Miracle-dz/UniFAR-FTUs huggingface dataset:
Spectral–Spatial Anomaly Detection of Hyperspectral Data Based on Improved Isolation Forest

Anomaly detection in hyperspectral image (HSI) is affected by redundant bands and the limited utilization capacity of spectral–spatial information. In this article, we propose a novel improved Isolation Forest (IIF) algorithm based on the assumption that anomaly pixels are more susceptible to isolation than background pixels. The proposed IIF is a modified version of the Isolation Forest (iForest) algorithm, which addresses the poor performance of iForest in detecting local anomalies and anomaly detection in high-dimensional data. Furthermore, we propose a spectral–spatial anomaly detector based on IIF (SSIIFD) to make full use of global and local information, as well as spectral and spatial information. To be specific, first, we apply the Gabor filter to extract spatial features, which are then employed as input to the relative mass isolation forest (ReMass-iForest) detector to obtain the spatial anomaly score. Next, original images are divided into several homogeneous regions via the entropy rate segmentation (ERS) algorithm, and the preprocessed images are then employed as input to the proposed IIF detector to obtain the spectral anomaly score. Finally, we fuse the spatial and spectral anomaly scores by combining them linearly to predict anomaly pixels. The experimental results on four real hyperspectral datasets demonstrate that the proposed detector outperforms other state-of-the-art methods.
Evaluation of Morphodynamic Controls on the Preservation of Fluvial Meander‐Belt Deposits

The way river morphodynamics influence the preservation of point‐bar deposits at multiple scales is not fully understood. Employing time‐lapse trajectories of natural rivers, a numerical model is used here to simulate planform evolutions of meander‐belt reaches that embody different transformation behaviors and cutoff processes. Proxies for temporal durations are obtained considering the surface area over which a river migrated and channel migration rates that relate to average channel radius of curvature through constant, monotonic, and non‐monotonic relationships. The preservation of meander‐belt deposits over different timescales is assessed at three architectural hierarchies: (a) pairs and (b) sets of accretion packages, and (c) meander‐belts. Results confirm that sediment preservation decreases in a predictable way with the accumulation time; however, accretion rates decay with time in a way that does not follow the expected power‐law. This is interpreted to reflect the effect of the onset of geomorphic thresholds of channel transformation and cutoff.
Near Miss Archive: a Challenge to Share Knowledge Among Inspectors and Improve Seveso Inspections

In European Seveso Legislation for the control of the hazard of major accidents (Directive 2015/12/UE), the Safety Management System SMS is an essential obligation for managers and the authorities are required to periodically verify its adequateness through periodical inspections at Seveso sites. One of the pillars of the SMS is the collection and analysis of documents on accidents, near misses and possibly anomalies, in order to identify weaknesses and implement continuous improvement. In Italy, for a few years, the documents, gathered from all Italian Seveso sites by the inspectors, have been archived and used for research purposes. The archive currently contains some 4000 reports, collected in five years by some 100 inspectors throughout Italy. The paper discusses in the detail the challenges faced to extract the knowledge hidden in the documents and make it usable through the design of a robust model. For this aim, Machine Learning techniques have been used as a preprocessing of the reports for extracting the concepts and their relations, organized into an entity-relation model. The effectiveness of this methodology and its potentiality are pointed out by investigating a few hot topics, exploiting the information contained in the repository.
La prise de contrôle étrangère : Déterminants micro-économiques et impact sur la performance des entreprises

