Miracle-dz/UniFAR-FTUs huggingface dataset:
Hyperspectral Image Clustering with Spatially-Regularized Ultrametrics

We propose a method for the unsupervised clustering of hyperspectral images based on spatially regularized spectral clustering with ultrametric path distances. The proposed method efficiently combines data density and spectral-spatial geometry to distinguish between material classes in the data, without the need for training labels. The proposed method is efficient, with quasilinear scaling in the number of data points, and enjoys robust theoretical performance guarantees. Extensive experiments on synthetic and real HSI data demonstrate its strong performance compared to benchmark and state-of-the-art methods. Indeed, the proposed method not only achieves excellent labeling accuracy, but also efficiently estimates the number of clusters. Thus, unlike almost all existing hyperspectral clustering methods, the proposed algorithm is essentially parameter-free.
Evaluation of Skid-Steering Kinematic Models for Subarctic Environments

In subarctic and arctic areas, large and heavy skid-steered robots are preferred for their robustness and ability to operate on difficult terrain. State estimation, motion control and path planning for these robots rely on accurate odometry models based on wheel velocities. However, the state-of-the-art odometry models for skid-steer mobile robots (SSMRs) have usually been tested on relatively lightweight platforms. In this paper, we focus on how these models perform when deployed on a large and heavy (590kg) SSMR. We collected more than 2km of data on both snow and concrete. We compare the ideal differential-drive, extended differential-drive, radius-of-curvature-based, and full linear kinematic models commonly deployed for SSMRs. Each of the models is fine-tuned by searching their optimal parameters on both snow and concrete. We then discuss the relationship between the parameters, the model tuning, and the final accuracy of the models.
Beamforming and Precoding Techniques

Beamforming and precoding/combining are techniques aimed at processing multiantenna signals at the transmitter and/or at the receiver of a wireless communication system. While they have been routinely used to improve performance in current and previous generations of mobile communications systems, they are expected to play a more fundamental role in 5th Generation (5G) New Radio (NR) cellular systems, whose functionalities have been defined in the first phase of 3GPP 5G standardization process. Besides operating in traditional cellular sub-6 GHz frequency band, 5G NR has been natively designed also to work in the higher millimeter-wave (MMW) band. At lower frequencies, multiantenna techniques for 5G NR are mainly refinements of those originally designed for 4G Long Term Evolution (LTE). On the contrary, to cope with the peculiarities of MMW scenarios, such as the larger number of antenna elements, the more directional transmission, and the higher path loss values, new dynamic, user-specific, and computationally-efficient multiantenna solutions and procedures have been incorporated in 5G NR specifications. In particular, since multiantenna techniques for 5G NR generally need detailed channel state information (CSI), a complete redesign of the set of reference signals and procedures used for CSI acquisition and reporting was carried out. 5G NR is continuously evolving and new features will be added, while the existing ones will be enhanced in the second phase of 5G standardization, with emphasis on reduction of CSI overhead, robustness against spatial correlation among channels, unconventional transmission methods, and software-based reconfigurable antennas.
Estimating Individual Treatment Effects through Causal Populations Identification

Estimating the Individual Treatment Effect from observational data, defined as the difference between outcomes with and without treatment or intervention, while observing just one of both, is a challenging problems in causal learning. In this paper, we formulate this problem as an inference from hidden variables and enforce causal constraints based on a model of four exclusive causal populations. We propose a new version of the EM algorithm, coined as Expected-Causality-Maximization (ECM) algorithm and provide hints on its convergence under mild conditions. We compare our algorithm to baseline methods on synthetic and real-world data and discuss its performances.
Spin splitting spectroscopy of heavy quark anti quark systems

Phenomenological potentials describe the quarkonium systems like Charmonia, Bottomonia and $B_c$ Meson. They give a good accuracy for the mass spectra. In the present work we extend one of our previous works in the central case by adding spin dependent terms to allow for relativistic corrections. By using non central terms, we get better accuracy than other previous theoretical calculation. In the present work, the mass spectra of the bound states of heavy quarks are studied within the framework of the non relativistic Schrodinger s equation. First, we solve Schrodinger's equation by Nikiforov Uvarov (NU) method. The energy eigenvalues are presented using a non central potential. The results obtained are in good agreement with the experiment and are better than previous theoretical estimates.
On the spectrum of dense random geometric graphs

