Miracle-dz/UniFAR-FTUs huggingface dataset:
A Numerical Study on the Effects of a Mountain on the Land and Sea Breezes

Thermal and dynamical effects of mountain on the land and sea breezes are studied numerically, by paying special attention to the growth and decay of the circulations and the extent of them. A two dimensional model in a vertical plane perpendicular to a seacoast line and a mo untain chain is used. The horizontal extent and the depth of the computational region are assumed to be 130km and 3km, respectively. The mountain is assumed to have a simple trapezoidal form with 8km in width and 450m in height, and is located at 18km from the coastal line. In order to estimate the thermal and dynamical effects of the mountain, numerical experiments are conducted for the following three cases: case (a) no mountain case (b) mountain with thermally insulated boundary condition (insulated mountain) case (c) mountain with the diurnal change of its surface potential temperature (heating mountain) Main conclusions are summarized as follows: (1) The sea breeze can not invade inland beyond the heating mountain, while the breeze invades inland beyond the insulated mountain faster and deeper than expected in case of no mountain. (2) The land breeze develops strong in case of the heating mountain. This is due to the down-slope winds. In case of the insulated mountain, this down-slope winds do not develop and the land breeze remains weaker than that of no mountain case. (3) The phase difference between the time of the maximum land-and-sea surface temperature contrast and that of the strongest induced circulation is much reduced, compared to the case of no mountain, regardless of the thermal boundary condition of the mountain surface.
Unsupervised Deep Feature Transfer for Low Resolution Image Classification /Author=Zhang, Z.; Wu, Y.; Wang, G. /CreationDate=October 31, 2019 /Subject=Artificial Intelligence, Computer Vision, Machine Learning

In this paper, we propose a simple while effective unsupervised deep feature transfer algorithm for low resolution image classification. No fine-tuning on convenet filters is required in our method. We use pre-trained convenet to extract features for both high and low resolution images, and then feed them into a two-layer feature transfer network for knowledge transfer. A SVM classifier is learned directly using these transferred low resolution features. Our network can be embedded into the state-of-the-art deep neural networks as a plug-in feature enhancement module. It preserves data structures in feature space for high resolution images, and transfers the distinguishing features from a wellstructured source domain (high resolution features space) to a not well-organized target domain (low resolution features space). Extensive experiments on VOC2007 test set show that the proposed method achieves significant improvements over the baseline of using feature extraction. IEEE International Conference on Computer Vision Workshops (ICCV) This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Research Laboratories, Inc.; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Research Laboratories, Inc. All rights reserved. Copyright c © Mitsubishi Electric Research Laboratories, Inc., 2019 201 Broadway, Cambridge, Massachusetts 02139 Unsupervised Deep Feature Transfer for Low Resolution Image Classification Yuanwei Wu∗, Guanghui Wang EECS, The University of Kansas 1450 Jayhawk Blvd., Lawrence, KS 66045 {y262w558, ghwang}@ku.edu Ziming Zhang† Mitsubishi Electric Research Laboratories 201 Broadway, Cambridge, MA 02139-1955
Growth Outstrips Publication Growth in Computer Science and Publication Quality Correlates with Collaboration

Although the computer science community successfully harnessed exponential increases in computer performance to drive societal and economic change, the exponential growth in publications is proving harder to accommodate. To gain a deeper understanding of publication growth and inform how the computer science community should handle this growth, we analyzed publication practices from several perspectives: ACM sponsored publications in the ACM Digital Library as a whole; subdisciplines captured by ACMs Special Interest Groups (SIGs); ten top conferences; institutions; four top U.S. departments; authors; faculty; and PhDs between 1990 and 2012. ACM publishes a large fraction of all computer science research.
Hydrogenation of carboxylic acids with a homogeneous cobalt catalyst

A direct route from acids to alcohols Making alcohols via hydrogen addition to C=O bonds is among the most widely applied reactions in chemistry. The transformation has also garnered renewed interest for generating commodity chemicals from biomass. Korstanje et al. now show that a cobalt compound can catalyze hydrogenation of the C=O bonds in carboxylic acids. These constitute a particularly challenging substrate class, given the propensity of many other catalysts to degrade under acidic conditions. The cobalt catalyst tolerates a versatile substrate range, and the Earth abundance of the metal bodes well for long-term utility. Science, this issue p. 298 Earth-abundant cobalt catalyzes a broadly useful chemical conversion of acids to alcohols. The reduction of esters and carboxylic acids to alcohols is a highly relevant conversion for the pharmaceutical and fine-chemical industries and for biomass conversion. It is commonly performed using stoichiometric reagents, and the catalytic hydrogenation of the acids previously required precious metals. Here we report the homogeneously catalyzed hydrogenation of carboxylic acids to alcohols using earth-abundant cobalt. This system, which pairs Co(BF4)2·6H2O with a tridentate phosphine ligand, can reduce a wide range of esters and carboxylic acids under relatively mild conditions (100°C, 80 bar H2) and reaches turnover numbers of up to 8000.
Defining Crowd Movement as Parabola and Classifying These Definitions

