Miracle-dz/UniFAR-FTUs huggingface dataset:
Adversarial Images through Stega Glasses

This paper explores the connection between steganography and adversarial images. On the one hand, ste-ganalysis helps in detecting adversarial perturbations. On the other hand, steganography helps in forging adversarial perturbations that are not only invisible to the human eye but also statistically undetectable. This work explains how to use these information hiding tools for attacking or defending computer vision image classification. We play this cat and mouse game with state-of-art classifiers, steganalyzers, and steganographic embedding schemes. It turns out that steganography helps more the attacker than the defender.
Uncertainty Aware Wildfire Management

Recent wildfires in the United States have resulted in loss of life and billions of dollars, destroying countless structures and forests. Fighting wildfires is extremely complex. It is difficult to observe the true state of fires due to smoke and risk associated with ground surveillance. There are limited resources to be deployed over a massive area and the spread of the fire is challenging to predict. This paper proposes a decision-theoretic approach to combat wildfires. We model the resource allocation problem as a partially-observable Markov decision process. We also present a data-driven model that lets us simulate how fires spread as a function of relevant covariates. A major problem in using data-driven models to combat wildfires is the lack of comprehensive data sources that relate fires with relevant covariates. We present an algorithmic approach based on large-scale raster and vector analysis that can be used to create such a dataset. Our data with over 2 million data points is the first open-source dataset that combines existing fire databases with covariates extracted from satellite imagery. Through experiments using real-world wildfire data, we demonstrate that our forecasting model can accurately model the spread of wildfires. Finally, we use simulations to demonstrate that our response strategy can significantly reduce response times compared to baseline methods.
Mortality rates of some Finnish passerines

It is possible to calculate the annual mortality or survival rates of birds e.g . from reported recoveries of ringed birds or by studying the colour-marked population of a species which returns regularly to the same nesting site . The former method has been used about twentyfive years (LACK 1943 a, b, c) and the latter about forty years (BURKITT 1926, NICE 1937 etc .) . For this paper I have calculated from ringing data annual mortality rates for 19 Passerine species. Because it is evident that the recoveries which form the basis of the calculations do not always represent an unbiased sample for the whole population I have made special efforts to select the recoveries used in order to get as unbiased mortality rates as possible .
Dimension reduction for thin films prestrained by shallow curvature

We are concerned with the dimension reduction analysis for thin three-dimensional elastic films, prestrained via Riemannian metrics with weak curvatures. For the prestrain inducing the incompatible version of the Föppl–von Kármán equations, we find the Γ-limits of the rescaled energies, identify the optimal energy scaling laws, and display the equivalent conditions for optimality in terms of both the prestrain components and the curvatures of the related Riemannian metrics. When the stretching-inducing prestrain carries no in-plane modes, we discover similarities with the previously described shallow shell models. In higher prestrain regimes, we prove new energy upper bounds by constructing deformations as the Kirchhoff–Love extensions of the highly perturbative, Hölder-regular solutions to the Monge–Ampere equation obtained by means of convex integration.
Mitigating Byzantine Attacks in Federated Learning

Prior solutions for mitigating Byzantine failures in federated learning, such as element-wise median of the stochastic gradient descent (SGD) based updates from the clients, tend to leverage the similarity of updates from the non-Byzantine clients. However, when data is non-IID, as is typical in mobile networks, the updates received from non-Byzantine clients are quite diverse, resulting in poor convergence performance of such approaches. On the other hand, current algorithms that address heterogeneous data distribution across clients are limited in scope and do not perform well when there is variability in the number and identities of the Byzantine clients, or when general non-convex loss functions are considered. We propose `DiverseFL' that jointly addresses three key challenges of Byzantine resilient federated learning -- (i) non-IID data distribution across clients, (ii) variable Byzantine fault model, and (iii) generalization to non-convex and non-smooth optimization. DiverseFL leverages computing capability of the federated learning server that for each iteration, computes a `guiding' gradient for each client over a tiny sample of data received only once from the client before start of the training. The server uses `per client' criteria for flagging Byzantine clients, by comparing the corresponding guiding gradient with the client's gradient update. The server then updates the model using the gradients received from the non-flagged clients. As we demonstrate in our experiments with benchmark datasets and popular Byzantine attacks, our proposed approach performs better than the prior algorithms, almost matching the performance of the `Oracle SGD', where the server knows the identities of the Byzantine clients.
NUIG-Shubhanker@Dravidian-CodeMix- FIRE2020: Sentiment Analysis of Code-Mixed Dravidian text using XLNet