Les operations de prise de controle etrangere (PCE) ont ete marquees par un essor spectaculaire notamment durant les deux dernieres decennies (Moeller, Schlingemann et Stulz (2004); Seth, Song et Pettit (2002)). Une monte marquante des PCE essentiellement sur le plan des marches emergents a ete affichee tout au long des annees 90s. De ce fait et dans l’horizon de cette etude, nous posons la question suivante : Quels sont les determinants micro-economiques qui influencent la prise de controle etrangere dans le capital des societes cotees en Tunisie et quel est l’impact de cette prise de controle sur la performance ? L’analyse des donnees s’est basee sur une regression lineaire multiple estimee par la methode des moindres carres ordinaires. En choisissant un panel de donnees incluant 180 observations et en ayant recours au test d'Hausman (1978) en vue de choisir entre le modele a effets fixes et le modele a effets aleatoires pour avoir les meilleures estimations de nos variables, nous avons conclu que parmi les determinants classiques : la taille et le statut sont des determinants qui influencent positivement ainsi que significativement sur la PCE. Par opposition, l’appartenance au meme secteur d’activite impacte negativement et d’une maniere significative notre variable a expliquer. Ensuite, pour les determinants de gouvernance nous avons selectionne, en se basant sur la revue anterieure, le mecanisme de conseil d’administration pour lequel nous avons confirme une relation significativement positive avec la PCE. Enfin, en vue de mettre en evidence l’impact de la performance financiere de l’entreprise cible sur la PCE, nos resultats nous ont permis de confirmer une association significativement negative entre la performance financiere et la PCE.
Wood Adhesives Based on Natural Resources: A Critical Review: Part IV. Special Topics

Various naturally-based chemicals can be used directly as wood adhesives or are precursors for the synthesis of adhesive resins. Liquefaction and pyrolysis of wood yield various smaller chemicals derived from the different wood components, which then are used in the preparation of adhesives by replacing mainly phenol as raw material. The possible replacement of formaldehyde in aminoplastic and phenolic resins would solve the question of the subsequent formaldehyde emission. The multiple unsaturations of the triglycerides in vegetable oils enable polymerization for the direct synthesis of thermosets, as well as bases for polyfunctionalization and crosslinking. Natural polymers, such as poly(lactic acid)s (PLAs), natural rubber, or poly(hyhydroxyalkanoate)s (PHAs) are thermoplastics and can be used for various special applications in wood bonding, in case they can also be crosslinked. For other thermoplastic wood adhesives, such as PUR or PA, chemicals based on natural resources can at least replace a part or even all synthetic raw materials (monomers); these monomers derive from targeted decomposition of the wood material in biorefineries. Cellulose nanofibrils (CNFs) can be used as as sole adhesives or as components of adhesives. Hydrogen bonding has a key function in binder applications related to adhesion between cellulose nanoparticles and other materials. CNFs are able to establish strong bonding between wood particles/fibres through flexible and strong films by a simple drying process. Cashew nut shell liquid (CNSL) is a by-product of the cashew nut processing with cardanol (CD) as main component. CD-formaldehyde resins show improved flexibility compared to phenol-formaldehyde (PF) resins; CD can replace up to 40% of the phenol.
APLICACIÓN DEL ALGORITMO K-MEDOID PARA LA SEGMENTACIÓN DE LOS ALUMNOS INGRESANTES DE UNA UNIVERSIDAD.

Actualmente, en el área de educación superior se ha vuelto indispensable la gestión de los datos para la toma de decisiones académicas y la mejora de los procesos educativos, para ello la analítica y estadística han sido llevados al ámbito tecnológico, donde prima la automatización de procesos y la gestión de grandes bases de datos a través de algoritmos de Machine Learning, uno de los más utilizados son los algoritmos clustering, cuyo propósito es agrupar datos por similitud. El presente estudio tuvo como objetivo encontrar tipos de estudiantes universitarios respecto a sus variables sociodemográficas, económicas y de rendimiento académico, utilizando el algoritmo K-medoid en datos de alumnos ingresantes a la Universidad Nacional Agraria La Molina de Lima, Perú. Se pudo determinar que los ingresantes en estudio se pueden segmentar en 3 grupos, cada uno con características propias, lo que permitirá impulsar cambios a favor de la calidad educativa y promover la renovación de los espacios de enseñanza de manera personalizada en torno al tipo de estudiante que la universidad gestiona.
OS DETERMINANTES DAS EXPORTAÇÕES CONVENCIONAIS E DAS CADEIAS GLOBAIS DE VALOR: UMA ANÁLISE EM PAINEL

