Miracle-dz/UniFAR-FTUs huggingface dataset:
Microcontroller based wearable healthcare monitoring system

Wearable remote healthcare monitoring systems have started to become one of the main building blocks of modern healthcare. It offers innovative and better ways to monitor chronic diseases or detect early deterioration. It acquires physiological signals using sensors and wirelessly transmits them to a remote healthcare station where they are monitored and analyzed for further diagnosis. This study presents the design and development of a healthcare system that monitors vital parameters namely heart rate, respiration rate, body temperature and orientation. The sensors used for them are pulse sensor, temperature sensor LM 35, a force sensitive resistor and MPU9150 motion sensor. The sensors have been tested at different locations and the best location for each sensor has been used. The data has been sent wirelessly to a nearby laptop using Bluetooth and subsequently it has been displayed on the laptop using a graphical user interface (GUI) made in NI LabVIEW software. The proposed system has been compared with calibrated instruments and results are encouraging.
Therapeutic application of machine learning in psoriasis: A Prisma systematic review

Dermatology, being a predominantly visual‐based diagnostic field, has found itself to be at the epitome of artificial intelligence (AI)‐based advances. Machine learning (ML), a subset of AI, goes a step further by recognizing patterns from data and teaches machines to automatically learn tasks. Although artificial intelligence in dermatology is mostly developed in melanoma and skin cancer diagnosis, advances in AI and ML have gone far ahead and found its application in ulcer assessment, psoriasis, atopic dermatitis, onychomycosis, etc. This article is focused on the application of ML in the therapeutic aspect of psoriasis.
Mixed π type structure in Wilkinson power divider design with 3rd harmonic suppression

A compact microstrip Wilkinson power divider with band‐pass performance and 3rd harmonic suppression is introduced in this article. λ/4 transmission line is replaced by mixed π type structure which includes three elements: two open stubs and one capacitor. Through even‐ and odd‐mode analysis, design equations for the proposed topology are derived in closed form. Capacitor is only determined by designed center frequency; characteristic impedance for each open stub is only determined by its electrical length, therefore, these three elements in mixed π type structure can be designed independently. By selecting different electrical lengths, transmission zeros can be adjusted accordingly, 3rd harmonic suppression can be realized. Finally, open stubs convert short stubs for miniaturization. One example divider is designed and fabricated, and measured results show good agreement with theoretical results. © 2017 Wiley Periodicals, Inc. Microwave Opt Technol Lett 59:1245–1248, 2017
TIP: Task-Informed Motion Prediction for Intelligent Vehicles

When predicting trajectories of road agents, motion predictors often approximate the future distribution by a limited number of samples. This constraint requires the predictors to generate samples that best support the task given task specifications. However, existing predictors are often optimized and evaluated via task-agnostic measures without accounting for the use of predictions in downstream tasks, and thus could result in sub-optimal task performance. In this paper, we propose a task-informed motion prediction model that better supports the tasks through its predictions by jointly reasoning about prediction accuracy and the utility of the downstream tasks during training. The task utility function is commonly used to evaluate task performance. It does not require the full task information, but rather a specification of the utility of the task, resulting in predictors that are tailored to different downstream tasks. We demonstrate our approach on two use cases of common decision making tasks and their utility functions, in the context of autonomous driving and parallel autonomy. Experiment results show that our predictor produces accurate predictions that improve the task performance by a large margin in both tasks when compared to task-agnostic baselines on the Waymo Open Motion dataset.
A Rotated Hyperbolic Wrapped Normal Distribution for Hierarchical Representation Learning

We present a rotated hyperbolic wrapped normal distribution (RoWN), a simple yet effective alteration of a hyperbolic wrapped normal distribution (HWN). The HWN expands the domain of probabilistic modeling from Euclidean to hyperbolic space, where a tree can be embedded with arbitrary low distortion in theory. In this work, we analyze the geometric properties of the diagonal HWN, a standard choice of distribution in probabilistic modeling. The analysis shows that the distribution is inappropriate to represent the data points at the same hierarchy level through their angular distance with the same norm in the Poincar\'e disk model. We then empirically verify the presence of limitations of HWN, and show how RoWN, the proposed distribution, can alleviate the limitations on various hierarchical datasets, including noisy synthetic binary tree, WordNet, and Atari 2600 Breakout. The code is available at https://github.com/ml-postech/RoWN.
New Photometric Calibration of the Wide Field Camera 3 Detectors

