Miracle-dz/UniFAR-FTUs huggingface dataset:
Language guided machine action

Here we build a hierarchical modular network called Language guided machine action (LGMA), whose modules process information stream mimicking human cortical network that allows to achieve multiple general tasks such as language guided action, intention decomposition and mental simulation before action execution etc. LGMA contains 3 main systems: (1) primary sensory system that multimodal sensory information of vision, language and sensorimotor. (2) association system involves and Broca modules to comprehend and synthesize language, BA14/40 module to translate between sensorimotor and language, midTemporal module to convert between language and vision, and superior parietal lobe to integrate attended visual object and arm state into cognitive map for future spatial actions. Pre-supplementary motor area (pre-SMA) can converts high level intention into sequential atomic actions, while SMA can integrate these atomic actions, current arm and attended object state into sensorimotor vector to apply corresponding torques on arm via pre-motor and primary motor of arm to achieve the intention. The high-level executive system contains PFC that does explicit inference and guide voluntary action based on language, while BG is the habitual action control center.
Large Scale Neural Architecture Search with Polyharmonic Splines

Neural Architecture Search (NAS) is a powerful tool to automatically design deep neural networks for many tasks, including image classification. Due to the significant computational burden of the search phase, most NAS methods have focused so far on small, balanced datasets. All attempts at conducting NAS at large scale have employed small proxy sets, and then transferred the learned architectures to larger datasets by replicating or stacking the searched cells. We propose a NAS method based on polyharmonic splines that can perform search directly on large scale, imbalanced target datasets. We demonstrate the effectiveness of our method on the ImageNet22K benchmark[16], which contains 14 million images distributed in a highly imbalanced manner over 21,841 categories. By exploring the search space of the ResNet [23] and Big-Little Net ResNext [11] architectures directly on ImageNet22K, our polyharmonic splines NAS method designed a model which achieved a top-1 accuracy of 40.03% on ImageNet22K, an absolute improvement of 3.13% over the state of the art with similar global batch size [15].
Machine Predictions and Human Decisions with Variation in Payoffs and Skills

Human decision-making differs due to variation in both incentives and available information. This generates substantial challenges for the evaluation of whether and how machine learning predictions can improve decision outcomes. We propose a framework that incorporates machine learning on large-scale administrative data into a choice model featuring heterogeneity in decision maker payoff functions and predictive skill. We apply our framework to the major health policy problem of improving the efficiency in antibiotic prescribing in primary care, one of the leading causes of antibiotic resistance. Our analysis reveals large variation in physicians’ skill to diagnose bacterial infections and in how physicians trade off the externality inherent in antibiotic use against its curative benefit. Counterfactual policy simulations show the combination of machine learning predictions with physician diagnostic skill achieves a 25.4 percent reduction in prescribing and the largest welfare gains compared to alternative policies for estimated as well as plausible hypothetical weights on the antibiotic resistance externality.
Compliance with COVID-19 Preventive and Control Measures among Food and Drink Establishments in Bench-Sheko and West-Omo Zones, Ethiopia, 2020

Introduction Despite the implementation of many preventive and control systems developed by governments, the spread of COVID-19 and its resulting infection rate are alarmingly increasing from time to time all over the world. In Ethiopia, public places visited by large numbers of people where preventive and control measures are poorly practiced are considered to be potentially contributing to the spread of the disease. Food and drink establishments are among the highly susceptible public establishments visited by large numbers of people who interact among themselves and with employees. Hence, this study aimed to measure the compliance with COVID-19 preventive and control measures among food and drink establishments in the selected towns of Bench-Sheko and West-Omo Zones in Ethiopia. Methods A cross-sectional study was conducted among food and drink establishments in selected towns of Bench-Sheko and West-Omo zones from May 15, 2020 to June 15, 2020. A census of all 324 food and drink establishments found in the study area was conducted, and data were obtained from managers of the establishments through face-to-face interviews. Data were entered in to Epidata manager and exported to SPSS version 24.0 for analysis. Percentage compliance score was computed to describe the level of compliance. Ethical approval was obtained from Mizan-Tepi University Institutional Review Board, and written informed consent was obtained from every participant. Results The overall compliance level with COVID-19 preventive and control measures was 55.5%. The majority (89%) of the food and drink establishments had functional hand washing facilities at the main entrance gate. Less than half of the food and drink establishments had posted written materials promoting hand washing, arranged tables and chairs in a manner that they accommodate not more than four people at once and at least 2 meters apart, carry out daily cleaning and disinfection of frequented touched surfaces, and provided education or training for their workers about COVID-19. Conclusion The overall compliance level with COVID-19 preventive and control measures among food and drink establishments was very poor. Thus, it is highly recommended that the federal government of Ethiopia, the federal ministry of health, and local health authorities consider a move towards more solid, strict, and comprehensive compulsory measures, including fines that can lead up to the closure of non-compliant establishments.
The Selectivity and Competition of the Mind's Eye in Visual Perception

