Miracle-dz/UniFAR-FTUs huggingface dataset:
Jet-HR2: A Flying Bipedal Robot Based on Thrust Vector Control

Achieving short-distance flight helps improve the efficiency of bipedal robots moving in complex environments (e.g., crossing large obstacles or reaching high places) for rapid emergency missions. This study proposes a design of a flying bipedal robot named Jet-HR2 (Fig. 1(a)). The robot has 10 joints driven by brushless motors and harmonic drives for locomotion. To overcome the challenge of the stable-attitude takeoff under low thrust-to-weight conditions, the robot was designed based on the concept of thrust vectoring. The propulsion system consists of four ducted fans, that is, two fixed on the waist of the robot and the other two mounted on the feet for thrust vector control. The thrust vector is controlled by adjusting the attitude of the foot during the flight. A simplified model and control strategies are proposed to solve the problem of attitude instability caused by mass errors and joint position errors during takeoff. The experimental results showed that the robot’s spin and dive behaviors during takeoff were effectively suppressed by controlling the thrust vector of the ducted fan on the foot. The robot successfully achieved takeoff at a thrust-to-weight ratio of 1.17 (17 kg / 20 kg) and maintained a stable attitude, reaching a takeoff height of over 1000 mm.
Complexity as information in spin-glass Gibbs states and metastates: Upper bounds at nonzero temperature and long-range models.

In classical finite-range spin systems, especially those with disorder such as spin glasses, a low-temperature Gibbs state may be a mixture of a number of pure or ordered states; the complexity of the Gibbs state has been defined in the past roughly as the logarithm of this number, assuming the question is meaningful in a finite system. As nontrivial pure-state structure is lost in finite size, in a recent paper [Phys. Rev. E 101, 042114 (2020)2470-004510.1103/PhysRevE.101. 042114] Höller and the author introduced a definition of the complexity of an infinite-size Gibbs state as the mutual information between the pure state and the spin configuration in a finite region, and applied this also within a metastate construction. (A metastate is a probability distribution on Gibbs states.) They found an upper bound on the complexity for models of Ising spins in which each spin interacts with only a finite number of others, in terms of the surface area of the region, for all T≥0. In the present paper, the complexity of a metastate is defined likewise in terms of the mutual information between the Gibbs state and the spin configuration. Upper bounds are found for each of these complexities for general finite-range (i.e., short- or long-range, in a sense we define) mixed p-spin interactions of discrete or continuous spins (such as m-vector models), but only for T>0. For short-range models, the bound reduces to the surface area. For long-range interactions, the definition of a Gibbs state has to be modified, and for these models we also prove that the states obtained within the metastate constructions are Gibbs states under the modified definition. All results are valid for a large class of disorder distributions.
Shifted Chunk Transformer for Spatio-Temporal Representational Learning

Spatio-temporal representational learning has been widely adopted in various fields such as action recognition, video object segmentation, and action anticipation. Previous spatio-temporal representational learning approaches primarily employ ConvNets or sequential models,e.g., LSTM, to learn the intra-frame and inter-frame features. Recently, Transformer models have successfully dominated the study of natural language processing (NLP), image classification, etc. However, the pure-Transformer based spatio-temporal learning can be prohibitively costly on memory and computation to extract fine-grained features from a tiny patch. To tackle the training difficulty and enhance the spatio-temporal learning, we construct a shifted chunk Transformer with pure self-attention blocks. Leveraging the recent efficient Transformer design in NLP, this shifted chunk Transformer can learn hierarchical spatio-temporal features from a local tiny patch to a global video clip. Our shifted self-attention can also effectively model complicated inter-frame variances. Furthermore, we build a clip encoder based on Transformer to model long-term temporal dependencies. We conduct thorough ablation studies to validate each component and hyper-parameters in our shifted chunk Transformer, and it outperforms previous state-of-the-art approaches on Kinetics-400, Kinetics-600, UCF101, and HMDB51.
A ug 2 02 1 Implementation of residual nucleus de-excitations associated with proton decays in 12 C based on the GENIE and TALYS generators

We implement the de-excitation processes of residual nuclei associated with proton decays in C based on the GENIE and TALYS generators. To derive the reasonable excitation energy spectra of residual nuclei B, B and Be, the default GENIE nucleon decay generator is modified in terms of the Spectral Function nuclear model. Then we use the TALYS code to estimate the de-excitation processes of residual nuclei. The TALYS calculations are partly consistent with the experimental data. The de-excitation neutron emission is more crucial than other particle emissions for liquid scintillator detectors due to the neutron identification capability. We find that the predicted branching ratio of neutron emission can well account for the current experimental measurements.
Mining Contextual Information Beyond Image for Semantic Segmentation

