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In this work, we address the limitation of surface fitting-based grasp planning algorithm, which primarily focuses on geometric alignment between the gripper and object surface while overlooking the stability of contact point distribution, often resulting in unstable grasps due to inadequate contact configurations. To overcome this limitation, we propose a novel surface fitting algorithm that integrates contact stability while preserving geometric compatibility. Inspired by human grasping behavior, our method disentangles the grasp pose optimization into three sequential steps: (1) rotation optimization to align contact normals, (2) translation refinement to improve Center of Mass (CoM) alignment, and (3) gripper aperture adjustment to optimize contact point distribution. We validate our approach through simulations on ten YCB dataset objects, demonstrating an 80% improvement in grasp success over conventional surface fitting methods that disregard contact stability. Further details can be found on our project page: https://tomoya-yamanokuchi.github.io/disf-project-page/., Comment: 18 pages, 6 figures, 1 table

Given an extensive, semi-infinite collection of multivariate coevolving data sequences (e.g., sensor/web activity streams) whose observations influence each other, how can we discover the time-changing cause-and-effect relationships in co-evolving data streams? How efficiently can we reveal dynamical patterns that allow us to forecast future values? In this paper, we present a novel streaming method, ModePlait, which is designed for modeling such causal relationships (i.e., time-evolving causality) in multivariate co-evolving data streams and forecasting their future values. The solution relies on characteristics of the causal relationships that evolve over time in accordance with the dynamic changes of exogenous variables. ModePlait has the following properties: (a) Effective: it discovers the time-evolving causality in multivariate co-evolving data streams by detecting the transitions of distinct dynamical patterns adaptively. (b) Accurate: it enables both the discovery of time-evolving causality and the forecasting of future values in a streaming fashion. (c) Scalable: our algorithm does not depend on data stream length and thus is applicable to very large sequences. Extensive experiments on both synthetic and real-world datasets demonstrate that our proposed model outperforms state-of-the-art methods in terms of discovering the time-evolving causality as well as forecasting., Comment: Accepted by KDD'25

Learning control policies for real-world robotic tasks often involve challenges such as multimodality, local discontinuities, and the need for computational efficiency. These challenges arise from the complexity of robotic environments, where multiple solutions may coexist. To address these issues, we propose Composite Gaussian Processes Flows (CGP-Flows), a novel semi-parametric model for robotic policy. CGP-Flows integrate Overlapping Mixtures of Gaussian Processes (OMGPs) with the Continuous Normalizing Flows (CNFs), enabling them to model complex policies addressing multimodality and local discontinuities. This hybrid approach retains the computational efficiency of OMGPs while incorporating the flexibility of CNFs. Experiments conducted in both simulated and real-world robotic tasks demonstrate that CGP-flows significantly improve performance in modeling control policies. In a simulation task, we confirmed that CGP-Flows had a higher success rate compared to the baseline method, and the success rate of GCP-Flow was significantly different from the success rate of other baselines in chi-square tests.

Symbolic task representation is a powerful tool for encoding human instructions and domain knowledge. Such instructions guide robots to accomplish diverse objectives and meet constraints through reinforcement learning (RL). Most existing methods are based on fixed mappings from environmental states to symbols. However, in inspection tasks, where equipment conditions must be evaluated from multiple perspectives to avoid errors of oversight, robots must fulfill the same symbol from different states. To help robots respond to flexible symbol mapping, we propose representing symbols and their mapping specifications separately within an RL policy. This approach imposes on RL policy to learn combinations of symbolic instructions and mapping specifications, requiring an efficient learning framework. To cope with this issue, we introduce an approach for learning flexible policies called Symbolic Instructions with Adjustable Mapping Specifications (SIAMS). This paper represents symbolic instructions using linear temporal logic (LTL), a formal language that can be easily integrated into RL. Our method addresses the diversified completion patterns of instructions by (1) a specification-aware state modulation, which embeds differences in mapping specifications in state features, and (2) a symbol-number-based task curriculum, which gradually provides tasks according to the learning's progress. Evaluations in 3D simulations with discrete and continuous action spaces demonstrate that our method outperforms context-aware multitask RL comparisons., Comment: 8 pages, Accepted by IEEE Robotics and Automation Letters (RA-L)

We present a method for generating hexagonal aperiodic tilings that are topologically equivalent to the triangular and dice lattices. This approach incorporates aperiodic sequences into the spacing between three sets of grids for the triangular lattice, resulting in "modulated triangular lattices". Subsequently, by replacing the triangles with rhombuses, parallelograms, or hexagons, modulated dice or honeycomb lattices are constructed. Using generalized Fibonacci, Thue-Morse, and tribonacci sequences, we demonstrate several examples of hexagonal aperiodic tilings. Structural analysis confirms that their diffraction patterns reflect the properties of the one-dimensional aperiodic sequences, namely pure point (Bragg peaks) or singular continuous. Our method establishes a general framework for constructing a broad range of hexagonal aperiodic systems, advancing aperiodic-crystal research into higher dimensions that were previously focused on one-dimensional aperiodic sequences., Comment: 13 pages, 19 figures

We investigate spin-flip scattering processes of electrons when they pass a chiral interface, which is the boundary between right- and left-handed one-dimensional chain. We construct a minimal $p$-orbital model consisting of the right- and left-handed one-dimensional threefold helical chains connected at $z=0$ with the nearest neighbor hopping and the spin-orbit coupling. The dynamics of spin-polarized wave packet passing through the interface, the Green's functions, and electronic states near the interface are analyzed numerically. We find that the microscopic structure of the interface is important and this strongly affects the local electronic orbital state. This in addition to the spin-orbit coupling determines whether the spin flip occurs or not at the chiral interface and suggests a possible spin transport control by the orbital configuration at the chiral interface., Comment: 8 pages, 7 figures

