Miracle-dz/UniFAR-FTUs huggingface dataset:
Clinical Outcomes of Human Rhinovirus/Enterovirus Infection in Pediatric Hemopoietic Cell Transplant Patients

Abstract Background Respiratory viral infections are common among pediatric transplant patients, with human rhinovirus (HRV) being the most frequent. In pediatric patients undergoing hemopoietic cell transplant (HCT), infection with HRV has been associated with progression to lower respiratory tract infection (LRTI) and adverse outcomes. We describe the clinical presentation and outcomes of HRV infection in children undergoing HCT. Methods Single-center retrospective study. HCT recipients who were positive for HRV/EV (HRV+) or negative for any respiratory virus (VN) by BioFire® FilmArray® panel between October 2014 and December 2017, were included. Primary outcomes were progression to LRTI, ICU admission, all-cause mortality at 3 and 6 months, and respiratory event-related mortality at 6 months. Results 227 patients (160 allogeneic HCT) were included. Of all patients, 108/227 (47.6%) were HRV+. From all HRV+, 95/108 (88%) were symptomatic and 68/107 (63.6%) of the diagnosis were made pretransplant. The median age of HRV+ was significantly lower than VN patients (5 vs 10 years). Cough and rhinorrhea were more frequently observed in HRV+ (53.7 and 60% vs 19.8 and 22.8%, respectively). No differences were found between both groups pretransplant and overall in rates progression to LRTI, ICU admission, mechanical ventilation, all-cause within 3 and 6 months, and mortality related with respiratory failure. No significant association was found between the severity of respiratory disease and the type of conditioning, type of transplant, or absolute lymphocyte count. Conclusions HRV infection is frequently detected in HCT recipients but is not associated with severity of respiratory disease, need for intensive care unit or mortality, including those diagnosed before transplant, suggesting that delaying HCT in this scenario may not be needed. Multicenter larger studies are required to confirm these findings.
GraspCaps: A Capsule Network Approach for Familiar 6DoF Object Grasping

As robots become more widely available outside industrial settings, the need for reliable object grasping and manipulation is increasing. In such environments, robots must be able to grasp and manipulate novel objects in various situations. This paper presents GraspCaps, a novel architecture based on Capsule Networks for generating per-point 6D grasp configurations for familiar objects. GraspCaps extracts a rich feature vector of the objects present in the point cloud input, which is then used to generate per-point grasp vectors. This approach allows the network to learn specific grasping strategies for each object category. In addition to GraspCaps, the paper also presents a method for generating a large object-grasping dataset using simulated annealing. The obtained dataset is then used to train the GraspCaps network. Through extensive experiments, we evaluate the performance of the proposed approach, particularly in terms of the success rate of grasping familiar objects in challenging real and simulated scenarios. The experimental results showed that the overall object-grasping performance of the proposed approach is significantly better than the selected baseline. This superior performance highlights the effectiveness of the GraspCaps in achieving successful object grasping across various scenarios.
DECISÃO DE COMPRA: FATORES IGUAIS, PESOS DIFERENTES; VISÃO DO CONSUMIDOR, SOBRE AS PEQUENAS E GRANDES REDES DE FARMÁCIA 1 DECISION TO PURCHASE: FACTORS SAME, DIFFERENT WEIGHTS; VISION OF THE CONSUMER, ON SMALL AND LARGE NETWORK OF PHARMACY

O objetivo do presente estudo, foi discutir a decisao de compra, como um fenomeno que pode se configurar diferentemente para o consumidor, mesmo que os seus objetivos, o produto, os fatores influenciadores em tal decisao, o negocio e os agentes envolvidos, sejam os mesmos, pela natureza. Fundamentada no metodo indutivo, a pesquisa exploratoria, descritiva e quali-quantitativa, foi feita atraves de um formulario semi-estruturado, aplicado pelos proprios pesquisadores, junto a consumidores de produtos farmaceuticos, na cidade de Ituiutaba (MG), no periodo de 05 a 30 de julho de 2010. Tal preocupacao se justifica, considerando que as empresas envolvidas no processo, embora atendendo o mesmo publico, podem agir de forma diferente, pela filosofia, pelo tamanho, pelas politicas adotadas e pela localizacao. Os principais resultados, revelam que o consumidor de produtos farmaceuticos, da as pequenas farmacias a preferencia de compra, porque julga que elas oferecem o melhor conjunto de atributos ou de fatores influenciadores no processo de compra, ou a melhor relacao custo x beneficio, do que as farmacias das grandes redes. Revela ainda que o consumidor, consegue perfeitamente, hierarquizar aqueles fatores.
Exploiting Diffusion Prior for Generalizable Pixel-Level Semantic Prediction