This paper studies the context aggregation problem in semantic image segmentation. The existing researches focus on improving the pixel representations by aggregating the contextual information within individual images. Though impressive, these methods neglect the significance of the representations of the pixels of the corresponding class beyond the input image. To address this, this paper proposes to mine the contextual information beyond individual images to further augment the pixel representations. We first set up a feature memory module, which is updated dynamically during training, to store the dataset-level representations of various categories. Then, we learn class probability distribution of each pixel representation under the supervision of the ground-truth segmentation. At last, the representation of each pixel is augmented by aggregating the dataset-level representations based on the corresponding class probability distribution. Furthermore, by utilizing the stored dataset-level representations, we also propose a representation consistent learning strategy to make the classification head better address intra-class compactness and inter-class dispersion. The proposed method could be effortlessly incorporated into existing segmentation frameworks (e.g., FCN, PSPNet, OCRNet and DeepLabV3) and brings consistent performance improvements. Mining contextual information beyond image allows us to report state-of-the-art performance on various benchmarks: ADE20K, LIP, Cityscapes and COCO-Stuff 1.
On variational principles for polarization in electromechanical systems

Classical electrodynamics uses a dielectric constant to describe the polarization response of electromechanical systems to changes in an electric field. We generalize that description to include a wide variety of responses to changes in the electric field, as found in most systems and applications. Electromechanical systems can be found in many physical and biological applications, such as ion transport in membranes, batteries, and dielectric elastomers. We present a unified, thermodynamically consistent, variational framework for modeling electromechanical systems as they respond to changes in the electric field; that is to say, as they polarize. This framework is motivated and developed using the classical energetic variational approach (EnVarA). The coupling between the electric part and the chemo-mechanical parts of the system is described either by Lagrange multipliers or various energy relaxations. The classical polarization and its dielectrics and dielectric constants appear as outputs of this analysis. The Maxwell equations then become universal conservation laws of charge and current, conjoined to an electromechanical description of polarization Polarization describes the entire electromechanical response to changes in the electric field and can sometimes be approximated as a dielectric constant or dielectric dispersion.
Connections between galaxy properties and halo formation time in the cosmic web

By linking galaxies in Sloan Digital Sky Survey to subhaloes in the ELUCID simulation, we investigate the relation between subhalo formation time and the galaxy properties, and the dependence of galaxy properties on the cosmic web environment. We find that central and satellite subhaloes have different formation time, where satellite subhaloes are older than central subhaloes at fixed mass. At fixed mass, the galaxy stellar-to-subhalo mass ratio is a good proxy of the subhalo formation time, and increases with the subhalo formation redshifts, especially for massive galaxies. The subhalo formation time is dependent on the cosmic web environment. For central subhaloes, there is a characteristic subhalo mass of ${\sim}10^{12} \, \mathrm{ h}^{-1}\,{\rm M}_\odot$, below which subhaloes in knots are older than subhaloes of the same mass in filaments, sheets, or voids, while above which it reverses. The cosmic web environmental dependence of stellar-to-subhalo mass ratio is similar to that of the subhalo formation time. For centrals, there is a characteristic subhalo mass of ${\sim}10^{12} \, \mathrm{ h}^{-1}\,{\rm M}_\odot$, below which the stellar-to-subhalo mass ratio is higher in knots than in filaments, sheets and voids, above which it reverses. Galaxies in knots have redder colours below $10^{12} \, \mathrm{ h}^{-1}\,{\rm M}_\odot$, while above $10^{12} \, \mathrm{ h}^{-1}\,{\rm M}_\odot$, the environmental dependence vanishes. Satellite fraction is strongly dependent on the cosmic web environment, and decreases from knots to filaments to sheets to voids, especially for low-mass galaxies.
A Deep Learning Loss Function based on Auditory Power Compression for Speech Enhancement

Deep learning technology has been widely applied to speech enhancement. While testing the effectiveness of various network structures, researchers are also exploring the improvement of the loss function used in network training. Although the existing methods have considered the auditory characteristics of speech or the reasonable expression of signal-to-noise ratio, the correlation with the auditory evaluation score and the applicability of the calculation for gradient optimization still need to be improved. In this paper, a signal-to-noise ratio loss function based on auditory power compression is proposed. The experimental results show that the overall correlation between the proposed function and the indexes of objective speech intelligibility, which is better than other loss functions. For the same speech enhancement model, the training effect of this method is also better than other comparison methods.
Approximation algorithms for the random-field Ising model