Social media has penetrated into multilingual societies, however most of them use English to be a preferred language for communication. So it looks natural for them to mix their cultural language with English during conversations resulting in abundance of multilingual data, call this code-mixed data, available in todays' world.Downstream NLP tasks using such data is challenging due to the semantic nature of it being spread across multiple languages.One such Natural Language Processing task is sentiment analysis, for this we use an auto-regressive XLNet model to perform sentiment analysis on code-mixed Tamil-English and Malayalam-English datasets.
Auto-STGCN: Autonomous Spatial-Temporal Graph Convolutional Network Search

In recent years, many spatial-temporal graph convolutional network (STGCN) models are proposed to deal with the spatial-temporal network data forecasting problem. These STGCN models have their own advantages, i.e., each of them puts forward many effective operations and achieves good prediction results in the real applications. If users can effectively utilize and combine these excellent operations integrating the advantages of existing models, then they may obtain more effective STGCN models thus create greater value using existing work. However, they fail to do so due to the lack of domain knowledge, and there is lack of automated system to help users to achieve this goal. In this article, we fill this gap and propose Auto-STGCN algorithm, which makes use of existing models to automatically explore high-performance STGCN model for specific scenarios. Specifically, we design Unified-STGCN framework, which summarizes the operations of existing architectures, and use parameters to control the usage and characteristic attributes of each operation, so as to realize the parameterized representation of the STGCN architecture and the reorganization and fusion of advantages. Then, we present Auto-STGCN, an optimization method based on reinforcement learning, to quickly search the parameter search space provided by Unified-STGCN, and generate optimal STGCN models automatically. Extensive experiments on real-world benchmark datasets show that our Auto-STGCN can find STGCN models superior to existing STGCN models used for search space construction, which demonstrates the effectiveness of our proposed method.
Probabilistic Sequential Shrinking: A Best Arm Identification Algorithm for Stochastic Bandits with Corruptions

We consider a best arm identification (BAI) problem for stochastic bandits with adversarial corruptions in the fixed-budget setting of $T$ steps. We design a novel randomized algorithm, Probabilistic Sequential Shrinking$(u)$ (PSS$(u)$), which is agnostic to the amount of corruptions. When the amount of corruptions per step (CPS) is below a threshold, PSS$(u)$ identifies the best arm or item with probability tending to $1$ as $T\rightarrow\infty$. Otherwise, the optimality gap of the identified item degrades gracefully with the CPS. We argue that such a bifurcation is necessary. In addition, we show that when the CPS is sufficiently large, no algorithm can achieve a BAI probability tending to $1$ as $T\rightarrow \infty$. In PSS$(u)$, the parameter $u$ serves to balance between the optimality gap and success probability. En route, the injection of randomization is shown to be essential to mitigate the impact of corruptions. Indeed, we show that PSS$(u)$ has a better performance than its deterministic analogue, the Successive Halving (SH) algorithm by Karnin et al. (2013). PSS$(2)$'s performance guarantee matches SH's when there is no corruption. Finally, we identify a term in the exponent of the failure probability of PSS$(u)$ that generalizes the common $H_2$ term for BAI under the fixed-budget setting.
A reassessment of apheloriine millipede phylogeny: additional taxa, Bayesian inference, and direct optimization (Polydesmida: Xystodesmidae)

Millipedes in the tribe Apheloriini occur throughout the eastern United States, predominately in the deciduous forests of the Appalachian Mountains. Herein we present a reassessment of apheloriine millipede phylogeny using mitochondrial DNA sequences and an additional 29 exemplar taxa (including 15 undescribed species and all of the species in the genus Brachoria, except one). In this study, first we check the results of the previous phylogeny of the tribe (Marek and Bond, 2006) with different alignment and phylogenetic techniques (direct optimization and maximum likelihood), and second reconstruct a new phylogeny evaluating it in the same way with Bayesian, maximum likelihood, and direct optimization. Using this updated and expanded phylogeny, we tested historical classifications with Bayes factor and Shimodaira-Hasegawa hypothesis testing, consistently finding very strong evidence against their implied phylogenetic hypotheses. Lastly, using the new phylogeny as a foundation, we make taxonomic modifications and provide an updated species list of Apheloriini (106 species/17 genera).
Optimal Assistance for Object-Rearrangement Tasks in Augmented Reality

