Miracle-dz/UniFAR-FTUs huggingface dataset:
Acta Botanica Brasilica

Distributions of aquatic macrophyte species are commonly associated with water chemistry characteristics. However, other environmental factors that can lead to the occurrence of aquatic plants in lotic ecosystems, such as dynamic habitats linked to the surrounding landscape, have been underestimated. This study aims to evaluate landscape features that may influence the occurrence of species of euhydrophyte aquatic macrophytes in a tropical river basin. We assessed the occurrence of the following seven species: Egeria densa, Cabomba furcata, Potamogeton pusillus, Potamogeton polygonus, Utricularia foliosa, Pistia stratiotes and Salvinia molesta. We also measured environmental variables related to three spatial scales, local (limnological), channel and riparian landscape, along 25.5 Km of the Itanhaém River basin (São Paulo, Brazil). We found that local (limnological) and channel characteristics were important variables in determining the occurrence of aquatic macrophyte species while the landscape scale had little influence on species composition. Channel depth and margin slope were especially relevant abiotic variables in explaining the occurrence of four of the species but not P. pusillus, P. polygonus and U. foliosa. Our results highlight the importance of channel morphology for understanding aquatic plant occurrence and community composition in tropical rivers.
Wasserstein Autoencoders for Collaborative Filtering

The recommender systems have long been investigated in the literature. Recently, users' implicit feedback like `click' or `browse' are considered to be able to enhance the recommendation performance. Therefore, a number of attempts have been made to resolve this issue. Among them, the variational autoencoders (VAE) approach already achieves a superior performance. However, the distributions of the encoded latent variables overlap a lot which may restrict its recommendation ability. To cope with this challenge, this paper tries to extend the Wasserstein autoencoders (WAE) for collaborative filtering. Particularly, the loss function of the adapted WAE is re-designed by introducing two additional loss terms: (1) the mutual information loss between the distribution of latent variables and the assumed ground truth distribution, and (2) the L1 regularization loss introduced to restrict the encoded latent variables to be sparse. Two different cost functions are designed for measuring the distance between the implicit feedback data and its re-generated version of data. Experiments are valuated on three widely adopted data sets, i.e., ML-20M, Netflix and LASTFM. Both the baseline and the state-of-the-art approaches are chosen for the performance comparison which are Mult-DAE, Mult-VAE, CDAE and Slim. The performance of the proposed approach outperforms the compared methods with respect to evaluation criteria Recall@1, Recall@5 and NDCG@10, and this demonstrates the efficacy of the proposed approach.
It is Okay to Not Be Okay: Overcoming Emotional Bias in Affective Image Captioning by Contrastive Data Collection - Supplementary Material

Cowen and Keltner [3] proposed a novel theory to explain the emotional experiences. He proposed to label emotional experience with categories such as love, joy, sadness, etc. Contrary to universal emotions proposed by Ekman [9], Semantic space theory expands the set of emotional categories to over twenty emotions compared to only eight in the universal emotions. It also argues that these emotional categories are embedded in a high dimensional space and have continuous gradients between them, with some categories such as joy and admiration being closer to each other compared to joy and sadness. Cowen et al. [2, 4, 5, 6] showed via human experiments that the Semantic space theory is better at explaining emotional experiences of human subjects compared to universal emotions as well as Affective dimension theory. Demszky et al. [7] collected GoEmotions dataset which have reddit comments labeled according to the emotional categories of the Semantic space theory. In order to show that our Combined dataset result in a better distribution over the emotional categories, we pretrained a BERT [8] model from HuggingFace [10] on GoEmotions and then used the pretrained model to predict the full set of emotional categories present in Combined and ArtEmis. To highlight the quality of labeling in Combined we calculate Pearson’s correlation coefficient between all pairs of emotional categories. We present the results in Fig. 1. The histogram shows how Combined is more balanced especially for the negative emotions such as fear, disgust, and sadness. The heat maps show that Combined have less correlation between emotions which is desirable since it reflects the ability of the dataset to
Oxidative stress, aging, and diseases

