Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning Bipedal Walking On Planned Footsteps For Humanoid Robots

Deep reinforcement learning (RL) based controllers for legged robots have demonstrated impressive ro-bustness for walking in different environments for several robot platforms. To enable the application of RL policies for humanoid robots in real-world settings, it is crucial to build a system that can achieve robust walking in any direction, on 2D and 3D terrains, and be controllable by a user-command. In this paper, we tackle this problem by learning a policy to follow a given step sequence. The policy is trained with the help of a set of procedurally generated step sequences (also called footstep plans). We show that simply feeding the upcoming 2 steps to the policy is sufficient to achieve omnidirectional walking, turning in place, standing, and climbing stairs. Our method employs curriculum learning on the complexity of terrains, and circumvents the need for reference motions or pre-trained weights. We demonstrate the application of our proposed method to learn RL policies for 2 new robot platforms - HRP5P and JVRC-1 - in the MuJoCo simulation environment. The code for training and evaluation is available online. ††https://github.com/rohanpsingh/LearningHumanoidWalking.
Investigating field-induced magnetic order in Han purple by neutron scattering up to 25.9 T

quantum antiferromagnet containing three different types of stacked, square-lattice bilayer hosting spin-1 / 2 dimers. Although this compound has been studied extensively over the last two decades, the critical applied magnetic ﬁeld required to close the dimer spin gap and induce magnetic order, which exceeds 23 T, has to date precluded any kind of neutron scattering investigation. However, the HFM / EXED instrument at the Helmholtz-Zentrum Berlin made this possible at magnetic ﬁelds up to 25.9 T. Thus we have used HFM / EXED to investigate the ﬁeld-induced ordered phase, in particular to look for quasi-2D physics arising from the layered structure and from the different bilayer types. From neutron diffraction data, we determined the global dependence of the magnetic order parameter on both magnetic ﬁeld and temperature, ﬁnding a form consistent with 3D quantum critical scaling; from this we deduce that the quasi-2D interactions and nonuniform layering of BaCuSi 2 O 6 are not anisotropic enough to induce hallmarks of 2D physics. From neutron spectroscopy data, we measured the dispersion of the strongly Zeeman-split magnetic excitations, ﬁnding good agreement with the zero-ﬁeld interaction parameters of BaCuSi 2 O 6 . We conclude that HFM / EXED allowed a signiﬁcant extension in the application of neutron scattering techniques to the ﬁeld range above 20 T and in particular opened previously unavailable possibilities in the study of ﬁeld-induced magnetic quantum phase transitions.
Active Learning of Ordinal Embeddings: A User Study on Football Data

Humans innately measure distance between instances in an unlabeled dataset using an unknown similarity function. Distance metrics can only serve as proxy for similarity in information retrieval of similar instances. Learning a good similarity function from human annotations improves the quality of retrievals. This work uses deep metric learning to learn these user-defined similarity functions from few annotations for a large football trajectory dataset. We adapt an entropy-based active learning method with recent work from triplet mining to collect easy-to-answer but still informative annotations from human participants and use them to train a deep convolutional network that generalizes to unseen samples. Our user study shows that our approach improves the quality of the information retrieval compared to a previous deep metric learning approach that relies on a Siamese network. Specifically, we shed light on the strengths and weaknesses of passive sampling heuristics and active learners alike by analyzing the participants' response efficacy. To this end, we collect accuracy, algorithmic time complexity, the participants' fatigue and time-to-response, qualitative self-assessment and statements, as well as the effects of mixed-expertise annotators and their consistency on model performance and transfer-learning.
On the Interaction between Test-Suite Reduction and Regression-Test Selection Strategies

Unit testing is one of the most established quality-assurance techniques for software development. One major advantage of unit testing is the adjustable trade-oﬀ between eﬃciency (i.e., testing eﬀort) and eﬀectiveness (i.e., fault-detection probability). To this end, various strategies have been proposed to exploit this trade-oﬀ. In particular, test-suite reduction (TSR) reduces the number of (presumably redundant) test cases while testing a single program version. Regression-test selection (RTS) selects test cases for testing consecutive program revisions. However, both TSR and RTS may inﬂuence—or even obstruct— each others’ performance when used in combination. For instance, test cases discarded during TSR for a particular program version may become relevant again for RTS. However, ﬁnding a combination of both strategies leading to a reasonable trade-oﬀ throughout the version history of a program is an open question. The goal of this paper is to gain a better understanding of the interactions between TSR and RTS with respect to eﬃciency and eﬀectiveness. To this end, we present a conﬁgurable framework called RegreTS for automated unit-testing of C programs. The framework comprises diﬀerent strategies for TSR and RTS and possible combinations thereof. We apply this framework to a collection of subject systems, delivering several crucial insights. First, TSR has almost always a negative impact on the eﬀectiveness of RTS, yet a positive impact on eﬃciency. Second, test cases revealing to testers the eﬀect of program modiﬁcations between consecutive program versions are far more eﬀective than test cases simply covering modiﬁed code parts, yet causing much more testing eﬀort.
On the benefits of non-linear weight updates