Research has shown that neurons within the brain are selective to certain stimuli. For example, the fusiform face area (FFA) region is known by neuroscientists to selectively activate when people see faces over non-face objects. However, the mechanisms by which the primary visual system directs information to the correct higher levels of the brain are currently unknown. In our work, we advance the understanding of the neural mechanisms of perception by creating a novel computational model that incorporates lateral and top down feedback in the form of hierarchical competition. We show that these elements can help explain the information flow and selectivity of high level areas within the brain. Additionally, we present both quantitative and qualitative results that demonstrate consistency with general themes and specific responses observed in the visual system. Finally, we show that our generative framework enables a wide range of applications in computer vision, including overcoming issues of bias that have been discovered in standard deep learning models.
Statistical and computational thresholds for the planted k-densest sub-hypergraph problem

Recovery a planted signal perturbed by noise is a fundamental problem in machine learning. In this work, we consider the problem of recovery a planted $k$-densest sub-hypergraph on $h$-uniform hypergraphs over $n$ nodes. This fundamental problem appears in different contexts, e.g., community detection, average case complexity, and neuroscience applications. We first observe that it can be viewed as a structural variant of tensor PCA in which the hypergraph parameters $k$ and $h$ determine the structure of the signal to be recovered when the observations are contaminated by Gaussian noise. In this work, we provide tight information-theoretic upper and lower bounds for the recovery problem, as well as the first non-trivial algorithmic bounds based on approximate message passing algorithms. The problem exhibits a typical information-to-computational-gap observed in analogous settings, that widens with increasing sparsity of the problem. Interestingly, the bounds show that the structure of the signal does have an impact on the existing bounds of tensor PCA that the unstructured planted signal does not capture.
Rephased CLuP

In \cite{Stojnicclupint19,Stojnicclupcmpl19,Stojnicclupplt19} we introduced CLuP, a \bl{\textbf{Random Duality Theory (RDT)}} based algorithmic mechanism that can be used for solving hard optimization problems. Due to their introductory nature, \cite{Stojnicclupint19,Stojnicclupcmpl19,Stojnicclupplt19} discuss the most fundamental CLuP concepts. On the other hand, in our companion paper \cite{Stojniccluplargesc20} we started the story of going into a bit deeper details that relate to many of other remarkable CLuP properties with some of them reaching well beyond the basic fundamentals. Namely, \cite{Stojniccluplargesc20} discusses how a somewhat silent RDT feature (its algorithmic power) can be utilized to ensure that CLuP can be run on very large problem instances as well. In particular, applying CLuP to the famous MIMO ML detection problem we showed in \cite{Stojniccluplargesc20} that its a large scale variant, $\text{CLuP}^{r_0}$, can handle with ease problems with \textbf{\emph{several thousands}} of unknowns with theoretically minimal complexity per iteration (only a single matrix-vector multiplication suffices). In this paper we revisit MIMO ML detection and discuss another remarkable phenomenon that emerges within the CLuP structure, namely the so-called \bl{\textbf{\emph{rephasing}}}. As MIMO ML enters the so-called low $\alpha$ regime (fat system matrix with ratio of the number of rows and columns, $\alpha$, going well below $1$) it becomes increasingly difficult even for the basic standard CLuP to handle it. However, the discovery of the rephasing ensures that CLuP remains on track and preserves its ability to achieve the ML performance. To demonstrate the power of the rephasing we also conducted quite a few numerical experiments, compared the results we obtained through them to the theoretical predictions, and observed an excellent agreement.
DmifNet: 3D Shape Reconstruction based on Dynamic Multi-Branch Information Fusion

3D object reconstruction from a single-view image is a long-standing challenging problem. Previous work was difficult to accurately reconstruct 3D shapes with a complex topology which has rich details at the edges and corners. Moreover, previous works used synthetic data to train their network, but domain adaptation problems occurred when tested on real data. In this paper, we propose a Dynamic Multi-branch Information Fusion Network (DmifNet) which can recover a high-fidelity 3D shape of arbitrary topology from a 2D image. Specifically, we design several side branches from the intermediate layers to make the network produce more diverse representations to improve the generalization ability of network. In addition, we utilize DoG (Difference of Gaussians) to extract edge geometry and corners information from input images. Then, we use a separate side branch network to process the extracted data to better capture edge geometry and corners feature information. Finally, we dynamically fuse the information of all branches to gain final predicted probability. Extensive qualitative and quantitative experiments on a large-scale publicly available dataset demonstrate the validity and efficiency of our method. Code and models are publicly available at https://github.com/leilimaster/DmifNet.
Pose2Pose: 3D Positional Pose-Guided 3D Rotational Pose Prediction for Expressive 3D Human Pose and Mesh Estimation