Approximating the partition function of the ferromagnetic Ising model with general external fields is known to be #BIS-hard in the worst case, even for bounded-degree graphs, and it is widely believed that no polynomial-time approximation scheme exists. This motivates an average-case question: are there classes of instances for which polynomial-time approximation schemes exist? We investigate this question for the random field Ising model on graphs with maximum degree $\Delta$. We establish the existence of fully polynomial-time approximation schemes and samplers with high probability over the random fields if the external fields are IID Gaussians with variance larger than a constant depending only on the inverse temperature and $\Delta$. The main challenge comes from the positive density of vertices at which the external field is small. These regions, which may have connected components of size $\Theta(\log n)$, are a barrier to algorithms based on establishing a zero-free region, and cause worst-case analyses of Glauber dynamics to fail. The analysis of our algorithm is based on percolation on a self-avoiding walk tree.
PULSEE: A software for the quantum simulation of an extensive set of nuclear magnetic and quadrupole resonance observables

We present an open-source software for the simulation of observables in nuclear magnetic/quadrupole resonance experiments (NMR/NQR) on solidstate samples, developed to assist experimental research in the design of new strategies for the investigation of quantum materials inspired by the early NMR/NQR quantum computation protocols. The software is based on a quantum mechanical description of nuclear spin dynamics in NMR/NQR experiments and has been widely tested on both theoretical and experimental available results. Moreover, the structure of the software allows an easy generalization of basic experiments to more sophisticated ones, as it includes all the libraries required for the numerical simulation of generic spin systems. In order to make the program easily accessible to a large user base, we developed a user-friendly graphical interface and fully-detailed documentation. Lastly, we portray several examples of the execution of the code that demonstrate the potential of NMR/NQR for the scopes of quantum control and quantum information processing.
Consistent Relative Confidence and Label-Free Model Selection for Convolutional Neural Networks

In this paper, we are concerned with image classification with deep convolutional neural networks (CNNs). We focus on the following question: given a set of candidate CNN models, how to select the right one with the best generalization property for the current task? Current model selection methods all require access to a batch of labeled data for computing a pre-specified performance metric, such as the cross-entropy loss, the classification error rate and the negative log-likelihood. In many practical cases, labels are not available in time as labeling itself is a time-consuming and expensive task. To this end, we propose an approach to CNN model selection using only unlabeled data. We develop this method based on a principle termed consistent relative confidence. Experimental results on benchmark datasets demonstrate the effectiveness and efficiency of our method.
Hardware-assisted Trusted Memory Disaggregation for Secure Far Memory

Memory disaggregation provides efficient memory utilization across network-connected systems. It allows a node to use part of memory in remote nodes in the same cluster. Recent studies have improved RDMA-based memory disaggregation systems, supporting lower latency and higher bandwidth than the prior generation of disaggregated memory. However, the current disaggregated memory systems manage remote memory only at coarse granularity due to the limitation of the access validation mechanism of RDMA. In such systems, to support fine-grained remote page allocation, the trustworthiness of all participating systems needs to be assumed, and thus a security breach in a node can propagate to the entire cluster. From the security perspective, the memory-providing node must protect its memory from memory-requesting nodes. On the other hand, the memory-requesting node requires the confidentiality and integrity protection of its memory contents even if they are stored in remote nodes. To address the weak isolation support in the current system, this study proposes a novel hardware-assisted memory disaggregation system. Based on the security features of FPGA, the logic in each per-node FPGA board provides a secure memory disaggregation engine. With its own networks, a set of FPGA-based engines form a trusted memory disaggregation system, which is isolated from the privileged software of each participating node. The secure memory disaggregation system allows fine-grained memory management in memory-providing nodes, while the access validation is guaranteed with the hardware-hardened mechanism. In addition, the proposed system hides the memory access patterns observable from remote nodes, supporting obliviousness. Our evaluation with FPGA implementation shows that such fine-grained secure disaggregated memory is feasible with comparable performance to the latest software-based techniques.
H2OPUS-TLR: High Performance Tile Low Rank Symmetric Factorizations using Adaptive Randomized Approximation

Tile low rank representations of dense matrices partition them into blocks of roughly uniform size, where each off-diagonal tile is compressed and stored as its own low rank factorization. They offer an attractive representation for many data-sparse dense operators that appear in practical applications, where substantial compression and a much smaller memory footprint can be achieved. TLR matrices are a compromise between the simplicity of a regular perfectly-strided data structure and the optimal complexity of the unbalanced trees of hierarchically low rank matrices, and provide a convenient performance-tuning parameter through their tile size that can be proportioned to take into account the cache size where the tiles reside in the memory hierarchy. There are currently no high-performance algorithms that can generate Cholesky and $LDL^T$ factorizations, particularly on GPUs. The difficulties in achieving high performance when factoring TLR matrices come from the expensive compression operations that must be performed during the factorization process and the adaptive rank distribution of the tiles that causes an irregular work pattern for the processing cores. In this work, we develop a dynamic batching operation and combine it with batched adaptive randomized approximations to achieve high performance both on GPUs and CPUs. Our implementation attains over 1.2 TFLOP/s in double precision on the V100 GPU, and is limited by the performance of batched GEMM operations. The Cholesky factorization of covariance matrix of size $N = 131K$ arising in spatial statistics can be factored to an accuracy $\epsilon=10^{-2}$ in just a few seconds. We believe the proposed GEMM-centric algorithm allows it to be readily ported to newer hardware such as the tensor cores that are optimized for small GEMM operations.
Visual-and-Language Navigation: A Survey and Taxonomy