A previously established frequency distribution model combining a log-normal distribution with a logarithmic equation describes fluctuations in the email size during send requests. Although the frequency distribution fit was considered satisfactory, the underlying mechanism driving this distribution remains inadequately explained. To address this gap, this study introduced a novel email-send model that characterizes the sending process as an exponential function modulated by noise from a normal distribution. This model is consistent with both the observed frequency distribution and the previously proposed frequency distribution model., Comment: 12 pages, 7 figures

We examined the anomalies in the light curves of the lensing events MOA-2022-BLG-033, KMT-2023-BLG-0119, and KMT-2023-BLG-1896. We conducted detailed modeling of the light curves to uncover the nature of the anomalies. This modeling revealed that all signals originated from planetary companions to the primary lens. The planet-to-host mass ratios are very low: $q\sim 7.5\times 10^{-5}$ for MOA-2022-BLG-033, $q\sim 3.6\times 10^{-4}$ for KMT-2023-BLG-0119, and $q\sim 6.9\times 10^{-5}$ for KMT-2023-BLG-1896. The anomalies occurred as the source passed through the negative deviation region behind the central caustic along the planet-host axis. The solutions are subject to a common inner-outer degeneracy, resulting in variations in estimating the projected planet-host separation. For KMT-2023-BLG-1896, although the planetary scenario provides the best explanation of the anomaly, the binary companion scenario is marginally possible. We estimate the physical parameters of the planetary systems through Bayesian analyses based on the lensing observables. The analysis identifies MOA-2022-BLG-033L as a planetary system with an ice giant, approximately 12 times the mass of Earth, orbiting an early M dwarf star. The companion of KMT-2023-BLG-1896L is also an ice giant, with a mass around 16 Earth masses, orbiting a mid-K-type main-sequence star. The companion of KMT-2023-BLG-0119L, which has a mass about the mass of Saturn, orbits a mid-K-type dwarf star. The lens for MOA-2022-BLG-033 is highly likely to be located in the disk, whereas for the other events, the probabilities of the lens being in the disk or the bulge are roughly comparable., Comment: 9 pages, 10 figures, 6 tables

Split computing ($\neq$ split learning) is a promising approach to deep learning models for resource-constrained edge computing systems, where weak sensor (mobile) devices are wirelessly connected to stronger edge servers through channels with limited communication capacity. State-of-theart work on split computing presents methods for single tasks such as image classification, object detection, or semantic segmentation. The application of existing methods to multitask problems degrades model accuracy and/or significantly increase runtime latency. In this study, we propose Ladon, the first multi-task-head supervised compression model for multi-task split computing. Experimental results show that the multi-task supervised compression model either outperformed or rivaled strong lightweight baseline models in terms of predictive performance for ILSVRC 2012, COCO 2017, and PASCAL VOC 2012 datasets while learning compressed representations at its early layers. Furthermore, our models reduced end-to-end latency (by up to 95.4%) and energy consumption of mobile devices (by up to 88.2%) in multi-task split computing scenarios., Comment: Accepted at WACV 2025. Code and models are available at https://github.com/yoshitomo-matsubara/ladon-multi-task-sc2

This paper investigates a novel nonlinear update rule based on temporal difference (TD) errors in reinforcement learning (RL). The update rule in the standard RL states that the TD error is linearly proportional to the degree of updates, treating all rewards equally without no bias. On the other hand, the recent biological studies revealed that there are nonlinearities in the TD error and the degree of updates, biasing policies optimistic or pessimistic. Such biases in learning due to nonlinearities are expected to be useful and intentionally leftover features in biological learning. Therefore, this research explores a theoretical framework that can leverage the nonlinearity between the degree of the update and TD errors. To this end, we focus on a control as inference framework, since it is known as a generalized formulation encompassing various RL and optimal control methods. In particular, we investigate the uncomputable nonlinear term needed to be approximately excluded in the derivation of the standard RL from control as inference. By analyzing it, Weber-Fechner law (WFL) is found, namely, perception (a.k.a. the degree of updates) in response to stimulus change (a.k.a. TD error) is attenuated by increase in the stimulus intensity (a.k.a. the value function). To numerically reveal the utilities of WFL on RL, we then propose a practical implementation using a reward-punishment framework and modifying the definition of optimality. Analysis of this implementation reveals that two utilities can be expected i) to increase rewards to a certain level early, and ii) to sufficiently suppress punishment. We finally investigate and discuss the expected utilities through simulations and robot experiments. As a result, the proposed RL algorithm with WFL shows the expected utilities that accelerate the reward-maximizing startup and continue to suppress punishments during learning., Comment: 36 pages 9 figures

Deep learning models have recently shown great success in classifying epileptic patients using EEG recordings. Unfortunately, classification-based methods lack a sound mechanism to detect the onset of seizure events. In this work, we propose a two-stage framework, \method, that explicitly models seizure onset through a novel task formulation of subsequence clustering. Given an EEG sequence, the framework first learns a set of second-level embeddings with label supervision. It then employs model-based clustering to explicitly capture long-term temporal dependencies in EEG sequences and identify meaningful subsequences. Epochs within a subsequence share a common cluster assignment (normal or seizure), with cluster or state transitions representing successful onset detections. Extensive experiments on three datasets demonstrate that our method can correct misclassifications, achieving 5%-11% classification improvements over other baselines and accurately detecting seizure onsets., Comment: Accepted at AAAI 2025

Phase transitions are fundamental in nature. A small parameter change near a critical point leads to a qualitative change in system properties. Across a regular phase transition, the system remains in thermal equilibrium and, therefore, experiences a change of static properties, like the emergence of a magnetisation upon cooling a ferromagnet below the Curie temperature. When driving a system far from equilibrium, novel, otherwise inaccessible quantum states of matter may arise. Such states are typically non-Hermitian, that is, their dynamics break time-reversal symmetry, a basic law of equilibrium physics. Phase transitions in non-Hermitian systems are of fundamentally new nature in that the dynamical behaviour rather than static properties may undergo a qualitative change at a critical, here called exceptional point. Here we experimentally realize a non-Hermitian phase transition in a bulk condensed-matter system. Optical excitation creates charge carriers in the ferromagnetic semiconductor EuO. In a temperature-dependent interplay with the Hermitian transition to ferromagnetic order, a non-Hermitian change of the relaxation dynamics occurs, manifesting in our time-resolved reflection data as a transition from bi-exponential real to single-exponential complex decay. Our theory models this behavior and predicts non-Hermitian phase transitions for a large class of condensed-matter systems, where they may be exploited to sensitively control bulk-dynamic properties., Comment: 15 pages, 10 figures