Contents generated by recent advanced Text-to-Image (T2I) diffusion models are sometimes too imaginative for existing off-the-shelf property semantic predictors to estimate due to the immitigable domain gap. We introduce DMP, a pipeline utilizing pre-trained T2I models as a prior for pixel-level semantic prediction tasks. To address the misalignment between deterministic prediction tasks and stochastic T2I models, we reformulate the diffusion process through a sequence of interpolations, establishing a deterministic mapping between input RGB images and output prediction distributions. To preserve generalizability, we use low-rank adaptation to fine-tune pre-trained models. Extensive experiments across five tasks, including 3D property estimation, semantic segmentation, and intrinsic image decomposition, showcase the efficacy of the proposed method. Despite limited-domain training data, the approach yields faithful estimations for arbitrary images, surpassing existing state-of-the-art algorithms.
Efficient Model-Based Concave Utility Reinforcement Learning through Greedy Mirror Descent

Many machine learning tasks can be solved by minimizing a convex function of an occupancy measure over the policies that generate them. These include reinforcement learning, imitation learning, among others. This more general paradigm is called the Concave Utility Reinforcement Learning problem (CURL). Since CURL invalidates classical Bellman equations, it requires new algorithms. We introduce MD-CURL, a new algorithm for CURL in a finite horizon Markov decision process. MD-CURL is inspired by mirror descent and uses a non-standard regularization to achieve convergence guarantees and a simple closed-form solution, eliminating the need for computationally expensive projection steps typically found in mirror descent approaches. We then extend CURL to an online learning scenario and present Greedy MD-CURL, a new method adapting MD-CURL to an online, episode-based setting with partially unknown dynamics. Like MD-CURL, the online version Greedy MD-CURL benefits from low computational complexity, while guaranteeing sub-linear or even logarithmic regret, depending on the level of information available on the underlying dynamics.
An Exponential Reduction in Training Data Sizes for Machine Learning Derived Entanglement Witnesses

We propose a support vector machine (SVM) based approach for generating an entanglement witness that requires exponentially less training data than previously proposed methods. SVMs generate hyperplanes represented by a weighted sum of expectation values of local observables whose coefficients are optimized to sum to a positive number for all separable states and a negative number for as many entangled states as possible near a specific target state. Previous SVM-based approaches for entanglement witness generation used large amounts of randomly generated separable states to perform training, a task with considerable computational overhead. Here, we propose a method for orienting the witness hyperplane using only the significantly smaller set of states consisting of the eigenstates of the generalized Pauli matrices and a set of entangled states near the target entangled states. With the orientation of the witness hyperplane set by the SVM, we tune the plane's placement using a differential program that ensures perfect classification accuracy on a limited test set as well as maximal noise tolerance. For $N$ qubits, the SVM portion of this approach requires only $O(6^N)$ training states, whereas an existing method needs $O(2^{4^N})$. We use this method to construct witnesses of 4 and 5 qubit GHZ states with coefficients agreeing with stabilizer formalism witnesses to within 6.5 percent and 1 percent, respectively. We also use the same training states to generate novel 4 and 5 qubit W state witnesses. Finally, we computationally verify these witnesses on small test sets and propose methods for further verification.
Bilinearly indexed random processes - stationarization of fully lifted interpolation