O objetivo deste trabalho é investigar empiricamente o desempenho e asdiferenças entre as exportações convencionais e as exportações relacionadas às cadeias globais de valor (CGV) em resposta aos seguintes determinantes: taxa de câmbioem três conceitos (nível, volatilidade e desalinhamento), efeito renda e as consequências da crise financeira internacional. Para tanto, utilizam-se três métodos de dadosem painel estático e dinâmico para um conjunto de 59 países, no período de 2000 a2011. Os resultados das estimações confirmam: a) a elasticidade-preço das exportaçõesrelacionadas às CGV são maiores em comparação à elasticidade-preço das exportaçõesconvencionais; b) o aumento da volatilidade da taxa de câmbio possui menores ounenhum impacto nas exportações vinculadas às CGV; e c) países com maiores participações a jusante nas CGV estão sujeitos a menores efeitos advindos de um desalinhamento cambial. Além disso, pode-se afirmar que a crise do subprime gerou efeitosnegativos mais proeminentes nas exportações associadas às CGV em relação às exportações tradicionais.
Bayesian Semi-nonparametric ARCH Models

A Bayesian seminonparametric approach to ARCH models is developed with the advantage that small sample results are obtained even when the likelihood function is subject to nonlinear inequality constraints (as in the ARCH models used in this paper). The seminonparametric nature of the approach allows for the relaxation of the assumption of normal errors. An application and a small Monte Carlo study indicate that the methods the author advocates are both feasible and necessary. Copyright 1994 by MIT Press.
ROAR CPV NH Coral Transplantation : A Method to Create , preserve , and Manage Coral Reefs

G'oraL reefs in Haxoaii and other tropical areas are valuable economic and recreational resources. He+ever, in recent years Mbanisation and other human activities have accelerated the destruction and pollution of ref msources. Past studies have indicated that reefs are very sensitive to mm-made stresses and, once denuded, may take decades to mcover. The feasibility of using transplanted co~2s as a method to shoWen recovery time and to create near cor'a2 reefs is discussed in this report. C'orals of Mo common Haaraiian species, Porites compressa and Montipora verrucosa, ue~ transplanted in three areas of Kaneohe Bay, Oahu and ><ere monitored for gr~th and suan'val over an 18-month period. lllatura2 rates of coz'al alcove~ over a period of 84 years in Kaneohe Bay vere aLso studied to assess the importance and feasibility of the transpLantation method. Results of' the study shmed that cora2 transplantation may be an effective procedure for pmserving and creating coral reefs in Hawaii, especially in some environments rhea natural recovery is 2ikely to fail.
Feedback Interconnected Density Estimation and Control

—Swarm robotic systems have foreseeable applications in the near future. Recently, there has been an increasing amount of literature that employs partial differential equations (PDEs) to model the time-evolution of the probability density of swarm robotic systems and uses density feedback to design stable control laws that act on individuals such that their density converges to a target proﬁle. However, it remains largely unexplored considering problems of how to estimate the real-time density, how the density estimation algorithms affect the control performance, and whether the estimation performance in turn depends on the control algorithms. In this work, we focus on studying the interplay of these algorithms. Speciﬁcally, we propose new density control laws which use the real-time density and its gradient as feedback, and prove that they are globally input-to-state stable (ISS) with respect to estimation errors. Then, we design ﬁltering algorithms to obtain estimates of the density and its gradient, and prove that these estimates are convergent assuming the control laws are known. Finally, we show that the feedback interconnection of these estimation and control algorithms is still globally ISS, which is attributed to the bilinearity of the PDE system. An agent-based simulation is included to verify the stability of these algorithms and their feedback interconnection.
Attenuation Coefficient Estimation for PET/MRI With Bayesian Deep Learning Pseudo-CT and Maximum-Likelihood Estimation of Activity and Attenuation