Automating object shaping by grinding with a robot is a crucial industrial process that involves removing material with a rotating grinding belt. This process generates removal resistance depending on such process conditions as material type, removal volume, and robot grinding posture, all of which complicate the analytical modeling of shape transitions. Additionally, a data-driven approach based on real-world data is challenging due to high data collection costs and the irreversible nature of the process. This paper proposes a Cutting Sequence Diffuser (CSD) for object shaping by grinding. The CSD, which only requires simple simulation data for model learning, offers an efficient way to plan long-horizon action sequences transferable to the real world. Our method designs a smooth action space with constrained small removal volumes to suppress the complexity of the shape transitions caused by removal resistance, thus reducing the reality gap in simulations. Moreover, by using a diffusion model to generate long-horizon action sequences, our approach reduces the planning time and allows for grinding the target shape while adhering to the constraints of a small removal volume per step. Through evaluations in both simulation and real robot experiments, we confirmed that our CSD was effective for grinding to different materials and various target shapes in a short time., Comment: 8 pages, Accepted by Robotics and Automation Letter

We present a comprehensive analysis of a planetary microlensing event OGLE-2015-BLG-1609. The planetary anomaly was detected by two survey telescopes, OGLE and MOA. Each of these surveys collected enough data over the planetary anomaly to allow for an unambiguous planet detection. Such survey detections of planetary anomalies are needed to build a robust sample of planets that could improve studies on the microlensing planetary occurrence rate by reducing biases and statistical uncertainties. In this work, we examined different methods for modeling microlensing events using individual datasets, particularly we incorporated a Galactic model prior to better constrain poorly defined microlensing parallax. Ultimately, we fitted a comprehensive model to all available data, identifying three potential typologies, with two showing comparably high Bayesian evidence. Our analysis indicates that the host of the planet is a brown dwarf with a probability of 34%, or a low-mass stellar object (M-dwarf) with the probability of 66%., Comment: 12 pages, 7 figures

The appearance of broken time-reversal symmetry (TRS) in superconducting states is an intriguing issue in solid-state physics because of the incompatibility of the spontaneous magnetic field and the Meissner effect. We identify broken TRS in Pd-doped CaAgP (CaAg$_{0.9}$Pd$_{0.1}$P) by tunneling spectroscopy through the magnetic field response of conductance spectra. CaAg$_{0.9}$Pd$_{0.1}$P is a nodal-line semimetal with exotic electronic states such as drumhead surface states and surface superconductivity. Tunneling conductance spectra acquired at the side surfaces of CaAg$_{0.9}$Pd$_{0.1}$P under an applied magnetic field exhibit broad zero-bias peaks with small asymmetric structures. Surprisingly, the asymmetric structures are reversed exactly by flipping the field direction. On the basis of an analysis which stands on the formula of tunneling junctions for unconventional superconductors, these results are consistent with the pair potential of the superconductivity breaks the TRS and is strongly coupled to an external magnetic field. We reveal the novel character of superconducting nodal-line semimetals by developing the TRS sensitivity of tunneling spectroscopy. Our results serve as an exploration of broken TRS in superconducting states realized in topological materials., Comment: 12 pages, 4 figures

We study the problem of modeling a non-linear dynamical system when given a time series by deriving equations directly from the data. Despite the fact that time series data are given as input, models for dynamics and estimation algorithms that incorporate long-term temporal dependencies are largely absent from existing studies. In this paper, we introduce a latent state to allow time-dependent modeling and formulate this problem as a dynamics estimation problem in latent states. We face multiple technical challenges, including (1) modeling latent non-linear dynamics and (2) solving circular dependencies caused by the presence of latent states. To tackle these challenging problems, we propose a new method, Latent Non-Linear equation modeling (LaNoLem), that can model a latent non-linear dynamical system and a novel alternating minimization algorithm for effectively estimating latent states and model parameters. In addition, we introduce criteria to control model complexity without human intervention. Compared with the state-of-the-art model, LaNoLem achieves competitive performance for estimating dynamics while outperforming other methods in prediction., Comment: Accepted by AAAI'25

In earthwork and construction, excavators often encounter large rocks mixed with various soil conditions, requiring skilled operators. This paper presents a framework for achieving autonomous excavation using reinforcement learning (RL) through a rock excavation simulator. In the simulation, resolution can be defined by the particle size/number in the whole soil space. Fine-resolution simulations closely mimic real-world behavior but demand significant calculation time and challenging sample collection, while coarse-resolution simulations enable faster sample collection but deviate from real-world behavior. To combine the advantages of both resolutions, we explore using policies developed in coarse-resolution simulations for pre-training in fine-resolution simulations. To this end, we propose a novel policy learning framework called Progressive-Resolution Policy Distillation (PRPD), which progressively transfers policies through some middle-resolution simulations with conservative policy transfer to avoid domain gaps that could lead to policy transfer failure. Validation in a rock excavation simulator and nine real-world rock environments demonstrated that PRPD reduced sampling time to less than 1/7 while maintaining task success rates comparable to those achieved through policy learning in a fine-resolution simulation.

We introduce a novel method for human shape and pose recovery that can fully leverage multiple static views. We target fixed-multiview people monitoring, including elderly care and safety monitoring, in which calibrated cameras can be installed at the corners of a room or an open space but whose configuration may vary depending on the environment. Our key idea is to formulate it as neural optimization. We achieve this with HeatFormer, a neural optimizer that iteratively refines the SMPL parameters given multiview images, which is fundamentally agonistic to the configuration of views. HeatFormer realizes this SMPL parameter estimation as heat map generation and alignment with a novel transformer encoder and decoder. We demonstrate the effectiveness of HeatFormer including its accuracy, robustness to occlusion, and generalizability through an extensive set of experiments. We believe HeatFormer can serve a key role in passive human behavior modeling.