: Reactive oxygen and nitrogen species (RONS) are produced by several endogenous and exogenous processes, and their negative effects are neutralized by antioxidant defenses. Oxidative stress occurs from the imbalance between RONS production and these antioxidant defenses. Aging is a process characterized by the progressive loss of tissue and organ function. The oxidative stress theory of aging is based on the hypothesis that age-associated functional losses are due to the accumulation of RONS-induced damages. At the same time, oxidative stress is involved in several age-related conditions (ie, cardiovascular diseases [CVDs], chronic obstructive pulmonary disease, chronic kidney disease, neurodegenerative diseases, and cancer), including sarcopenia and frailty. Different types of oxidative stress biomarkers have been identified and may provide important information about the efficacy of the treatment, guiding the selection of the most effective drugs/dose regimens for patients and, if particularly relevant from a pathophysiological point of view, acting on a specific therapeutic target. Given the important role of oxidative stress in the pathogenesis of many clinical conditions and aging, antioxidant therapy could positively affect the natural history of several diseases, but further investigation is needed to evaluate the real efficacy of these therapeutic interventions. The purpose of this paper is to provide a review of literature on this complex topic of ever increasing interest.
Study on Mass Transports in Evolution of Separation Bubbles Using LCSs and Lobe Dynamics

The lobe dynamics andmass transport between separation bubble and main flow in flow over airfoil are studied in detail, using Lagrangian coherent structures (LCSs), in order to understand the nature of evolution of the separation bubble. For this problem, the transient flow over NACA0012 airfoil with low Reynolds number is simulated numerically by characteristic based split (CBS) scheme, in combination with dual time stepping. Then, LCSs and lobe dynamics are introduced and developed to investigate themass transport between separation bubble and main flow, from viewpoint of nonlinear dynamics. The results show that stable manifolds and unstable manifolds could be tangled with each other as time evolution, and the lobes are formed periodically to induce mass transport between main flow and separation bubble, with dynamic behaviors. Moreover, the evolution of the separation bubble depends essentially on the mass transport which is induced by lobes, ensuing energy and momentum transfers. As the results, it can be drawn that the dynamics of flow separation could be studied using LCSs and lobe dynamics, and could be controlled feasibly if an appropriate control is applied to the upstream boundary layer with high momentum.
Resource Allocation for Device-to-Device Communications Overlaying Two-Way Cellular Networks

Device-to-device (D2D) communications has been proposed in the literature as an underlay approach to cellular networks to allow direct transmission between two cellular devices with local communication needs. In this paper, we consider a scenario of D2D communications overlaying a cellular network and propose a new spectrum sharing protocol, which allows the D2D users to communicate bi-directionally with each other while assisting the two-way communications between the cellular base station (BS) and the cellular user (CU). We derive the achievable rate region of the sum rate of the D2D transmissions versus that of the cellular transmissions. The Pareto boundary of the region is found by optimizing the transmit power at BS and CU as well as the power splitting factor at the relay D2D node. Since either of the two D2D users can be the relay and there can exist multiple pairs of D2D users, we also consider the relay selection from the potential D2D users. We find through numerical results that the proposed two-way protocol with power control at the BS and CU is effective to improve the sum rate for both the D2D and cellular users. In addition, relay selection can achieve further improvement in the sum rate of the cellular links.
Total Variation-Based Reconstruction and Phase Retrieval for Diffraction Tomography

In optical diffraction tomography (ODT), the three-dimensional scattering potential of a microscopic object rotating around its center is recovered by a series of illuminations with coherent light. Reconstruction algorithms such as the filtered backpropagation require knowledge of the complex-valued wave at the measurement plane, whereas often only intensities, i.e., phaseless measurements, are available in practice. We propose a new reconstruction approach for ODT with unknown phase information based on three key ingredients. First, the light propagation is modeled using Born's approximation enabling us to use the Fourier diffraction theorem. Second, we stabilize the inversion of the non-uniform discrete Fourier transform via total variation regularization utilizing a primal-dual iteration, which also yields a novel numerical inversion formula for ODT with known phase. The third ingredient is a hybrid input-output scheme. We achieved convincing numerical results, which indicate that ODT with phaseless data is possible. The so-obtained 2D and 3D reconstructions are even comparable to the ones with known phase.
Supplementary for: UCLID-Net: Single View Reconstruction in Object Space