In this paper we study the spectrum of the random geometric graph $G(n,r)$, in a regime where the graph is dense and highly connected. In the \erdren $G(n,p)$ random graph it is well known that upon connectivity the spectrum of the normalized graph Laplacian is concentrated around $1$. We show that such concentration does not occur in the $G(n,r)$ case, even when the graph is dense and almost a complete graph. In particular, we show that the limiting spectral gap is strictly smaller than $1$. In the special case where the vertices are distributed uniformly in the unit cube and $r=1$, we show that for every $0\le k \le d$ there are at least $\binom{d}{k}$ eigenvalues near $1-2^{-k}$, and the limiting spectral gap is exactly $1/2$. We also show that the corresponding eigenfunctions in this case are tightly related to the geometric configuration of the points.
Quenched and annealed equilibrium states for random Ruelle expanding maps and applications

Abstract We find generalized conformal measures and equilibrium states for random dynamics generated by Ruelle expanding maps, under which the dynamics exhibits exponential decay of correlations. This extends results by Baladi [Correlation spectrum of quenched and annealed equilibrium states for random expanding maps. Comm. Math. Phys. 186 (1997), 671–700] and Carvalho et al [Semigroup actions of expanding maps. J. Stat. Phys. 116(1) (2017), 114–136], where the randomness is driven by an independent and identically distributed process and the phase space is assumed to be compact. We give applications in the context of weighted non-autonomous iterated function systems, free semigroup actions and introduce a boundary of equilibria for not necessarily free semigroup actions.
Person Re-Identification via Active Hard Sample Mining

Annotating a large-scale image dataset is very tedious, yet necessary for training person re-identification models. To alleviate such a problem, we present an active hard sample mining framework via training an effective re-ID model with the least labeling efforts. Considering that hard samples can provide informative patterns, we first formulate an uncertainty estimation to actively select hard samples to iteratively train a re-ID model from scratch. Then, intra-diversity estimation is designed to reduce the redundant hard samples by maximizing their diversity. Moreover, we propose a computer-assisted identity recommendation module embedded in the active hard sample mining framework to help human annotators to rapidly and accurately label the selected samples. Extensive experiments were carried out to demonstrate the effectiveness of our method on several public datasets. Experimental results indicate that our method can reduce 57%, 63%, and 49% annotation efforts on the Market1501, MSMT17, and CUHK03, respectively, while maximizing the performance of the re-ID model.
Rényi entanglement entropy of Fermi and non-Fermi liquids: Sachdev-Ye-Kitaev model and dynamical mean field theories

We present a new method for calculating Renyi entanglement entropies for fermionic field-theories originating from microscopic Hamiltonians. The method builds on an operator identity which we discover for the first time. The identity leads to the representation of traces of operator products, and thus Renyi entropies of a subsystem, in terms of fermionic-displacement operators. This allows for a very transparent path-integral formulation, both in and out-of-equilibrium, having a simple boundary condition on the fermionic fields. The method is validated by reproducing well known expressions for entanglement entropy in terms of the correlation matrix for non-interacting fermions. We demonstrate the effectiveness of the method by explicitly formulating the field theory for Renyi entropy in a few zero and higher-dimensional large-$N$ interacting models based on the Sachdev-Ye-Kitaev (SYK) model, and for the Hubbard model within dynamical mean-field theory (DMFT) approximation. We use the formulation to compute Renyi entanglement entropy of interacting Fermi liquid (FL) and non-Fermi liquid (NFL) states in the large-$N$ models and compare successfully with the results obtained via exact diagonalization for finite $N$. We elucidate the connection between entanglement entropy and residual entropy of the NFL ground state in the SYK model and extract sharp signatures of quantum phase transition in the entanglement entropy across an NFL to FL transition. Furthermore, we employ the method to obtain nontrivial system-size scaling of entanglement in an interacting diffusive metal described by a chain of SYK dots.
Polarization-insensitive SWG and its Application into Holographic Antenna Design

Surface-wave waveguides (SWG) are generally sensitive to the polarization direction due to different dispersion characteristics for TE and TM modes and therefore limitations exist in many practical applications. In this paper, a polarization-insensitive SWG, which could support both TE and TM modes with the same phase velocity is introduced and holographic antennas with broadside radiation and polarization-reconfigurable property are implemented based on the proposed SWG.
Coordinated Control Strategy for the Multiple Single Phase Converters in the Series Side of Distributed Power Flow Controller

In this paper, a simulation model of the distributed power flow controller is established by PSCAD/EMTDC, where the shunt converter is equivalent to the current source when the interaction between the shunt and series converters is ignored. The coordinated control strategy for the multiple single phase converters in the series side is proposed. The interaction of the series of converters through the line is studied from the three aspects: switching on/off at the same time, switching on/off at the same interval, and being installed in different electrical distance. The simulation results show that the proposed control strategy is the best switching on/off at the equal interval, and there is a short negative interaction among the converters. The increase of the electrical distance will weaken the interaction of converters.
Copper Electrodeposition by Hydrogen Evolution Assisted Electroplating (HEA) for Wearable Electronics