The Euler discriminant of a family of very affine varieties is defined as the locus where the Euler characteristic drops. In this work, we study the Euler discriminant of families of complements of hyperplanes. We prove that the Euler discriminant is a hypersurface in the space of coefficients, and provide its defining equation in two cases: (1) when the coefficients are generic, and (2) when they are constrained to a proper subspace. In the generic case, we show that the multiplicities of the components can be recovered combinatorially. This analysis also recovers the singularities of an Euler integral. In the appendix, we discuss a relation to cosmological correlators., Comment: 33 pages. Fixed printing issue in Figure 5

Cooperative grasping and transportation require effective coordination to complete the task. This study focuses on the approach leveraging force-sensing feedback, where robots use sensors to detect forces applied by others on an object to achieve coordination. Unlike explicit communication, it avoids delays and interruptions; however, force-sensing is highly sensitive and prone to interference from variations in grasping environment, such as changes in grasping force, grasping pose, object size and geometry, which can interfere with force signals, subsequently undermining coordination. We propose multi-agent reinforcement learning (MARL) with ternary force representation, a force representation that maintains consistent representation against variations in grasping environment. The simulation and real-world experiments demonstrate the robustness of the proposed method to changes in grasping force, object size and geometry as well as inherent sim2real gap.

Mobile grasping enhances manipulation efficiency by utilizing robots' mobility. This study aims to enable a commercial off-the-shelf robot for mobile grasping, requiring precise timing and pose adjustments. Self-supervised learning can develop a generalizable policy to adjust the robot's velocity and determine grasp position and orientation based on the target object's shape and pose. Due to mobile grasping's complexity, action primitivization and step-by-step learning are crucial to avoid data sparsity in learning from trial and error. This study simplifies mobile grasping into two grasp action primitives and a moving action primitive, which can be operated with limited degrees of freedom for the manipulator. This study introduces three fully convolutional neural network (FCN) models to predict static grasp primitive, dynamic grasp primitive, and residual moving velocity error from visual inputs. A two-stage grasp learning approach facilitates seamless FCN model learning. The ablation study demonstrated that the proposed method achieved the highest grasping accuracy and pick-and-place efficiency. Furthermore, randomizing object shapes and environments in the simulation effectively achieved generalizable mobile grasping., Comment: 8 pages, 9 figures

We carried out a project involving the systematic analysis of microlensing data from the Korea Microlensing Telescope Network survey. The aim of this project is to identify lensing events with complex anomaly features that are difficult to explain using standard binary-lens or binary-source models. Our investigation reveals that the light curves of microlensing events KMT-2021-BLG-0284, KMT-2022-BLG-2480, and KMT-2024-BLG-0412 display highly complex patterns with three or more anomaly features. These features cannot be adequately explained by a binary-lens (2L1S) model alone. However, the 2L1S model can effectively describe certain segments of the light curve. By incorporating an additional source into the modeling, we identified a comprehensive model that accounts for all the observed anomaly features. Bayesian analysis, based on constraints provided by lensing observables, indicates that the lenses of KMT-2021-BLG-0284 and KMT-2024-BLG-0412 are binary systems composed of M dwarfs. For KMT-2022-BLG-2480, the primary lens is an early K-type main-sequence star with an M dwarf companion. The lenses of KMT-2021-BLG-0284 and KMT-2024-BLG-0412 are likely located in the bulge, whereas the lens of KMT-2022-BLG-2480 is more likely situated in the disk. In all events, the binary stars of the sources have similar magnitudes due to a detection bias favoring binary source events with a relatively bright secondary source star, which increases detection efficiency., Comment: 9 pages, 9 figures

We develop a new theoretical scheme for charge transport and thermoelectric response in two-dimensional disordered systems exhibiting crossover from weak localization (WL) to strong localization (SL). The scheme is based on the scaling theory for Anderson localization combined with the Kubo-Luttinger theory. Key aspects of the scheme include introducing a unified $\beta$ function that seamlessly connects the WL and SL regimes, as well as describing the temperature ($T$) dependence of the conductance from high to low $T$ regions on the basis of the dephasing length. We found that the Seebeck coefficient, $S$, behaves as $S\propto T$ in the WL limit and as $S\propto T^{1-p}$ ($p < 1$) in the SL limit, both with possible logarithmic corrections. The scheme is applied to analyze experimental data for thin films of the p-type organic semiconductor poly[2,5-bis(3-alkylthiophen-2-yl)thieno(3,2-b)thiophene] (PBTTT)., Comment: 6 pages, 2 figures

The magnetised near detector (ND280) of the T2K long-baseline neutrino oscillation experiment has been recently upgraded aiming to satisfy the requirement of reducing the systematic uncertainty from measuring the neutrinonucleus interaction cross section, which is the largest systematic uncertainty in the search for leptonic charge-parity symmetry violation. A key component of the upgrade is SuperFGD, a 3D segmented plastic scintillator detector made of approximately 2,000,000 optically-isolated 1 cm3 cubes. It will provide a 3D image of GeV neutrino interactions by combining tracking and stopping power measurements of final state particles with sub-nanosecond time resolution. The performance of SuperFGD is characterized by the precision of its response to charged particles as well as the systematic effects that might affect the physics measurements. Hence, a detailed Geant4 based optical simulation of the SuperFGD building block, i.e. a plastic scintillating cube read out by three wavelength shifting fibers, has been developed and validated with the different datasets collected in various beam tests. In this manuscript the description of the optical model as well as the comparison with data are reported., Comment: 31 pages, 15 figures

