Miracle-dz/UniFAR-FTUs huggingface dataset:
3D Invisible Cloak

In this paper, we propose a novel physical stealth attack against the person detectors in real world. The proposed method generates an adversarial patch, and prints it on real clothes to make a three dimensional (3D) invisible cloak. Anyone wearing the cloak can evade the detection of person detectors and achieve stealth. We consider the impacts of those 3D physical constraints (i.e., radian, wrinkle, occlusion, angle, etc.) on person stealth attacks, and propose 3D transformations to generate 3D invisible cloak. We launch the person stealth attacks in 3D physical space instead of 2D plane by printing the adversarial patches on real clothes under challenging and complex 3D physical scenarios. The conventional and 3D transformations are performed on the patch during its optimization process. Further, we study how to generate the optimal 3D invisible cloak. Specifically, we explore how to choose input images with specific shapes and colors to generate the optimal 3D invisible cloak. Besides, after successfully making the object detector misjudge the person as other objects, we explore how to make a person completely disappeared, i.e., the person will not be detected as any objects. Finally, we present a systematic evaluation framework to methodically evaluate the performance of the proposed attack in digital domain and physical world. Experimental results in various indoor and outdoor physical scenarios show that, the proposed person stealth attack method is robust and effective even under those complex and challenging physical conditions, such as the cloak is wrinkled, obscured, curved, and from different angles. The attack success rate in digital domain (Inria data set) is 86.56%, while the static and dynamic stealth attack performance in physical world is 100% and 77%, respectively, which are significantly better than existing works.
UvA-DARE (Digital Academic Repository) The Smeared Null Energy Condition

: We propose a new bound on a weighted average of the null energy along a (cid:12)nite portion of a null geodesic: the Smeared Null Energy Condition (SNEC). We believe our bound is valid on scales small compared to the radius of curvature in any quantum (cid:12)eld theory that is consistently coupled to gravity. If correct, our bound implies that regions of negative energy density are never strongly gravitating, and that isolated regions of negative energy are forbidden.
3DSNet: Unsupervised Shape-to-Shape 3D Style Transfer

Transferring the style from one image onto another is a popular and widely studied task in computer vision. Yet, learning-based style transfer in the 3D setting remains a largely unexplored problem. To our knowledge, we propose the first learning-based generative approach for style transfer between 3D objects. Our method allows to combine the content and style of a source and target 3D model to generate a novel shape that resembles in style the target while retaining the source content. The proposed framework can synthesize new 3D shapes both in the form of point clouds and meshes. Furthermore, we extend our technique to implicitly learn the underlying multimodal style distribution of the individual category domains. By sampling style codes from the learned distributions, we increase the variety of styles that our model can confer to a given reference object. Experimental results validate the effectiveness of the proposed 3D style transfer method on a number of benchmarks.
GraphSeam: Supervised Graph Learning Framework for Semantic UV Mapping

Recently there has been a significant effort to automate UV mapping, the process of mapping 3D-dimensional surfaces to the UV space while minimizing distortion and seam length. Although state-of-the-art methods, Autocuts and OptCuts, addressed this task via energy-minimization approaches, they fail to produce semantic seam styles, an essential factor for professional artists. The recent emergence of Graph Neural Networks (GNNs), and the fact that a mesh can be represented as a particular form of a graph, has opened a new bridge to novel graph learning-based solutions in the computer graphics domain. In this work, we use the power of supervised GNNs for the first time to propose a fully automated UV mapping framework that enables users to replicate their desired seam styles while reducing distortion and seam length. To this end, we provide augmentation and decimation tools to enable artists to create their dataset and train the network to produce their desired seam style. We provide a complementary post-processing approach for reducing the distortion based on graph algorithms to refine low-confidence seam predictions and reduce seam length (or the number of shells in our supervised case) using a skeletonization method.
Evolution of the grain size distribution in Milky Way-like galaxies in post-processed IllustrisTNG simulations

We model dust evolution in Milky Way-like galaxies by post-processing the IllustrisTNG cosmological hydrodynamical simulations in order to predict dust-to-gas ratios and grain size distributions. We treat grain-size-dependent dust growth and destruction processes using a 64-bin discrete grain size evolution model without spatially resolving each galaxy. Our model broadly reproduces the observed dust--metallicity scaling relation in nearby galaxies. The grain size distribution is dominated by large grains at $z\gtrsim 3$ and the small-grain abundance rapidly increases by shattering and accretion (dust growth) at $z\lesssim 2$. The grain size distribution approaches the so-called MRN distribution at $z\sim 1$, but a suppression of large-grain abundances occurs at $z 2$, and become consistent with the Milky Way extinction curve at $z\lesssim 1$ at $1/\lambda < 6~\rm \mu m^{-1}$. However, typical extinction curves predicted by our model have a steeper slope at short wavelengths than is observed in the Milky Way. This is due to the low-redshift decline of gas-phase metallicity and the dense gas fraction in our TNG Milky Way analogues that suppresses the formation of large grains through coagulation.
CERN-TH-2018-190 Tensionless Strings and the Weak Gravity Conjecture