Augmented-reality (AR) glasses that will have access to onboard sensors and an ability to display relevant information to the user present an opportunity to provide user assistance in quotidian tasks. Many such tasks can be characterized as object-rearrangement tasks. We introduce a novel framework for computing and displaying AR assistance that consists of (1) associating an optimal action sequence with the policy of an embodied agent and (2) presenting this sequence to the user as suggestions in the AR system's heads-up display. The embodied agent comprises a "hybrid" between the AR system and the user, with the AR system's observation space (i.e., sensors) and the user's action space (i.e., task-execution actions); its policy is learned by minimizing the task-completion time. In this initial study, we assume that the AR system's observations include the environment's map and localization of the objects and the user. These choices allow us to formalize the problem of computing AR assistance for any object-rearrangement task as a planning problem, specifically as a capacitated vehicle-routing problem. Further, we introduce a novel AR simulator that can enable web-based evaluation of AR-like assistance and associated at-scale data collection via the Habitat simulator for embodied artificial intelligence. Finally, we perform a study that evaluates user response to the proposed form of AR assistance on a specific quotidian object-rearrangement task, house cleaning, using our proposed AR simulator on mechanical turk. In particular, we study the effect of the proposed AR assistance on users' task performance and sense of agency over a range of task difficulties. Our results indicate that providing users with such assistance improves their overall performance and while users report a negative impact to their agency, they may still prefer the proposed assistance to having no assistance at all.
Deep Convolutional Neural Network-Based Inverse Filtering Approach for Speech De-Reverberation

In this paper, we introduce a spectral-domain inverse filtering approach for single-channel speech de-reverberation using deep convolutional neural network (CNN). The main goal is to better handle realistic reverberant conditions where the room impulse response (RIR) filter is longer than the short-time Fourier transform (STFT) analysis window. To this end, we consider the convolutive transfer function (CTF) model for the reverberant speech signal. In the proposed framework, the CNN architecture is trained to directly estimate the inverse filter of the CTF model. Among various choices for the CNN structure, we consider the U-net which consists of a fully-convolutional auto-encoder network with skip-connections. Experimental results show that the proposed method provides better dereverberation performance than the prevalent benchmark algorithms under various reverberation conditions.
One-dimensional model for deconfined criticality with $\mathbb{Z}_3 \times \mathbb{Z}_3$ symmetry

We continue recent efforts to discover examples of deconfined quantum criticality in one-dimensional models. In this work we investigate the transition between a $\mathbb{Z}_3$ ferromagnet and a phase with valence bond solid (VBS) order in a spin chain with $\mathbb{Z}_3\times\mathbb{Z}_3$ global symmetry. We study a model with alternating projective representations on the sites of the two sublattices, allowing the Hamiltonian to connect to an exactly solvable point having VBS order with the character of SU(3)-invariant singlets. Such a model does not admit a Lieb-Schultz-Mattis theorem typical of systems realizing deconfined critical points. Nevertheless, we find evidence for a direct transition from the VBS phase to a $\mathbb{Z}_3$ ferromagnet. Finite-entanglement scaling data are consistent with a second-order or weakly first-order transition. We find in our parameter space an integrable lattice model apparently describing the phase transition, with a very long, finite, correlation length of 190878 lattice spacings. Based on exact results for this model, we propose that the transition is extremely weakly first order, and is part of a family of DQCP described by walking of renormalization group flows.
XPDNet for MRI Reconstruction: an Application to the fastMRI 2020 Brain Challenge

We present a modular cross-domain neural network the XPDNet and its application to the MRI reconstruction task. This approach consists in unrolling the PDHG algorithm as well as learning the acceleration scheme between steps. We also adopt state-of-the-art techniques specific to Deep Learning for MRI reconstruction. At the time of writing, this approach is the best performer in PSNR on the fastMRI leaderboards for both knee and brain at acceleration factor 4.
The effect of sex ratio on male reproductive success in painted turtles, Chrysemys picta

The effect of sex ratio on male reproductive success in painted turtles (Chrysemys picta) Elinor Hughes Advisors: University of Guelph, 2011 Dr. Ronald J. Brooks Dr. Jinzhong Fu Sex ratio theory suggests that the strength of intersexual selection will increase as a population more male-biased; reflecting increased selectivity in mate choice. Populations of pond turtle have varying adult sex ratios, in painted turtles (Chrysemys picta), reported sex ratios range from female biased (1:3) to male biased (3:1). I investigated the effect of sex ratio on male reproductive success (quantified as “fertilization success”) in painted turtles. I examined the mating system of painted turtles in a female-biased population using microsatellite paternity analysis, relating variation in male fertilization success to male phenotype and offspring survival, employed ex situ behavioural observation to clarify the mechanism behind the variation in male fertilization success and used agentbased modeling to simulate the effects of changing sex ratio, population density and proportions of male phenotype on male fertilization success. Small males contributed sperm to a greater number of clutches than did larger males, but were not more likely to reproduce in a season than larger males. There was no offspring fitness advantage related to male body size and no relationship between male claw length and fertilization success. Large male painted turtles courted at a higher frequency than small males. I found no relationship between male courtship behavior and claw length. Females showed no preference for males of any phenotype. Agent-based simulations were based on the distribution of best fit from the observed data; an amalgam of two Poisson distributions, each with its own probability of success and proportional representation in the final distribution. Increased female sex ratio bias, increased population density and increased proportions of “more successful” males all increased the mean and variance of male fertilization success, based on increased encounter rate among turtles. Small and large male painted turtles enjoy different fertilization success. It is uncertain whether this difference is based on active female choice, cryptic female choice, sperm competition or a combination of factors. Sex ratio simulations predict the opposite result as that predicted by sex ratio theory. These contrary results should be compared to simulations manipulating choosiness and field data from painted turtle populations to clarify mechanisms influencing male reproductive success.
Trisections of Nonorientable 4-Manifolds

