Miracle-dz/UniFAR-FTUs huggingface dataset:
DMSO_A_231981 267..276

Bokun Kim 1 Seungyeol Kim 2 1Faculty of Sports Health Care, Inje University, Gimhae, Gyeongsangnamdo, Republic of Korea; 2College of Arts and Sports, Dong-A University, Busan, Republic of Korea Purpose: The purpose of this study was to comparatively investigate changes in physiological and physical fitness in previously inactive men with obesity in response to aerobic exercise (AE) or resistance exercise (RE). Methods: A total of 27 inactive men with obesity, aged 34–60 years, attended a 90-min AE or RE program 3 days/week for 12 weeks. The subjects underwent assessments of energy intake (by a 3-day weighted dietary record), body weight (by a digital scale), body composition (by whole-body dual-energy X-ray absorptiometry), VO2max (by a cycling ergometer), muscle strength (by a Biodex System 3 dynamometer) and blood analysis. Results: There were no significant interactions (P = 0.100~0.730) among energy intake variables. RE led to decreased fat mass (−4.39%, P < 0.05) and improved cardiorespiratory capacity (+11.66%, P < 0.05), as well as increases in lean mass (+2.12%, P < 0.01) and muscle strength variables (+8.41~+11.00%, P < 0.01 for all), without significant weight change. Although AE induced decreases in fat mass (−5.91%, P < 0.05) and weight (−2.28%, P < 0.05) and improved cardiorespiratory capacity (+19.07%, P < 0.01), lean mass and muscle strength variables remained unchanged. RE showed a stronger positive influence than AE on lean mass (P = 0.003) and muscle strength variables (P = 0.001~0.015), and RE and AE had similar influences on weight, fat mass, cardiorespiratory capacity and blood markers. Conclusion: It may be an efficient exercise regimen to perform RE first and then utilize AE to maintain the changes that occur in response to RE.
Dimensionality Reduction of Movement Primitives in Parameter Space

Movement primitives are an important policy class for real-world robotics. However, the high dimensionality of their parametrization makes the policy optimization expensive both in terms of samples and computation. Enabling an efficient representation of movement primitives facilitates the application of machine learning techniques such as reinforcement on robotics. Motions, especially in highly redundant kinematic structures, exhibit high correlation in the configuration space. For these reasons, prior work has mainly focused on the application of dimensionality reduction techniques in the configuration space. In this paper, we investigate the application of dimensionality reduction in the parameter space, identifying principal movements. The resulting approach is enriched with a probabilistic treatment of the parameters, inheriting all the properties of the Probabilistic Movement Primitives. We test the proposed technique both on a real robotic task and on a database of complex human movements. The empirical analysis shows that the dimensionality reduction in parameter space is more effective than in configuration space, as it enables the representation of the movements with a significant reduction of parameters.
Autonomous Driving at Intersections: A Critical-Turning-Point Approach for Left Turns

Left-turn planning is one of the formidable challenges for autonomous vehicles, especially at unsignalized intersections due to the unknown intentions of oncoming vehicles. This paper addresses the challenge by proposing a critical turning point (CTP) based hierarchical planning approach. This includes a high-level candidate path generator and a low-level partially observable Markov decision process (POMDP) based planner. The proposed CTP concept, inspired by human-driving behaviors at intersections, aims to increase the computational efficiency of the low-level planner and to enable human-friendly autonomous driving. The POMDP based low-level planner takes unknown intentions of oncoming vehicles into considerations to perform less conservative yet safe actions. With proper integration, the proposed hierarchical approach is capable of achieving safe planning results with high commute efficiency at unsignalized intersections in real time.
Constraining a neutron star merger origin for localized fast radio bursts