Our companion paper \cite{Stojnicnflgscompyx23} introduced a very powerful \emph{fully lifted} (fl) statistical interpolating/comparison mechanism for bilinearly indexed random processes. Here, we present a particular realization of such fl mechanism that relies on a stationarization along the interpolating path concept. A collection of very fundamental relations among the interpolating parameters is uncovered, contextualized, and presented. As a nice bonus, in particular special cases, we show that the introduced machinery allows various simplifications to forms readily usable in practice. Given how many well known random structures and optimization problems critically rely on the results of the type considered here, the range of applications is pretty much unlimited. We briefly point to some of these opportunities as well.
A Robust Hessian-based Trust Region Algorithm for Spherical Conformal Parameterizations

Surface parameterizations are widely applied in computer graphics, medical imaging and transformation optics. In this paper, we rigorously derive the gradient vector and Hessian matrix of the discrete conformal energy for spherical conformal parameterizations of simply connected closed surfaces of genus-$0$. In addition, we give the sparsity structure of the Hessian matrix, which leads to a robust Hessian-based trust region algorithm for the computation of spherical conformal maps. Numerical experiments demonstrate the local quadratic convergence of the proposed algorithm with low conformal distortions. We subsequently propose an application of our method to surface registrations that still maintains local quadratic convergence.
Global Information System On Water ResourcesAnd Uses For Hydro Agricultural Development InLebanon

The FAO'S AQUASTAT is a standard global information system on water for agriculture and rural development. LEBANON was inserted recently into this system, which encompasses the whole members' countries including the 29 countries of the MENA Region, by working out a thorough national survey which led to set up a standardized country profile presenting the situation of the water resources, irrigation and drainage sub-sector and its major characteristics, trends, constraints and perspectives. Recent evolution and trends were briefly described as well as major constraints and environmental aspects facing water resources development and issues to be addressed. Finally, the LEBANON'S AQUASTAT could be considered, as a large-scale planning and forecasting tool for the sub-sector of water for agriculture provided a continuous action of updating and improvement of its effectiveness and efficiency.
Nitrogen and sulfur emissions from the burning of forest products near large urban areas

Airborne measuremens of trace gases and particles in the smoke from a prescribed burn of forest products in the Los Angeles basin show significantly higher emissions of NOx, SO2, and particulate NO3 than do measurements in smokes from the burning of biomass in rural areas. It is postulated that the high emissions are due to the revolatilization of previously deposited pollutants. Implications for pollutant source inventories and the nuclear winter hypothesis are briefly discussed.
Probing quantum phase transition via quantum speed limit

Quantum speed limit (QSL) is the lower bound on the time required for a state to evolve to a desired final state under a given Hamiltonian evolution. Three well-known QSLs exist Mandelstam-Tamm (MT), Margolus-Levitin (ML), and dual ML (ML$^*$) bounds. We consider one-dimensional systems that undergoes delocalization-localization transition in the presence of quasiperiodic and linear potential. By performing sudden quenches across the phase boundary, we find that the exact dynamics get captured very well by QSLs. We show that the MT bound is always tighter in the short time limit for any arbitrary state, while the optimal bound for the time of orthogonalization (time required to reach the orthogonal state) depends on the choice of the initial state. Further, for extreme quenches, we prove that the MT bound remains tighter for the time of orthogonalization, and it can qualitatively describe the non-analyticity in free energy for dynamical quantum phase transition (DQPT). Finally, we also demonstrate that the localization-delocalization transition point can be exactly identified from QSLs, whose computation cost is much less compared to many other diagnostic tools.
Digging into the Interior of Hot Cores with ALMA: Spiral Accretion into the High-mass Protostellar Core G336.01–0.82