where ℘(N,M) is the set of all possible uniform transport plans from a point cloud of N points to 16 one of M points, i.e. ℘(N,M) is the set of all N ×M matrices with real coefficients larger than or 17 equal to 0, such that the sum of each line equals 1/N and the sum of each column equals 1/M . 18 The high computational cost of EMD implies that it is mostly used for validation only, and in an 19 approximated form. On ShapeNet, we use the implementation from [5] on point clouds normalized 20
Exact quasi-optical theory of self-diffraction, finite-waist focusing and reflection of matter wave trajectories

Standard Quantum Mechanics (SQM) is the most widely accepted "route" starting from the common “main road” provided by de Broglie’s and Schrodinger’s discovery of matter waves and of their eigen-functions and eigen-values. Another "route" is offered by Bohm’s Mechanics, able to reproduce the same results of SQM, while interpreting the streamlines of probability current density as a set of "quantum trajectories”. We exploit here a further and somewhat different "route", based on the fact that the stationary Schrodinger equation (as well as any other Helmholtz-like equation) may be treated, bypassing any kind of quasi-optical approximation, in terms of exact trajectories, coupled and guided by a stationary, energy dependent function (which we call “Wave Potential”) encoded in the structure itself of Helmholtz-like equations and acting normally to the relevant particle momentum. The basic property of the Wave Potential is therefore to leave the particle energy unchanged: a property which cannot be shared (in spite of its formal analogy) by the time-dependent "Quantum Potential” of the Bohmian theory, involving the entire spectrum of possible eigen-energies. This property turns out to allow the numerical computation of the particle trajectories in stationary external force fields, and to show that all diffractive processes consist of energy-preserving exchanges between the longitudinal and transverse momentum components of each single particle. We compute and discuss the trajectories of particles moving, under the guiding rule of the Wave Potential, in many different force-fields, such as a constant field and the ones due to a potential barrier, a potential step and a focalizing potential, respectively. The very existence of these exact, point-particle, trajectory-based solutions appears to reserve the uncertainty relations to wave-packet particle representations.
Acta Universitatis Upsaliensis Uppsala 2015 List of Papers

Executive functions are higher-order cognitive functions underlying self-regulation of behavior. That is, executive functions make it possible to resolve internal conflicts and behave according to future goals rather than acting on sudden impulses or going on automatic. Very similarly, the temperamental construct of effortful control is defined as being able to inhibit a dominant response, instead acting on a subdominant response. In children, poor executive functions and low levels of effortful control have both been associated with several negative outcomes, such as lower academic achievements and externalizing behavior problems. Although these self-regulatory functions seem to play a very important role in child development, little is still known about them during the first years of life. Furthering the knowledge of early executive functions and effortful control would likely increase the chances of early detection of risks of poor development. The present thesis aimed to investigate individual differences in executive functions and effortful control in infancy and toddlerhood, as well as the early development of, and the relation between, these two functions. The thesis further aimed to investigate the relationship between the self-regulatory functions and activity level, and the possibility of predicting toddlerhood self-regulatory functions with sustained attention in infancy. In Study I, individual differences in 10-month-olds' rudimentary executive functions were found, and these were related to temperamental activity level. In Study II, individual differences in sustained attention in infancy were found to predict toddlerhood executive functions and effortful control. Both these self-regulatory functions improved significantly from infancy to toddlerhood although the individual stability was low. Executive functions and effortful control were related in toddlerhood but not in infancy. In Study III we replicated and extended the finding of a longitudinal relation between infant sustained attention and toddlerhood executive functions. In addition, partial support for the proposition that executive functions develop in a hierarchical fashion was found, with simple inhibition being predictive of more complex forms of working memory two years later. The results from the three studies combined contribute to a better understanding of the early development of the self-regulatory functions executive functions and effortful control. To my grandmother Britta, for encouraging and appreciating academic work like no one else. This thesis is based on the following papers, which are referred to in the text by their Roman numerals. (2015). Sustained attention in infancy as a longitudinal predictor of self-regulatory functions. Individual differences in early executive functions: A longitudinal study from …
Optical variability patterns of radio-quiet and radio-loud quasars

