Miracle-dz/UniFAR-FTUs huggingface dataset:
A Value-Function-based Interior-point Method for Non-convex Bi-level Optimization

Bi-level optimization model is able to capture a wide range of complex learning tasks with practical interest. Due to the witnessed efficiency in solving bi-level programs, gradient-based methods have gained popularity in the machine learning community. In this work, we propose a new gradient-based solution scheme, namely, the Bi-level Value-Function-based Interior-point Method (BVFIM). Following the main idea of the log-barrier interior-point scheme, we penalize the regularized value function of the lower level problem into the upper level objective. By further solving a sequence of differentiable unconstrained approximation problems, we consequently derive a sequential programming scheme. The numerical advantage of our scheme relies on the fact that, when gradient methods are applied to solve the approximation problem, we successfully avoid computing any expensive Hessian-vector or Jacobian-vector product. We prove the convergence without requiring any convexity assumption on either the upper level or the lower level objective. Experiments demonstrate the efficiency of the proposed BVFIM on non-convex bi-level problems.
Physion: Evaluating Physical Prediction from Vision in Humans and Machines

While current vision algorithms excel at many challenging tasks, it is unclear how well they understand the physical dynamics of real-world environments. Here we introduce Physion, a dataset and benchmark for rigorously evaluating the ability to predict how physical scenarios will evolve over time. Our dataset features realistic simulations of a wide range of physical phenomena, including rigid and soft-body collisions, stable multi-object configurations, rolling, sliding, and projectile motion, thus providing a more comprehensive challenge than previous benchmarks. We used Physion to benchmark a suite of models varying in their architecture, learning objective, input-output structure, and training data. In parallel, we obtained precise measurements of human prediction behavior on the same set of scenarios, allowing us to directly evaluate how well any model could approximate human behavior. We found that vision algorithms that learn object-centric representations generally outperform those that do not, yet still fall far short of human performance. On the other hand, graph neural networks with direct access to physical state information both perform substantially better and make predictions that are more similar to those made by humans. These results suggest that extracting physical representations of scenes is the main bottleneck to achieving human-level and human-like physical understanding in vision algorithms. We have publicly released all data and code to facilitate the use of Physion to benchmark additional models in a fully reproducible manner, enabling systematic evaluation of progress towards vision algorithms that understand physical environments as robustly as people do.
Color2Style: Real-Time Exemplar-Based Image Colorization with Self-Reference Learning and Deep Feature Modulation

Legacy black-and-white photos are riddled with people’s nostalgia and glorious memories of the past. To better re-live the elapsed frozen moments, in this paper, we present a deep exemplar-based image colorization approach named Color2Style to resurrect these grayscale image media by ﬁlling them with vibrant colors. Generally, for learning-based exemplar-based colorization, unsupervised and un-paired training are usually adopted, due to the difﬁculty of obtaining input and ground truth image pairs. To train an exemplar-based colorization model, current algorithms usually strive to achieve two procedures: i) retrieving a large number of reference images with high similarity in advance, which is inevitably time-consuming and tedious; ii) designing complicated modules to transfer the colors of the reference image to the grayscale image, by calculating and leveraging the deep semantic correspondence be-tween them (e.g., non-local operation). Contrary to the previous methods, we solve and simplify the above two steps in one end-to-end learning procedure. First, we adopt a self-augmented self-reference training scheme, where the
Potato Crop Stress Identification in Aerial Images using Deep Learning-based Object Detection

Recent research on the application of remote sensing and deep learning-based analysis in precision agriculture demonstrated a potential for improved crop management and reduced environmental impacts of agricultural production. Despite the promising results, the practical relevance of these technologies for actual field deployment requires novel algorithms that are customized for analysis of agricultural images and robust to implementation on natural field imagery. The paper presents an approach for analyzing aerial images of a potato crop using deep neural networks. The main objective is to demonstrate automated spatial recognition of a healthy versus stressed crop at a plant level. Specifically, we examine premature plant senescence resulting in drought stress on Russet Burbank potato plants. The proposed deep learning model, named Retina-UNet-Ag, is a variant of Retina-UNet (Jaeger et al., 2018) and includes connections from low-level semantic dense representation maps to the feature pyramid network. The paper also introduces a dataset of field images acquired with a Parrot Sequoia camera carried by a Solo unmanned aerial vehicle. Experimental validation demonstrated the ability for distinguishing healthy and stressed plants in field images, achieving an average Dice score coefficient of 0.74. A comparison to related state-of-the-art deep learning models for object detection revealed that the presented approach is effective for the task at hand. The method applied here is conducive toward the assessment and recognition of potato crop stress (early plant senescence resulting from drought stress in this case) in natural aerial field images collected under real conditions.
Neuromodulatory control over nonlinear spiking of layer V pyramidal neurons mediates adaptive slightly subcritical network dynamics

