Miracle-dz/UniFAR-FTUs huggingface dataset:
An Interactive City Choice Model and Its Application for Measuring the Intercity Interaction

Measuring the interaction between cities is an important research topic in many disciplines, such as sociology, geography, economics, and transportation science. The traditional and most widely used spatial interaction model is the gravity model, but it requires the parameters to be artificially set. In this paper, we propose a parameter-free interactive city choice (ICC) model that measures intercity interaction from the perspective of individual choice behavior. The ICC model assumes that the probability of an individual choosing to interact with a city is proportional to the number of opportunities in the destination city and inversely proportional to the number of intervening opportunities between the origin city and the destination city, calculated using the travel time in the transportation network. The intercity interaction intensity can be obtained by calculating the product of this probability and the origin city’s population. We apply the ICC model to measure the interaction intensity among 339 cities in China and analyze the impact of changes in the Chinese land transportation network from 2005 to 2018 on the intercity and city interaction intensity. The results show that our model provides an alternative method for measuring the intercity interaction.
Behavior coordination for self-adaptive robots using constraint-based configuration

Autonomous robots may be able to adapt their behavior in response to changes in the environment. This is useful, for example, to efficiently handle limited resources or to respond appropriately to unexpected events such as faults. The architecture of a self-adaptive robot is complex because it should include automatic mechanisms to dynamically configure the elements that control robot behaviors. To facilitate the construction of this type of architectures, it is useful to have general solutions in the form of software tools that may be applicable to different robotic systems. This paper presents an original algorithm to dynamically configure the control architecture, which is applicable to the development of self-adaptive autonomous robots. This algorithm uses a constraint-based configuration approach to decide which basic robot behaviors should be activated in response to both reactive and deliberative events. The algorithm uses specific search heuristics and initialization procedures to achieve the performance required by robotic systems. The solution has been implemented as a software development tool called Behavior Coordinator CBC (Constraint-Based Configuration), which is based on ROS and open source, available to the general public. This tool has been successfully used for building multiple applications of autonomous aerial robots.
TRADE: TRusted Anonymous Data Exchange: Threat Sharing Using Blockchain Technology

Cyber attacks are becoming more frequent and sophisticated, introducing significant challenges for organizations to protect their systems and data from threat actors. Today, threat actors are highly motivated, persistent, and well-founded and operate in a coordinated manner to commit a diversity of attacks using various sophisticated tactics, techniques, and procedures. Given the risks these threats present, it has become clear that organizations need to collaborate and share cyber threat information (CTI) and use it to improve their security posture. In this paper, we present TRADE – TRusted Anonymous Data Exchange – a collaborative, distributed, trusted, and anonymized CTI sharing platform based on blockchain technology. TRADE uses a blockchain-based access control framework designed to provide essential features and requirements to incentivize and encourage organizations to share threat intelligence information. In TRADE, organizations can fully control their data by defining sharing policies enforced by smart contracts used to control and manage CTI sharing in the network. TRADE allows organizations to preserve their anonymity while keeping organizations fully accountable for their action in the network. Finally, TRADE can be easily integrated within existing threat intelligence exchange protocols such as trusted automated exchange of intelligence information (TAXII) and OpenDXL, thereby allowing a fast and smooth technology adaptation.
Fast convergence of primal-dual dynamics and algorithms with time scaling for linear equality constrained convex optimization problems ⋆

We propose a primal-dual dynamic with time scaling for a linear equality constrained convex optimization problem, which consists of a second-order ODE for the primal variable and a ﬁrst-order ODE for the dual variable. Without assuming strong convexity, we prove its fast convergence property and show that the obtained fast convergence property is preserved under a small perturbation. We also develop an inexact primal-dual algorithm derived by a time discretization, and derive the fast convergence property matching that of the underlying dynamic. Finally, we give numerical experiments to illustrate the validity of the proposed algorithm
Structure-Aware Face Clustering on a Large-Scale Graph with 107 Nodes