Abstract We analyzed the light curves (LCs) of several radio-quiet and radio-loud quasars belonging to the same parameter space volume in the 4D Eigenvector 1 (4DE1) quasar sequence, using data from the Catalina Real-time Transient Survey (CRTS). We report preliminary results on detected variability pattern, and discuss possible cases of periodic variability.
Competitive Collaboration : Joint Unsupervised Learning of Depth , Camera Motion , Optical Flow and Motion Segmentation — Supplementary Material

Digit classification is the task of classifying a given image I into one of the 10 digit classes t ∈ {0, 1, 2, . , 9}. Two most widely used datasets for digit classification include images of the postal code digits, MNIST [6] and street view house numbers, SVHN [9]. For our setup, we take the samples from both of the datasets, and shuffle them together. This means that, although an image and a target, (Ii, ti) form a pair, there is no information if the digits came from MNIST or SVHN. We now train our model under Competitive Collaboration framework given the mixed-domain dataset MNIST+SVHN, a mixture of MNIST and SVHN. The model consists of two networks Rx and Fx that compete with each other regulated by a moderatorMy which assigns training data to each of the competitors. Here, x denotes the combined weights of the two competitor networks (R,F ) and y denotes the weight of the moderator network M . The networks are trained using an alternate optimization procedure consisting of two phases. In the competition phase, we train the competitors by fixing the moderator M and minimizing,
Hydrodynamical Evolution of Black-Hole Binaries Embedded in AGN Discs

Stellar-mass binary black holes (BBHs) embedded in active galactic nucleus (AGN) discs are possible progenitors of black-hole mergers detected in gravitational waves by LIGO/VIRGO. To better understand the hydrodynamical evolution of BBHs interacting with the disc gas, we perform a suite of high-resolution 2D simulations of binaries in local disc (shearing-box) models, considering various binary mass ratios, eccentricities and background disc properties. We use the γ-law equation of state and adopt a robust post-processing treatment to evaluate the mass accretion rate, torque and energy transfer rate on the binary to determine its long-term orbital evolution. We find that circular comparable-mass binaries contract, with an orbital decay rate of a few times the mass doubling rate. Eccentric binaries always experience eccentricity damping. Prograde binaries with higher eccentricities or smaller mass ratios generally have slower orbital decay rates, with some extreme cases exhibiting orbital expansion. The averaged binary mass accretion rate depends on the physical size of the accretor. The accretion flows are highly variable, and the dominant variability frequency is the apparent binary orbital frequency (in the rotating frame around the central massive BH) for circular binaries but gradually shifts to the radial epicyclic frequency as the binary eccentricity increases. Our findings demonstrate that the dynamics of BBHs embedded in AGN discs is quite different from that of isolated binaries in their own circumbinary discs. Furthermore, our results suggest that the hardening timescales of the binaries are much shorter than their migration timescales in the disc, for all reasonable binary and disc parameters.
Nonparametric Regression ( and Classification ) Statistical Machine Learning , Spring 2017

• Note for i.i. d. samples (xi, yi) ∈ R × R, i = 1, . , n, we can always write yi = f0(xi) + i, i = 1, . , n, where i, i = 1, . , n are i.i. d. random errors, with mean zero. Therefore we can think about the sampling distribution as follows: (xi, i), i = 1, . , n are i.i. d. draws from some common joint distribution, where E( i) = 0, and yi, i = 1, . , n are generated from the above model • It is typical to assume that each i is independent of xi. This is a pretty strong assumption, and you should think about it skeptically. We too will make this assumption, for simplicity. It should be noted that a good portion of theoretical results that we cover (or at least, similar theory) also holds without this assumption
Measuring risks in the extreme tail: The extreme VaR and its confidence interval