Previous 3D human pose and mesh estimation methods mostly rely on only global image feature to predict 3D rotations of human joints (i.e., 3D rotational pose) from an input image. However, local features on the position of human joints (i.e., positional pose) can provide joint-specific information, which is essential to understand human articulation. To effectively utilize both local and global features, we present Pose2Pose, a 3D positional pose-guided 3D rotational pose prediction network, along with a positional pose-guided pooling and joint-specific graph convolution. The positional pose-guided pooling extracts useful joint-specific local and global features. Also, the joint-specific graph convolution effectively processes the joint-specific features by learning joint-specific characteristics and different relationships between different joints. We use Pose2Pose for expressive 3D human pose and mesh estimation and show that it outperforms all previous part-specific and expressive methods by a large margin. The codes will be publicly available.
The Field Substellar Mass Function Based on the Full-sky 20 pc Census of 525 L, T, and Y Dwarfs

We present final Spitzer trigonometric parallaxes for 361 L, T, and Y dwarfs. We combine these with prior studies to build a list of 525 known L, T, and Y dwarfs within 20 pc of the Sun, 38 of which are presented here for the first time. Using published photometry and spectroscopy as well as our own follow-up, we present an array of color–magnitude and color–color diagrams to further characterize census members, and we provide polynomial fits to the bulk trends. Using these characterizations, we assign each object a T eff value and judge sample completeness over bins of T eff and spectral type. Except for types ≥T8 and T eff < 600 K, our census is statistically complete to the 20 pc limit. We compare our measured space densities to simulated density distributions and find that the best fit is a power law ( ) with α = 0.6 ± 0.1. We find that the evolutionary models of Saumon & Marley correctly predict the observed magnitude of the space density spike seen at 1200 K < T eff < 1350 K, believed to be caused by an increase in the cooling timescale across the L/T transition. Defining the low-mass terminus using this sample requires a more statistically robust and complete sample of dwarfs ≥Y0.5 and with T eff < 400 K. We conclude that such frigid objects must exist in substantial numbers, despite the fact that few have so far been identified, and we discuss possible reasons why they have largely eluded detection.
Sound modes in collisional superfluid and normal Bose gases.

We theoretically investigate sound modes in a weakly-interacting collisional Bose gas in $D$ dimensions. Using the Landau's two-fluid hydrodynamics and working within the Bogoliubov theory, we observe the hybridization of the first and second sound modes for $D\ge 2$. To model the recent measurements of the sound velocities in 2D, obtained around the Berezinskii-Kosterlitz-Thouless transition temperature and outside the very weakly-interacting regime, we derive a refined calculation of the superfluid density, finding a fair agreement with the experiment. In the 1D case, for which experimental results are currently unavailable, we find no hybridization, triggering the necessity of future experimental investigations. Our analysis provides a systematic understanding of sound propagation in a collisional weakly-interacting Bose gas in $D$ dimensions.
Topological Data Analysis of copy number alterations in cancer

Identifying subgroups and properties of cancer biopsy samples is a crucial step towards obtaining precise diagnoses and being able to perform personalized treatment of cancer patients. Recent data collections provide a comprehensive characterization of cancer cell data, including genetic data on copy number alterations (CNAs). We explore the potential to capture information contained in cancer genomic information using a novel topology-based approach that encodes each cancer sample as a persistence diagram of topological features, i.e., high-dimensional voids represented in the data. We find that this technique has the potential to extract meaningful low-dimensional representations in cancer somatic genetic data and demonstrate the viability of some applications on finding substructures in cancer data as well as comparing similarity of cancer types.
An N-body population synthesis framework for the formation of moons around Jupiter-like planets

The moons of giant planets are believed to form in situ in circumplanetary discs (CPDs). Here, we present an N-body population synthesis framework for satellite formation around a Jupiter-like planet, in which the dust-to-gas ratio, the accretion rate of solids from the protoplanetary disc, the number, and the initial positions of protosatellites were randomly chosen from realistic distributions. The disc properties were from 3D radiative simulations sampled in 1D and 2D grids and evolved semi-analytically with time. The N-body satellitesimals accreted mass from the solid component of the disc, interacted gravitationally with each other, experienced close-encounters, both scattering and colliding. With this improved modeling, we found that only about $15{{\ \rm per\ cent}}$ of the resulting population is more massive than the Galilean one, causing migration rates to be low and resonant captures to be uncommon. In 10 per cent of the cases, moons are engulfed by the planet, and 1 per cent of the satellite-systems lose at least 1 Earth-mass into the planet, contributing only in a minor part to the giant planet’s envelope’s heavy element content. We examined the differences in outcome between the 1D and 2D disc models and used machine learning techniques (Randomized Dependence Coefficient together with t-SNE) to compare our population with the Galilean system. Detecting our population around known transiting Jupiter-like planets via transits and TTVs would be challenging, but $14{{\ \rm per\ cent}}$ of the moons could be spotted with an instrumental transit sensitivity of 10−5.
Neural Network Gaussian Process Considering Input Uncertainty for Composite Structure Assembly