A major remaining challenge for magnetic resonance-based attenuation correction methods (MRAC) is their susceptibility to sources of magnetic resonance imaging (MRI) artifacts (e.g., implants and motion) and uncertainties due to the limitations of MRI contrast (e.g., accurate bone delineation and density, and separation of air/bone). We propose using a Bayesian deep convolutional neural network that in addition to generating an initial pseudo-CT from MR data, it also produces uncertainty estimates of the pseudo-CT to quantify the limitations of the MR data. These outputs are combined with the maximum-likelihood estimation of activity and attenuation (MLAA) reconstruction that uses the PET emission data to improve the attenuation maps. With the proposed approach uncertainty estimation and pseudo-CT prior for robust MLAA (UpCT-MLAA), we demonstrate accurate estimation of PET uptake in pelvic lesions and show recovery of metal implants. In patients without implants, UpCT-MLAA had acceptable but slightly higher root-mean-squared-error (RMSE) than Zero-echo-time and Dixon Deep pseudo-CT when compared to CTAC. In patients with metal implants, MLAA recovered the metal implant; however, anatomy outside the implant region was obscured by noise and crosstalk artifacts. Attenuation coefficients from the pseudo-CT from Dixon MRI were accurate in normal anatomy; however, the metal implant region was estimated to have attenuation coefficients of air. UpCT-MLAA estimated attenuation coefficients of metal implants alongside accurate anatomic depiction outside of implant regions.
Considering User Agreement in Learning to Predict the Aesthetic Quality

How to robustly rank the aesthetic quality of given images has been a long-standing ill-posed topic. Such challenge stems mainly from the diverse subjective opinions of different observers about the varied types of content. There is a growing interest in estimating the user agreement by considering the standard deviation (σ) of the scores, instead of only predicting the mean aesthetic opinion score (µ). Nevertheless, when comparing a pair of contents, few studies consider how confident are we regarding the difference in the aesthetic scores. In this paper, we thus propose (1) a re-adapted multi-task attention network to predict both the mean opinion score and the standard deviation in an end-to-end manner; (2) a brand-new confidence interval ranking loss that encourages the model to focus on image-pairs that are less certain about the difference of their aesthetic scores. With such loss, the model is encouraged to learn the uncertainty of the content that is relevant to the diversity of observers’ opinions, i.e., user disagreement. Extensive experiments have demonstrated that the proposed multi-task aesthetic model achieves state-of-the-art performance on two different types of aesthetic datasets, i.e., AVA and TMGA.
Bugs in our Pockets: The Risks of Client-Side Scanning

Our increasing reliance on digital technology for personal, economic, and government affairs has made it essential to secure the communications and devices of private citizens, businesses, and governments. This has led to pervasive use of cryptography across society. Despite its evident advantages, law enforcement and national security agencies have argued that the spread of cryptography has hindered access to evidence and intelligence. Some in industry and government now advocate a new technology to access targeted data: client-side scanning (CSS). Instead of weakening encryption or providing law enforcement with backdoor keys to decrypt communications, CSS would enable on-device analysis of data in the clear. If targeted information were detected, its existence and, potentially, its source, would be revealed to the agencies; otherwise, little or no information would leave the client device. Its proponents claim that CSS is a solution to the encryption versus public safety debate: it offers privacy -- in the sense of unimpeded end-to-end encryption -- and the ability to successfully investigate serious crime. In this report, we argue that CSS neither guarantees efficacious crime prevention nor prevents surveillance. Indeed, the effect is the opposite. CSS by its nature creates serious security and privacy risks for all society while the assistance it can provide for law enforcement is at best problematic. There are multiple ways in which client-side scanning can fail, can be evaded, and can be abused.
On Downlink Interference Decoding In Multi-Cell Massive MIMO Systems