What the progenitors of fast radio bursts (FRBs) are, and whether there are multiple types of progenitors are open questions. The advent of localized FRBs with host galaxy redshifts allows the various emission models to be directly tested for the first time. Given the recent localizations of two non-repeating FRBs (FRB 180924 and FRB 190523), we discuss a selection of FRB emission models and demonstrate how we can place constraints on key model parameters such as e magnetic field strength and age of the putative FRB-emitting neutron star. In particular, we focus on models related to compact binary merger events involving at least one neutron star, motivated by commonalities between the host galaxies of the FRBs and the hosts of such merger events/short gamma-ray bursts (SGRBs). We rule out the possibility that either FRB was produced during the final inspiral stage of a merging binary system. Where possible, we predict the light curve of electromagnetic emission associated with a given model and use it to recommend multiwavelength follow-up strategies that may help confirm or rule out models for future FRBs. In addition, we conduct a targeted sub-threshold search in Fermi Gamma-ray Burst Monitor data for potential SGRB candidates associated with either FRB, and show what a non-detection means for relevant models. The methodology presented in this study may be easily applied to future localized FRBs, and adapted to sources with possibly core-collapse supernova progenitors, to help constrain potential models for the FRB population at large.
Orbital Refinement and Stellar Properties for the HD 9446, HD 43691, and HD 179079 Planetary Systems

The Transit Ephemeris Refinement and Monitoring Survey is a project that aims to detect transits of intermediate-long period planets by refining orbital parameters of the known radial velocity planets using additional data from ground-based telescopes, calculating a revised transit ephemeris for the planet, then monitoring the planet host star during the predicted transit window. Here we present the results from three systems that had high probabilities of transiting planets: HD 9446 b and c, HD 43691 b, and HD 179079 b. We provide new radial velocity (RV) measurements that are then used to improve the orbital solution for the known planets. We search the RV data for indications of additional planets in orbit and find that HD 9446 shows a strong linear trend of 4. 8σ. Using the newly refined planet orbital solutions, which include a new best-fit solution for the orbital period of HD 9446 c, and an improved transit ephemerides, we found no evidence of transiting planets in the photometry for each system. Transits of HD 9446 b can be ruled out completely and transits HD 9446 c and HD 43691 b can be ruled out for impact parameters up to b = 0.5778 and b = 0.898, respectively, due to gaps in the photometry. A transit of HD 179079 b cannot be ruled out, however, due to the relatively small size of this planet compared to the large star and thus low signal to noise. We determine properties of the three host stars through spectroscopic analysis and find through photometric analysis that HD 9446 exhibits periodic variability.
Learning in the Sky: An Efficient 3D Placement of UAVs

Deployment of unmanned aerial vehicles (UAVs) as aerial base stations can deliver a fast and flexible solution for serving varying traffic demand. In order to adequately benefit of UAVs deployment, their efficient placement is of utmost importance, and requires to intelligently adapt to the environment changes. In this paper, we propose a learning-based mechanism for the three-dimensional deployment of UAVs assisting terrestrial cellular networks in the downlink. The problem is modeled as a non-cooperative game among UAVs in satisfaction form. To solve the game, we utilize a low complexity algorithm, in which unsatisfied UAVs update their locations based on a learning algorithm. Simulation results reveal that the proposed UAV placement algorithm yields significant performance gains up to about 52% and 74% in terms of throughput and the number of dropped users, respectively, compared to an optimized baseline algorithm.
SSFN -- Self Size-estimating Feed-forward Network with Low Complexity, Limited Need for Human Intervention, and Consistent Behaviour across Trials.

We design a self size-estimating feed-forward network (SSFN) using a joint optimization approach for estimation of number of layers, number of nodes and learning of weight matrices. The learning algorithm has a low computational complexity, preferably within few minutes using a laptop. In addition the algorithm has a limited need for human intervention to tune parameters. SSFN grows from a small-size network to a large-size network, guaranteeing a monotonically non-increasing cost with addition of nodes and layers. The learning approach uses judicious a combination of `lossless flow property' of some activation functions, convex optimization and instance of random matrix. Consistent performance -- low variation across Monte-Carlo trials -- is found for inference performance (classification accuracy) and estimation of network size.
KMT-2019-BLG-1339L: An M Dwarf with a Giant Planet or a Companion near the Planet/Brown Dwarf Boundary