We present a new photometric calibration of the WFC3-UVIS and WFC3-IR detectors based on observations collected from 2009 to 2020 for four white dwarfs—namely, GRW +70 5824, GD 153 , GD 71 , G191B2B—and a G-type star, P330E. These calibrations include recent updates to the Hubble Space Telescope primary standard white dwarf models and a new reference flux for Vega. Time-dependent inverse sensitivities for the two WFC3-UVIS chips, UVIS1 and UVIS2, were calculated for all 42 full-frame filters after accounting for temporal changes in the observed count rates with respect to a reference epoch in 2009. We also derived new encircled energy values for a few filters and improved sensitivity ratios for the two WFC3-UVIS chips by correcting for sensitivity changes with time. Updated inverse sensitivity values for the 20 WFC3-UVIS quad filters and 15 WFC3-IR filters were derived by using the new models for the primary standards, the new Vega reference flux, and, in the case of the IR detector, new flat fields. However, these values do not account for any sensitivity changes with time. The new calibration provides a photometric internal precision better than 0.5% for the wide-, medium-, and narrowband WFC3-UVIS filters; 5% for the quad filters; and 1% for the WFC3-IR filters. As of 2020 October 15, an updated set of photometric keywords were populated in the WFC3 image headers.
Criteria for energy conditions

In model building studies, it is important to check the energy conditions for the corresponding energy–momentum tensor determined by the gravitational field equations in order to single out physically reasonable models. In this process, one often encounters a situation where the energy–momentum tensor has one off-diagonal ‘space–time’ component in the frame with an orthonormal basis in a given spacetime. We derive useful criteria of energy–momentum tensors for their Hawking–Ellis types and the standard energy conditions in such situations. As demonstrations, we apply those criteria to four different systems.
An Acceleration of Fixed Point Iterations for M/G/1-type Markov Chains by Means of Relaxation Techniques

We present some accelerated variants of ﬁxed point iterations for computing the minimal nonnegative solution of the unilateral matrix equation associated with an M/G/1-type Markov chain. These schemes derive from certain staircase regular splittings of the block Hessenberg M-matrix associated with the Markov chain. By exploiting the staircase proﬁle we introduce a two-step ﬁxed point iteration. The iteration can be further accelerated by computing a weighted average between the approximations obtained in two consecutive steps. The convergence of the basic two-step ﬁxed point iteration and of its relaxed modiﬁcation is proved. Our theoretical analysis along with several numerical experiments show that the proposed variants generally outperform the classical iterations.
On Collective Robustness of Bagging Against Data Poisoning

Bootstrap aggregating (bagging) is an effective ensemble protocol, which is believed can enhance robustness by its majority voting mechanism. Recent works further prove the sample-wise robustness certificates for certain forms of bagging (e.g. partition aggregation). Beyond these particular forms, in this paper, \emph{we propose the first collective certification for general bagging to compute the tight robustness against the global poisoning attack}. Specifically, we compute the maximum number of simultaneously changed predictions via solving a binary integer linear programming (BILP) problem. Then we analyze the robustness of vanilla bagging and give the upper bound of the tolerable poison budget. Based on this analysis, \emph{we propose hash bagging} to improve the robustness of vanilla bagging almost for free. This is achieved by modifying the random subsampling in vanilla bagging to a hash-based deterministic subsampling, as a way of controlling the influence scope for each poisoning sample universally. Our extensive experiments show the notable advantage in terms of applicability and robustness.
The Effect of Task Ordering in Continual Learning

We investigate the effect of task ordering on continual learning performance. We conduct an extensive series of empirical experiments on synthetic and naturalistic datasets and show that reordering tasks significantly affects the amount of catastrophic forgetting. Connecting to the field of curriculum learning, we show that the effect of task ordering can be exploited to modify continual learning performance, and present a simple approach for doing so. Our method computes the distance between all pairs of tasks, where distance is defined as the source task curvature of a gradient step toward the target task. Using statistically rigorous methods and sound experimental design, we show that task ordering is an important aspect of continual learning that can be modified for improved performance.
Deep Reinforcement Learning with Adaptive Hierarchical Reward for Multi-Phase Multi-Objective Dexterous Manipulation

— Dexterous manipulation tasks usually have multiple objectives, and the priorities of these objectives may vary at different phases of a manipulation task. Varying priority makes a robot hardly or even failed to learn an optimal policy with a deep reinforcement learning (DRL) method. To solve this problem, we develop a novel Adaptive Hierarchical Reward Mechanism (AHRM) to guide the DRL agent to learn manipulation tasks with multiple prioritized objectives. The AHRM can determine the objective priorities during the learning process and update the reward hierarchy to adapt to the changing objective priorities at different phases. The proposed method is validated in a multi-objective manipulation task with a JACO robot arm in which the robot needs to manipulate a target with obstacles surrounded. The simulation and physical experiment results show that the proposed method improved robot learning in task performance and learning efficiency.
Open Arms: Open-Source Arms, Hands & Control