We present measurements of the microlensing optical depth and event rate toward the Galactic bulge using the dataset from the 2006--2014 MOA-II survey, which covers 22 bulge fields spanning ~42 deg^2 between -5 deg < l < 10 deg and -7 deg < b < -1 deg. In the central region with |l|<5 deg, we estimate an optical depth of {\tau} = [1.75+-0.04]*10^-6exp[(0.34+-0.02)(3 deg-|b|)] and an event rate of {\Gamma} = [16.08+-0.28]*10^-6exp[(0.44+-0.02)(3 deg-|b|)] star^-1 year^-1 using a sample consisting of 3525 microlensing events, with Einstein radius crossing times of tE < 760 days and source star magnitude of IsWe confirm our results are consistent with the latest measurements from OGLE-IV 8 year dataset (Mr\'oz et al. 2019). We find our result is inconsistent with a prediction based on Galactic models, especially in the central region with |b|<3 deg. These results can be used to improve the Galactic bulge model, and more central regions can be further elucidated by future microlensing experiments, such as The PRime-focus Infrared Microlensing Experiment (PRIME) and Nancy Grace Roman Space Telescope.

Retrieving gene functional networks from knowledge databases presents a challenge due to the mismatch between disease networks and subtype-specific variations. Current solutions, including statistical and deep learning methods, often fail to effectively integrate gene interaction knowledge from databases or explicitly learn subtype-specific interactions. To address this mismatch, we propose GeSubNet, which learns a unified representation capable of predicting gene interactions while distinguishing between different disease subtypes. Graphs generated by such representations can be considered subtype-specific networks. GeSubNet is a multi-step representation learning framework with three modules: First, a deep generative model learns distinct disease subtypes from patient gene expression profiles. Second, a graph neural network captures representations of prior gene networks from knowledge databases, ensuring accurate physical gene interactions. Finally, we integrate these two representations using an inference loss that leverages graph generation capabilities, conditioned on the patient separation loss, to refine subtype-specific information in the learned representation. GeSubNet consistently outperforms traditional methods, with average improvements of 30.6%, 21.0%, 20.1%, and 56.6% across four graph evaluation metrics, averaged over four cancer datasets. Particularly, we conduct a biological simulation experiment to assess how the behavior of selected genes from over 11,000 candidates affects subtypes or patient distributions. The results show that the generated network has the potential to identify subtype-specific genes with an 83% likelihood of impacting patient distribution shifts. The GeSubNet resource is available: https://anonymous.4open.science/r/GeSubNet/, Comment: Under review as a conference paper at ICLR 2025

Deep learning has achieved great success in modeling dynamical systems, providing data-driven simulators to predict complex phenomena, even without known governing equations. However, existing models have two major limitations: their narrow focus on mechanical systems and their tendency to treat systems as monolithic. These limitations reduce their applicability to dynamical systems in other domains, such as electrical and hydraulic systems, and to coupled systems. To address these limitations, we propose Poisson-Dirac Neural Networks (PoDiNNs), a novel framework based on the Dirac structure that unifies the port-Hamiltonian and Poisson formulations from geometric mechanics. This framework enables a unified representation of various dynamical systems across multiple domains as well as their interactions and degeneracies arising from couplings. Our experiments demonstrate that PoDiNNs offer improved accuracy and interpretability in modeling unknown coupled dynamical systems from data.

While end-to-end multi-channel electroencephalography (EEG) learning approaches have shown significant promise, their applicability is often constrained in neurological diagnostics, such as intracranial EEG resources. When provided with a single-channel EEG, how can we learn representations that are robust to multi-channels and scalable across varied tasks, such as seizure prediction? In this paper, we present SplitSEE, a structurally splittable framework designed for effective temporal-frequency representation learning in single-channel EEG. The key concept of SplitSEE is a self-supervised framework incorporating a deep clustering task. Given an EEG, we argue that the time and frequency domains are two distinct perspectives, and hence, learned representations should share the same cluster assignment. To this end, we first propose two domain-specific modules that independently learn domain-specific representation and address the temporal-frequency tradeoff issue in conventional spectrogram-based methods. Then, we introduce a novel clustering loss to measure the information similarity. This encourages representations from both domains to coherently describe the same input by assigning them a consistent cluster. SplitSEE leverages a pre-training-to-fine-tuning framework within a splittable architecture and has following properties: (a) Effectiveness: it learns representations solely from single-channel EEG but has even outperformed multi-channel baselines. (b) Robustness: it shows the capacity to adapt across different channels with low performance variance. Superior performance is also achieved with our collected clinical dataset. (c) Scalability: With just one fine-tuning epoch, SplitSEE achieves high and stable performance using partial model layers., Comment: This paper has been accepted by ICDM2024

The problem of uncertainty is a feature of real world robotics problems and any control framework must contend with it in order to succeed in real applications tasks. Reinforcement Learning is no different, and epistemic uncertainty arising from model uncertainty or misspecification is a challenge well captured by the sim-to-real gap. A simple solution to this issue is domain randomization (DR), which unfortunately can result in conservative agents. As a remedy to this conservativeness, the use of universal policies that take additional information about the randomized domain has risen as an alternative solution, along with recurrent neural network-based controllers. Uncertainty-aware universal policies present a particularly compelling solution able to account for system identification uncertainties during deployment. In this paper, we reveal that the challenge of efficiently optimizing uncertainty-aware policies can be fundamentally reframed as solving the convex coverage set (CCS) problem within a multi-objective reinforcement learning (MORL) context. By introducing a novel Markov decision process (MDP) framework where each domain's performance is treated as an independent objective, we unify the training of uncertainty-aware policies with MORL approaches. This connection enables the application of MORL algorithms for domain randomization (DR), allowing for more efficient policy optimization. To illustrate this, we focus on the linear utility function, which aligns with the expectation in DR formulations, and propose a series of algorithms adapted from the MORL literature to solve the CCS, demonstrating their ability to enhance the performance of uncertainty-aware policies., Comment: 27 pages, 9 figures, 12 tables, under review by IJRR