We analyze KMT-2019-BLG-1339, a microlensing event with an obvious but incompletely resolved brief anomaly feature around the peak of the light curve. Although the origin of the anomaly is identified to be a companion to the lens with a low mass ratio q, the interpretation is subject to two different degeneracy types. The first type is the ambiguity in ρ, representing the angular source radius scaled to the angular radius of the Einstein ring, θE, and the other is the s ↔ s−1 degeneracy. The former type, “finite-source degeneracy,” causes ambiguities in both s and q, while the latter induces an ambiguity only in s. Here, s denotes the separation (in units of θE) in projection between the lens components. We estimate that the lens components have masses and according to the two solutions subject to the finite-source degeneracy, indicating that the lens comprises an M dwarf and a companion with a mass around the planet/brown dwarf boundary or a Jovian-mass planet. It is possible to lift the finite-source degeneracy by conducting future observations utilizing a high-resolution instrument because the relative lens–source proper motion predicted by the solutions is widely different.
What went wrong and when? Instance-wise Feature Importance for Time-series Models

Explanations of time series models are useful for high stakes applications like healthcare but have received little attention in machine learning literature. We propose FIT, a framework that evaluates the importance of observations for a multivariate time-series black-box model, by quantifying the shift in the predictive distribution over time. FIT defines the importance of an observation based on its contribution to the distributional shift under a KL-divergence that contrasts the predictive distribution against a counterfactual where the rest of the features are unobserved. We also demonstrate the need to control for time-dependent distribution shifts. We compare with state-of-the-art baselines on simulated and real-world clinical data and demonstrate that our approach is superior in identifying important time points and observations over the course of the time series.
SAFE: Scalable Automatic Feature Engineering Framework for Industrial Tasks

Machine learning techniques have been widely applied in Internet companies for various tasks, acting as an essential driving force, and feature engineering has been generally recognized as a crucial tache when constructing machine learning systems. Recently, a growing effort has been made to the development of automatic feature engineering methods, so that the substantial and tedious manual effort can be liberated. However, for industrial tasks, the efficiency and scalability of these methods are still far from satisfactory. In this paper, we proposed a staged method named SAFE (Scalable Automatic Feature Engineering), which can provide excellent efficiency and scalability, along with requisite interpretability and promising performance. Extensive experiments are conducted and the results show that the proposed method can provide prominent efficiency and competitive effectiveness when comparing with other methods. What’s more, the adequate scalability of the proposed method ensures it to be deployed in large scale industrial tasks.
Distributed Stochastic Gradient Descent and Convergence to Local Minima

In centralized settings, it is well known that stochastic gradient descent (SGD) avoids saddle points. However, similar guarantees are lacking for distributed first-order algorithms in nonconvex optimization.The paper studies distributed stochastic gradient descent (D-SGD)--a simple network-based implementation of SGD. Conditions under which D-SGD converges to local minima are studied. In particular, it is shown that, for each fixed initialization, with probability 1 we have that: (i) D-SGD converges to critical points of the objective and (ii) D-SGD avoids nondegenerate saddle points. To prove these results, we use ODE-based stochastic approximation techniques. The algorithm is approximated using a continuous-time ODE which is easier to study than the (discrete-time) algorithm. Results are first derived for the continuous-time process and then extended to the discrete-time algorithm. Consequently, the paper studies continuous-time distributed gradient descent (DGD) alongside D-SGD. Because the continuous-time process is easier to study, this approach allows for simplified proof techniques and builds important intuition that is obfuscated when studying the discrete-time process alone.
Knot selection in sparse Gaussian processes with a variational objective function