—An agent that can understand natural-language instruction and carry out corresponding actions in the visual world is one of the long-term challenges of Artiﬁcial Intelligent (AI). Due to multifarious instructions from humans, it requires the agent can combine natural language to vision and action in unstructured, previously unseen environments. If the instruction given by human is a navigation task, this challenge is called Visual-and-Language Navigation (VLN). It is a booming multi-disciplinary ﬁeld of increasing importance and with extraordi- nary practicality. Instead of focusing on the details of speciﬁc methods, this paper provides a comprehensive survey on VLN tasks and makes a classiﬁcation carefully according the different characteristics of language instructions in these tasks. According to when the instructions are given, the tasks can be divided into single-turn and multi-turn . For single-turn tasks, we further divided them into goal-orientation and route-orientation based on whether the instructions contain a route. For multi-turn tasks, we divided them into imperative task and interactive task based on whether the agent responses to the instructions. This taxonomy enable researchers to better grasp the key point of a speciﬁc task and identify directions for future research.
The Bregman Proximal Average

We provide a proximal average with repect to a 1-coercive Legendre function. In the sense of Bregman distance, the Bregman envelope of the proximal average is a convex combination of Bregman envelopes of individual functions. The Bregman proximal mapping of the average is a convex combination of convexified proximal mappings of individual functions. Techniques from variational analysis provide the keys for the Bregman proximal average. 2000 Mathematics Subject Classification: Primary 49J53, 49J52; Secondary 47H05, 90C26, 52A01.
Influence of gravitational waves upon light in the Minkowski background: From null geodesics to interferometry

We have recently derived a manifestly covariant evolution law, under the geometrical optics (or eikonal) approximation of the vacuum Maxwell’s equations, for the electric field along null geodesics in a general spacetime, relative to an arbitrary set of instantaneous observers [1]. As one of its applications, we derive here the final detected intensity signal arising from a prototypical laser interferometric gravitational wave (GW) Michelson-Morley detector, comoving with transverse traceless (TT) observers, valid for both long and short GW wavelengths (as compared to the lengths of the interferometer’s arms). The motion of the test particles and light is described through the covariant kinematic and optical parameters. One of our main results is the presentation of the integrated null geodesic parametric equations exchanged between two TT observers in terms of explicitly observable quantities (laser initial frequency and positions of the observers) and the profile of the plane GW packet. This allows us to revisit the derivation of the consequential radar distance and Doppler shift, taking the opportunity to discuss some related subtle conceptual issues and how they might affect the interferometric process. Another achievement is the calculation of the electric field in each arm up to the detection event, for any relative orientations of the arms and the GW direction. The main quantitative result is the new expression for the final interference pattern, for normal GW incidence, which turns out to have three contributions: (i) the well-known traditional one due to the difference in optical paths, and two new ones due to (ii) the Doppler effect, and (iii) the divergence of the laser beams. The quantitative relevance of the last two contributions is compared to the traditional one and shown to be negligible within the geometrical optics regime of light. Although in general further contributions from the non-parallel transport of the polarization vector are expected (cf. [1]), again in the case of GW normal incidence, such a vector is indeed parallel transported, and those contributions are absent.
Evaluating Students Perspectives on ICT Readiness in Somali Higher Education towards Teaching - Learning Acceptance

Along the rapid development of Information and communication technology (ICT) tools and growth of Internet access offer opportunities that facilitate teaching and learning activities in the context of higher education. However, the study of ICTs readiness and acceptance in Somalia higher education is meagre. This research aims to examine the current state of ICT readiness among university students and explores the factors that affect their readiness acceptance. It proposes an extended model, based on the Technology Acceptance Model (TAM), which explains how University students’ beliefs influence their readiness to accept ICT applications in their learning. Survey responses of 304 students from undergraduate and Graduate in Somalia higher education were collected and analyzed using structural equation modelling. The results of the data analysis demonstrated that the TAM explained university students’ readiness acceptance of ICT applications reasonably well. More specifically, perceived usefulness, Ease of Use, ICT Selfefficacy, Teaching-Learning autonomy, Student’s Optimism and Availability of ICT infrastructure are robust predictors of Students ICT readiness acceptance. Results also showed that internet affordability, network speed and quality, innovativeness, discomfort and insecurity do not have a meaningful effect on perceived usefulness and Ease of Use towards ICT readiness acceptance. Through the empirical results, this study helped us understand why students choose to engage in ICT applications for their learning context.