Miracle-dz/UniFAR-FTUs huggingface dataset:
Self context-aware emotion perception on human-robot interaction

Emotion recognition plays a crucial role in various domains of human-robot interaction. In long-term interactions with humans, robots need to respond continuously and accurately, however, the mainstream emotion recognition methods mostly focus on short-term emotion recognition, disregarding the context in which emotions are perceived. Humans consider that contextual information and different contexts can lead to completely different emotional expressions. In this paper, we introduce self context-aware model (SCAM) that employs a two-dimensional emotion coordinate system for anchoring and re-labeling distinct emotions. Simultaneously, it incorporates its distinctive information retention structure and contextual loss. This approach has yielded significant improvements across audio, video, and multimodal. In the auditory modality, there has been a notable enhancement in accuracy, rising from 63.10% to 72.46%. Similarly, the visual modality has demonstrated improved accuracy, increasing from 77.03% to 80.82%. In the multimodal, accuracy has experienced an elevation from 77.48% to 78.93%. In the future, we will validate the reliability and usability of SCAM on robots through psychology experiments.
Computation of the Schl\"{a}fli function

In this work a method for numerical computation of the Schl\"afli function $f_n(x)$, for $x\in[n-1,n+1]$ and $n\geq4$ is presented. The computation is based on the Chebyshev approximation of the function $q_{n}(x)$, which is related to the Schl\"afli function and regular in the interval.
A Saliency Enhanced Feature Fusion based multiscale RGB-D Salient Object Detection Network

Multiscale convolutional neural network (CNN) has demonstrated remarkable capabilities in solving various vision problems. However, fusing features of different scales alwaysresults in large model sizes, impeding the application of multiscale CNNs in RGB-D saliency detection. In this paper, we propose a customized feature fusion module, called Saliency Enhanced Feature Fusion (SEFF), for RGB-D saliency detection. SEFF utilizes saliency maps of the neighboring scales to enhance the necessary features for fusing, resulting in more representative fused features. Our multiscale RGB-D saliency detector uses SEFF and processes images with three different scales. SEFF is used to fuse the features of RGB and depth images, as well as the features of decoders at different scales. Extensive experiments on five benchmark datasets have demonstrated the superiority of our method over ten SOTA saliency detectors.
Superfluidity of indirect momentum space dark dipolar excitons in a double layer with massive anisotropic tilted semi-Dirac bands

We have theoretically investigated the spin- and valley-dependent superfluidity properties of indirect momentum space dark dipolar excitons in double layers with massive anisotropic tilted semi-Dirac bands in the presence of circularly polarized irradiation. An external vertical electric field is also applied to the structure and is responsible for tilting and gap opening for the band structure. For our calculations we used the parameters of a double layer of 1T$^\prime$-MoS$_2$. Closed form analytical expressions are presented for the energy spectrum for excitons, their associated wave functions and binding energies. Additionally, we examine the effects which the intensity and frequency of circularly polarized irradiation has for 1T$^\prime$-MoS$_2$ on the effective mass of the excitons since it has been demonstrated that the application of an external high-frequency dressing field tailors the crucial electronic including the exciton binding energy, as well as the critical temperature for superfluidity. We also calculate the sound velocity in the anisotropic weakly-interacting Bose gas of two-component indirect momentum space dark excitons for a double layer of 1T$^\prime$-MoS$_2$. We show that the critical velocity of superfluidity, the spectrum of collective excitations, concentrations of the superfluid and normal component, and mean field critical temperature for superfluidity are anisotropic and formed by a two-component system. The critical temperature for superfluidity is increased when the exciton concentration and interlayer separation are increased. We propose the use of phonon-assisted photoluminescence to experimentally confirm directional superfluidity of indirect momentum space dark excitons in a double layer with massive anisotropic tilted semi-Dirac bands.
Modeling Stereo-Confidence Out of the End-to-End Stereo-Matching Network via Disparity Plane Sweep

We propose a novel stereo-confidence that can be measured externally to various stereo-matching networks, offering an alternative input modality choice of the cost volume for learning-based approaches, especially in safety-critical systems. Grounded in the foundational concepts of disparity definition and the disparity plane sweep, the proposed stereo-confidence method is built upon the idea that any shift in a stereo-image pair should be updated in a corresponding amount shift in the disparity map. Based on this idea, the proposed stereo-confidence method can be summarized in three folds. 1) Using the disparity plane sweep, multiple disparity maps can be obtained and treated as a 3-D volume (predicted disparity volume), like the cost volume is constructed. 2) One of these disparity maps serves as an anchor, allowing us to define a desirable (or ideal) disparity profile at every spatial point. 3) By comparing the desirable and predicted disparity profiles, we can quantify the level of matching ambiguity between left and right images for confidence measurement. Extensive experimental results using various stereo-matching networks and datasets demonstrate that the proposed stereo-confidence method not only shows competitive performance on its own but also consistent performance improvements when it is used as an input modality for learning-based stereo-confidence methods.
Optimal Control of Malware Propagation in IoT Networks