Face clustering is a promising method for annotating un-labeled face images. Recent supervised approaches have boosted the face clustering accuracy greatly, however their performance is still far from satisfactory. These methods can be roughly divided into global-based and local-based ones. Global-based methods suffer from the limitation of training data scale, while local-based ones are difficult to grasp the whole graph structure information and usually take a long time for inference. Previous approaches fail to tackle these two challenges simultaneously. To address the dilemma of large-scale training and efficient inference, we propose the STructure-AwaRe Face Clustering (STAR-FC) method. Specifically, we design a structure-preserved subgraph sampling strategy to explore the power of large-scale training data, which can increase the training data scale from 105 to 107. During inference, the STAR-FC performs efficient full-graph clustering with two steps: graph parsing and graph refinement. And the concept of node intimacy is introduced in the second step to mine the local structural information. The STAR-FC gets 91.97 pairwise F-score on partial MS1M within 310s which surpasses the state-of-the-arts. Furthermore, we are the first to train on very large-scale graph with 20M nodes, and achieve superior inference results on 12M testing data. Overall, as a simple and effective method, the proposed STAR-FC provides a strong baseline for large-scale face clustering. Code is available at https://sstzal.github. io/STAR-FC/.
Vulnerability of Appearance-based Gaze Estimation

Appearance-based gaze estimation has achieved significant improvement by using deep learning. However, many deep learning-based methods suffer from the vulnerability property, i.e., perturbing the raw image using noise confuses the gaze estimation models. Although the perturbed image visually looks similar to the original image, the gaze estimation models output the wrong gaze direction. In this paper, we investigate the vulnerability of appearance-based gaze estimation. To our knowledge, this is the first time that the vulnerability of gaze estimation to be found. We systematically characterized the vulnerability property from multiple aspects, the pixel-based adversarial attack, the patch-based adversarial attack and the defense strategy. Our experimental results demonstrate that the CA-Net shows superior performance against attack among the four popular appearance-based gaze estimation networks, Full-Face, Gaze-Net, CA-Net and RT-GENE. This study draws the attention of researchers in the appearance-based gaze estimation community to defense from adversarial attacks.
Power Modeling for Effective Datacenter Planning and Compute Management

Over the past decade, there has been a global growth in datacenter capacity, power consumption and the associated costs. Accurate mapping of datacenter resource usage (CPU, RAM, etc.) and hardware configurations (servers, accelerators, etc.) to its power consumption is necessary for efficient long-term infrastructure planning and real-time compute load management. This paper presents two types of statistical power models that relate CPU usage of Google’s Power Distribution Units (PDUs, commonly referred to as power domains) to their power consumption. The models are deployed in production and are used for cost- and carbon-aware load management, power provisioning and infrastructure rightsizing. They are simple, interpretable and exhibit uniformly high prediction accuracy in modeling power domains with large diversity of hardware configurations and workload types across Google fleet. A multi-year validation of the deployed models demonstrate that they can predict power with less than 5% Mean Absolute Percent Error (MAPE) for more than 95% diverse PDUs across Google fleet. This performance matches the best reported accuracies coming from studies that focus on specific workload types, hardware platforms and, typically, more complex statistical models.
Learning Fine-Grained Segmentation of 3D Shapes without Part Labels

Learning-based 3D shape segmentation is usually formulated as a semantic labeling problem, assuming that all parts of training shapes are annotated with a given set of tags. This assumption, however, is impractical for learning fine-grained segmentation. Although most off-the-shelf CAD models are, by construction, composed of fine-grained parts, they usually miss semantic tags and labeling those fine-grained parts is extremely tedious. We approach the problem with deep clustering, where the key idea is to learn part priors from a shape dataset with fine-grained segmentation but no part labels. Given point sampled 3D shapes, we model the clustering priors of points with a similarity matrix and achieve part segmentation through minimizing a novel low rank loss. To handle highly densely sampled point sets, we adopt a divide-and-conquer strategy. We partition the large point set into a number of blocks. Each block is segmented using a deep-clustering-based part prior network trained in a category-agnostic manner. We then train a graph convolution network to merge the segments of all blocks to form the final segmentation result. Our method is evaluated with a challenging benchmark of fine-grained segmentation, showing state-of-the-art performance.
Shift-and-Balance Attention