Smart surveillance systems developed in recent years have made enormous contributions to providing safety and management of crowds. The aim of this study is to observe and try to understand how crowd movements presented in a video sequence show behaviour. For this end, the motion data at pixel level among the consecutive frames is obtained using optical flow initially. Then, this motion data is associated using the particle advection method and stable as well as moving areas in the image are obtained. After, the moving areas clustered using Mean-Shift method are described and classified as parabola, in addition to the studies in the literature. At the end of the study, the method developed was tested over UCF as well as Pets2009 datasets and the results are presented.
Nonlinear Projection Based Gradient Estimation for Query Efficient Blackbox Attacks

As discussed in Section 4.1, the generalized gradient estimator in Definition 2 unifies the boundary gradient estimator in HSJA (Chen et al., 2020), QEBA (Li et al., 2020), and our NonLinear-BA. In this section we discuss the instantiations of them in detail. In the generalized gradient estimator, the u1, u2, . , uB are a sampled subset of orthonormal basis, whereas in practice, all these methods only uniformly sample normalized vectors for efficiency concern. As implied by Lemma 1, when n becomes large, 〈ui, v〉’s PDF is highly concentrated at x = 0, implying that with high probability the sampled normalized vectors are close to orthogonal. Therefore, the orthonormal basis sampling can be approximated by normalized vector sampling. With this mindset, we express each gradient estimator using generalized gradient estimator (Definition 2).
Proof of Travel for Trust-Based Data Validation in V2I Communication Part I: Methodology

—Previous work on misbehavior detection and trust management for Vehicle-to-Everything (V2X) communication can identify falsiﬁed and malicious messages, enabling witness vehicles to report observations about high-criticality trafﬁc events. However, there may not exist enough ”benign” vehicles with V2X connectivity or vehicle owners who are willing to opt-in in the early stages of connected-vehicle deployment. In this paper, we propose a security protocol for the communication between vehicles and infrastructure, titled Proof-of-Travel (POT), to answer the research question: How can we transform the power of cryptography techniques embedded within the protocol into social and economic mechanisms to simultaneously incentivize Vehicle-to-Infrastructure (V2I) data sharing activities and validate the data? The key idea is to determine the reputation of and the contribution made by a vehicle based on its distance traveled and the information it shared through V2I channels. In particular, the total vehicle miles traveled for a vehicle must be testiﬁed by digital signatures signed by each infrastructure component along the path of its movement. While building a chain of proofs of spatial movement creates burdens for malicious vehicles, acquiring proofs does not result in extra cost for normal vehicles, which naturally want to move from the origin to the destination. The proof of travel for a vehicle can then be used to determine vehicle reputation by its altruistic behaviors. Based on the POT protocol, we enhance previous V2I data validation algorithms and evaluate their security and performance through theoretical analysis and simulations.
Inference and Properties of Mixture Two Extreme Lower Bound Distributions

In this paper, we discuss the mixture model of two extreme lower bound distributions. First, some properties we obtain of the model with hazard function are discussed. In addition, the estimates of the unknown parameters via the EM algorithm are obtained. The performance of the findings in the paper is showed by demonstrating some numerical illustrations through Monte Carlo simulation.
Orderly Roulette Selection Based Ant Colony Algorithm for Hierarchical Multilabel Protein Function Prediction

Ant colony optimization (ACO) algorithms have been successfully applied to identify classification rules in data mining. This paper proposes a new ant colony optimization algorithm, named , for the hierarchical multilabel classification problem in protein function prediction. The proposed algorithm is characterized by an orderly roulette selection strategy that distinguishes the merits of the data attributes through attributes importance ranking in classification model construction. A new pheromone update strategy is introduced to prevent the algorithm from getting trapped in local optima and thus leading to more efficient identification of classification rules. The comparison studies to other closely related algorithms on 16 publicly available datasets reveal the efficiency of the proposed algorithm.
Mental Representations of Fractions and Decimals: Differences, Commonalities and Implications for Understanding