To remain adaptable to a dynamic environment, coordinated neural activity in the brain must be simultaneously flexible and reliable. There is evidence that the brain facilitates this adaptive response using highly-conserved metabotropic neuromodulatory neurotransmitters, such as noradrenaline and acetylcholine. While we understand how these neuromodulators alter individual neuronal dynamics, precisely how neuromodulation operates in networks of neurons to give rise to observed large-scale dynamics remains unknown. By viewing neural dynamics as a critical phase transition, neuromodulators can be framed as control parameters that modulate the order inherent within a network of linear neurons – analogous to magnetic domains in the Ising model. While powerful, this view fails to account for two significant biological aspects: distinct arms of the neuromodulatory system possess differential anatomical projection patterns; and, neurons are not simple linear point sources. Neurons can spike nonlinearly, whereby the same input can elicit different responses. Here, we investigate this disparity by demonstrating correspondence between adaptive information processing modes – calculated on in vivo electrophysiological recordings of bursting layer V neurons in awake mice – and fluctuations in neuromodulatory tone – assessed by dynamic changes in pupil diameter. We theoretically validate these results by creating a novel, biologically plausible dual-compartment model of nonlinear layer V pyramidal neurons – capable of both regular spike and bursting modes – that reproduce our main empirical findings. We then probe our model at a resolution impossible in vivo to demonstrate that the adrenergic and cholinergic neuromodulatory systems shift the brain into a neuroprotective (i.e., slightly subcritical) regime – assessed by the branching parameter – while facilitating flexible and reliable dynamics, respectively. This unexpected result demonstrates that the brain has circumvented the necessity to shift the system closer to criticality for variability by differentially augmenting an intrinsic neuronal nonlinearity. Our analyses establish that these distinct arms of the ascending arousal system modulate ensembles of layer V pyramidal neurons to augment critical dynamics and facilitate distinct adaptive information processing modes within the brain.
GRAVITY K-band spectroscopy of HD 206893 B

Context. Near-infrared interferometry has become a powerful tool for studying the orbital and atmospheric parameters of substellar companions. Aims. We aim to reveal the nature of the reddest known substellar companion HD 206893 B by studying its near-infrared colors and spectral morphology and by investigating its orbital motion. Methods. We fit atmospheric models for giant planets and brown dwarfs and perform spectral retrievals with petitRADTRANS and ATMO on the observed GRAVITY, SPHERE, and GPI spectra of HD 206893 B. To recover its unusual spectral features, first and foremost its extremely red near-infrared color, we include additional extinction by high-altitude dust clouds made of enstatite grains in the atmospheric model fits. However, forsterite, corundum, and iron grains predict similar extinction curves for the grain sizes considered here. We also infer the orbital parameters of HD 206893 B by combining the ~100 μas precision astrometry from GRAVITY with data from the literature and constrain the mass and position of HD 206893 C based on the Gaia proper motion anomaly of the system. Results. The extremely red color and the very shallow 1.4 μm water absorption feature of HD 206893 B can be fit well with the adapted atmospheric models and spectral retrievals. By comparison with AMES-Cond evolutionary tracks, we find that only some atmosphericmodels predict physically plausible objects. Altogether, our analysis suggests an age of ~ 3–300 Myr and a mass of ~ 5–30 MJup for HD 206893 B, which is consistent with previous estimates but extends the parameter space to younger and lower-mass objects. The GRAVITY astrometry points to an eccentric orbit (e = 0.29−0.11+0.06) with a mutual inclination of <34.4 deg with respectto the debris disk of the system. Conclusions. While HD 206893 B could in principle be a planetary-mass companion, this possibility hinges on the unknown influence of the inner companion on the mass estimate of 10−4+5 MJup from radial velocity and Gaia as well as a relatively small but significant Argus moving group membership probability of ~ 61%. However, we find that if the mass of HD 206893 B is <30 MJup, then the inner companion HD 206893 C should have a mass between ~ 8–15 MJup. Finally, further spectroscopic or photometric observations at higher signal-to-noise and longer wavelengths are required to learn more about the composition and dust cloud properties of HD 206893 B.
Compositional Sketch Search

We present an algorithm for searching image collections using free-hand sketches that describe the appearance and relative positions of multiple objects1 Sketch based image retrieval (SBIR) methods predominantly match queries containing a single, dominant object invariant to its position within an image. Our work exploits drawings as a concise and intuitive representation for specifying entire scene compositions. We train a convolutional neural network (CNN) to encode masked visual features from sketched objects, pooling these into a spatial descriptor encoding the spatial relationships and appearances of objects in the composition. Training the CNN backbone as a Siamese network under triplet loss yields a metric search embedding for measuring compositional similarity which may be efficiently leveraged for visual search by applying product quantization.
Proximate composition and antioxidant analysis of some good mode foods and their sensory analysis