Attention is an effective mechanism to improve the deep model capability. Squeeze-and-Excite (SE) introduces a light-weight attention branch to enhance the network's representational power. The attention branch is gated using the Sigmoid function and multiplied by the feature map's trunk branch. It is too sensitive to coordinate and balance the trunk and attention branches' contributions. To control the attention branch's influence, we propose a new attention method, called Shift-and-Balance (SB). Different from Squeeze-and-Excite, the attention branch is regulated by the learned control factor to control the balance, then added into the feature map's trunk branch. Experiments show that Shift-and-Balance attention significantly improves the accuracy compared to Squeeze-and-Excite when applied in more layers, increasing more size and capacity of a network. Moreover, Shift-and-Balance attention achieves better or close accuracy compared to the state-of-art Dynamic Convolution.
Investigation of Spectroscopic (FT-IR and NMR), Structural and Non-linear Optical Properties of 4-Chloro-2-((4-chlorobenzylidene)amino)phenol by using DFT Quantum Chemical Calculations

In this study, 4-chloro-2-((4-chlorobenzylidene)-amino)phenol molecule (C13H9Cl2NO) was investigated using experimental spectroscopic techniques and quantum chemical methods. The structure of molecule has been characterized by using both FT-IR and NMR ( 1 H, 13 C) spectroscopic techniques. The IR spectrum of the title molecule was recorded in the 400-4000 cm -1 region. The 1 H and 13 C-NMR spectra of the molecule were recorded. The geometry, vibrational frequencies, 1 H and 13 C-NMR chemical shift values of the title molecule in the ground state were calculated by using the DFT/B3LYP method with 6-31G*, 6-311G** and LANL2DZ basis sets. The calculated geometric parameters, vibrational frequencies and chemical shift values were compared with experimental data of title molecule. In addition to all this non-linear optical properties, potential energy surface (PES), frontier molecular orbitals (FMOs), molecular electrostatic potential and Mulliken population analysis for corresponding molecule were theoretically investigated using quantum mechanical calculations. The results showed a good
Generic Merging of Structure from Motion Maps with a Low Memory Footprint

With the development of cheap image sensors, the amount of available image data have increased enormously, and the possibility of using crowdsourced collection methods has emerged. This calls for development of ways to handle all these data. In this paper, we present new tools that will enable efficient, flexible and robust map merging. Assuming that separate optimisations have been performed for the individual maps, we show how only relevant data can be stored in a low memory footprint representation. We use these representations to perform map merging so that the algorithm is invariant to the merging order and independent of the choice of coordinate system. The result is a robust algorithm that can be applied to several maps simultaneously. The result of a merge can also be represented with the same type of low-memory footprint format, which enables further merging and updating of the map in a hierarchical way. Furthermore, the method can perform loop closing and also detect changes in the scene between the capture of the different image sequences. Using both simulated and real data — from both a hand held mobile phone and from a drone — we verify the performance of the proposed method.
BIOREMEDIATION OF XENOBIOTICS: USE OF DEAD FUNGAL BIOMASS AS BIOSORBENT

Biosorption is bioengineering where metabolism independent adsorption of xenobiotics to living or dead cells takes place.Microorganisms dead or alive are successfully exploited for bioremediation of xenobiotics by biosorption. In the present study bioremediation of xenobiotics of textile industry effluent was carried out by biosorption using dead fungus biomass of Aspergillus flavus. The dead biomass of fungus Aspergillus flavus shows maximum biosorption for three toxic components of textile industry effluent under different parameters. Methyl orange biosorption was found to be 53.62% at room temperature, at pH 5.5, with biomass concentration of 2g/L having contact time of 40 min and the dye concentration was 1ppm. Chromium biosorption was 72.18%, at pH 6, at room temperature with biomass concentration of 2g/L having contact time of 10 min and solution concentration 200ppm. Lead biosorption was found to be 76.12%, at pH 7, at room temperature with biomass concentration 2g/L having contact time of 40 min and solution concentration 1ppm. Desorption studies were also performed and was found that dead fungal biomass can be reused further.
Towards Optimal Algorithms for Multi-Player Bandits without Collision Sensing Information

