines the impact of audit technology usage and corporate governance as well as the factors that affect on financial reporting timeliness in the Jordanian listed company in Amman Stock Exchange in the Kingdom of Jordan. The sample internal auditors of the company. To achieve the study objectives, the researchers developed questioner and annual report, which was administered in a survey. The collected data were analyzed by using SPSS. The analysis of the descriptive statistics and correlations indicated that audit technology and selection as well as audit technology and corporate governance on financial reporting timeliness of Jordanian listed company. The study also recommended the steps to improve financial reporting timeliness in the Jordanian listed company.
Climate regime shifts in paleoclimate time series from the Yucatán Peninsula: from the Preclassic to Classic period

(1) Basque Centre for Climate Change -BC3, Bilbao, Spain (josue.polanco@bc3research.org), (2) Department of Geology, Amherst College, Amherst MA, USA (mmedinaelizalde@amherst.edu), (3) Department of Geosciences, University of Massachusetts, Amherst MA, USA (sburns@geo.umass.edu), (4) College of Geography Science, Fujian Normal University, Fuzhou 350007, China (xyjiang@fjnu.edu.cn), (5) Department of Geosciences, National Taiwan University, Taipei, Taiwan (river@ntu.edu.tw)
Words to Music Synthesis

This paper discusses the design of a musical synthesizer that takes words as input, and attempts to generate music that somehow underscores those words. This is considered as a tool for sound designers who could, for example, enter dialogue from a ﬁlm script and generate appropriate background music. The synthesizer uses emotional valence and arousal as a common representation between words and music. It draws on previous studies that relate words and musical features to valence and arousal. The synthesizer was evaluated with a user study. Participants listened to music generated by the synthesizer, and described the music with words. The arousal of the words they entered was highly correlated with the intended arousal of the music. The same was, surprisingly, not true for valence. The synthesizer is at
FINANCE-GROWTH NEXUS IN SUDAN: EMPIRICAL ASSESSMENT BASED ON AN APPLICATION OF THE AUTOREGRESSIVE DISTRIBUTED LAG (ARDL) MODEL Correspondence

This study investigates the effect of financial development on economic performance in Sudan over the period (19702004). The short-run and long-run relationship between financial development and economic growth and other conditioning variables on economic growth are estimated by using the most recently developed autoregressive distributed lag (ARDL) approach to cointegration analysis proposed by Pesaran and Shin (1999). The results overwhelmingly indicate a weak relationship between financial development and economic growth in Sudan. Although the coefficient of M3/GDP is significant, it is negative, whilst the impact of credit to private sector by banks to real GDP is negative and insignificant. These findings may be attributed to the inefficient allocation of resources by banks, along with the absence of an appropriate investment climate required to foster significant private investment and promote growth in the long run, and to the poor quality of credit disbursal of the banking sector in Sudan. Moreover, the econometric analysis suggest that the speed of adjustment in the estimated models is relatively high and had the expected significant and negative sign. نادوسلا في يداصتقلاا ومنلاب ليالما روطتلا ةقلاع : جذونم مادختسا ىلع ةينبم ةيقيبطت ةسارد عزولما ءاطبلإل يتاذلا رادنحلاا
Learning the dynamics of cell–cell interactions in confined cell migration

