Miracle-dz/UniFAR-FTUs huggingface dataset:
Biodiversity monitoring in developing countries: what are we trying to achieve?

, 2002). manyexistingprogrammessuCerfromtwomajordesign One could argue that these constraints are unrelateddeﬁciencies: a lack of well-articulated objectives, and a to biodiversity monitoring and should be addressed byneglect of diCerent sources of error in the estimation of strengthening the government institutions responsiblebiological diversity. The paper was primarily devoted and providing additional training and funds (Bawa &to the sources of data uncertainty. While we agree Menon, 1997). However, whether we like it or not, wewith the conclusion that substantial thought should be are not likely to see signiﬁcantly improved environmentalgiven to the question of design, and to ‘how’, ‘what’ and departments in developing countries for some time. By‘why’ to monitor, it is worth considering what we want neglecting these constraints and emphasizing the needto achieve by monitoring and, considering the socio- for more standardized approaches, the recommendationseconomic context, what is practically feasible. Design of Yoccoz
A New Basis for Sparse PCA

The statistical and computational performance of sparse principal component analysis (PCA) can be dramatically improved when the principal components are allowed to be sparse in a rotated eigenbasis. For this, we propose a new method for sparse PCA. In the simplest version of the algorithm, the component scores and loadings are initialized with a low-rank singular value decomposition. Then, the singular vectors are rotated with orthogonal rotations to make them approximately sparse. Finally, soft-thresholding is applied to the rotated singular vectors. This approach differs from prior approaches because it uses an orthogonal rotation to approximate a sparse basis. Our sparse PCA framework is versatile; for example, it extends naturally to the two-way analysis of a data matrix for simultaneous dimensionality reduction of rows and columns. We identify the close relationship between sparse PCA and independent component analysis for separating sparse signals. We provide empirical evidence showing that for the same level of sparsity, the proposed sparse PCA method is more stable and can explain more variance compared to alternative methods. Through three applications---sparse coding of images, analysis of transcriptome sequencing data, and large-scale clustering of Twitter accounts, we demonstrate the usefulness of sparse PCA in exploring modern multivariate data.
Fighting Failures with FIRE: Failure Identification to Reduce Expert Burden in Intervention-Based Learning

Supervised imitation learning, also known as behavioral cloning, suffers from distribution drift leading to failures during policy execution. One approach to mitigate this issue is to allow an expert to correct the agent's actions during task execution, based on the expert's determination that the agent has reached a `point of no return. ' The agent's policy is then retrained using this new corrective data. This approach alone can enable high-performance agents to be learned, but at a substantial cost: the expert must vigilantly observe execution until the policy reaches a specified level of success, and even at that point, there is no guarantee that the policy will always succeed. To address these limitations, we present FIRE (Failure Identification to Reduce Expert Burden in intervention-based learning), a system that can predict when a running policy will fail, halt its execution, and request a correction from the expert. Unlike existing approaches that learn only from expert data, our approach learns from both expert and non-expert data, akin to adversarial learning. We demonstrate experimentally for a series of challenging manipulation tasks that our method is able to recognize state-action pairs that lead to failures. This permits seamless integration into an intervention-based learning system, where we show an order-of-magnitude gain in sample efficiency compared with a state-of-the-art inverse reinforcement learning method and dramatically improved performance over an equivalent amount of data learned with behavioral cloning.
Thermal Conductivity Measurement of Supported Thin Film Materials Using the 3$\omega$ method

In this article, we are proposing a thorough analysis of the cross, and the in-plane thermal conductivity of thin-film materials based on the 3$\omega$ method. The analysis accommodates a 2D mathematical heat transfer model of a semi-infinite body and the details of the sample preparation followed by the measurement process. The presented mathematical model for the system considers a two-dimensional space for its solution. It enables the calculation of the cross-plane thermal conductivity with a single frequency measurement, the derived equation opens new opportunities for frequency-based and penetration-depth dependent thermal conductivity analysis. The derived equation for the in-plane thermal conductivity is dependent on the cross-plane thermal conductivity. Both in and cross-plane thermal conductivities enable the measurements in two steps of measurements, the resistance-temperature slope measurement and another set of measures that extracts the third harmonic of the voltage signal. We evaluated the methodology in two sets of samples, silicon nitride and boron nitride, both on silicon wafers. We observed anisotropic thermal conductivity in the cross and the in-plane direction, despite the isotropic nature of the thin films, which we relate to the total anisotropy of the thin film-substrate system. The technique is conducive to the thermal analysis of next-generation nanoelectronic devices.
Twitter and Census Data Analytics to Explore Socioeconomic Factors for Post-COVID-19 Reopening Sentiment