A novel technique called hydrogen evolution assisted (HEA) electroplating, has dramatically shown enhancement to the deposition rate of copper compared to galvanostatic conventional electroplating methods, opening new venues for the direct integration of devices to fabrics leading to the development of useful wearable electronics. HEA can be used for both printing copper tracks on a multi-wall carbon nanotubes (MWCNTs) coated template tracks and soldering surface mount electronic devices (SMD) to such tracks, demonstrating its versatility to be used for specific applications in which fabric mutilation wants to be prevented. However, in this project we studied how copper deposition takes place at different voltage ranges using 1000 Denier Coated Cordura Nylon, Laminated Polyester Ripstop and 100% Virgin Vinyl in the constant presence of the hydrogen evolution technique. Cupric sulfate (CUSO4) and sulfuric acid (H2SO4) were used as the medium to allow a lateral deposition over a multi-wall carbon nanotube track of 0.1mm by the application of a voltage ranging between - 0.5V to −2.0V using a potentiostat to employ the cyclic voltammeter technique in order to achieve a uniform deposition. Structure of the fabrics and variation of the copper deposits with respect to the type of fabric used were observed using a scanning electron microscopy (SEM).
Optimal design of a printed patch antenna using Water Cycle Algorithm

In this article, the Water Cycle Algorithm (WCA) Optimization method is applied to design and optimize a printed patch antenna. The optimization goals are set to reach an antenna with a S11 less than −10dB and a high-gain radiation pattern. The operational bandwidth of the antenna is based on the center frequency of 2.45GHz, which also covers the ISM band (2.4∼2.5GHz) applications. This method is implemented by simulating the antenna in HFSS with MATLAB co-simulation using VBScript and optimized with WCA. Compared with the results of the previous studies, the best available structure has been achieved by implementing this simple closed-loop algorithm. The simulation results verify the performance of the WCA method.
Recursive State-Value Function: A Method to Reduce the Complexity of Online Computation of Dynamic Programming

This paper proposed a method to reduce the computation quantity of dynamic programming to make the time consumption be acceptable for on-line control. The proposed method is the combination of model predictive control and state-value function. This method consist of two parts, the off-line part and the on-line part, where the former part is to generate an approximation of <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-step recursive state-value function which represents the cumulative reward from a state in <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> steps under the optimal control policy, and the latter part is to work out the best action in real time using the <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-step recursive state-value function both individually and in combination with MPC. At the end of this paper, some numerical examples are taken to illustrate the effectiveness of our method. Results show that compared to model predictive control and deep Q-learning, our method has some superiority.
Key Attributes for a Quality Mobile Application

The innovative advancement of cell phones, the significance of the Internet in the present society and the blasting market of the mobile devices have upset the mobile software programming altogether known as the product quality of portable intuitive gadgets. The mobile software programming gets increasingly competent and complex, which enables designers to apply entrenched quality strategies and models, from the work area of software programming advancement to mobile software programming. But still, mobile software programming moreover still has its portable explicit qualities, comparing models and techniques that must be balanced for its use in the larger domain. In the following research, some of the key attributes that must be incorporated and taken care for developing a portable quality mobile applications are identified. The key attributes determined by investigating before developed quality models which allows enhancing knowledge that can be drifted in the near future.
Combined Weighted Method for TDOA-Based Localization

A novel and practical combined weighted (COM-W) method for target localization based on time difference of arrival (TDOA) is proposed in this paper. For 2-D situations with more than three nodes, the main idea of the proposed method is to obtain multiple preliminary localization results corresponding to the different three-node combinations first, and then use the weighted averaging technique based on the Cramér–Rao lower bound (CRLB) to obtain the final estimation. Considering target localization with a set of fixed-positioning nodes employing TDOA measurements as an example, the performance of the proposed method is evaluated by both simulations and acoustic indoor positioning experiments. The experimental results show that the COM-W method is a closed-form and computationally efficient method. It has an excellent positioning performance under both narrow and square geometric arrangements, specifically in the central cross-shaped area and in the vicinity of the nodes. Compared to the three classic weighted linear least squares (WLS)-based positioning methods, the proposed COM-W method is less sensitive to the measurement noise and the NLOS environment. The proposed method has an important application value for the realization of a TDOA-based indoor target positioning system.
Data Consistency in the 5G Specification