We test various conjectures about quantum gravity for six-dimensional string compactifications in the framework of F-theory. Starting with a gauge theory coupled to gravity, we analyze the limit in Kähler moduli space where the gauge coupling tends to zero while gravity is kept dynamical. We show that such a limit must be located at infinite distance in the moduli space. As expected, the low-energy effective theory breaks down in this limit due to a tower of charged particles becoming massless. These are the excitations of an asymptotically tensionless string, which is shown to coincide with a critical heterotic string compactified to six dimensions. For a more quantitative analysis, we focus on a U(1) gauge symmetry and use a chain of dualities and mirror symmetry to determine the elliptic genus of the nearly tensionless string, which is given in terms of certain meromorphic weak Jacobi forms. Their modular properties in turn allow us to determine the charge-to-mass ratios of certain string excitations near the tensionless limit. We then provide evidence that the tower of asymptotically massless charged states satisfies the (sub-)Lattice Weak Gravity Conjecture, the Completeness Conjecture, and the Swampland Distance Conjecture. Quite remarkably, we find that the number theoretic properties of the elliptic genus conspire with the balance of gravitational and scalar forces of extremal black holes, such as to produce a narrowly tuned charge spectrum of superextremal states. As a byproduct, we show how to compute elliptic genera of both critical and non-critical strings, when refined by Mordell-Weil U(1) symmetries in F-theory. seung.joo. lee, wolfgang.lerche, timo.weigand @cern.ch ar X iv :1 80 8. 05 95 8v 3 [ he pth ] 2 3 O ct 2 01 8
Hawkes Processes Modeling, Inference and Control: An Overview

Hawkes Processes are a type of point process which models self-excitement among time events. It has been used in a myriad of applications, ranging from finance and earthquakes to crime rates and social network activity analysis.Recently, a surge of different tools and algorithms have showed their way up to top-tier Machine Learning conferences. This work aims to give a broad view of the recent advances on the Hawkes Processes modeling and inference to a newcomer to the field.
Deinterlacing Network for Early Interlaced Videos

With the rapid development of image restoration techniques, high-definition reconstruction of early videos has achieved impressive results. However, there are few studies about the interlacing artifacts that often appear in early videos and significantly affect visual perception. Traditional deinterlacing approaches are mainly focused on early interlacing scanning systems and thus cannot handle the complex and complicated artifacts in real-world early interlaced videos. Hence, this paper proposes a specific deinterlacing network (DIN), which is motivated by the traditional deinterlacing strategy. The proposed DIN consists of two stages, i.e., a cooperative vertical interpolation stage for split fields, and a merging stage that is applied to perceive movements and remove ghost artifacts. Experimental results demonstrate that the proposed method can effectively remove complex artifacts in early interlaced videos.
Learning from Lexical Perturbations for Consistent Visual Question Answering

Existing Visual Question Answering (VQA) models are often fragile and sensitive to input variations. In this paper, we propose a novel approach to address this issue based on modular networks, which creates two questions related by linguistic perturbations and regularizes the visual reasoning process between them to be consistent during training. We show that our framework markedly improves consistency and generalization ability, demonstrating the value of controlled linguistic perturbations as a useful and currently underutilized training and regularization tool for VQA models. We also present VQA Perturbed Pairings (VQA P2), a new, low-cost benchmark and augmentation pipeline to create controllable linguistic variations of VQA questions. Our benchmark uniquely draws from large-scale linguistic resources, avoiding human annotation effort while maintaining data quality compared to generative approaches. We benchmark existing VQA models using VQA P2 and provide robustness analysis on each type of linguistic variation.
Effect of pH and salinity on pathogenicity of Flavobacterium columnare and Myxobacterium sp . in Indian cat fish , Clarias batrachus ( Linn . ) and Heteropneustes fossilis ( Bloch . ) Author Details