Recent normalization-based methods have shown great success in tackling the distribution shift issue, facilitating non-stationary time series forecasting. Since these methods operate in the time domain, they may fail to fully capture the dynamic patterns that are more apparent in the frequency domain, leading to suboptimal results. This paper first theoretically analyzes how normalization methods affect frequency components. We prove that the current normalization methods that operate in the time domain uniformly scale non-zero frequencies, and thus, they struggle to determine components that contribute to more robust forecasting. Therefore, we propose FredNormer, which observes datasets from a frequency perspective and adaptively up-weights the key frequency components. To this end, FredNormer consists of two components: a statistical metric that normalizes the input samples based on their frequency stability and a learnable weighting layer that adjusts stability and introduces sample-specific variations. Notably, FredNormer is a plug-and-play module, which does not compromise the efficiency compared to existing normalization methods. Extensive experiments show that FredNormer improves the averaged MSE of backbone forecasting models by 33.3% and 55.3% on the ETTm2 dataset. Compared to the baseline normalization methods, FredNormer achieves 18 top-1 results and 6 top-2 results out of 28 settings.

The electric dipole strength distribution in $^{58}$Ni between 6 and 20 MeV has been determined from proton inelastic scattering experiments at very forward angles at RCNP, Osaka. The experimental data are rather well reproduced by quasiparticle random-phase approximation calculations including vibration coupling, despite a mild dependence on the adopted Skyrme interaction. They allow an estimate of the experimentally inaccessible high-energy contribution above 20 MeV, leading to an electric dipole polarizability $\alpha_\mathrm{D}(^{58}{\rm Ni}) = 3.48(31)$ fm$^3$. This serves as a test case for recent extensions of coupled-cluster calculations with chiral effective field theory interactions to nuclei with two nucleons on top of a closed-shell system., Comment: 8 pages, 4 figures

A search for proton decay into $e^+/\mu^+$ and a $\eta$ meson has been performed using data from a 0.373 Mton$\cdot$year exposure (6050.3 live days) of Super-Kamiokande. Compared to previous searches this work introduces an improved model of the intranuclear $\eta$ interaction cross section, resulting in a factor of two reduction in uncertainties from this source and $\sim$10\% increase in signal efficiency. No significant data excess was found above the expected number of atmospheric neutrino background events resulting in no indication of proton decay into either mode. Lower limits on the proton partial lifetime of $1.4\times\mathrm{10^{34}~years}$ for $p\rightarrow e^+\eta$ and $7.3\times\mathrm{10^{33}~years}$ for $p\rightarrow \mu^+\eta$ at the 90$\%$ C.L. were set. These limits are around 1.5 times longer than our previous study and are the most stringent to date.

Multivariate time series data suffer from the problem of missing values, which hinders the application of many analytical methods. To achieve the accurate imputation of these missing values, exploiting inter-correlation by employing the relationships between sequences (i.e., a network) is as important as the use of temporal dependency, since a sequence normally correlates with other sequences. Moreover, exploiting an adequate network depending on time is also necessary since the network varies over time. However, in real-world scenarios, we normally know neither the network structure nor when the network changes beforehand. Here, we propose a missing value imputation method for multivariate time series, namely MissNet, that is designed to exploit temporal dependency with a state-space model and inter-correlation by switching sparse networks. The network encodes conditional independence between features, which helps us understand the important relationships for imputation visually. Our algorithm, which scales linearly with reference to the length of the data, alternatively infers networks and fills in missing values using the networks while discovering the switching of the networks. Extensive experiments demonstrate that MissNet outperforms the state-of-the-art algorithms for multivariate time series imputation and provides interpretable results., Comment: Accepted by KDD 2024

Machine learning has shown great potential in the field of cancer multi-omics studies, offering incredible opportunities for advancing precision medicine. However, the challenges associated with dataset curation and task formulation pose significant hurdles, especially for researchers lacking a biomedical background. Here, we introduce the CMOB, the first large-scale cancer multi-omics benchmark integrates the TCGA platform, making data resources accessible and usable for machine learning researchers without significant preparation and expertise.To date, CMOB includes a collection of 20 cancer multi-omics datasets covering 32 cancers, accompanied by a systematic data processing pipeline. CMOB provides well-processed dataset versions to support 20 meaningful tasks in four studies, with a collection of benchmarks. We also integrate CMOB with two complementary resources and various biological tools to explore broader research avenues.All resources are open-accessible with user-friendly and compatible integration scripts that enable non-experts to easily incorporate this complementary information for various tasks. We conduct extensive experiments on selected datasets to offer recommendations on suitable machine learning baselines for specific applications. Through CMOB, we aim to facilitate algorithmic advances and hasten the development, validation, and clinical translation of machine-learning models for personalized cancer treatments. CMOB is available on GitHub (\url{https://github.com/chenzRG/Cancer-Multi-Omics-Benchmark}).

The development of good thermoelectric materials exhibiting high $ZT$ (=$\frac{PF}{\kappa} T$) requires maximizing power factor, $PF$, mainly governed by electrons, and minimizing thermal conductivity, $\kappa$, associated not only with electrons but also with phonons. In the present work, we focus on the GeTe and Mg$_3$Sb$_2$ as high $ZT$ materials with inhomogeneous structures and analyze both electrical conductivity, $L_{11}$, and Seebeck coefficient, $S$, with help of Sommerfeld-Bethe formula, resulting in understanding the temperature dependence of $PF$ and the identification of electrons contribution to thermal conductivity, $\kappa_{\rm el}$. Comparing the obtained $\kappa_{\rm el}$ and experimentally measured $\kappa$, the temperature dependence of phonons contribution to thermal conductivity, $\kappa_{\rm ph}=\kappa-\kappa_{\rm el}$, is inferred and analyzed based on the formula by Holland. Comparison of the GeTe and Mg$_3$Sb$_2$ with different types of crystal structures, i.e., GeTe being of a semiordered zigzag nanostructure like a disrupted herringbone structure while Mg$_3$Sb$_2$ of rather uniform amorphous structure, discloses that size effects on temperature dependence of $\kappa_{\rm ph}$ is large in the former, while very small in the latter. Hence, it is concluded that not only the size of the grain but also its shape has an important influence on $\kappa_{\rm ph}$ and then $ZT$.