Investigating and classifying sentiments of social media users (e.g., positive, negative) towards an item, situation, and system are very popular among the researchers. However, they rarely discuss the underlying socioeconomic factor associations for such sentiments. This study attempts to explore the factors associated with positive and negative sentiments of the people about reopening the economy, in the United States (US) amidst the COVID-19 global crisis. It takes into consideration the situational uncertainties (i.e., changes in work and travel pattern due to lockdown policies), economic downturn and associated trauma, and emotional factors such as depression. To understand the sentiment of the people about the reopening economy, Twitter data was collected, representing the 51 states including Washington DC of the US. State-wide socioeconomic characteristics of the people (e.g., education, income, family size, and employment status), built environment data (e.g., population density), and the number of COVID-19 related cases were collected and integrated with Twitter data to perform the analysis. A binary logit model was used to identify the factors that influence people toward a positive or negative sentiment. The results from the logit model demonstrate that family households, people with low education levels, people in the labor force, low-income people, and people with higher house rent are more interested in reopening the economy. In contrast, households with a high number of members and high income are less interested to reopen the economy. The accuracy of the model is good (i.e., the model can correctly classify 56.18% of the sentiments). The Pearson chi2 test indicates that overall this model has high goodness-of-fit. This study provides a clear indication to the policymakers where to allocate resources and what policy options they can undertake to improve the socioeconomic situations of the people and mitigate the impacts of pandemics in the current situation and as well as in the future.
Understanding phishers' strategies of mimicking uniform resource locators to leverage phishing attacks: A machine learning approach

Phishing is a type of social engineering attack with an intention to steal user data, including login credentials and credit card numbers, leading to financial losses for both organizations and individuals. It occurs when an attacker, pretending as a trusted entity, lure a victim into click on a link or attachment in an email, or in a text message. Phishing is often launched via email messages or text messages over social networks. Previous research has revealed that phishing attacks can be identified just by looking at uniform resource locator (URLs). Identifying the techniques which are used by phishers to mimic a phishing URL is rather a challenging issue. At present, we have limited knowledge and understanding of how cyber‐criminals attempt to mimic URLs with the same look and feel of the legitimate ones, to entice people into clicking links. Therefore, this paper investigates the feature selection of phishing URLs (uniform resource locators), aiming to explore the strategies employed by phishers to mimic URLs that can obviously trick people into clicking links. We employed an information gain (IG) and Chi‐Squared feature selection methods in machine learning (ML) on a phishing dataset. The dataset contains a total of 48 features extracted from 5000 phishing and another 5000 legitimate URL from web pages downloaded from January to May 2015 and from May to June 2017. Our results revealed that there were 10 techniques that phishers used to mimic URLs to manipulate humans into clicking links. Identifying these phishing URL manipulation techniques would certainly help to educate individuals and organizations and keep them safe from phishing attacks. In addition, the findings of this research will also help develop anti‐phishing tools, framework or browser plugins for phishing prevention.
A Generalized Reinforcement Learning Algorithm for Online 3D Bin-Packing

We propose a Deep Reinforcement Learning (Deep RL) algorithm for solving the online 3D bin packing problem for an arbitrary number of bins and any bin size. The focus is on producing decisions that can be physically implemented by a robotic loading arm, a laboratory prototype used for testing the concept. The problem considered in this paper is novel in two ways. First, unlike the traditional 3D bin packing problem, we assume that the entire set of objects to be packed is not known a priori. Instead, a fixed number of upcoming objects is visible to the loading system, and they must be loaded in the order of arrival. Second, the goal is not to move objects from one point to another via a feasible path, but to find a location and orientation for each object that maximises the overall packing efficiency of the bin(s). Finally, the learnt model is designed to work with problem instances of arbitrary size without retraining. Simulation results show that the RL-based method outperforms state-of-the-art online bin packing heuristics in terms of empirical competitive ratio and volume efficiency.
Conditional Gradient Methods for Convex Optimization with Function Constraints