Recent work has suggested that the generalisation performance of a DNN is related to the extent to which the Signal-to-Noise Ratio is optimised at each of the nodes. In contrast, Gradient Descent methods do not always lead to SNR-optimal weight configurations. One way to improve SNR performance is to suppress large weight updates and amplify small weight updates. Such balancing is already implicit in some common optimizers, but we propose an approach that makes this explicit. The method applies a non-linear function to gradients prior to making DNN parameter updates. We investigate the performance with such non-linear approaches. The result is an adaptation to existing optimizers that improves performance for many problem types.
Robust and Efficient Segmentation of Cross-domain Medical Images

—Efﬁcient medical image segmentation aims to provide accurate pixel-wise prediction for the medical images with the lightweight implementation framework. However, lightweight frameworks generally fail to achieve high performance, and suffer from the poor generalizable ability on cross-domain tasks. In this paper, we propose a generalizable knowledge distillation method for robust and efﬁcient segmentation of cross-domain medical images. Primarily, we propose the Model-Speciﬁc Alignment Networks (MSAN) to provide the domain-invariant representations which are regularized by a Pre-trained Semantic AutoEncoder (P-SAE). Meanwhile, a customized Alignment Consistency Training (ACT) strategy is designed to promote the MSAN training. With the domain-invariant representative vectors in MSAN, we propose two generalizable knowledge distillation schemes, Dual Contrastive Graph Distillation (DCGD) and Domain-Invariant Cross Distillation (DICD). Speciﬁcally, in DCGD, two types of implicit contrastive graphs are designed to represent the intra-coupling and inter-coupling semantic correlations from the perspective of data distribution. In DICD, the domain-invariant semantic vectors from the two models ( i.e. , teacher and student) are leveraged to cross-reconstruct features by the header exchange of MSAN, which achieves generalizable improvement for both the encoder and decoder in the student model. Furthermore, a metric named Fr´echet Semantic Distance (FSD) is tailored to verify the effectiveness of the regularized domain-invariant features. Extensive experiments conducted on the Liver and Retinal Vessel Segmentation datasets demonstrate the priority of our method, in terms of performance and generalization on lightweight frameworks.
Stability version of Dirac's theorem and its applications for generalized Turán problems

In 1952, Dirac proved that every 2‐connected n$n$ ‐vertex graph with the minimum degree k+1$k+1$ contains a cycle of length at least min{n,2(k+1)}$\min \lbrace n, 2(k+1)\rbrace$ . Here we obtain a stability version of this result by characterizing those graphs with minimum degree k$k$ and circumference at most 2k+1$2k+1$ . We present applications of the above‐stated result by obtaining generalized Turán numbers. In particular, for all ℓ⩾5$\ell \geqslant 5$ we determine how many copies of a five‐cycle as well as four‐cycle are necessary to guarantee that the graph has a circumference larger than ℓ$\ell$ . In addition, we give a new proof of Luo's theorem for cliques using our stability result.
Recovery of a Distributed Order Fractional Derivative in an Unknown Medium

In this work, we study an inverse problem of recovering information about the weight in distributed-order time-fractional diffusion from the observation at one single point on the domain boundary. In the absence of an explicit knowledge of the medium, we prove that the one-point observation can uniquely determine the support bound of the weight. The proof is based on asymptotics of the data, analytic continuation and Titchmarch convolution theorem. When the medium is known, we give an alternative proof of an existing result, i.e., the one-point boundary observation uniquely determines the weight. Several numerical experiments are also presented to complement the analysis.
Efficient Algorithms for Sparse Moment Problems without Separation

We consider the sparse moment problem of learning a $k$-spike mixture in high-dimensional space from its noisy moment information in any dimension. We measure the accuracy of the learned mixtures using transportation distance. Previous algorithms either assume certain separation assumptions, use more recovery moments, or run in (super) exponential time. Our algorithm for the one-dimensional problem (also called the sparse Hausdorff moment problem) is a robust version of the classic Prony's method, and our contribution mainly lies in the analysis. We adopt a global and much tighter analysis than previous work (which analyzes the perturbation of the intermediate results of Prony's method). A useful technical ingredient is a connection between the linear system defined by the Vandermonde matrix and the Schur polynomial, which allows us to provide tight perturbation bound independent of the separation and may be useful in other contexts. To tackle the high-dimensional problem, we first solve the two-dimensional problem by extending the one-dimensional algorithm and analysis to complex numbers. Our algorithm for the high-dimensional case determines the coordinates of each spike by aligning a 1d projection of the mixture to a random vector and a set of 2d projections of the mixture. Our results have applications to learning topic models and Gaussian mixtures, implying improved sample complexity results or running time over prior work.
Peduncle Gripping and Cutting Force for Strawberry Harvesting Robotic End-effector Design