Three different good mood food cereals or breakfast cereals viz; Cornflakes, Dalia and Oats were analyzed for different physic-chemical properties. The ash and protein content of three different breakfast cereals does not vary significantly with each other while moisture content showed significant difference. The protein content of Dalia and Oats does not vary with each other, however both varied significantly with Cornflakes. The crude fiber of Cornflakes and Oats differs significantly with each other Based on organoleptic evaluation by semi-trained panelists; the appearance of all the three breakfast cereals does not vary significantly with each other. Overall acceptability of all the three breakfast cereals does not vary with each
1 3 M ay 2 02 1 Multiparticle Entanglement Dynamics of Quantum Chaos in a Bose-Einstein condensate

We study the particle-entanglement dynamics witnessed by the quantum Fisher information (QFI) of a trapped Bose-Einstein condensate governed by the kicked rotor Hamiltonian. The dynamics is investigated with a beyond mean-field approach. We link the time scales of the validity of this approximation in, both, classical regular and chaotic regions, with the maximum Lyapunov exponents of the classical system. This establishes an effective connection between the classical chaos and the QFI. We finally study the critical point of a quantum phase transition using the beyond mean-field approximation by considering a two-mode bosonic Josephson junction with attractive interparticle interaction.
MobILE: Model-Based Imitation Learning From Observation Alone

This paper studies Imitation Learning from Observations alone (ILFO) where the learner is presented with expert demonstrations that consist only of states visited by an expert (without access to actions taken by the expert). We present a provably efficient model-based framework MobILE to solve the ILFO problem. MobILE involves carefully trading off strategic exploration against imitation - this is achieved by integrating the idea of optimism in the face of uncertainty into the distribution matching imitation learning (IL) framework. We provide a unified analysis for MobILE, and demonstrate that MobILE enjoys strong performance guarantees for classes of MDP dynamics that satisfy certain well studied notions of structural complexity. We also show that the ILFO problem is strictly harder than the standard IL problem by presenting an exponential sample complexity separation between IL and ILFO. We complement these theoretical results with experimental simulations on benchmark OpenAI Gym tasks that indicate the efficacy of MobILE. Code for implementing the MobILE framework is available at https://github.com/rahulkidambi/MobILE-NeurIPS2021.
Federated stochastic gradient Langevin dynamics

Stochastic gradient MCMC methods, such as stochastic gradient Langevin dynamics (SGLD), employ fast but noisy gradient estimates to enable large-scale posterior sampling. Although we can easily extend SGLD to distributed settings, it suffers from two issues when applied to federated non-IID data. First, the variance of these estimates increases significantly. Second, delaying communication causes the Markov chains to diverge from the true posterior even for very simple models. To alleviate both these problems, we propose conducive gradients, a simple mechanism that combines local likelihood approximations to correct gradient updates. Notably, conducive gradients are easy to compute, and since we only calculate the approximations once, they incur negligible overhead. We apply conducive gradients to distributed stochastic gradient Langevin dynamics (DSGLD) and call the resulting method federated stochastic gradient Langevin dynamics (FSGLD). We demonstrate that our approach can handle delayed communication rounds, converging to the target posterior in cases where DSGLD fails. We also show that FSGLD outperforms DSGLD for non-IID federated data with experiments on metric learning and neural networks.
Single-file dynamics of colloids in circular channels: Time scales, scaling laws and their universality

In colloidal systems, Brownian motion emerges from the massive separation of time and length scales associated to characteristic dynamics of the solute and solvent constituents. This separation of scales produces several temporal regimes in the colloidal dynamics when combined with the effects of the interaction between the particles, confinement conditions, and state variables, such as density and temperature. Some examples are the short- and long-time regimes in two- and three-dimensional open systems and the diffusive and sub-diffusive regimes observed in the single-file dynamics along a straight line. This work studies the way in which a confining geometry induces new time scales. We report on the dynamics of interacting colloidal particles moving along a circle by combining a heuristic theoretical analysis of the involved scales, Brownian Dynamics computer simulations, and video-microscopy experiments with paramagnetic colloids confined to lithographic circular channels subjected to an external magnetic field. The systems display four temporal regimes in this order: one-dimensional free diffusion, single-file sub-diffusion, free-cluster rotational diffusion, and the expected saturation due to the confinement. We also report analytical expressions for the mean-square angular displacement and crossover times obtained from scaling arguments, which accurately reproduce both experiments and simulations. Our generic approach can be used to predict the long-time dynamics of many other confined physical systems.
Aligning an interface terminology to the Logical Observation Identifiers Names and Codes (LOINC®)