We propose a novel algorithm for multi-player multi-armed bandits without collision sensing information. Our algorithm circumvents two problems shared by all state-of-the-art algorithms: it does not need as an input a lower bound on the minimal expected reward of an arm, and its performance does not scale inversely proportionally to the minimal expected reward. We prove a theoretical regret upper bound to justify these claims. We complement our theoretical results with numerical experiments, showing that the proposed algorithm outperforms state-of-the-art in practice as well.
Convex Online Video Frame Subset Selection using Multiple Criteria for Data Efficient Autonomous Driving

Training vision-based Urban Autonomous driving models is a challenging problem, which is highly researched in recent times. Training such models is a data-intensive task requiring the storage and processing of vast volumes of (possibly redundant) driving video data. In this paper, we study the problem of developing data-efficient autonomous driving systems. In this context, we study the problem of multi-criteria online video frame subset selection. We study convex optimization-based solutions and show that they are unable to provide solutions with high weightage to the loss of selected video frames. We design a novel convex optimization-based multi-criteria online subset selection algorithm that uses a thresholded concave function of selection variables. We also propose and study a submodular optimization-based algorithm. Extensive experiments using the driving simulator CARLA show that we are able to drop 80% of the frames while succeeding to complete 100% of the episodes w.r. t. the model trained on 100% data, in the most difficult task of taking turns. This results in a training time of less than 30% compared to training on the whole dataset. We also perform detailed experiments on prediction performances of various affordances used by the Conditional Affordance Learning (CAL) model and show that our subset selection improves performance on the crucial affordance"Relative Angle"during turns.
Distributed Visual-Inertial Cooperative Localization

In this paper we present a consistent and distributed state estimator for multi-robot cooperative localization (CL) which efficiently fuses environmental features and loop-closure constraints across time and robots. In particular, we leverage covariance intersection (CI) to allow each robot to only estimate its own state and autocovariance and compensate for the unknown correlations between robots. Two novel multi-robot methods for utilizing common environmental SLAM features are introduced and evaluated in terms of accuracy and efficiency. Moreover, we adapt CI to enable drift-free estimation through the use of loop-closure measurement constraints to other robots’ historical poses without a significant increase in computational cost. The proposed distributed CL estimator is validated against its non-realtime centralized counterpart extensively in both simulations and real-world experiments.
Noise Activated DNA Translocation for Faster Screening