Robotic harvesting of strawberries has gained much interest in the recent past. Although there are many innovations, they haven't yet reached a level that is comparable to an expert human picker. The end effector unit plays a major role in defining the efficiency of such a robotic harvesting system. Even though there are reports on various end effectors for strawberry harvesting, but there they lack a picture of certain parameters that the researchers can rely upon to develop new end effectors. These parameters include the limit of gripping force that can be applied on the peduncle for effective gripping, the force required to cut the strawberry peduncle, etc. These estimations would be helpful in the design cycle of the end effectors that target to grip and cut the strawberry peduncle during the harvesting action. This paper studies the estimation and analysis of these parameters experimentally. It has been estimated that the peduncle gripping force can be limited to 10 N. This enables an end effector to grip a strawberry of mass up to 50 grams with a manipulation acceleration of 50 m/s2 without squeezing the peduncle. The study on peduncle cutting force reveals that a force of 15 N is sufficient to cut strawberry peduncle using a blade with a wedge angle of 16.6° at 30° orientation.
Thermal and Non-thermal Properties of Active Region Recurrent Coronal Jets

We present observations of recurrent active region coronal jets, and derive their thermal and non-thermal properties, by studying the physical properties of the plasma simultaneously at the base footpoint and along the outflow of jets. The sample of analyzed solar jets were observed by SDO-AIA in extreme ultraviolet and by RHESSI in the X-ray domain. The main thermal plasma physical parameters, such as temperature, density, energy flux contributions, etc., are calculated using multiple inversion techniques to obtain the differential emission measure from extreme-ultraviolet filtergrams. The underlying models are assessed, and their limitations and applicability are scrutinized. Complementarily, we perform source reconstruction and spectral analysis of higher energy X-ray observations to further assess the thermal structure and identify non-thermal plasma emission properties. We discuss a peculiar penumbral magnetic reconnection site, which we previously identified as a “Coronal Geyser. ” Evidence supporting cool and hot thermal emission, as well as non-thermal emission, is presented for a subset of geyser jets. These active region jets are found to be energetically stronger than their polar counterparts, but we find their potential influence on heliospheric energetics and dynamics to be limited. We scrutinize whether the geyser does fit the non-thermal erupting microflare picture, finding that our observations at peak flaring times can only be explained by a combination of thermal and non-thermal emission models. This analysis of geysers provides new information and observational constraints applicable to theoretical modeling of solar jets.
Sizing Co-located Storage for Uncertain Renewable Energy Sold Through Forward Contracts

In this paper, we propose a high-level Stochastic steady-state model to analyze the value of co-located energy storage systems for wind power producers that participate in an electricity market through Forward or Day Ahead contracts. In particular, we try to find optimal sizing and contracting and stationary operating policies for profit maximization in the long-run. We obtain a stylized model calibrated to actual wind power production and electricity wholesale price data that allows us to asses the value of storage size and perform sensitivity analysis on key parameters such as contract prices, storage cost and storage efficiency.
The Von Zeipel–Lidov–Kozai Effect inside Mean Motion Resonances with Applications to Trans-Neptunian Objects

Secular dynamics inside mean motion resonances (MMRs) plays an essential role in governing the dynamical structure of the trans-Neptunian region and sculpting the orbital distribution of trans-Neptunian objects (TNOs). In this study, semianalytical developments are made to explore the von Zeipel–Lidov–Kozai resonance inside MMRs. To this end, a semi-secular model is formulated from averaging theory and then a single-degree-of-freedom integrable model is achieved based on the adiabatic invariance approximation. In particular, we introduce a modified adiabatic invariant, which is continuous around the separatrices of MMRs. During long-term evolution, both the resonant Hamiltonian and the adiabatic invariant remain unchanged, thus phase portraits can be produced by plotting level curves of the adiabatic invariant with a given Hamiltonian. The phase portraits provide global pictures to predict long-term behaviors of the eccentricity, inclination, and argument of pericenter. Applications to some representative TNOs inside MMRs (2018 VO137, 2005 SD278, 2015 PD312, Pluto, 2004 HA79, 1996 TR66, and 2014 SR373) show good agreements between the numerically propagated trajectories under the full N-body model and the level curves arising in phase portraits. Interestingly, 2018 VO137 and 2005 SD278 exhibit switching behaviors during their long-term evolution and currently they are inside 2:5 MMR with Neptune.
Analyzing Sharpness along GD Trajectory: Progressive Sharpening and Edge of Stability