We observed the high-mass star-forming core G336.01–0.82 at 1.3 mm and 0. ″05 (∼150 au) angular resolution with the Atacama Large Millimeter/submillimeter Array (ALMA) as part of the Digging into the Interior of Hot Cores with ALMA survey. These high-resolution observations reveal two spiral streamers feeding a circumstellar disk at opposite sides in great detail. Molecular line emission from CH3OH shows velocity gradients along the streamers consistent with infall. Similarly, a flattened envelope model with rotation and infall implies a mass larger than 10 M ☉ for the central source and a centrifugal barrier of 300 au. The location of the centrifugal barrier is consistent with local peaks in the continuum emission. We argue that gas brought by the spiral streamers is accumulating at the centrifugal barrier, which can result in future accretion burst events. A total high infall rate of ∼4 × 10−4 M ☉ yr−1 is derived by matching models to the observed velocity gradient along the streamers. Their contribution accounts for 20%–50% the global infall rate of the core, indicating streamers play an important role in the formation of high-mass stars.
Focal depths of intracontinental and intraplate earthquakes and their implications for the thermal and mechanical properties of the lithosphere

We investigate the distribution of focal depths for earthquakes that do not appear to be associated with zones of recent subduction, using both new results from analyses of individual events recorded at teleseismic distances and published data for both microearthquakes and larger events. The deepest events in oceanic regions occur in old lithosphere (≥100 Ma), and excluding earthquakes in active mountain belts, the deepest crustal events occur in old cratons (tectonic age ≥800 Ma). Therefore, the temperature at the source region is likely to be an important factor determining whether deformation occurs seismically or not. From estimates of the temperatures at depths of the deepest events, we conclude that those limiting temperatures are about 250°–450°C and 600°–800°C for crustal and mantle materials, respectively. In several regions of recent continental convergence, in addition to shallow crustal seismicity, there is seismic activity in the uppermost mantle. The lower crust, however, is essentially aseismic. We infer that both the upper crustal and the mantle seismic regions correspond to zones of relatively high strength and that they are separated by a zone of lower strength in the lower crust where aseismic, ductile deformation predominates. This simple interpretation is qualitatively in agreement with extrapolated values of brittle and ductile strengths of geologic materials studied under appropriate pressure and temperature conditions in the laboratory. A low-strength zone in the lower crust might allow detachment of crystalline nappes from the underlying mantle (and lower crustal) lithosphere. The apparently greater strength of mantle materials than crustal materials at the same temperature implies that oceanic lithosphere is much stronger than continental lithosphere, and this difference may account for why plate tectonics works well in oceanic regions but not in continents.
Stress control in non-ideal topological Maxwell lattices via geometry

Topological mechanical metamaterials have demonstrated exotic and robust mechanical properties which led to promising engineering applications. One of such properties is the focusing of stress at the interface connecting domains of topological Maxwell lattices of opposite topological polarizations, which protects the bulk of the material against fracturing. Here we generalize this theory to non-ideal Maxwell lattices, incorporate real material features that leads to interactions beyond previous ideal models. By quantitative analysis of stress distributions of topological Maxwell lattices with self-stress interfaces theoretically and computationally, we propose a design rule that minimizes stress in the bulk of the material. This design rule can guide the realization of stress focusing and fracturing protection in real materials.
DiffusionAvatars: Deferred Diffusion for High-fidelity 3D Head Avatars

DiffusionAvatars synthesizes a high-fidelity 3D head avatar of a person, offering intuitive control over both pose and expression. We propose a diffusion-based neural renderer that leverages generic 2D priors to produce compelling images of faces. For coarse guidance of the expression and head pose, we render a neural parametric head model (NPHM) from the target viewpoint, which acts as a proxy geometry of the person. Additionally, to enhance the modeling of intricate facial expressions, we condition DiffusionAvatars directly on the expression codes obtained from NPHM via cross-attention. Finally, to synthesize consistent surface details across different viewpoints and expressions, we rig learnable spatial features to the head's surface via TriPlane lookup in NPHM's canonical space. We train DiffusionAvatars on RGB videos and corresponding tracked NPHM meshes of a person and test the obtained avatars in both self-reenactment and animation scenarios. Our experiments demonstrate that DiffusionAvatars generates temporally consistent and visually appealing videos for novel poses and expressions of a person, outperforming existing approaches.
Unsupervised learning architecture based on neural Darwinism and Hopfield networks recognizes symbols with high accuracy