Flavobacterium columnare (FC) and Myxobacterium sp. recorded persistently associated in fish hatchery and culture system of Himalayan and Sub Himalayan regions were found to be pathogenic. The pH and salinity played a significant role on the pathogenicity of these potent pathogens in case of Clarias batrachus and Heteropneustes fossilis. LD 50 value of FC was 10 CFU in both the fishes and those of Myxobacterium sp it was 10 CFU ml fish. Fish challenged with F. columnare and Myxobacterium sp. (@ 0.2 ml fish) individually consisting 10-cfu ml exhibited explicit symptoms of columnaris disease and marked with ulceration and saddle back lesion on the dorsal side of body. Maximum reisolation of inoculated bacteria was recorded at pH 7.0 and 7.5 and at 0.0-0.5 (F. columnare) and 0.0-1.0% (Myxobacterium sp.) salinity. Foregoing results elucidated that F. columnare was more sensitive to salinity in comparison to Myxobacterium sp. and their pathogenicity significantly (p<0.05) depends on the salinity and pH that might be one of the physical factors to control their proliferation.
Virufy: Global Applicability of Crowdsourced and Clinical Datasets for AI Detection of COVID-19 from Cough

Rapid and affordable methods of testing for COVID-19 infections are essential to reduce infection rates and prevent medical facilities from becoming overwhelmed. Current approaches of detecting COVID-19 require in-person testing with expensive kits that are not always easily accessible. This study demonstrates that crowdsourced cough audio samples acquired on smartphones from around the world can be used to develop a AI-based method that accurately predicts COVID-19 infection with an ROC-AUC of 77.1% (75.2%-78.3%). Furthermore, we show that our method is able to generalize to crowdsourced samples from Latin America and clinical samples from South Asia, without further training using the specific samples from those regions. As more crowdsourced data is collected, further development can be implemented using various respiratory audio samples to create a cough analysis-based machine learning (ML) solution for COVID-19 detection that can likely generalize globally to all demographic groups in both clinical and non-clinical settings.
Autonomous learning of multiple, context-dependent tasks

When facing the problem of autonomously learning multiple tasks with reinforcement learning systems, researchers typically focus on solutions where just one parametrised policy per task is sufficient to solve them. However, in complex environments presenting different contexts, the same task might need a set of different skills to be solved. These situations pose two challenges: (a) to recognise the different contexts that need different policies; (b) quickly learn the policies to accomplish the same tasks in the new discovered contexts. These two challenges are even harder if faced within an open-ended learning framework where an agent has to autonomously discover the goals that it might accomplish in a given environment, and also to learn the motor skills to accomplish them. We propose a novel open-ended learning robot architecture, C-GRAIL, that solves the two challenges in an integrated fashion. In particular, the architecture is able to detect new relevant contests, and ignore irrelevant ones, on the basis of the decrease of the expected performance for a given goal. Moreover, the architecture can quickly learn the policies for the new contexts by exploiting transfer learning importing knowledge from already acquired policies. The architecture is tested in a simulated robotic environment involving a robot that autonomously learns to reach relevant target objects in the presence of multiple obstacles generating several different obstacles. The proposed architecture outperforms other models not using the proposed autonomous context-discovery and transfer-learning mechanisms.
A new version of distributional chaos and the relations between distributional chaos in a sequence and other concepts of chaos.

In this paper we consider relations between distributional chaos in a sequence with distributional chaos, w-chaos, R-T chaos, DC 3, respectively). We give a sufficient condition and prove that the distributional chaos is equivalent to the distributional chaos in a sequence under this condition. Besides, we get that distributional chaos in a sequence and w-chaos(R-T chaos, DC 3, respectively)do not imply each other. Finally, we give a new definition of chaos, named DC 2', which is similar to DC 2, and show that for any integer N > 0, f is DC 2' if and only if f^N is also DC 2'.
A Study of Prevalence of Obsessive and Compulsive Symptoms in Persons with Schizophrenia

DOI: 10.21276/sjams.2017. 5.10. 62 Abstract: The presence of obsessive-compulsive symptoms in patients with schizophrenia was observed at the very beginning of the descriptions of the illness. Since the 19th century, cases with both psychotic and obsessive-compulsive symptom have been reported [1]. The relationship between OCD and schizophrenia has aroused curiosity among researchers, and has challenged the practicing clinicians. Earlier studies [2,3] reported a low prevalence rate and benign clinical course in schizophrenia patients with obsessive-compulsive symptoms, which have been challenged in recent years. To study the prevalence and clinical characteristics of obsessive-compulsive symptoms in patients with schizophrenia. This is a cross sectional study done on patients with schizophrenia, who attended outpatient department at Institute of Mental Health Hyderabad. In a period of ten months, consecutive out-patients who fulfilled the ICD-10 criteria for schizophrenia and also met the inclusion, and exclusion criteria mentioned in study were enrolled for the study. By the end, a total of 70 patients had been included in the study. Subject were later administered to clinical instruments like M.I. N.I, PANSS and Y-BOCS. Subjects were divided into two groups based the presence of obsessive-compulsive symptoms as Schizophrenia patients with and without obsessive compulsive symptoms. These two groups were compared to find out if there exists any statistically significant difference between them. Out of the 70 patients studied, 13[18.5%] patients were having obsessive compulsive symptoms. Mean age at onset of obsessive-compulsive symptoms: 17.54 ± 4.02 years. The most common type of obsessive symptom was obsession of contamination, which was found in 10 patients (76.92%). Other most frequent obsessions were aggressive (23%), need for symmetry (23%), and sexual (15.38%). The most commom compulsion in the study was cleaning/washing compulsion [92.3]. Among other common compulsions are ordering/rearranging, checking, and counting. This study may conclude that the coexistence of OCS is a relatively frequent finding in patients with schizophrenia, and clinician awareness of the possible co-occurrence of OCS and schizophrenia is warranted.
A Visual Analytics Approach to Monitor Time-Series Data with Incremental and Progressive Functional Data Analysis