A neurochip is a device that reproduces the signal processing mechanisms of brain neurons and calculates Spiking Neural Networks (SNNs) with low power consumption and at high speed. Thus, neurochips are attracting attention from edge robot applications, which suffer from limited battery capacity. This paper aims to achieve deep reinforcement learning (DRL) that acquires SNN policies suitable for neurochip implementation. Since DRL requires a complex function approximation, we focus on conversion techniques from Floating Point NN (FPNN) because it is one of the most feasible SNN techniques. However, DRL requires conversions to SNNs for every policy update to collect the learning samples for a DRL-learning cycle, which updates the FPNN policy and collects the SNN policy samples. Accumulative conversion errors can significantly degrade the performance of the SNN policies. We propose Robust Iterative Value Conversion (RIVC) as a DRL that incorporates conversion error reduction and robustness to conversion errors. To reduce them, FPNN is optimized with the same number of quantization bits as an SNN. The FPNN output is not significantly changed by quantization. To robustify the conversion error, an FPNN policy that is applied with quantization is updated to increase the gap between the probability of selecting the optimal action and other actions. This step prevents unexpected replacements of the policy's optimal actions. We verified RIVC's effectiveness on a neurochip-driven robot. The results showed that RIVC consumed 1/15 times less power and increased the calculation speed by five times more than an edge CPU (quad-core ARM Cortex-A72). The previous framework with no countermeasures against conversion errors failed to train the policies. Videos from our experiments are available: https://youtu.be/Q5Z0-BvK1Tc., Comment: Accepted by Robotics and Autonomous Systems

Building on previous works to construct a homogeneous sample of brown dwarfs in binary systems, we investigate microlensing events detected by the Korea Microlensing Telescope Network (KMTNet) survey during the 2022 and 2023 seasons. Given the difficulty in distinguishing brown-dwarf events from those produced by binary lenses with nearly equal-mass components, we analyze all lensing events detected during the seasons that exhibit anomalies characteristic of binary-lens systems. Using the same criteria consistently applied in previous studies, we identify six additional brown dwarf candidates through the analysis of lensing events KMT-2022-BLG-0412, KMT-2022-BLG-2286, KMT-2023-BLG-0201, KMT-2023-BLG-0601, KMT-2023-BLG-1684, and KMT-2023-BLG-1743. An examination of the mass posteriors shows that the median mass of the lens companions ranges from 0.02 $M_\odot$ to 0.05 $M_\odot$, indicating that these companions fall within the brown-dwarf mass range. The mass of the primary lenses ranges from 0.11 $M_\odot$ to 0.68 $M_\odot$, indicating that they are low-mass stars with substantially lower masses compared to the Sun., Comment: 13 pages, 17 figures, 12 tables

The medical community believes binary medical event outcomes in EHR data contain sufficient information for making a sensible recommendation. However, there are two challenges to effectively utilizing such data: (1) modeling the relationship between massive 0,1 event outcomes is difficult, even with expert knowledge; (2) in practice, learning can be stalled by the binary values since the equally important 0 entries propagate no learning signals. Currently, there is a large gap between the assumed sufficient information and the reality that no promising results have been shown by utilizing solely the binary data: visiting or secondary information is often necessary to reach acceptable performance. In this paper, we attempt to build the first successful binary EHR data-oriented drug recommendation system by tackling the two difficulties, making sensible drug recommendations solely using the binary EHR medical records. To this end, we take a statistical perspective to view the EHR data as a sample from its cohorts and transform them into continuous Bernoulli probabilities. The transformed entries not only model a deterministic binary event with a distribution but also allow reflecting \emph{event-event} relationship by conditional probability. A graph neural network is learned on top of the transformation. It captures event-event correlations while emphasizing \emph{event-to-patient} features. Extensive results demonstrate that the proposed method achieves state-of-the-art performance on large-scale databases, outperforming baseline methods that use secondary information by a large margin. The source code is available at \url{https://github.com/chenzRG/BEHRMecom}

Atoms in Rydberg states are an important building block for emerging quantum technologies. While the excitation to the Rydberg orbitals are typically achieved in more than tens of nanoseconds, the physical limit is in fact much faster, at the ten picoseconds level. Here, we tackle such ultrafast Rydberg excitation of a Rubidium atom by designing a dedicated pulsed laser system generating 480 nm pulses of 10 ps duration. In particular, we improved upon our previous design by using an injection-seeded optical parametric amplifier (OPA) to obtain stable pulsed energy, decreasing the fluctuation from 30 % to 6 %. We then succeeded in ultrafast excitation of Rydberg atoms with excitation probability of ~90 %, not limited anymore by energy fluctuation but rather by the atomic state preparation, addressable in future works. This achievement broadens the range of applications of Rydberg atoms.

In the pursuit of understanding the population of stellar remnants within the Milky Way, we analyze the sample of $\sim 950$ microlensing events observed by the Spitzer Space Telescope between 2014 and 2019. In this study we focus on a sub-sample of nine microlensing events, selected based on their long timescales, small microlensing parallaxes and joint observations by the Gaia mission, to increase the probability that the chosen lenses are massive and the mass is measurable. Among the selected events we identify lensing black holes and neutron star candidates, with potential confirmation through forthcoming release of the Gaia time-series astrometry in 2026. Utilizing Bayesian analysis and Galactic models, along with the Gaia Data Release 3 proper motion data, four good candidates for dark remnants were identified: OGLE-2016-BLG-0293, OGLE-2018-BLG-0483, OGLE-2018-BLG-0662, and OGLE-2015-BLG-0149, with lens masses of $2.98^{+1.75}_{-1.28}~M_{\odot}$, $4.65^{+3.12}_{-2.08}~M_{\odot}$, $3.15^{+0.66}_{-0.64}~M_{\odot}$ and $1.4^{+0.75}_{-0.55}~M_{\odot}$, respectively. Notably, the first two candidates are expected to exhibit astrometric microlensing signals detectable by Gaia, offering the prospect of validating the lens masses. The methodologies developed in this work will be applied to the full Spitzer microlensing sample, populating and analyzing the time-scale ($t_{\rm E}$) vs. parallax ($\pi_{\rm E}$) diagram to derive constraints on the population of lenses in general and massive remnants in particular., Comment: submitted to ApJ