This paper introduces a novel unsupervised learning paradigm inspired by Gerald Edelman's theory of neuronal group selection ("Neural Darwinism"). The presented automaton learns to recognize arbitrary symbols (e.g., letters of an alphabet) when they are presented repeatedly, as they are when children learn to read. On a second hierarchical level, the model creates abstract categories representing the learnt symbols. The fundamental computational unit are simple McCulloch-Pitts neurons arranged into fully-connected groups (Hopfield networks with randomly initialized weights), which are"selected", in an evolutionary sense, through symbol presentation. The learning process is fully tractable and easily interpretable for humans, in contrast to most neural network architectures. Computational properties of Hopfield networks enabling pattern recognition are discussed. In simulations, the model achieves high accuracy in learning the letters of the Latin alphabet, presented as binary patterns on a grid. This paper is a proof of concept with no claims to state-of-the-art performance in letter recognition, but hopefully inspires new thinking in bio-inspired machine learning.
Match me if you can: Semantic Correspondence Learning with Unpaired Images

Recent approaches for semantic correspondence have focused on obtaining high-quality correspondences using a complicated network, refining the ambiguous or noisy matching points. Despite their performance improvements, they remain constrained by the limited training pairs due to costly point-level annotations. This paper proposes a simple yet effective method that performs training with unlabeled pairs to complement both limited image pairs and sparse point pairs, requiring neither extra labeled keypoints nor trainable modules. We fundamentally extend the data quantity and variety by augmenting new unannotated pairs not primitively provided as training pairs in benchmarks. Using a simple teacher-student framework, we offer reliable pseudo correspondences to the student network via machine supervision. Finally, the performance of our network is steadily improved by the proposed iterative training, putting back the student as a teacher to generate refined labels and train a new student repeatedly. Our models outperform the milestone baselines, including state-of-the-art methods on semantic correspondence benchmarks.
Periodic Vibration Gaussian: Dynamic Urban Scene Reconstruction and Real-time Rendering

Modeling dynamic, large-scale urban scenes is challenging due to their highly intricate geometric structures and unconstrained dynamics in both space and time. Prior methods often employ high-level architectural priors, separating static and dynamic elements, resulting in suboptimal capture of their synergistic interactions. To address this challenge, we present a unified representation model, called Periodic Vibration Gaussian (PVG). PVG builds upon the efficient 3D Gaussian splatting technique, originally designed for static scene representation, by introducing periodic vibration-based temporal dynamics. This innovation enables PVG to elegantly and uniformly represent the characteristics of various objects and elements in dynamic urban scenes. To enhance temporally coherent and large scene representation learning with sparse training data, we introduce a novel temporal smoothing mechanism and a position-aware adaptive control strategy respectively. Extensive experiments on Waymo Open Dataset and KITTI benchmarks demonstrate that PVG surpasses state-of-the-art alternatives in both reconstruction and novel view synthesis for both dynamic and static scenes. Notably, PVG achieves this without relying on manually labeled object bounding boxes or expensive optical flow estimation. Moreover, PVG exhibits 900-fold acceleration in rendering over the best alternative.
Zooming Out on Zooming In: Advancing Super-Resolution for Remote Sensing

Super-Resolution for remote sensing has the potential for huge impact on planet monitoring by producing accurate and realistic high resolution imagery on a frequent basis and a global scale. Despite a lot of attention, several inconsistencies and challenges have prevented it from being deployed in practice. These include the lack of effective metrics, fragmented and relatively small-scale datasets for training, insufficient comparisons across a suite of methods, and unclear evidence for the use of super-resolution outputs for machine consumption. This work presents a new metric for super-resolution, CLIPScore, that corresponds far better with human judgments than previous metrics on an extensive study. We use CLIPScore to evaluate four standard methods on a new large-scale dataset, S2-NAIP, and three existing benchmark datasets, and find that generative adversarial networks easily outperform more traditional L2 loss-based models and are more semantically accurate than modern diffusion models. We also find that using CLIPScore as an auxiliary loss can speed up the training of GANs by 18x and lead to improved outputs, resulting in an effective model in diverse geographies across the world which we will release publicly. The dataset, pre-trained model weights, and code are available at https://github.com/allenai/satlas-super-resolution/.