The rapid proliferation of Internet of Things (IoT) devices in recent years has resulted in a significant surge in the number of cyber-attacks targeting these devices. Recent data indicates that the number of such attacks has increased by over 100 percent, highlighting the urgent need for robust cybersecurity measures to mitigate these threats. In addition, a cyber-attack will begin to spread malware across the network once it has successfully compromised an IoT network. However, to mitigate this attack, a new patch must be applied immediately. In reality, the time required to prepare and apply the new patch can vary significantly depending on the nature of the cyber-attack. In this paper, we address the issue of how to mitigate cyber-attacks before the new patch is applied by formulating an optimal control strategy that reduces the impact of malware propagation and minimise the number of infected devices across IoT networks in the smart home. A novel node-based epidemiological model susceptible, infected high, infected low, recover first, and recover complete(SI_HI_LR_FR_C) is established with immediate response state for the restricted environment. After that, the impact of malware on IoT devices using both high and low infected rates will be analyzed. Finally, to illustrate the main results, several numerical analyses are carried out in addition to simulate the real-world scenario of IoT networks in the smart home, we built a dataset to be used in the experiments.
Synergistic signatures of group mechanisms in higher-order systems

The interplay between causal mechanisms and emerging collective behaviors is a central aspect of the understanding, control, and prediction of complex networked systems. Here we study this interplay in the context of higher-order mechanisms and behaviors in two representative models: a simplicial Ising model and a simplicial social contagion model. In both systems, we find that group (higher-order) interactions show emergent synergistic (higher-order) behavior. The emergent synergy appears only at the group level and depends in a complex non-linear way on the tradeoff between the strengths of the low- and higher-order mechanisms, and is invisible to low-order behavioral observables. Finally, we present a simple method to detect higher-order mechanisms by using this signature.
NeuroSynt: A Neuro-symbolic Portfolio Solver for Reactive Synthesis

We introduce NeuroSynt, a neuro-symbolic portfolio solver framework for reactive synthesis. At the core of the solver lies a seamless integration of neural and symbolic approaches to solving the reactive synthesis problem. To ensure soundness, the neural engine is coupled with model checkers verifying the predictions of the underlying neural models. The open-source implementation of NeuroSynt provides an integration framework for reactive synthesis in which new neural and state-of-the-art symbolic approaches can be seamlessly integrated. Extensive experiments demonstrate its efficacy in handling challenging specifications, enhancing the state-of-the-art reactive synthesis solvers, with NeuroSynt contributing novel solves in the current SYNTCOMP benchmarks.
Observation of discrete charge states of a coherent two-level system in a superconducting qubit

We report observations of discrete charge states of a coherent dielectric two-level system (TLS) that is strongly coupled to an offset-charge-sensitive superconducting transmon qubit. We measure an offset charge of 0.072$e$ associated with the two TLS eigenstates, which have a transition frequency of 2.9 GHz and a relaxation time exceeding 3 ms. Combining measurements in the strong dispersive and resonant regime, we quantify both transverse and longitudinal couplings of the TLS-qubit interaction. We further perform joint tracking of TLS transitions and quasiparticle tunneling dynamics but find no intrinsic correlations. This study demonstrates microwave-frequency TLS as a source of low-frequency charge noise.
Multi-level Cross-modal Alignment for Image Clustering

Recently, the cross-modal pretraining model has been employed to produce meaningful pseudo-labels to supervise the training of an image clustering model. However, numerous erroneous alignments in a cross-modal pretraining model could produce poor-quality pseudo labels and degrade clustering performance. To solve the aforementioned issue, we propose a novel Multi-level Cross-modal Alignment method to improve the alignments in a cross-modal pretraining model for downstream tasks, by building a smaller but better semantic space and aligning the images and texts in three levels, i.e., instance-level, prototype-level, and semantic-level. Theoretical results show that our proposed method converges, and suggests effective means to reduce the expected clustering risk of our method. Experimental results on five benchmark datasets clearly show the superiority of our new method.
Regularized Estimation of Sparse Spectral Precision Matrices