Developing machine-learning-enabled smart manufacturing is promising for a composite structure assembly process. To improve production quality and efficiency of the assembly process, accurate predictive analysis on dimensional deviations and residual stress of the composite structures is required. The novel composite structure assembly involves two challenges: 1) the highly nonlinear and anisotropic properties of composite materials; and 2) inevitable uncertainty in the assembly process. To overcome those problems, in this article, we propose a neural network Gaussian process model considering input uncertainty for composite structure assembly. Deep architecture of our model allows us to approximate a complex process better, and consideration of input uncertainty enables robust modeling with complete incorporation of the process uncertainty. Based on simulation and case study, the neural network Gaussian process considering input uncertainty can outperform other benchmark methods when the response function is nonsmooth and nonlinear. Although we use composite structure assembly as an example, the proposed methodology can be applied to other engineering systems with intrinsic uncertainties.
A non-autonomous equation discovery method for time signal classification

Certain neural network architectures, in the infinite-layer limit, lead to systems of nonlinear differential equations. Motivated by this idea, we develop a framework for analyzing time signals based on non-autonomous dynamical equations. We view the time signal as a forcing function for a dynamical system that governs a time-evolving hidden variable. As in equation discovery, the dynamical system is represented using a dictionary of functions and the coefficients are learned from data. This framework is applied to the time signal classification problem. We show how gradients can be efficiently computed using the adjoint method, and we apply methods from dynamical systems to establish stability of the classifier. Through a variety of experiments, on both synthetic and real datasets, we show that the proposed method uses orders of magnitude fewer parameters than competing methods, while achieving comparable accuracy. We created the synthetic datasets using dynamical systems of increasing complexity; though the ground truth vector fields are often polynomials, we find consistently that a Fourier dictionary yields the best results. We also demonstrate how the proposed method yields graphical interpretability in the form of phase portraits.
Balance Regularized Neural Network Models for Causal Effect Estimation

Estimating individual and average treatment effects from observational data is an important problem in many domains such as healthcare and e-commerce. In this paper, we advocate balance regularization of multi-head neural network architectures. Our work is motivated by representation learning techniques to reduce differences between treated and untreated distributions that potentially arise due to confounding factors. We further regularize the model by encouraging it to predict control outcomes for individuals in the treatment group that are similar to control outcomes in the control group. We empirically study the bias-variance trade-off between different weightings of the regularizers, as well as between inductive and transductive inference.
Improved Confidence Bounds for the Linear Logistic Model and Applications to Linear Bandits

We propose improved fixed-design confidence bounds for the linear logistic model. Our bounds significantly improve upon the state-of-the-art bounds of Li et al. (2017) by leveraging the self-concordance of the logistic loss inspired by Faury et al. (2020). Specifically, our confidence width does not scale with the problem dependent parameter $1/\kappa$, where $\kappa$ is the worst-case variance of an arm reward. At worse, $\kappa$ scales exponentially with the norm of the unknown linear parameter $\theta^*$. Instead, our bound scales directly on the local variance induced by $\theta^*$. We present two applications of our novel bounds on two logistic bandit problems: regret minimization and pure exploration. Our analysis shows that the new confidence bounds improve upon previous state-of-the-art performance guarantees.
Generative Adversarial Simulator

Knowledge distillation between machine learning models has opened many new avenues for parameter count reduction, performance improvements, or amortizing training time when changing architectures between the teacher and student network. In the case of reinforcement learning, this technique has also been applied to distill teacher policies to students. Until now, policy distillation required access to a simulator or real world trajectories. In this paper we introduce a simulator-free approach to knowledge distillation in the context of reinforcement learning. A key challenge is having the student learn the multiplicity of cases that correspond to a given action. While prior work has shown that data-free knowledge distillation is possible with supervised learning models by generating synthetic examples, these approaches to are vulnerable to only producing a single prototype example for each class. We propose an extension to explicitly handle multiple observations per output class that seeks to find as many exemplars as possible for a given output class by reinitializing our data generator and making use of an adversarial loss. To the best of our knowledge, this is the first demonstration of simulator-free knowledge distillation between a teacher and a student policy. This new approach improves over the state of the art on data-free learning of student networks on benchmark datasets (MNIST, Fashion-MNIST, CIFAR-10), and we also demonstrate that it specifically tackles issues with multiple input modes. We also identify open problems when distilling agents trained in high dimensional environments such as Pong, Breakout, or Seaquest.