Sparse, knot‐based Gaussian processes have enjoyed considerable success as scalable approximations of full Gaussian processes. Certain sparse models can be derived through specific variational approximations to the true posterior, and knots can be selected to minimize the Kullback‐Leibler divergence between the approximate and true posterior. While this has been a successful approach, simultaneous optimization of knots can be slow due to the number of parameters being optimized. Furthermore, there have been few proposed methods for selecting the number of knots, and no experimental results exist in the literature. We propose using a one‐at‐a‐time knot selection algorithm based on Bayesian optimization to select the number and locations of knots. We showcase the competitive performance of this method relative to optimization of knots simultaneously on three benchmark datasets, but at a fraction of the computational cost.
TIME: A Transparent, Interpretable, Model-Adaptive and Explainable Neural Network for Dynamic Physical Processes

Partial Differential Equations are infinite dimensional encoded representations of physical processes. However, imbibing multiple observation data towards a coupled representation presents significant challenges. We present a fully convolutional architecture that captures the invariant structure of the domain to reconstruct the observable system. The proposed architecture is significantly low-weight compared to other networks for such problems. Our intent is to learn coupled dynamic processes interpreted as deviations from true kernels representing isolated processes for model-adaptivity. Experimental analysis shows that our architecture is robust and transparent in capturing process kernels and system anomalies. We also show that high weights representation is not only redundant but also impacts network interpretability. Our design is guided by domain knowledge, with isolated process representations serving as ground truths for verification. These allow us to identify redundant kernels and their manifestations in activation maps to guide better designs that are both interpretable and explainable unlike traditional deep-nets.
Toward an Effective CFT$_2$ from ${\cal N}=4$ Super Yang-Mills and Aspects of Hawking Radiation

Using ${\cal N}=4$ supersymmetric Yang-Mills theory we recover important aspects of the near-extremal thermodynamics of AdS$_5$ black holes including both the outer and the inner horizons with their corresponding entropy and energy. This $\mathcal{N}=4$ supersymmetric Yang-Mills theory approach to black hole thermodynamics leads to an effective CFT$_2$ interpretation similar to the work by Callan and Maldacena. We corroborate this effective CFT$_2$ by implementing a particular near-horizon limit that geometrizes the Virasoro algebras as asymptotic symmetries. Using the effective CFT$_2$ picture, we discuss aspects of the Hawking radiation rate for a region of the near-extremal AdS$_5$ black hole quantum evolution.
Whole-Body Control of a Mobile Manipulator using End-to-End Reinforcement Learning

Mobile manipulation is usually achieved by sequentially executing base and manipulator movements. This simplification, however, leads to a loss in efficiency and in some cases a reduction of workspace size. Even though different methods have been proposed to solve Whole-Body Control (WBC) online, they are either limited by a kinematic model or do not allow for reactive, online obstacle avoidance. In order to overcome these drawbacks, in this work, we propose an end-to-end Reinforcement Learning (RL) approach to WBC. We compared our learned controller against a state-of-the-art sampling-based method in simulation and achieved faster overall mission times. In addition, we validated the learned policy on our mobile manipulator RoyalPanda in challenging narrow corridor environments.
Workload Scheduling on heterogeneous Mobile Edge Cloud in 5G networks to Minimize SLA Violation

Smart devices have become an indispensable part of our lives and gain increasing applicability in almost every area. Latency-aware applications such as Augmented Reality (AR), autonomous driving, and online gaming demand more resources such as network bandwidth and computational capabilities. Since the traditional mobile networks cannot fulfill the required bandwidth and latency, Mobile Edge Cloud (MEC) emerged to provide cloud computing capabilities in the proximity of users on 5G networks. In this paper, we consider a heterogeneous MEC network with numerous mobile users that send their tasks to MEC servers. Each task has a maximum acceptable response time. Non-uniform distribution of users makes some MEC servers hotspots that cannot take more. A solution is to relocate the tasks among MEC servers, called Workload Migration. We formulate this problem of task scheduling as a mixed-integer non-linear optimization problem to minimize the number of Service Level Agreement (SLA) violations. Since solving this optimization problem has high computational complexity, we introduce a greedy algorithm called MESA, Migration Enabled Scheduling Algorithm, which reaches a near-optimal solution quickly. Our experiments show that in the term of SLA violation, MESA is only 8% and 11% far from the optimal choice on the average and the worst-case, respectively. Moreover, the migration enabled solution can reduce SLA violations by about 30% compare to assigning tasks to MEC servers without migration.
Weyl, Pontryagin, Euler, Eguchi and Freund