Spectral precision matrix, the inverse of a spectral density matrix, is an object of central interest in frequency-domain analysis of multivariate time series. Estimation of spectral precision matrix is a key step in calculating partial coherency and graphical model selection of stationary time series. When the dimension of a multivariate time series is moderate to large, traditional estimators of spectral density matrices such as averaged periodograms tend to be severely ill-conditioned, and one needs to resort to suitable regularization strategies involving optimization over complex variables. In this work, we propose complex graphical Lasso (CGLASSO), an $\ell_1$-penalized estimator of spectral precision matrix based on local Whittle likelihood maximization. We develop fast $\textit{pathwise coordinate descent}$ algorithms for implementing CGLASSO on large dimensional time series data sets. At its core, our algorithmic development relies on a ring isomorphism between complex and real matrices that helps map a number of optimization problems over complex variables to similar optimization problems over real variables. This finding may be of independent interest and more broadly applicable for high-dimensional statistical analysis with complex-valued data. We also present a complete non-asymptotic theory of our proposed estimator which shows that consistent estimation is possible in high-dimensional regime as long as the underlying spectral precision matrix is suitably sparse. We compare the performance of CGLASSO with competing alternatives on simulated data sets, and use it to construct partial coherence network among brain regions from a real fMRI data set.
Extending Polaris to Support Transactions

In Polaris, we introduced a cloud-native distributed query processor to perform analytics at scale. In this paper, we extend the underlying Polaris distributed computation framework, which can be thought of as a read-only transaction engine, to execute general transactions (including updates, deletes, inserts and bulk loads, in addition to queries) for Tier 1 warehousing workloads in a highly performant and predictable manner. We take advantage of the immutability of data files in log-structured data stores and build on SQL Server transaction management to deliver full transactional support with Snapshot Isolation semantics, including multi-table and multi-statement transactions. With the enhancements described in this paper, Polaris supports both query processing and transactions for T-SQL in Microsoft Fabric.
Mutual Coupling in RIS-Aided Communication: Model Training and Experimental Validation

This paper investigates the mutual coupling in reconfigurable intelligent surface (RIS)-aided communication. Despite the existence of several mutual coupling-aware models for RIS-aided communication, the absence of experimental validation remains a notable gap. To fill this void, this work delves into the recently proposed mutual coupling-aware communication model based on scattering matrices. Utilizing a fabricated 1bit quasi-passive RIS prototype operating in the mmWave band, we first propose a novel model training approach based on a single 3D full-wave simulation of the RIS radiation pattern, enabling the estimation of the scattering matrix among RIS unit cells. Subsequently, the trained model is validated via both full-wave simulations and experimental measurements on the real RIS prototype. Compared with the conventional communication model without accounting for mutual coupling in RIS, the utilized mutual coupling-aware model, incorporating trained scattering parameters, showcases a more accurate prediction performance. Benchmarked against the full-wave simulated RIS radiation pattern, the trained model can reduce the prediction error by up to 10.6 %. Meanwhile, the S-parameter between the Tx and Rx antennas is measured, validating that the trained model exhibits a closer alignment with the experimental measurements. Such results affirm the correctness of the adopted model and the effectiveness of the proposed model training method.
ORBGRAND: Achievable Rate for General Bit Channels and Application in BICM

Guessing random additive noise decoding (GRAND) has received widespread attention recently, and among its variants, ordered reliability bits GRAND (ORBGRAND) is particularly attractive due to its efficient utilization of soft information and its amenability to hardware implementation. It has been recently shown that ORBGRAND is almost capacity-achieving in additive white Gaussian noise channels under antipodal input. In this work, we first extend the analysis of ORBGRAND achievable rate to memoryless binary-input bit channels with general output conditional probability distributions. The analytical result also sheds insight into understanding the gap between the ORBGRAND achievable rate and the channel mutual information. As an application of the analysis, we study the ORBGRAND achievable rate of bit-interleaved coded modulation (BICM). Numerical results indicate that for BICM, the gap between the ORBGRAND achievable rate and the channel mutual information is typically small, and hence suggest the feasibility of ORBGRAND for channels with high-order coded modulation schemes.
Detecting Hidden Triggers: Mapping Non-Markov Reward Functions to Markov

Many Reinforcement Learning algorithms assume a Markov reward function to guarantee optimality. However, not all reward functions are known to be Markov. In this paper, we propose a framework for mapping non-Markov reward functions into equivalent Markov ones by learning a Reward Machine - a specialized reward automaton. Unlike the general practice of learning Reward Machines, we do not require a set of high-level propositional symbols from which to learn. Rather, we learn \emph{hidden triggers} directly from data that encode them. We demonstrate the importance of learning Reward Machines versus their Deterministic Finite-State Automata counterparts, for this task, given their ability to model reward dependencies in a single automaton. We formalize this distinction in our learning objective. Our mapping process is constructed as an Integer Linear Programming problem. We prove that our mappings provide consistent expectations for the underlying process. We empirically validate our approach by learning black-box non-Markov Reward functions in the Officeworld Domain. Additionally, we demonstrate the effectiveness of learning dependencies between rewards in a new domain, Breakfastworld.
Approximate solutions to a nonlinear functional differential equation