Nonlinear solid friction between the gel matrix and DNA molecules inhibits the motion of DNA during electrophoresis. We report enhanced mobility of the DNA using external noise to alleviate the effect of solid friction. In presence of noise, the mobility of 1 kbp DNA increases ~ 86% compared to the conventional gel electrophoresis, whereas the increment is more than ~113 % for 6 kbp DNA. At low power of the noise, super Arrhenius kinetics suggest the collective behavior of the activated motion of DNA molecules. Stochastic simulation following modified Langevin equation with the asymmetric pore size distribution of the agarose gel successfully predicts the mobility of DNA molecules and estimates the huge frictional force at the DNA-gel matrix interface. One-Sentence Summary: Noise-activated electrophoresis results enhanced mobility of DNA by more than 100 %. A child learns to release a stuck (due to some tiny obstacles) toy car on an inclined plank by tapping it, from his/her experience. One can also tap on a horizontal platform having a pile of sand to allow free spreading of the sand from the ‘jammed’ state. The tapping acts as an external perturbation or noise that provides sufficient energy to the system to overcome the surface defects on the plank or the jamming potential barrier in the case of a sand pile. Similar scenarios are encountered almost every day such as the motion of a stuck tiny water droplet either on a windshield of a car on a rainy day or on a shower curtain during a bath. Noise is generated from the random blow of the wind in the former case, whereas, for later, it is the self-excited oscillations due to the condensation of steam and/or coalescence of multiple droplets (1). The potential barriers in such cases originate from the solid friction or its surrogate, contact angle hysteresis, at the solid-solid or solid-liquid interface, respectively. The solid friction restricts the diffusive as well as biased motion of the nano-micro (2) to the large-scale macroscopic objects (3–5). Burlatsky and Deutch first pointed out that the electrophoretic motion of DNA is hindered by solid friction originates from the DNA-gel matrix interactions along with the viscous dissipation between DNA and the buffer-solvent (6, 7). This solid friction engendered from the adhesive interactions between the pore-wall of the gel matrix and the flexible DNA molecules, rubbing between them, distortion of the gel fibers, and other forms of local kinetic energy dissipation. To detach from the wall of the gel matrix, an electric field-driven DNA molecule has to overcome these interactions, characterized by critical forces. Constrictions, defined by the dimensions of the pores, govern the solid frictional forces. As the distribution of the pore-size spans over a wide range, one can expect a wide distribution of critical forces. These critical forces can be overcome when enough energy is supplied to the DNA molecules. Here we report a novel
Dual‐Polymer‐Functionalized Nanoscale Graphene Oxide as a Highly Effective Gene Transfection Agent for Insect Cells with Cell‐Type‐Dependent Cellular Uptake Mechanisms

Efficient and safe gene transfection carriers, especially for hard‐to‐transfect cells, are urgently demanded in basic biological research and gene therapy applications. Many insect cell lines widely used in molecular cell biology exhibit relatively low transfection efficiencies when treated by conventional non‐viral agents. Herein, we develop a novel gene delivery vector by coating graphene oxide (GO) with both polyethylene glycol (PEG) and polyethylenimine (PEI), obtaining a dual‐polymer‐functionalized nanoscale GO (nGO‐PEG‐PEI) to transfect insect cells. While exhibiting remarkably reduced cytotoxicity compared with PEI, nGO‐PEG‐PEI, when used as the plasmid DNA transfection agent to treat Drosophila S2 cells, offers ≈7‐fold and ≈2.5‐fold higher efficiency compared with those achieved by using bare PEI and Lipofectamine 2000, a widely used commercial transfection agent, respectively. Interestingly, the advantages of nGO‐PEG‐PEI are even more dramatic when transfecting cells with lower‐quality linearized DNA. It is revealed that nGO‐PEG‐PEI/pDNA complexes enter insect cells via a unique pathway working even at a low temperature, rather different from their entry into mammalian adherent cells. Our results encourage the development of nano‐GO‐based gene carriers to treat special types of hard‐to‐transfect cells (e.g., insect cells), and indicate that nanomaterials would enter cells by cell‐type‐dependent mechanisms, which merit significantly more future attentions.
O C ] 2 4 M ar 2 02 1 Average Cost Markov Decision Processes with Semi-Uniform Feller Transition Probabilities March 25 , 2021

This paper studies average-cost Markov decision processes with semi-uniform Feller transition probabilities. This class of MDPs was recently introduced by the authors to study MDPs with incomplete information. This paper studies the validity of optimality inequalities, the existence of optimal policies, and the approximations of optimal policies by policies optimizing total discounted costs.
Towards a Model-Theoretic View of Narratives

In this paper, we propose the beginnings of a formal framework for modeling narrative qua narrative. Our framework affords the ability to discuss key qualities of stories and their communication, including the flow of information from a Narrator to a Reader, the evolution of a Reader’s story model over time, and Reader uncertainty. We demonstrate its applicability to computational narratology by giving explicit algorithms for measuring the accuracy with which information was conveyed to the Reader, along with two novel measurements of story coherence.