Miracle-dz/UniFAR-FTUs huggingface dataset:
Boson sampling enhanced quantum chemistry

In this work, we give a new variational quantum algorithm for solving electronic structure problems of molecules using only linear quantum optical systems. The variational ansatz we proposed is a hybrid of non-interacting Boson dynamics and classical computational chemistry methods, specifically, the Hartree-Fock method and the Configuration Interaction method. The Boson part is built by a linear optical interferometer which is easier to realize compared with the well-known Unitary Coupled Cluster (UCC) ansatz composed of quantum gates in conventional VQE and the classical part is merely classical processing acting on the Hamiltonian. We called such ansatzes Boson Sampling-Classic (BS-C). The appearance of permanents in the Boson part has its physical intuition to provide different kinds of resources from commonly used single-, double-, and higher-excitations in classical methods and the UCC ansatz to exploring chemical quantum states. Such resources can help enhance the accuracy of methods used in the classical parts. We give a scalable hybrid homodyne and photon number measurement procedure for evaluating the energy value which has intrinsic abilities to mitigate photon loss errors and discuss the extra measurement cost induced by the no Pauli exclusion principle for Bosons with its solutions. To demonstrate our proposal, we run numerical experiments on several molecules and obtain their potential energy curves reaching chemical accuracy.
ChatGPT Incorrectness Detection in Software Reviews

We conducted a survey of 135 software engineering (SE) practitioners to understand how they use Generative AI-based chatbots like ChatGPT for SE tasks. We find that they want to use ChatGPT for SE tasks like software library selection but often worry about the truthfulness of ChatGPT responses. We developed a suite of techniques and a tool called CID (ChatGPT Incorrectness Detector) to automatically test and detect the incorrectness in ChatGPT responses. CID is based on the iterative prompting to ChatGPT by asking it contextually similar but textually divergent questions (using an approach that utilizes metamorphic relationships in texts). The underlying principle in CID is that for a given question, a response that is different from other responses (across multiple incarnations of the question) is likely an incorrect response. In a benchmark study of library selection, we show that CID can detect incorrect responses from ChatGPT with an F1-score of 0.74 - 0.75.
ChebMixer: Efficient Graph Representation Learning with MLP Mixer

Graph neural networks have achieved remarkable success in learning graph representations, especially graph Transformer, which has recently shown superior performance on various graph mining tasks. However, graph Transformer generally treats nodes as tokens, which results in quadratic complexity regarding the number of nodes during self-attention computation. The graph MLP Mixer addresses this challenge by using the efficient MLP Mixer technique from computer vision. However, the time-consuming process of extracting graph tokens limits its performance. In this paper, we present a novel architecture named ChebMixer, a newly graph MLP Mixer that uses fast Chebyshev polynomials-based spectral filtering to extract a sequence of tokens. Firstly, we produce multiscale representations of graph nodes via fast Chebyshev polynomial-based spectral filtering. Next, we consider each node's multiscale representations as a sequence of tokens and refine the node representation with an effective MLP Mixer. Finally, we aggregate the multiscale representations of nodes through Chebyshev interpolation. Owing to the powerful representation capabilities and fast computational properties of MLP Mixer, we can quickly extract more informative node representations to improve the performance of downstream tasks. The experimental results prove our significant improvements in a variety of scenarios ranging from graph node classification to medical image segmentation.
Low‐Altitude Observations of Recurrent Short‐Lived keV Ion Microinjections Inside the Diffuse Auroral Zone

The AMBRE experiment onboard the ocean topography mapper JASON‐3 aims at measuring the spacecraft potential as well as auroral particle precipitation using two top‐hat analyzers for electrons and ions in the 20 eV–28 keV energy range. The JASON‐3 spacecraft has a nearly circular orbit at an altitude of 1,336 km with an inclination of 66°, at times probing the equatorward part of the auroral oval in a nearly tangential manner upon leaving the outer radiation belt. In this region of space, during periods of enhanced geomagnetic activity with small or moderate storms, AMBRE detected recurrent “microinjections” of ions with energies in the 200 eV–28 keV range and which exhibit clear time‐of‐flight dispersion. Ray tracing using single trajectory computations suggests that these ions are launched from a source located in the 8,000–12,000 km altitudinal range and subsequently propagate downward toward the ionosphere. Such observations of quasiperiodic dispersed downflowing ions are new, and we argue that these structures could be produced by ion‐wave interactions at midaltitudes.
Evidence that Orai1 does not contribute to store-operated TRPC1 channels in vascular smooth muscle cells