Open Arms is a novel open-source platform of realistic human-like robotic hands and arms hardware with 28 Degree-of-Freedom (DoF), designed to extend the capabilities and accessibility of humanoid robotic grasping and manipulation. The Open Arms framework includes an open SDK and development environment, simulation tools, and application development tools to build and operate Open Arms. This paper describes these hands’ controls, sensing, mechanisms, aesthetic design, and manufacturing and their real-world applications with a teleoperated nursing robot. From 2015 to 2022, the authors have designed and established the manufacturing of Open Arms as a low-cost, high functionality robotic arms hardware and software framework to serve both humanoid robot applications and the urgent demand for low-cost prosthetics, as part of the Hanson Robotics Sophia Robot platform. Using the techniques of consumer product manufacturing, we set out to define modular, low-cost techniques for approximating the dexterity and sensitivity of human hands. To demonstrate the dexterity and control of our hands, we present a Generative Grasping Residual CNN (GGR-CNN) model that can generate robust antipodal grasps from input images of various objects in real-time speeds (~22ms). We achieved state-of-the-art accuracy of 92.4% using our model architecture on a standard Cornell Grasping Dataset, which contains a diverse set of household objects.
Factorized Structured Regression for Large-Scale Varying Coefficient Models

Recommender Systems (RS) pervade many aspects of our everyday digital life. Proposed to work at scale, state-of-the-art RS allow the modeling of thousands of interactions and facilitate highly individualized recommendations. Conceptually, many RS can be viewed as instances of statistical regression models that incorporate complex feature effects and potentially non-Gaussian outcomes. Such structured regression models, including time-aware varying coefficients models, are, however, limited in their applicability to categorical effects and inclusion of a large number of interactions. Here, we propose Factorized Structured Regression (FaStR) for scalable varying coefficient models. FaStR overcomes limitations of general regression models for large-scale data by combining structured additive regression and factorization approaches in a neural network-based model implementation. This fusion provides a scalable framework for the estimation of statistical models in previously infeasible data settings. Empirical results confirm that the estimation of varying coefficients of our approach is on par with state-of-the-art regression techniques, while scaling notably better and also being competitive with other time-aware RS in terms of prediction performance. We illustrate FaStR's performance and interpretability on a large-scale behavioral study with smartphone user data.
O(N2) Universal Antisymmetry in Fermionic Neural Networks

Fermionic neural network (FermiNet) is a re-cently proposed wavefunction Ansatz, which is used in variational Monte Carlo (VMC) methods to solve the many-electron Schr¨odinger equation. FermiNet proposes permutation-equivariant architectures, on which a Slater determinant is applied to induce antisymmetry. FermiNet is proved to have universal approximation capabil-ity with a single determinant, namely, it sufﬁces to represent any antisymmetric function given sufﬁcient parameters. However, the asymptotic computational bottleneck comes from the Slater determinant, which scales with O ( N 3 ) for N electrons. In this paper, we substitute the Slater determinant with a pairwise antisymmetry construction, which is easy to implement and can re-duce the computational cost to O ( N 2 ) . We formally prove that the pairwise construction built upon permutation-equivariant architectures can universally represent any antisymmetric function. Besides, this can be achieved via continuous approximators when we aim to represent ground-state wavefunctions.
Automatic parameter selection for the TGV regularizer in image restoration under Poisson noise

We address the image restoration problem under Poisson noise corruption. The Kullback-Leibler divergence, which is typically adopted in the variational framework as data fidelity term in this case, is coupled with the second-order Total Generalized Variation (TGV$^2$). The TGV$^2$ regularizer is known to be capable of preserving both smooth and piece-wise constant features in the image, however its behavior is subject to a suitable setting of the parameters arising in its expression. We propose a hierarchical Bayesian formulation of the original problem coupled with a Maximum A Posteriori estimation approach, according to which the unknown image and parameters can be jointly and automatically estimated by minimizing a given cost functional. The minimization problem is tackled via a scheme based on the Alternating Direction Method of Multipliers, which also incorporates a procedure for the automatic selection of the regularization parameter by means of a popular discrepancy principle. Computational results show the effectiveness of our proposal.
On the Evolution of A.I. and Machine Learning: Towards Measuring and Understanding Impact, Influence, and Leadership at Premier A.I. Conferences