Meeting the goals of 5G networks - high bandwidth, low latency, massive connectivity, and resiliency demands improvements to the infrastructure that hosts the network components. Mobile Network Operators will rely on a geographically distributed and highly scalable infrastructure that must handle and replicate user data consistently. This paper explores the management of user data with regards to data consistency in the first 5G specification. In particular we will focus on how the 5G system procedures handle and update data, and discuss failure scenarios where the correctness properties of the user data may be violated. In this work we present the necessary properties that an underlying data store must deliver in order to maintain correctness in the presence of failures.
Making Better Use of Processing-in-Memory Through Potential-Based Task Offloading

There is an increasing demand for a novel computing structure for data-intensive applications such as artificial intelligence and virtual reality. The processing-in-memory (PIM) is a promising alternative to reduce the overhead caused by data movement. Many studies have been conducted on the utilization of the PIM taking advantage of the bandwidth increased by the through silicon via (TSV). One approach is to design an optimized PIM architecture for a specific application, the other is to find the tasks that will be more advantageous when offloading to PIM. The goal of this paper is to make the PIM, a newly introduced technology, be easily applied to various applications. The programmable GPU-based PIM is the target system. The essential but simple task offloading conditions are proposed to secure as many candidate tasks as possible when there is any potential benefit from the PIM. The PIM design options then are explored reflecting the characteristics of the candidate tasks actively. When determining offloading conditions, it is difficult to simultaneously consider three time-energy-power objectives. Thus, the problem is divided into two sub-problems. The first offloading condition is designed based on time-energy constraints, whereas the second offloading condition is modeled to satisfy time-power constraints. During the whole processes, the offloading conditions and the PIM design options are carefully configured in a complementary manner to reduce the tasks that are excluded from the offloading. In the simulation results, the suitability of the modeled two offloading conditions and the proposed PIM design are verified using various benchmarks and then, they are compared with previous works in terms of processing speed and energy.
An Improved Attention-Based Spatiotemporal-Stream Model for Action Recognition in Videos

Action recognition is an important yet challenging task in computer vision. Attention mechanism not only tells where to focus but when to focus. It plays a key role in extracting discriminative spatial and temporal features for solving the task. In this paper, we propose an improved spatiotemporal attention model based on the two-stream structure to recognize the different actions in videos. Specifically, we first extract the intra-frame spatial features and inter-frame optical flow features for each video data. Then we implement an effective attention module, which sequentially infers attention maps along three separate dimensions: channel, spatial and temporal. After adaptive feature refinement based on the attention maps, we perform a temporal pooling process to squeeze the temporal dimension. Then, these achieved spatial and temporal features are fed into the spatial LSTM and temporal LSTM, respectively. Finally, we fuse the spatial feature, temporal feature and two-stream fusion feature to classify the actions in videos. Additionally, we also collect and construct a new Ping-Pong action dataset for subsequent human-robot interaction task from YouTube. It contains 2400 labeled videos for 4 categories. We compare with other action recognition algorithms and validate the feasibility and effectiveness of the proposed method on Ping-Pong action dataset and HMDB51 dataset.
Hierarchical Multimodal Hub Location With Time Restriction for China Railway (CR) Express Network

Known as the Belt and Road Initiative, China Railway(CR) Express is a cross-continent railway container transport mode between China and Europe, and plays an important role for connecting China and Eurasia market to boost the regional economy. In order to balance cargo flow and improve transport efficiency, this paper proposes a hierarchical multimodal hub location problem with time restriction for CR Express network. A mix integer programming is formulated and derived, and some results are concluded based on our observation and investigation on CR Express. The results illustrate the rail hub location in China with different rail hub number input, and the hinterland of every hub is explored as well. The rail hubs tend to cover more regions first and then be spatially agglomerative in the central and western regions of China. Moreover, we find that the city Wulanchabu that is located in Inner Mongolia Province has the largest hinterland range, and that strong competition among hubs exists in central and western of China, especially for Zhengzhou, Xi’an, Chongqing and Chengdu.
Prediction Error Negativity in Physical Human-Robot Collaboration

Cognitive conflict is a fundamental phenomenon of human cognition, particularly during interaction with the real world. Understanding and detecting cognitive conflict can help to improve interactions in a variety of applications, such as in human-robot collaboration (HRC), which involves continuously guiding the semi-autonomous robot to perform a task in given settings. There have been several works to detect cognitive conflict in HRC but without physical control settings. In this work, we have conducted the first study to explore cognitive conflict using prediction error negativity (PEN) in physical human-robot collaboration (pHRC). Our results show that there was a statistically significant (p =. 047) higher PEN for conflict condition compared to normal conditions, as well as a statistically significant difference between different levels of PEN (p =. 020). These results indicate that cognitive conflict can be detected in pHRC settings and, consequently, provide a window of opportunities to improve the interaction in pHRC.