Significance When cells migrate collectively, such as to heal wounds or invade tissue, they coordinate through cell–cell interactions. While much is known about the molecular basis of these interactions, the system-level stochastic dynamics of interacting cell behavior remain poorly understood. Here, we design an experimental “cell collider,” providing a large ensemble of interacting cell trajectories. Based on these trajectories, we infer an interacting equation of motion, which accurately predicts characteristic pairwise collision behaviors of different cell lines, including reversal, following, or sliding events. This data-driven approach can be used to quantitatively study how molecular perturbations control cell–cell interactions and may be extended to larger cell collectives, where the inferred interactions could provide key insights into multicellular dynamics. The migratory dynamics of cells in physiological processes, ranging from wound healing to cancer metastasis, rely on contact-mediated cell–cell interactions. These interactions play a key role in shaping the stochastic trajectories of migrating cells. While data-driven physical formalisms for the stochastic migration dynamics of single cells have been developed, such a framework for the behavioral dynamics of interacting cells still remains elusive. Here, we monitor stochastic cell trajectories in a minimal experimental cell collider: a dumbbell-shaped micropattern on which pairs of cells perform repeated cellular collisions. We observe different characteristic behaviors, including cells reversing, following, and sliding past each other upon collision. Capitalizing on this large experimental dataset of coupled cell trajectories, we infer an interacting stochastic equation of motion that accurately predicts the observed interaction behaviors. Our approach reveals that interacting noncancerous MCF10A cells can be described by repulsion and friction interactions. In contrast, cancerous MDA-MB-231 cells exhibit attraction and antifriction interactions, promoting the predominant relative sliding behavior observed for these cells. Based on these experimentally inferred interactions, we show how this framework may generalize to provide a unifying theoretical description of the diverse cellular interaction behaviors of distinct cell types.
Selecting Adaptive Number of Nearest Neighbors in k -Nearest Neighbor Classifier Apply Diabetes Data

The k-nearest neighbours (knn) is a simple but effective method of classification. K is the most important parameter in medical data classification based on k -nearest neighbor algorithm (knn). The major drawback with respect to knn is dependency on the selection of a “good value” for k . The value of k is usually determined by the cross-validation method but if k is too large, big classes will overwhelm small ones. On the other hand, if k is too small, the advantage of the knn algorithm will not be exhibited. Therefore, it is very likely that a fixed k value will result in a bias on large classes. In this paper we propose a modified k -nearest neighbor method, which uses different k values for different regions in an entire data set, rather than a fixed k value for a complete data set. The number of nearest neighbors is selected locally based on P-value Rate criteria. We apply the modified knn method to diagnose type II diabetes dataset which includes 768 samples from diabetic patients taken from Pima Indians Dataset.
Effect of β-patchoulene on cerebral ischemia-reperfusion injury and the TLR4/NF-κB signaling pathway

β-patchoulene (β-PAE), an active constituent of the Pogostemon cablin, is well known for its anti-inflammatory and antioxidative functions in various diseases. However, little is known about the impact of β-PAE on the cerebral ischemia-reperfusion (I/R) injury. The current study aimed to determine the neuroprotective effect of β-PAE and the underlying mechanisms on cerebral I/R injury. Following pretreatment with β-PAE (10 mg/kg body weight) by tail intravenous injection for 1 h, Sprague-Dawley rats were subjected to middle cerebral artery occlusion for 2 h and reperfusion for 24 h. The results indicated that pretreatment with β-PAE could diminish the infarct volume, decrease the brain water content, reduce the neurological deficit score and restore the mitochondrial membrane potential, compared with the untreated I/R injury group. Furthermore, cell apoptosis was markedly suppressed by β-PAE, and this effect was associated with the decreased apoptosis regulator BAX/apoptosis regulator Bcl-2 expression ratio and caspase-3 activity. In addition, β-PAE significantly inhibited the release of proinflammatory factors, including tumor necrosis factor-α, interleukin (IL)-1β and IL-6. Superoxide generation and malondialdehyde levels were reduced while the levels of glutathione peroxidase and superoxide dismutase were elevated following treatment with β-PAE, indicating the antioxidative role of β-PAE in cerebral I/R injury. Furthermore, the Toll-like receptor 4 (TLR4)/nuclear factor-κB (NF-κB) signaling pathway was inhibited by β-PAE, as demonstrated by the decreased TLR4 expression and nuclear translocation of p65, and increased IκBα level. Taken together, the results suggested that β-PAE may exhibit a neuroprotective effect on cerebral I/R injury in rats through inactivating the TLR4/NF-κB signaling pathway.