Abstract Objective Our study consists in aligning the interface terminology of the Bordeaux university hospital (TLAB) to the Logical Observation Identifiers Names and Codes (LOINC). The objective was to facilitate the shared and integrated use of biological results with other health information systems. Materials and Methods We used an innovative approach based on a decomposition and re-composition of LOINC concepts according to the transversal relations that may be described between LOINC concepts and their definitional attributes. TLAB entities were first anchored to LOINC attributes and then aligned to LOINC concepts through the appropriate combination of definitional attributes. Finally, using laboratory results of the Bordeaux data-warehouse, an instance-based filtering process has been applied. Results We found a small overlap between the tokens constituting the labels of TLAB and LOINC. However, the TLAB entities have been easily aligned to LOINC attributes. Thus, 99.8% of TLAB entities have been related to a LOINC analyte and 61.0% to a LOINC system. A total of 55.4% of used TLAB entities in the hospital data-warehouse have been mapped to LOINC concepts. We performed a manual evaluation of all 1-1 mappings between TLAB entities and LOINC concepts and obtained a precision of 0.59. Conclusion We aligned TLAB and LOINC with reasonable performances, given the poor quality of TLAB labels. In terms of interoperability, the alignment of interface terminologies with LOINC could be improved through a more formal LOINC structure. This would allow queries on LOINC attributes rather than on LOINC concepts only.
Yuanhuacine Is a Potent and Selective Inhibitor of the Basal-Like 2 Subtype of Triple Negative Breast Cancer with Immunogenic Potential

Simple Summary Most breast cancers express the estrogen, progesterone, and/or HER2 receptors and patients are treated with inhibitors targeting these receptors. Triple negative breast cancers (TNBCs) lack these receptors and thus patients with TNBC do not benefit from existing targeted therapies. There is a continued search for effective targets for the treatment of these heterogeneous tumors. We identified a class of plant-derived natural products, including yuanhuacine, that selectively kill cells that represent a molecularly defined subtype of TNBC. These compounds also promote expression of an immunological profile that is beneficial for engaging the immune system, which could provide an added benefit in TNBC. The mechanism of action of both the TNBC selectivity and immunological phenotypes is associated with activation of protein kinase C. Yuanhuacine has potent antitumor efficacy in a mouse model of TNBC, identifying a new therapeutic target for the treatment of this deadly disease. Abstract The heterogeneity of triple negative breast cancer (TNBC) has led to efforts to further subtype this disease with the hope of identifying new molecular liabilities and drug targets. Furthermore, the finding that TNBC is the most inherently immunogenic type of breast cancer provides the potential for effective treatment with immune checkpoint inhibitors and immune adjuvants. Thus, we devised a dual screen to identify compounds from natural product extracts with TNBC subtype selectivity that also promote the expression of cytokines associated with antitumor immunity. These efforts led to the identification of yuanhuacine (1) as a potent and highly selective inhibitor of the basal-like 2 (BL2) subtype of TNBC that also promoted an antitumor associated cytokine signature in immune cells. The mechanism of action of yuanhuacine for both phenotypes depends on activation of protein kinase C (PKC), defining a novel target for the treatment of this clinical TNBC subtype. Yuanhuacine showed potent antitumor efficacy in animals bearing BL2 tumors further demonstrating that PKC could function as a potential pharmacological target for the treatment of the BL2 subtype of TNBC.
Atomic Simulations of Packing Structures, Local Stress and Mechanical Properties for One Silicon Lattice with Single Vacancy on Heating

The effect of vacancy defects on the structure and mechanical properties of semiconductor silicon materials is of great significance to the development of novel microelectronic materials and the processes of semiconductor sensors. In this paper, molecular dynamics is used to simulate the atomic packing structure, local stress evolution and mechanical properties of a perfect lattice and silicon crystal with a single vacancy defect on heating. In addition, their influences on the change in Young’s modulus are also analyzed. The atomic simulations show that in the lower temperature range, the existence of vacancy defects reduces the Young’s modulus of the silicon lattice. With the increase in temperature, the local stress distribution of the atoms in the lattice changes due to the migration of the vacancy. At high temperatures, the Young’s modulus of the silicon lattice changes in anisotropic patterns. For the lattice with the vacancy, when the temperature is higher than 1500 K, the number and degree of distortion in the lattice increase significantly, the obvious single vacancy and its adjacent atoms contracting inward structure disappears and the defects in the lattice present complex patterns. By applying uniaxial tensile force, it can be found that the temperature has a significant effect on the elasticity–plasticity behaviors of the Si lattice with the vacancy.