Contrary to the current regulatory trend concerning extreme risks, the purpose of this paper is to emphasize the necessity of considering the Value-at-Risk (VaR) with extreme confidence levels like 99.9%, as an alternative way to measure risks in the “extreme tail”. Although the mathematical definition of the extreme VaR is trivial, its computation is challenging in practice, because the uncertainty of the extreme VaR may not be negligible for a finite amount of data. We begin to build confidence intervals around the unknown VaR. We build them using two different approaches, the first using Smirnov's result (Smirnov, 1949 [24]) and the second Zhu and Zhou's result (Zhu and Zhou, 2009 [25]), showing that this last one is robust when we use finite samples. We compare our approach with other methodologies which are based on bootstrapping techniques, Christoffersen et al. (2005) [7], focusing on the estimation of the extreme quantiles of a distribution. Finally, we apply these confidence intervals to perform a stress testing exercice with historical stock returns during financial crisis, for identifying potential violations of the VaR during turmoil periods on financial markets.
Distinguishing Refracted Features Using Light Field Cameras With Application to Structure From Motion

Distinguishing Refracted Features Using Light Field Cameras With Application to Structure From Motion . D. Tsai, D. G. Dansereau, T. Peynot, and P. Corke 177 Adaptive Impedance Control Applied to Robot-Aided Neuro-Rehabilitation of the Ankle . J. C. Pérez-Ibarra, A. A. G. Siqueira, M. A. Silva-Couto, T. L. de Russo, and H. I. Krebs 185 Robot Cooperative Behavior Learning Using Single-Shot Learning From Demonstration and Parallel Hidden Markov Models . J.-F. Lafleche, S. Saunderson, and G. Nejat 193 Inferring 3D Shapes of Unknown Rigid Objects in Clutter Through Inverse Physics Reasoning . C. Song and A. Boularias 201 The Foldable Drone: A Morphing Quadrotor That Can Squeeze and Fly . D. Falanga, K. Kleber, S. Mintchev, D. Floreano, and D. Scaramuzza 209 Online Finger Control Using High-Density EMG and Minimal Training Data for Robotic Applications . M. Barsotti, S. Dupan, I. Vujaklija, S. Došen, A. Frisoli, and D. Farina 217 Inferring Control Intent During Seated Balance Using Inverse Model Predictive Control . A. Ramadan, J. Choi, C. J. Radcliffe, J. M. Popovich, Jr., and N. P. Reeves 224 3-D Image Reconstruction of Biological Organelles With a Robot-Aided Microscopy System for Intracellular Surgery . W. Gao, A. Shakoor, L. Zhao, Z. Jiang, and D. Sun 231 Incorporating Trust and Self-Confidence Analysis in the Guidance and Control of (Semi)Autonomous Mobile Robotic Systems . H. Saeidi and Y. Wang 239 Oscillator-Based Transparent Control of an Active/Semiactive Ankle-Foot Orthosis . Y. Zhang, K. J. Nolan, and D. Zanotto 247 Autonomous Data-Driven Manipulation of Unknown Anisotropic Deformable Tissues Using Unmodelled Continuum Manipulators . F. Alambeigi, Z. Wang, R. Hegeman, Y. -H. Liu, and M. Armand 254 Self-Assembly Magnetic Chain Unit for Bulk Biomaterial Actuation. H. Lu, Y. Liu, Y. Yang, X. Yang, R. Tan, and Y. Shen 262 Data Information Fusion From Multiple Access Points for WiFi-Based Self-localization . R. Miyagusuku, A. Yamashita, and H. Asama 269 Fluidic Elastomer Actuators for Haptic Interactions in Virtual Reality . J. Barreiros, H. Claure, B. Peele, O. Shapira, J. Spjut, D. Luebke, M. Jung, and R. Shepherd 277 Automated Abstraction of Manipulation Domains for Cost-Based Reactive Synthesis . K. He, M. Lahijanian, L. E. Kavraki, and M. Y. Vardi 285 Relationship Between the Order for Motor Skill Transfer and Motion Complexity in Reinforcement Learning. N. J. Cho, S. H. Lee, I. H. Suh, and H. -S. Kim 293 Task-Specific Manipulator Design and Trajectory Synthesis . J. Whitman and H. Choset 301