We study trisections of smooth, compact non-orientable 4-manifolds, and introduce trisections of non-orientable 4-manifolds with boundary. In particular, we prove a non-orientable analogue of a classical theorem of Laudenbach-Poenaru. As a consequence, trisection diagrams and Kirby diagrams of closed non-orientable 4-manifolds exist. We discuss how the theory of trisections may be adapted to the setting of non-orientable 4-manifolds with many examples.
Skeleton-bridged Point Completion: From Global Inference to Local Adjustment

Point completion refers to complete the missing geometries of objects from partial point clouds. Existing works usually estimate the missing shape by decoding a latent feature encoded from the input points. However, real-world objects are usually with diverse topologies and surface details, which a latent feature may fail to represent to recover a clean and complete surface. To this end, we propose a skeleton-bridged point completion network (SK-PCN) for shape completion. Given a partial scan, our method first predicts its 3D skeleton to obtain the global structure, and completes the surface by learning displacements from skeletal points. We decouple the shape completion into structure estimation and surface reconstruction, which eases the learning difficulty and benefits our method to obtain on-surface details. Besides, considering the missing features during encoding input points, SK-PCN adopts a local adjustment strategy that merges the input point cloud to our predictions for surface refinement. Comparing with previous methods, our skeleton-bridged manner better supports point normal estimation to obtain the full surface mesh beyond point clouds. The qualitative and quantitative experiments on both point cloud and mesh completion show that our approach outperforms the existing methods on various object categories.
Towards Accurate Quantization and Pruning via Data-free Knowledge Transfer

When large scale training data is available, one can obtain compact and accurate networks to be deployed in resource-constrained environments effectively through quantization and pruning. However, training data are often protected due to privacy concerns and it is challenging to obtain compact networks without data. We study data-free quantization and pruning by transferring knowledge from trained large networks to compact networks. Auxiliary generators are simultaneously and adversarially trained with the targeted compact networks to generate synthetic inputs that maximize the discrepancy between the given large network and its quantized or pruned version. We show theoretically that the alternating optimization for the underlying minimax problem converges under mild conditions for pruning and quantization. Our data-free compact networks achieve competitive accuracy to networks trained and fine-tuned with training data. Our quantized and pruned networks achieve good performance while being more compact and lightweight. Further, we demonstrate that the compact structure and corresponding initialization from the Lottery Ticket Hypothesis can also help in data-free training.
Singularities on toric fibrations

In this paper we investigate singularities on toric fibrations. In this context we study a conjecture of Shokurov (a special case of which is due to M Kernan which roughly says that if is an -lc Fano-type log Calabi-Yau fibration, then the singularities of the log base are bounded in terms of and where and are the discriminant and moduli divisors of the canonical bundle formula. A corollary of our main result says that if is a toric Fano fibration with being -lc, then the multiplicities of the fibres over codimension one points are bounded depending only on and . Bibliography: 20 titles.
An Inventory and Assessment of Exotic and Native Plant Species Diversity in the Kenyan Rangelands:-Case Study of Narok North Sub-County.

A botanical inventory and diversity of exotic and native species was assessed in the rangeland of Narok North sub-County, Kenya. A total of 72 species were recorded, composed of 64 species of trees and shrubs and eight species of herbs. They belonged to 34 families and 52 genera. Fabaceae was the richest family with 16 species, followed by Euphorbiaceae (five), Moraceae and Myrtaceae (four each). In total, 48 species were native and 24 exotics with a diversity index H of 1.082 and 0.604, respectively. Hypericum revolutum, a native shrub, was the most dominant with an importance value of 3.81, followed by three exotic species; Datura suaveolens (an invasive species), Dovyalis caffra and Hibiscus rosa-sinesis with importance values of 3.40, 3.35 and 3.23, respectively. The native species were abundant in undisturbed areas while the exotics were most common in areas with vegetation cover less than 60%. Four invasive alien species were encountered namely; D. suaveolens, Lantana camara, L. trifolia and Opuntia ficus-indica. They all had a high density that indicated greater establishment success. This inventory affirms that the rangelands of Narok North are undergoing serious changes in vegetation structure and composition due to human activities and requires urgent attention to conserve biodiversity and genetic resources. Key words: Dominance, importance values, invasive species, inventory, semi-arid lands