The purpose of this thesis is to seek evidence of commonalities in the mental representations of fractions and decimals between zero and one. The focus is on the mental representations of non-familiar fractions and decimals in adults. In addition, individual differences in the extent of common fraction and decimal mental representations are explored and their links to mathematical understanding of numbers between zero and one. For whole numbers, number comparison tasks have found evidence of an ordered, magnitude mental representation known as the mental number line through which the magnitude of a whole number is automatically processed. This evidence consists of phenomena such as the distance effect and SNARC effect. Here, indications of a similar magnitude representation common to both fractions and decimals are sought through a task in which a fraction is compared with a decimal. Substantial evidence of a distance effect is presented but not a SNARC effect, indicating that fractions and decimals can have mental representations containing or accessing a common magnitude but that this magnitude is not automatically processed. In addition, two emergent phenomena are reported. The first is an effect of location which is contrasted with the size effect in whole numbers and a previously reported anchor-point effect. The second is a larger-stimulus effect which is an indication of differences in the mental representations of fractions and decimals. These effects are explored in two additional, simple magnitude and location tasks. Furthermore, success but not speed within the comparison task is linked to strength of the distance effect for individuals. Therefore the number comparison task is repeated in series with a test designed to uncover common misconceptions of fractions and decimals. Patterns with the individual differences in responses to the test and comparison task are explored. By making links between the features and commonalities of individuals’ mental representations of fractions and decimals and quality of their understanding, this research hopes to be of value to mathematical educators.
VRED: A Position-Velocity Recurrent Encoder-Decoder for Human Motion Prediction

Human motion prediction, which aims to predict future human poses given past poses, has recently seen increased interest. Many recent approaches are based on Recurrent Neural Networks (RNN) which model human poses with exponential maps. These approaches neglect the pose velocity as well as temporal relation of different poses, and tend to converge to the mean pose or fail to generate natural-looking poses. We therefore propose a novel Position-Velocity Recurrent Encoder-Decoder (PVRED) for human motion prediction, which makes full use of pose velocities and temporal positional information. A temporal position embedding method is presented and a Position-Velocity RNN (PVRNN) is proposed. We also emphasize the benefits of quaternion parameterization of poses and design a novel trainable Quaternion Transformation (QT) layer, which is combined with a robust loss function during training. Experiments on two human motion prediction benchmarks show that our approach considerably outperforms the state-of-the-art methods for both short-term prediction and long-term prediction. In particular, our proposed approach can predict future human-like and meaningful poses in 4000 milliseconds.
Xj Instance Description Iterative Set Pooling Prediction Set Set labelFj

Multiple Instance learning (MIL) algorithms are tasked with learning how to associate sets of elements with specific setlevel outputs. Towards this goal, the main challenge of MIL lies in modelling the underlying structure that characterizes sets of elements. Existing methods addressing MIL problems are usually tailored to address either: a specific underlying set structure; specific prediction tasks, e.g. classification, regression; or a combination of both. Here we present an approach where a set representation is learned, iteratively, by looking at the constituent elements of each set one at a time. The iterative analysis of set elements enables our approach with the capability to update the set representation so that it reflects whether relevant elements have been detected and whether the underlying structure has been matched. These features provide our method with some model explanation capabilities. Despite its simplicity, the proposed approach not only effectively models different types of underlying set structures, but it is also capable of handling both classification and regression tasks – all this while requiring minimal modifications. An extensive empirical evaluation shows that the proposed method is able to reach and surpass the state-of-the-art.
DISAGGREGATION : THE HOLY GRAIL OF ENERGY EFFICIENCY ?

This paper aims to address two timely energy problems. First, significant low-cost energy reductions can be made in the residential and commercial sectors, but these savings have not been achievable to date. Second, billions of dollars are being spent to install smart meters, yet the energy saving and financial benefits of this infrastructure – without careful consideration of the human element – will not reach its full potential. We believe that we can address these problems by strategically marrying them, using disaggregation. Disaggregation refers to a set of statistical approaches for extracting end-use and/or appliance level data from an aggregate, or wholebuilding, energy signal. In this paper, we explain how appliance level data affords numerous benefits, and why using the algorithms in conjunction with smart meters is the most cost-effective and scalable solution for getting this data. We review disaggregation algorithms and their requirements, and evaluate the extent to which smart meters can meet those requirements. Research, technology, and policy recommendations are also outlined.
Deep Reinforcement Learning for Distributed and Uncoordinated Cognitive Radios Resource Allocation

This paper presents a novel deep reinforcement learning-based resource allocation technique for the multi-agent environment presented by a cognitive radio network where the interactions of the agents during learning may lead to a non-stationary environment. The resource allocation technique presented in this work is distributed, not requiring coordination with other agents. It is shown by considering aspects specific to deep reinforcement learning that the presented algorithm converges in an arbitrarily long time to equilibrium policies in a non-stationary multi-agent environment that results from the uncoordinated dynamic interaction between radios through the shared wireless environment. Simulation results show that the presented technique achieves a faster learning performance compared to an equivalent table-based Q-learning algorithm and is able to find the optimal policy in 99% of cases for a sufficiently long learning time. In addition, simulations show that our DQL approach requires less than half the number of learning steps to achieve the same performance as an equivalent table-based implementation. Moreover, it is shown that the use of a standard single-agent deep reinforcement learning approach may not achieve convergence when used in an uncoordinated interacting multi-radio scenario
NeX: Real-time View Synthesis with Neural Basis Expansion