Conditional gradient methods have attracted much attention in both machine learning and optimization communities recently. These simple methods can guarantee the generation of sparse solutions. In addition, without the computation of full gradients, they can handle huge-scale problems sometimes even with an exponentially increasing number of decision variables. This paper aims to significantly expand the application areas of these methods by presenting new conditional gradient methods for solving convex optimization problems with general affine and nonlinear constraints. More specifically, we first present a new constraint extrapolated condition gradient (CoexCG) method that can achieve an ${\cal O}(1/\epsilon^2)$ iteration complexity for both smooth and structured nonsmooth function constrained convex optimization. We further develop novel variants of CoexCG, namely constraint extrapolated and dual regularized conditional gradient (CoexDurCG) methods, that can achieve similar iteration complexity to CoexCG but allow adaptive selection for algorithmic parameters. We illustrate the effectiveness of these methods for solving an important class of radiation therapy treatment planning problems arising from healthcare industry. To the best of our knowledge, all the algorithmic schemes and their complexity results are new in the area of projection-free methods.
SLAM using ICP and graph optimization considering physical properties of environment

This paper describes a novel simultaneous localization and mapping (SLAM) scheme based on scan matching in an environment including various physical properties. In scan matching, localization is performed mainly focusing on the shape information of the environment. However, the localization cannot be performed correctly and matching may fail when a similar shape existing in different places if only the shape information is taken into consideration. Therefore, we propose a new method to improve the accuracy of scan matching by considering abundant physical features existing in the environment. In this method, it is possible to utilize not only the shape information but also the physical information of the environment as features by measuring such as heat and water sources. Experiment result in the real environment shows that a highly accurate map of the environment can be generated by utilizing this physical information.
Kolmogorov and Kelvin wave cascades in a generalized model for quantum turbulence

We performed numerical simulations of decaying quantum turbulence by using a generalized Gross-Pitaevskii equation that includes a beyond mean field correction and a nonlocal interaction potential. The nonlocal potential is chosen in order to mimic He II by introducing a roton minimum in the excitation spectrum. We observe that at large scales the statistical behavior of the flow is independent of the interaction potential, but at scales smaller than the intervortex distance a Kelvin wave cascade is enhanced in the generalized model. In this range, the incompressible kinetic energy spectrum obeys the weak wave turbulence prediction for Kelvin wave cascade not only for the scaling with wave numbers but also for the energy flux and the intervortex distance.
Root cortex death and subsequent initiation and growth of lateral roots from bare steles of chickpeas

The anatomical responses of chickpea (Cicer arietinum L.) roots to aging and severe water deficits and their capacity for subsequent initiation and growth of new lateral roots after rewatering are only partially known. This study was conducted to characterize the degeneration of epidermal and cortical cells of chickpea roots and the subsequent initiation and growth of new roots from bare steles, i.e., steles surrounded by an endodermis but lacking other parts of the cortex. Chickpea plants were grown in pots in a greenhouse under standard culture conditions (potting mix and daily watering) or drought treatments. For drought treatments, plants were grown in sand and exposed to repeated 10-d treatments of water deficits and rewatering. In standard culture conditions, older cortices collapsed and deteriorated but not simply as a function of chronological age. After one 10-d drought treatment, the epidermis and outer cortex of primary and some secondary and tertiary roots collapsed and after a subsequent rewa.
A unified framework for adjustable robust optimization with endogenous uncertainty

This work proposes a framework for multistage adjustable robust optimization that unifies the treatment of three different types of endogenous uncertainty, where decisions, respectively, (i) alter the uncertainty set, (ii) affect the materialization of uncertain parameters, and (iii) determine the time when the true values of uncertain parameters are observed. We provide a systematic analysis of the different types of endogenous uncertainty and highlight the connection between optimization under endogenous uncertainty and active learning. We consider decision-dependent polyhedral uncertainty sets and propose a decision rule approach that incorporates both continuous and binary recourse, including recourse decisions that affect the uncertainty set. The proposed method enables the modeling of decision-dependent nonanticipativity and results in a tractable reformulation of the problem. We demonstrate the effectiveness of the approach in computational experiments that cover a range of applications, including plant redesign, maintenance planning with inspections, optimizing revision points in capacity planning, and production scheduling with active parameter estimation. The results show significant benefits from the proper modeling of endogenous uncertainty and active learning.
Few-shots Parameter Tuning via Co-evolution

Generalization, i.e., the ability of addressing problem instances that are not available during the system design and development phase, is a critical goal for intelligent systems. A typical way to achieve good generalization is to exploit vast data to train a model. In the context of heuristic search, such a paradigm is termed parameter tuning or algorithm configuration, i.e., configuring the parameters of a search method based on a set of "training" problem instances. However, compared to its counterpart in machine learning, parameter tuning could more often suffer from the lack of training instances, and the obtained configuration may fail to generalize. This paper suggests competitive co-evolution as a remedy to this challenge and proposes a framework named Co-Evolution of Parameterized Search (CEPS). By alternately evolving a configuration population and an instance population, CEPS is capable of obtaining generalizable configurations with few training instances. The advantage of CEPS in improving generalization is analytically shown. Two concrete instantiations, namely CEPS-TSP and CEPS-VRPSPDTW, are also presented for the Traveling Salesman Problem (TSP) and the Vehicle Routing Problem with Simultaneous Pickup-Delivery and Time Windows (VRPSPDTW), respectively. Computational results on the two problems confirm the advantages of CEPS over state-of-the-art parameter tuning methods.
Estimating Forest Duiker (Cephalophinae) Density in Korup National Park: A Case Study on the Performance of Three Line Transect Methods