Artiﬁcial Intelligence is now recognized as a general-purpose technology with ample impact on human life. In this work, we aim to understand the evolution of AI and Machine learning over the years by analyzing researchers’ impact, inﬂuence, and leadership over the last decades. This work also intends to shed new light on the history and evolution of AI by exploring the dynamics involved in the ﬁeld’s evolution through the lenses of the papers published on AI conferences since the ﬁrst International Joint Conference on Artiﬁcial Intelligence (IJCAI) in 1969. AI development and evolution have led to increasing research output, reﬂected in the number of articles published over the last sixty years. We construct comprehensive citation-collaboration and paper-author datasets and compute corresponding centrality measures to carry out our analyses. These analyses allow a better understanding of how AI has reached its current state of affairs in research. Throughout the process, we correlate these datasets with the work of the ACM Turing Award winners and the so-called two AI winters the ﬁeld has gone through. We also look at self-citation trends and new authors’ behaviors. Finally, we present a novel way to infer the country of afﬁliation of a paper from its organization. Therefore, this work provides a deep analysis of Artiﬁcial Intelligence history from information gathered and analyzed from large technical venues datasets and suggests novel insights that can contribute to understanding and measuring AI’s evolution.
Complexity-optimal and Parameter-free First-order Methods for Finding Stationary Points of Composite Optimization Problems

This paper develops and analyzes an accelerated proximal descent method for finding stationary points of nonconvex composite optimization problems. The objective function is of the form $f+h$ where $h$ is a proper closed convex function, $f$ is a differentiable function on the domain of $h$, and $\nabla f$ is Lipschitz continuous on the domain of $h$. The main advantage of this method is that it is"parameter-free"in the sense that it does not require knowledge of the Lipschitz constant of $\nabla f$ or of any global topological properties of $f$. It is shown that the proposed method can obtain an $\varepsilon$-approximate stationary point with iteration complexity bounds that are optimal, up to logarithmic terms over $\varepsilon$, in both the convex and nonconvex settings. Some discussion is also given about how the proposed method can be leveraged in other existing optimization frameworks, such as min-max smoothing and penalty frameworks for constrained programming, to create more specialized parameter-free methods. Finally, numerical experiments are presented to support the practical viability of the method.
Cellular gradient flow structure connects single-cell-level rules and population-level dynamics

In multicellular systems, the single-cell behaviors should be coordinated consistently with the overall population dynamics and functions. However, the interrelation between single-cell rules and the population-level goal is still elusive. In this work, we reveal that these two levels are naturally connected via a gradient ﬂow structure of the heterogeneous cellular population and that biologically prevalent single-cell rules such as unidirectional type-switching and hierarchical order in types emerge from this structure. We also demonstrate the gradient ﬂow structure in a standard model of the T-cell immune response. This theoretical framework works as a basis for understanding multicellular dynamics and functions.
Broken symmetry and competing orders in Weyl semimetal interfaces

We consider interaction-induced broken symmetry states of two Weyl semimetal surfaces with multiple Fermi-arc (FA) states. In the presence of inter- and intra-surface Coulomb interactions, multiple broken symmetries may emerge which coexist and/or compete with one another. Interlayer exciton condensates involving different FA flavors are shown to form, with amplitudes determined by the strength of interactions and the degree of nesting among the arcs. For FA pairs which are well-separated in momentum with strong nesting, the resulting state is a particle-hole analog of a Fulde-Ferrell-Larkin-Ovchinnikov (FFLO) superconductor. Intralayer interactions moreover induce charge density wave (CDW) ordering, so that the most general state of the system is a supersolid. These orderings in principle carry signatures in non-linear behavior and narrow band noise in Coulomb drag transport measurements.
Quenched decay of correlations for nonuniformly hyperbolic random maps with an ergodic driving system

In this article we study random tower maps driven by an ergodic automorphism. We prove quenched exponential correlations decay for tower maps admitting exponential tails. Our technique is based on constructing suitable cones of functions, defined on the random towers, which contract with respect to the Hilbert metric under the action of appropriate transfer operators. We apply our results to obtain quenched exponential correlations decay for several non-iid random dynamical systems including small random perturbations of Lorenz maps and Axiom A attractors.
Learning the EFT likelihood with tree boosting

We develop a tree boosting algorithm for collider measurements of multiple Wilson coefficients in effective field theories describing phenomena beyond the standard model of particle physics. The design of the discriminant exploits per-event information of the simulated data sets that encodes the predictions for different values of the Wilson coefficients. This ``Boosted Information Tree'' algorithm provides nearly optimal discrimination power order-by-order in the expansion in the Wilson coefficients and approaches the optimal likelihood ratio test statistic. As a proof-of-principle, we apply the algorithm to the $\textrm{pp}\rightarrow\textrm{Zh}$ process for different types of modeling.
Statistical inference in social networks: how sampling bias and uncertainty shape decisions

We investigate how individuals form expectations about population behavior using statistical inference based on observations of their social relations. Misperceptions about others’ connected-ness and behavior arise from sampling bias stemming from the friendship paradox and uncertainty from small samples. In a game where actions are strategic complements, we characterize the equilibrium and analyze equilibrium behavior. We allow for agent sophistication to account for the sampling bias and demonstrate how sophistication aﬀects the equilibrium. We show how population behavior depends on both sources of misperceptions and illustrate when sampling uncertainty plays a critical role compared to sampling bias.