In a September 1976 PRL Eguchi and Freund considered two topological invariants: the Pontryagin number P∼∫d4xgR*R and the Euler number χ∼∫d4xgR*R* and posed the question: to what anomalies do they contribute? They found that P appears in the integrated divergence of the axial fermion number current, thus providing a novel topological interpretation of the anomaly found by Kimura in 1969 and Delbourgo and Salam in 1972. However, they found no analogous role for χ. This provoked my interest and, drawing on my April 1976 paper with Deser and Isham on gravitational Weyl anomalies, I was able to show that for conformal field theories the trace of the stress tensor depends on just two constants: gμν⟨Tμν⟩=1(4π)2(cF−aG) where F is the square of the Weyl tensor and ∫d4xgG/(4π)2 is the Euler number. For free CFTs with Ns massless fields of spin s 720c=6N0+18N1/2+72N1720a=2N0+11N1/2+124N1.
Cumulant-free closed-form formulas for some common (dis)similarities between densities of an exponential family

It is well-known that the Bhattacharyya, Hellinger, Kullback-Leibler, $\alpha$-divergences, and Jeffreys' divergences between densities belonging to a same exponential family have generic closed-form formulas relying on the strictly convex and real-analytic cumulant function characterizing the exponential family. In this work, we report (dis)similarity formulas which bypass the explicit use of the cumulant function and highlight the role of quasi-arithmetic means and their multivariate mean operator extensions. In practice, these cumulant-free formulas are handy when implementing these (dis)similarities using legacy Application Programming Interfaces (APIs) since our method requires only to partially factorize the densities canonically of the considered exponential family.
Efficient Uncertainty-aware Decision-making for Automated Driving Using Guided Branching

Decision-making in dense traffic scenarios is challenging for automated vehicles (AVs) due to potentially stochastic behaviors of other traffic participants and perception uncertainties (e.g., tracking noise and prediction errors, etc.). Although the partially observable Markov decision process (POMDP) provides a systematic way to incorporate these uncertainties, it quickly becomes computationally intractable when scaled to the real-world large-size problem. In this paper, we present an efficient uncertainty-aware decision-making (EUDM) framework, which generates long-term lateral and longitudinal behaviors in complex driving environments in real-time. The computation complexity is controlled to an appropriate level by two novel techniques, namely, the domain-specific closed-loop policy tree (DCP-Tree) structure and conditional focused branching (CFB) mechanism. The key idea is utilizing domain-specific expert knowledge to guide the branching in both action and intention space. The proposed framework is validated using both onboard sensing data captured by a real vehicle and an interactive multi-agent simulation platform. We also release the code of our framework to accommodate benchmarking.
A new measure for the comparison of signals without templates and the detection of gravitational polarization in the GW150914 event

We present a new measure for the comparison of unknown gravitational wave signals in two detectors, without recurring to an {\it a priori} template. We apply this measure to the LIGO data of the GW150914 event and discover that Hanford and Livingston observatories have recorded two different components of the gravitational wave polarization signal.
Dual link failure survivability with recovery time constraint: A Parallel cross connection backup route recovery strategy

In this paper, we proposed a fast recovery strategy for a dual link failure in elastic optical network. The elastic optical network is a promising solution to meet the next generation higher bandwidth demand. The survivability of high speed network is very crucial. As the network size increases the probability of the dual link failure and node failure also increases. Here, we proposed a parallel cross connection backup recovery strategy for dual link failure in the network. Proposed strategy shows lower bandwidth blocking probability (BBP), fast connection recovery, and bandwidth provisioning ratio when compared with the existing shared path protection (SPP) and dedicated path protection (DPP) approaches. Simulation is performed on ARPANET and COST239 topology networks.