In this paper, the downlink of a multi-cell massive MIMO system is considered where the channel state information (CSI) is estimated via pilot symbols that are orthogonal in a cell but re-used in other cells. Re-using the pilots, however, contaminates the CSI estimate at each base station (BS) by the channel of the users sharing the same pilot in other cells. The resulting inter-cell interference does not vanish even when the number of BS antennas $M$ is large, i.e., $M\rightarrow\infty$, and thus the rates achieved by treating interference as noise (TIN) saturate even if $M\rightarrow\infty$. In this paper, interference aware decoding schemes based on simultaneous unique decoding (SD) and simultaneous non-unique decoding (SND) of the full interference or a part of the interference (PD) are studied with two different linear precoding techniques: maximum ratio transmission (MRT) and zero forcing (ZF). The resulting rates are shown to grow unbounded as $M\rightarrow\infty$. In addition, the rates achievable via SD/SND/PD for finite $M$ are derived using a worst-case uncorrelated noise technique, which are shown to scale as $\mathcal{O}(\log M)$. To compare the performance of different schemes, the maximum symmetric rate problem is studied, where it is confirmed that with large, yet practical, values of $M$, SND strictly outperforms TIN, and also that PD strictly outperforms SND.
Simple Baseline for Single Human Motion Forecasting

Global human motion forecasting is important in many fields, which is the combination of global human trajectory prediction and local human pose prediction. Visual and social information are often used to boost model performance, however, they may consume too much computational resources. In this paper, we establish a simple but effective baseline for single human motion forecasting without visual and social information, equipped with useful training tricks. Our method "futuremotion_ICCV21" outperforms existing methods by a large margin on SoMoF benchmark1. We hope our work provide new ideas for future research.
SoGCN: Second-Order Graph Convolutional Networks

Graph Convolutional Networks (GCN) with multi-hop aggregation is more expressive than one-hop GCN but suffers from higher model complexity. Finding the shortest aggregation range that achieves comparable expressiveness and minimizes this side effect remains an open question. We answer this question by showing that multi-layer second-order graph convolution (SoGC) is sufficient to attain the ability of expressing polynomial spectral filters with arbitrary coefficients. Compared to models with one-hop aggregation, multi-hop propagation, and jump connections, SoGC possesses filter representational completeness while being lightweight, efficient, and easy to implement. Thereby, we suggest that SoGC is a simple design capable of forming the basic building block of GCNs, playing the same role as $3 \times 3$ kernels in CNNs. We build our Second-Order Graph Convolutional Networks (SoGCN) with SoGC and design a synthetic dataset to verify its filter fitting capability to validate these points. For real-world tasks, we present the state-of-the-art performance of SoGCN on the benchmark of node classification, graph classification, and graph regression datasets.
Modeling dynamic target deformation in camera calibration

Most approaches to camera calibration rely on calibration targets of well-known geometry. During data acquisition, calibration target and camera system are typically moved w.r. t. each other, to allow image coverage and perspective versatility. We show that moving the target can lead to small temporary deformations of the target, which can introduce significant errors into the calibration result. While static inaccuracies of calibration targets have been addressed in previous works, to our knowledge, none of the existing approaches can capture time-varying, dynamic deformations. To achieve high-accuracy calibrations despite moving the target, we propose a way to explicitly model dynamic target deformations in camera calibration. This is achieved by using a low-dimensional deformation model with only few parameters per image, which can be optimized jointly with target poses and intrinsics. We demonstrate the effectiveness of modeling dynamic deformations using different calibration targets and show its significance in a structure-from-motion application.
Integrated Path Planning and Tracking Control of Marine Current Turbine in Uncertain Ocean Environments

This paper presents an integrated path planning and tracking control of marine hydrokinetic energy harvesting devices. To address the highly nonlinear and uncertain oceanic environment, the path planner is designed based on a reinforcement learning (RL) approach by fully exploring the historical ocean current profiles. The planner will search for a path to optimize a chosen cost criterion, such as maximizing the total harvested energy for a given time. Model predictive control (MPC) is then utilized to design the tracking control for the optimal path command from the planner subject to problem constraints. The planner and the tracking control are accommodated in an integrated framework to optimize these two parts in a real-time manner. The proposed approach is validated on a marine current turbine (MCT) that executes vertical waypoint path searching to maximize the net power due to spatiotemporal uncertainties in the ocean environment, as well as the path following via an MPC tracking controller to navigate the MCT to the optimal path. Results demonstrate that the path planning increases harvested power compared to the baseline (i.e., maintaining MCT at an equilibrium depth), and the tracking controller can successfully follow the reference path under different shear profiles.