NanoTerasu, a new 3 GeV synchrotron light source in Japan, began user operation in April 2024. It provides high-brilliance soft to tender X-rays and covers a wide spectral range from ultraviolet to tender X-rays. Its compact storage ring with a circumference of 349 m is based on a four-bend achromat lattice to provide two straight sections in each cell for insertion devices with a natural horizontal emittance of 1.14 nm rad, which is small enough for soft X-rays users. The NanoTerasu accelerator incorporates several innovative technologies, including a full-energy injector C-band linear accelerator with a length of 110 m, an in-vacuum off-axis injection system, a four-bend achromat with B-Q combined bending magnets, and a TM020 mode accelerating cavity with built-in higher-order-mode dampers in the storage ring. This paper presents the accelerator machine commissioning over a half-year period and our model-consistent ring optics correction. The first user operation with a stored beam current of 160 mA is also reported. We summarize the storage ring parameters obtained from the commissioning. This is helpful for estimating the effective optical properties of synchrotron radiation at NanoTerasu., Comment: 30 pages, 24 figures, submitted to the journal

Various gravity theories beyond general relativity have been rigorously investigated in the literature such as Horndeski and degenerate higher-order scalar-tensor (DHOST) theories. In general, numerous model parameters are involved in such theories, which should be constrained to test the theories with experiments and observations. We construct the kurtosis consistency relations, calculated based on matter density fluctuations, in which the information of gravity theories is encoded. We derive two independent consistency relations that should hold in the framework of the DHOST theories and argue that such consistency relations would be useful for testing gravity theories., Comment: 19 pages, 3 figures

We investigated the nature of the anomalies appearing in four microlensing events KMT-2020-BLG-0757, KMT-2022-BLG-0732, KMT-2022-BLG-1787, and KMT-2022-BLG-1852. The light curves of these events commonly exhibit initial bumps followed by subsequent troughs that extend across a substantial portion of the light curves. We performed thorough modeling of the anomalies to elucidate their characteristics. Despite their prolonged durations, which differ from the usual brief anomalies observed in typical planetary events, our analysis revealed that each anomaly in these events originated from a planetary companion located within the Einstein ring of the primary star. It was found that the initial bump arouse when the source star crossed one of the planetary caustics, while the subsequent trough feature occurred as the source traversed the region of minor image perturbations lying between the pair of planetary caustics. The estimated masses of the host and planet, their mass ratios, and the distance to the discovered planetary systems are $(M_{\rm host}/M_\odot, M_{\rm planet}/M_{\rm J}, q/10^{-3}, \dl/{\rm kpc}) = (0.58^{+0.33}_{-0.30}, 10.71^{+6.17}_{-5.61}, 17.61\pm 2.25,6.67^{+0.93}_{-1.30})$ for KMT-2020-BLG-0757, $(0.53^{+0.31}_{-0.31}, 1.12^{+0.65}_{-0.65}, 2.01 \pm 0.07, 6.66^{+1.19}_{-1.84})$ for KMT-2022-BLG-0732, $(0.42^{+0.32}_{-0.23}, 6.64^{+4.98}_{-3.64}, 15.07\pm 0.86, 7.55^{+0.89}_{-1.30})$ for KMT-2022-BLG-1787, and $(0.32^{+0.34}_{-0.19}, 4.98^{+5.42}_{-2.94}, 8.74\pm 0.49, 6.27^{+0.90}_{-1.15})$ for KMT-2022-BLG-1852. These parameters indicate that all the planets are giants with masses exceeding the mass of Jupiter in our solar system and the hosts are low-mass stars with masses substantially less massive than the Sun., Comment: 10 pages, 12 figures, 7 tables

The Transformer model has shown leading performance in time series forecasting. Nevertheless, in some complex scenarios, it tends to learn low-frequency features in the data and overlook high-frequency features, showing a frequency bias. This bias prevents the model from accurately capturing important high-frequency data features. In this paper, we undertook empirical analyses to understand this bias and discovered that frequency bias results from the model disproportionately focusing on frequency features with higher energy. Based on our analysis, we formulate this bias and propose Fredformer, a Transformer-based framework designed to mitigate frequency bias by learning features equally across different frequency bands. This approach prevents the model from overlooking lower amplitude features important for accurate forecasting. Extensive experiments show the effectiveness of our proposed approach, which can outperform other baselines in different real-world time-series datasets. Furthermore, we introduce a lightweight variant of the Fredformer with an attention matrix approximation, which achieves comparable performance but with much fewer parameters and lower computation costs. The code is available at: https://github.com/chenzRG/Fredformer, Comment: This paper has been accepted by SIGKDD2024

This study proposes an approach to human-to-humanoid teleoperation using GAN-based online motion retargeting, which obviates the need for the construction of pairwise datasets to identify the relationship between the human and the humanoid kinematics. Consequently, it can be anticipated that our proposed teleoperation system will reduce the complexity and setup requirements typically associated with humanoid controllers, thereby facilitating the development of more accessible and intuitive teleoperation systems for users without robotics knowledge. The experiments demonstrated the efficacy of the proposed method in retargeting a range of upper-body human motions to humanoid, including a body jab motion and a basketball shoot motion. Moreover, the human-in-the-loop teleoperation performance was evaluated by measuring the end-effector position errors between the human and the retargeted humanoid motions. The results demonstrated that the error was comparable to those of conventional motion retargeting methods that require pairwise motion datasets. Finally, a box pick-and-place task was conducted to demonstrate the usability of the developed humanoid teleoperation system.