ABSTRACT Ca2+-permeable store-operated channels (SOCs) mediate Ca2+ entry pathways which are involved in many cellular functions such as contraction, growth, and proliferation. Prototypical SOCs are formed of Orai1 proteins and are activated by the endo/sarcoplasmic reticulum Ca2+ sensor stromal interaction molecule 1 (STIM1). There is considerable debate about whether canonical transient receptor potential 1 (TRPC1) proteins also form store-operated channels (SOCs), and if they do, is Orai1 involved. We recently showed that stimulation of TRPC1-based SOCs involves store depletion inducing STIM1-evoked Gαq/PLCβ1 activity in contractile vascular smooth muscle cells (VSMCs). Therefore the present work investigates the role of Orai1 in activation of TRPC1-based SOCs in freshly isolated mesenteric artery VSMCs from wild-type (WT) and Orai1−/− mice. Store-operated whole-cell and single channel currents recorded from WT and Orai1−/− VSMCs had similar properties, with relatively linear current-voltage relationships, reversal potentials of about +20mV, unitary conductances of about 2pS, and inhibition by anti-TRPC1 and anti-STIM1 antibodies. In Orai1−/− VSMCs, store depletion induced PLCβ1 activity measured with the fluorescent phosphatidylinositol 4,5-bisphosphate/inositol 1,4,5-trisphosphate biosensor GFP-PLCδ1-PH, which was prevented by knockdown of STIM1. In addition, in Orai1−/− VSMCs, store depletion induced translocation of STIM1 from within the cell to the plasma membrane where it formed STIM1-TRPC1 interactions at discrete puncta-like sites. These findings indicate that activation of TRPC1-based SOCs through a STIM1-activated PLCβ1 pathway are likely to occur independently of Orai1 proteins, providing evidence that TRPC1 channels form genuine SOCs in VSMCs with a contractile phenotype.
Investigating the production of emotional facial expressions: a combined electroencephalographic (EEG) and electromyographic (EMG) approach

Facial expressions are part of emotional reactions. However, humans can voluntarily pose a specific emotional expression without having the corresponding underlying feeling, or voluntarily modify (e.g. reduce or enhance) their spontaneous expression in reaction to an emotional event. Few studies have attempted to distinguish these different processes at the level of the central nervous system (CNS), even though spontaneous and voluntary facial expressions are long thought to rely upon distinct neural circuitries. Here, we review the neural bases of spontaneous and voluntary facial expressions, report the results of a first study assessing the Bereitschaftspotential (BP) before voluntary smiles, and outline a combined EEG/EMG approach for investigating facial expressions at the level of the CNS.
MIT Open Access Articles POLARIMETRY AND THE HIGH-ENERGY EMISSION MECHANISMS IN QUASAR JETS: THE CASE OF PKS 1136–135

Since the discovery of kiloparsec-scale X-ray emission from quasar jets, the physical processes responsible for their high-energy emission have been poorly deﬁned. A number of mechanisms are under active debate, including synchrotron radiation, inverse-Comptonized cosmic microwave background (IC / CMB) emission, and other Comptonization processes. In a number of cases, the optical and X-ray emission of jet regions are inked by a single spectral component, and in those, high-resolution multi-band imaging and polarimetry can be combined to yield a powerful diagnostic of jet emission processes. Here we report on deep imaging photometry of the jet of PKS 1136 − 135 obtained with the Hubble Space Telescope. We ﬁnd that several knots are highly polarized in the optical, with fractional polarization Π > 30%. When combined with the broadband spectral shape observed in these regions, this is very difﬁcult to explain via IC / CMB models, unless the scattering particles are at the lowest-energy tip of the electron energy distribution, with Lorentz factor γ ∼ 1, and the jet is also very highly beamed ( δ (cid:2) 20) and viewed within a few degrees of the line of sight. We discuss both the IC / CMB and synchrotron interpretation of the X-ray emission in the light of this new evidence, presenting new models of the spectral energy distribution and also the matter content of this jet. The high polarizations do not completely rule out the possibility of IC / CMB optical-to-X-ray emission in this jet, but they do strongly disfavor the model. We discuss the implications of this ﬁnding, and also the prospects for future work.
Who is bragging more online? A large scale analysis of bragging in social media