Many real-world applications involve analyzing time-dependent phenomena, which are intrinsically functional---consisting of curves varying over a continuum, which is time in this case. When analyzing continuous data, functional data analysis (FDA) provides substantial benefits, such as the ability to study the derivatives and to restrict the ordering of data. However, continuous data inherently has infinite dimensions, and FDA methods often suffer from high computational costs. This is even more critical when we have new incoming data and want to update the FDA results in real-time. In this paper, we present a visual analytics approach to consecutively monitor and review the changing time-series data with a focus on identifying outliers by using FDA. To perform such an analysis while addressing the computational problem, we introduce new incremental and progressive algorithms that promptly generate the magnitude-shape (MS) plot, which reveals both the functional magnitude and shape outlyingness of time-series data. In addition, by using an MS plot in conjunction with an FDA version of principal component analysis, we enhance the analyst's ability to investigate the visually-identified outliers. We illustrate the effectiveness of our approach with three case studies using real-world and synthetic datasets.
MEGARA-GTC stellar spectral library – II. MEGASTAR first release

MEGARA is an optical integral field and multiobject fibre based spectrograph for the 10.4m Gran Telescopio CANARIAS that offers medium to high spectral resolutions (FWHM) of R $\simeq$ 6000, 12000, 20000. Commissioned at the telescope in 2017, it started operation as a common-user instrument in 2018. We are creating an instrument-oriented empirical spectral library from MEGARA-GTC stars observations, MEGASTAR, crucial for the correct interpretation of MEGARA data. This piece of work describes the content of the first release of MEGASTAR, formed by the spectra of 414 stars observed with R $\simeq$ 20000 in the spectral intervals from 6420 to 6790 $\r{A}$ and from 8370 to 8885 $\r{A}$, and obtained with a continuum average signal to noise ratio around 260. We describe the release sample, the observations, the data reduction procedure and the MEGASTAR database. Additionally, we include in Appendix A, an atlas with the complete set of 838 spectra of this first release of the MEGASTAR catalogue.
Context-Conditioning as Cognitive Control: Guiding Meta-learning with Task Information

Cognitive control, the ability of a system to adapt to the demands of a task, is an integral part of cognition. A widely accepted fact about cognitive control is that it is context-sensitive: Adults and children alike infer information about a task's demands from contextual cues and use these inferences to learn from ambiguous cues. However, the precise way in which people use contextual cues to guide adaptation to a new task remains poorly understood. This work connects the context-sensitive nature of cognitive control to a method for meta-learning with context-conditioned adaptation. We begin by identifying an essential difference between human learning and current approaches to meta-learning: In contrast to humans, existing meta-learning algorithms do not make use of task-specific contextual cues but instead rely exclusively on online feedback in the form of task-specific labels or rewards. To remedy this, we introduce a framework for using contextual information about a task to guide the initialization of task-specific models before adaptation to online feedback. We show how context-conditioned meta-learning can capture human behavior in a cognitive task and how it can be scaled to improve the speed of learning in various settings, including few-shot classification and low-sample reinforcement learning. Our work demonstrates that guiding meta-learning with task information can capture complex, human-like behavior, thereby deepening our understanding of cognitive control.
Data-Efficient Classification of Radio Galaxies

The continuum emission from radio galaxies can be generally classified into different classes like FRI, FRII, Bent, or Compact. In this paper, we explore the task of radio galaxy classification based on morphology using deep learning methods with a focus on using a small scale dataset (~ 2000 samples). We apply few-shot learning techniques based on Siamese Networks and transfer learning techniques using a pre-trained DenseNet model with advanced techniques like cyclical learning rate, discriminative learning to train the model rapidly. We achieve a classification accuracy of over 92% using our best performing model with the biggest source of confusion being between Bent and FRII type galaxies. Our results show that focusing on a small but curated dataset along with the use of best practices to train the neural network can lead to good results. Automated classification techniques will be crucial for upcoming surveys with next generation radio telescopes which are expected to detect thousands of new radio galaxies in the future.