A functional differential equation related to the logistic equation is studied by a combination of numerical and perturbation methods. Parameter regions are identified where the solution to the nonlinear problem is approximated well by known series solutions of the linear version of the equation. The solution space for a certain class of functions is then mapped out using a continuation approach.
Room Temperature Quantum Control of N-Donor Electrons at Si/SiO2 Interface

The manuscript theoretically discusses various important aspects for donor atom based single qubit operations in silicon (Si) quantum computer architecture at room temperature using a single nitrogen (N) deep-donor close to the Si/SiO2 interface. Quantitative investigation of room temperature single electron shuttling between a single N-donor atom and the interface is the focus of attention under the influence of externally applied electric and magnetic field. To apprehend the realistic experimental configurations, central cell correction along with effective mass approach is adopted throughout the study. Furthermore, a detailed discussion currently explores the significant time scales implicated in the process and their suitability for experimental purposes. Theoretical estimates are also provided for all the external fields required to successfully achieve coherent single electron shuttling and their stable maintenance at the interface as required. The results presented in this work offer practical guidance for quantum electron control using N-donor atoms in Si at room temperature.
Agent-Based Modeling of C. Difficile Spread in Hospitals: Assessing Contribution of High-Touch vs. Low-Touch Surfaces and Inoculations' Containment Impact

Health issues and pandemics remain paramount concerns in the contemporary era. Clostridioides Difficile Infection (CDI) stands out as a critical healthcare-associated infection with global implications. Effectively understanding the mechanisms of infection dissemination within healthcare units and hospitals is imperative to implement targeted containment measures. In this study, we address the limitations of prior research by Sulyok et al., where they delineated two distinct categories of surfaces as high-touch and low-touch fomites, and subsequently evaluated the viral spread contribution of each surface utilizing mathematical modeling and Ordinary Differential Equations (ODE). Acknowledging the indispensable role of spatial features and heterogeneity in the modeling of hospital and healthcare settings, we employ agent-based modeling to capture new insights. By incorporating spatial considerations and heterogeneous patients, we explore the impact of high-touch and low-touch surfaces on contamination transmission between patients. Furthermore, the study encompasses a comprehensive assessment of various cleaning protocols, with differing intervals and detergent cleaning efficacies, in order to identify the most optimal cleaning strategy and the most important factor amidst the array of alternatives. Our results indicate that, among various factors, the frequency of cleaning intervals is the most critical element for controlling the spread of CDI in a hospital environment.
Machine learning based state observer for discrete time systems evolving on Lie groups

In this paper, a machine learning based observer for systems evolving on manifolds is designed such that the state of the observer is restricted to the Lie group on which the system evolves. Conventional techniques involving machine learning based observers on systems evolving on Lie groups involve designing charts for the Lie group, training a machine learning based observer for each chart, and switching between the trained models based on the state of the system. We propose a novel deep learning based technique whose predictions are restricted to a measure 0 subset of Euclidean space without using charts. Using this network, we design an observer ensuring that the state of the observer is restricted to the Lie group, and predicting the state using only one trained algorithm. The deep learning network predicts an ``error term'' on the Lie algebra of the Lie group, uses the map from the Lie algebra to the group, and uses the group action and the present state to estimate the state at the next epoch. This model being purely data driven does not require the model of the system. The proposed algorithm provides a novel framework for constraining the output of machine learning networks to a measure 0 subset of a Euclidean space without chart specific training and without requiring switching. We show the validity of this method using Monte Carlo simulations performed of the rigid body rotation and translation system.
Lie-Cartan modules and cohomology

As a sequel to [Duan-Shu-Yao], we introduce here a category $\mathscr{LC}$ arising from the BGG category $\mathcal{O}$ defined in [Duan-Shu-Yao] for Lie algebras of polynomial vector fields. The objects of $\mathscr{LC}$ are so-called Lie-Cartan modules which admit both Lie-module structure and compatible $R$-module structure ($R$ denotes the corresponding polynomial ring). This terminology is natural, coming from affine connections in differential geometry through which the structure sheaves in topology and the vector fields in geometry are integrated for differential manifolds. In this paper, we study Lie-Cartan modules and their categorical and cohomology properties. The category $\mathscr{LC}$ is abelian, and a ``highest weight category"with depths. Notably, the set of co-standard objects in the category $\mathcal{O}$ turns out to represent the isomorphism classes of simple objects of $\mathscr{LC}$. We then establish the cohomology for this category (called the $\mathscr{uLC}$-cohomology), extending Chevalley-Eilenberg cohomology theory. Another notable result says that in the fundamental case $\mathfrak{g}= W(n)$, the extension ring $\text{Ext}^\bullet_{\mathscr{uLC}}(R,R)$ for the polynomial algebra $R$ in the $\mathscr{uLC}$-cohomology is isomorphic to the usual cohomology ring $H^\bullet(\mathfrak{gl}(n))$ of the general linear Lie algebra $\mathfrak{gl}(n)$.