We calibrate a set of input images using an a Structurefrom-Motion (SfM) algorithm in an open-source software package COLMAP [4]. For COLMAP, we use a “simple radial” camera model with a single radial distortion coefficient and a shared intrinsic for all images. We use “sift feature guided matching” option in the exhaustive matcher step of SfM and also refine principle points of the intrinsic during the bundle adjustment. Note that accurate camera poses and intrinsic parameters are crucial for our method, and errors in these parameters can lead to poor results.
An entropy-based term weighting scheme and its application in e-commerce search engines

Term weighting schemes are commonly used in information retrieval field to extract the most relevant terms of documents. The main contribution of this paper consists in defining a new term weighting scheme based on entropy. We believe that this scheme is particularly well adapted to compare queries from e-commerce sites. These queries have their own specificities. They tend to be short and a large proportion of them are unique queries, i.e. have no historical record. We claim that widely used weighting schemes, such as tf-idf, are not well-adapted to this kind of queries. This claim is backed up by numerical experiments where the proposed entropy-based approach is incorporated into a collabora-tive filtering framework. In this framework, well suited to e-commerce search engines, we found out, on real e-commerce purchase data, that the proposed weighting scheme outperforms the tf-idf weighting scheme.
PREDICTION OF THE GEOMETRIC RENEWAL PROCESS

The first part of the paper presents major concepts and theoretical statements on prediction of processes. The second part presents the obtained results on the geometric renewal process by indicating its distribution which has a binomial distribution and is a process with independent and stationary increments. Further, having applied the theory introduced in the first part to the geometric renewal process, the sufficient and unbiased prediction with the minimum-variance has been found.
A Shared Pathogenic Mechanism for Valproic Acid and SHROOM3 Knockout in a Brain Organoid Model of Neural Tube Defects

Neural tube defects (NTDs) including anencephaly and spina bifida are common major malformations of fetal development resulting from incomplete closure of the neural tube. These conditions lead to either universal death (anencephaly) or life-long severe complications (spina bifida). Despite hundreds of genetic mouse models having neural tube defect phenotypes, the genetics of human NTDs are poorly understood. Furthermore, pharmaceuticals such as antiseizure medications have been found clinically to increase the risk of NTDs when administered during pregnancy. Therefore, a model that recapitulates human neurodevelopment would be of immense benefit to understand the genetics underlying NTDs and identify teratogenic mechanisms. Using our self-organizing single rosette spheroid (SOSRS) brain organoid system, we have developed a high-throughput image analysis pipeline for evaluating SOSRS structure for NTD-like phenotypes. Similar to small molecule inhibition of apical constriction, the antiseizure medication valproic acid (VPA), a known cause of NTDs, increases the apical lumen size and apical cell surface area in a dose-responsive manner. This expansion was mimicked by GSK3β and HDAC inhibitors; however, RNA sequencing suggests VPA does not inhibit GSK3β at these concentrations. Knockout of SHROOM3, a well-known NTD-related gene, also caused expansion of the lumen as well as reduced f-actin polarization. The increased lumen sizes were caused by reduced cell apical constriction suggesting that impingement of this process is a shared mechanism for VPA treatment and SHROOM3-KO, two well-known causes of NTDs. Our system allows the rapid identification of NTD-like phenotypes for both compounds and genetic variants and should prove useful for understanding specific NTD mechanisms and predicting drug teratogenicity.
Neural Camera Simulators

We analyze the design of the linear regression model in the exposure module. We adopt different b values for each channel in the RGBG Bayer pattern. When training this module, we exclude image pairs with aperture value changes. The final optimized w is 1.0038 and the optimize b is [−0.0003,−0.0004,−0.0004,−0.0005]. As we expect, w is close to 1 and b close to 0. We quantitatively analyze the exposure module by calculating the L2 loss on the test dataset. The mean squared error for simply multiplying the multiplier is 0.7729, and that for applying the exposure module is 0.0033.
- Supplemental Material Seeing through a Black Box: Toward High-Quality Terahertz Imaging via Subspace-and-Attention Guided Restoration

This supplementary material provides those details that could not be accom-modated in the main manuscript due to the limited space. Sec. 1 provides the detailed settings for our in-house THz system. Sec. 3.1 provides the additional quantitative evaluation results. Sec. 3.2 provides the evaluation results testing on the real-world objects. Finally, Sec. 3.3 demonstrates some sample views of 3D tomography reconstruction from 30 views restored by our model as part of the supplementary material.