Forest duikers (Cephalophinae) are important targets of the bushmeat trade but a reliable and efficient assessment of these shy animals is often regarded as problematic. Using a power analysis approach, we assessed the performance of three established methods (diurnal, nocturnal and indirect dung surveys) in producing density estimates for the blue and the Ogilby's duiker (Cephalophus monticola, Cephalophus ogilbyi ogilbyi) in a 16 km2 section of primary forest, systematically sampled using line transects. Direct survey methods (diurnal and nocturnal) yielded density estimates between 8.3 and 6.8 (blue duiker) and 6.5 and 4.3 animals/ km2 (Ogilby's). The coefficients of variation of density estimates implied a resolution of 44% density change detectable in diurnal surveys but only of 140% in nocturnal surveys. Density estimates derived from the dung survey were lower than those of direct surveys (1.5 animals/ km2 and 1.4 animals/ km2, respectively). Associated coefficients of variation imply a resolution of change in density estimates detectable for changes far larger than 100%. However, dung pile density was susceptible to variation in key parameters (decay and production rates) and difficulties in data acquisition lead us to reject the dung survey as a suitable method in our particular setting. Our results suggest that diurnal direct surveying from line transects can be reliable and effective even for shy forest ungulates under (some) hunting pressure.
Quantum spectral clustering

Spectral clustering is a powerful unsupervised machine learning algorithm for clustering data with non convex or nested structures. With roots in graph theory, it uses the spectral properties of the Laplacian matrix to project the data in a low dimensional space where clustering is more efficient. Despite its success in clustering tasks, spectral clustering suffers in practice from a fast-growing running time of $O(n^3)$, where $n$ is the number of points in the dataset. In this work, we propose the first algorithm performing spectral clustering on a quantum computer with provable guarantees, extending a number of works in quantum machine learning. The quantum algorithm is composed of two parts: the first is the efficient creation of the quantum state corresponding to the projected Laplacian matrix, and the second consists in applying the existing quantum analogue of the $k$-means algorithm. Both steps depend polynomially on the number of clusters, as well as precision and data parameters arising from quantum procedures, and polylogarithmically on the dimension of the input vectors. Our numerical simulations show an asymptotic linear growth with $n$ when all terms are taken into account, considerably better than the classical cubic growth. This work opens the path to other graph based quantum machine learning algorithms, as it provides the first routines for efficient computation and quantum access to the Incidence, Adjacency, and projected Laplacian matrices of a graph.
Intention-aware Residual Bidirectional LSTM for Long-term Pedestrian Trajectory Prediction

Trajectory prediction is one of the key capabilities for robots to safely navigate and interact with pedestrians. Critical insights from human intention and behavioral patterns need to be effectively integrated into long-term pedestrian behavior forecasting. We present a novel intention-aware motion prediction framework, which consists of a Residual Bidirectional LSTM (ReBiL) and a mutable intention filter. Instead of learning step-wise displacement, we propose learning offset to warp a nominal intention-aware linear prediction, giving residual learning a physical intuition. Our intention filter is inspired by genetic algorithms and particle filtering, where particles mutate intention hypotheses throughout the pedestrian motion with ReBiL as the motion model. Through experiments on a publicly available dataset, we show that our method outperforms baseline approaches and the robust performance of our method is demonstrated under abnormal intention-changing scenarios.
Towards Explainable Graph Representations in Digital Pathology

Explainability of machine learning (ML) techniques in digital pathology (DP) is of great significance to facilitate their wide adoption in clinics. Recently, graph techniques encoding relevant biological entities have been employed to represent and assess DP images. Such paradigm shift from pixel-wise to entity-wise analysis provides more control over concept representation. In this paper, we introduce a post-hoc explainer to derive compact per-instance explanations emphasizing diagnostically important entities in the graph. Although we focus our analyses to cells and cellular interactions in breast cancer subtyping, the proposed explainer is generic enough to be extended to other topological representations in DP. Qualitative and quantitative analyses demonstrate the efficacy of the explainer in generating comprehensive and compact explanations.