Recent findings (e.g., arXiv:2103.00065) demonstrate that modern neural networks trained by full-batch gradient descent typically enter a regime called Edge of Stability (EOS). In this regime, the sharpness, i.e., the maximum Hessian eigenvalue, first increases to the value 2/(step size) (the progressive sharpening phase) and then oscillates around this value (the EOS phase). This paper aims to analyze the GD dynamics and the sharpness along the optimization trajectory. Our analysis naturally divides the GD trajectory into four phases depending on the change of the sharpness. We empirically identify the norm of output layer weight as an interesting indicator of sharpness dynamics. Based on this empirical observation, we attempt to theoretically and empirically explain the dynamics of various key quantities that lead to the change of sharpness in each phase of EOS. Moreover, based on certain assumptions, we provide a theoretical proof of the sharpness behavior in EOS regime in two-layer fully-connected linear neural networks. We also discuss some other empirical findings and the limitation of our theoretical results.
“I Used To Carry A Wallet, Now I Just Need To Carry My Phone”: Understanding Current Banking Practices and Challenges Among Older Adults in China

Managing finances is crucial for older adults who are retired and may rely on savings to ensure their lives’ quality. As digital banking platforms (e.g., mobile apps, electronic payment) gradually replace physical ones, it is critical to understand how they adapt to digital banking and the potential frictions they experience. We conducted semi-structured interviews with 16 older adults in China, where the aging population is the largest and digital banking grows fast. We also interviewed bank employees to gain complementary perspectives of these help givers. Our findings show that older adults used both physical and digital platforms as an ecosystem based on perceived pros and cons. Perceived usefulness, self-confidence, and social influence were key motivators for learning digital banking. They experienced app-related (e.g., insufficient error-recovery support) and user-related challenges (e.g., trust, security and privacy concerns, low perceived self-efficacy) and developed coping strategies. We discuss design considerations to improve their banking experiences.
Contextualizing Online Conversational Networks

Online social connections occur within a specific conversational context. Prior work in network analysis of social media data attempts to contextualize data through filtering. We propose a method of contextualizing online conversational connections automatically and illustrate this method with Twitter data. Specifically, we detail a graph neural network model capable of representing tweets in a vector space based on their text, hashtags, URLs, and neighboring tweets. Once tweets are represented, clusters of tweets uncover conversational contexts. We apply our method to a dataset with 4.5 million tweets discussing the 2020 US election. We find that even filtered data contains many different conversational contexts, with users engaging in multiple conversations. While users engage in multiple conversations, the overlap between any two pairs of conversations tends to be only 30-40%, giving very different networks for different conversations. Even accounting for this variation, we show that the relative social status of users varies considerably across contexts, with tau=0.472 on average. Our findings imply that standard network analysis on social media data can be unreliable in the face of multiple conversational contexts.
Productivity meets Performance: Julia on A64FX

The Fujitsu A64FX ARM-based processor is used in supercomputers such as Fugaku in Japan and Isambard 2 in the UK and provides an interesting combination of hardware features such as Scalable Vector Extension (SVE), and native support for reduced-precision floating-point arithmetic. The goal of this paper is to explore performance of the Julia programming language on the A64FX processor, with a particular focus on reduced precision. Here, we present a performance study on axpy to verify the compilation pipeline, demonstrating that Julia can match the performance of tuned libraries. Additionally, we investigate Message Passing Interface (MPI) scalability and throughput analysis on Fugaku showing next to no significant overheads of Julia of its MPI interface. To explore the usability of Julia to target various floating-point precisions, we present results of ShallowWaters. j1, a shallow water model that can be executed a various levels of precision. Even for such complex applications, Julia's type-flexible programming paradigm offers both, productivity and performance.
AMF: Adaptable Weighting Fusion with Multiple Fine-tuning for Image Classification

—Fine-tuning is widely applied in image classiﬁcation tasks as a transfer learning approach. It re-uses the knowledge from a source task to learn and obtain a high performance in target tasks. Fine-tuning is able to alleviate the challenge of insufﬁcient training data and expensive labelling of new data. However, standard ﬁne-tuning has limited performance in complex data distributions. To address this issue, we propose the Adaptable Multi-tuning method, which adaptively determines each data sample’s ﬁne-tuning strategy. In this framework, multiple ﬁne-tuning settings and one policy network are deﬁned. The policy network in Adaptable Multi-tuning can dynamically adjust to an optimal weighting to feed different samples into models that are trained using different ﬁne-tuning strategies. Our method outperforms the standard ﬁne-tuning approach by 1.69 %, 2.79 % on the datasets FGVC-Aircraft, and Describ- able Texture, yielding comparable performance on the datasets Stanford Cars, CIFAR-10, and Fashion-MNIST. 1