Bragging is the act of uttering statements that are likely to be positively viewed by others and it is extensively employed in human communication with the aim to build a positive self-image of oneself. Social media is a natural platform for users to employ bragging in order to gain admiration, respect, attention and followers from their audiences. Yet, little is known about the scale of bragging online and its characteristics. This paper employs computational sociolinguistics methods to conduct the first large scale study of bragging behavior on Twitter (U.S.) by focusing on its overall prevalence, temporal dynamics and impact of demographic factors. Our study shows that the prevalence of bragging decreases over time within the same population of users. In addition, younger, more educated and popular users in the U.S. are more likely to brag. Finally, we conduct an extensive linguistics analysis to unveil specific bragging themes associated with different user traits.
Quantitative study of the pinning effect of the edge dislocation on domain wall motion in Barium Titanate thin films

Dislocation is a very important one-dimensional defect in ferroelectrics. This work introduces an easy and flexible model of implementing the edge dislocation by introducing eigenstrain at the interface, and it could be easily extended to incorporate the surface stress to refine the analysis of ferroelectric thin films. The influence of dislocations on the ferroelectric domain wall motion and hysteresis loop including the remanent polarization and coercive field using phase-field simulations is analyzed. The pinning effect of the dislocation on the domain wall motion is discussed and whether the domain wall is pined is the competition between the external loading and the magnitude of the burgers vector of the dislocation. This work could contribute to the understanding of the pining effect of the dislocation and provide guidance for the fabrication of ferroelectric thin films.
Homogenization of nonconvex viscous Hamilton-Jacobi equations in stationary ergodic media in one dimension

We establish homogenization for nondegenerate viscous Hamilton-Jacobi equations in one space dimension when the diffusion coefficient $a(x,\omega)>0$ and the Hamiltonian $H(p,x,\omega)$ are general stationary ergodic processes in $x$. Our result is valid under mild regularity assumptions on $a$ and $H$ plus standard coercivity and growth assumptions (in $p$) on the latter. In particular, we impose neither any additional condition on the law of the media nor any shape restriction on the graph of $p\mapsto H(p,x,\omega)$. Our approach consists of two main steps: (i) constructing a suitable candidate $\overline{H}$ for the effective Hamiltonian; (ii) proving homogenization. In the first step, we work with the set $E$ of all points at which $\overline{H}$ is naturally determined by correctors with stationary derivatives. We prove that $E$ is a closed subset of $\mathbb{R}$ that is unbounded from above and below, and, if $E\neq\mathbb{R}$, then $\overline{H}$ can be extended continuously to $\mathbb{R}$ by setting it to be constant on each connected component of $E^c$. In the second step, we use a key bridging lemma, comparison arguments and several general results to verify that homogenization holds with this $\overline{H}$ as the effective Hamiltonian.
Landmark-Guided Cross-Speaker Lip Reading with Mutual Information Regularization

Lip reading, the process of interpreting silent speech from visual lip movements, has gained rising attention for its wide range of realistic applications. Deep learning approaches greatly improve current lip reading systems. However, lip reading in cross-speaker scenarios where the speaker identity changes, poses a challenging problem due to inter-speaker variability. A well-trained lip reading system may perform poorly when handling a brand new speaker. To learn a speaker-robust lip reading model, a key insight is to reduce visual variations across speakers, avoiding the model overfitting to specific speakers. In this work, in view of both input visual clues and latent representations based on a hybrid CTC/attention architecture, we propose to exploit the lip landmark-guided fine-grained visual clues instead of frequently-used mouth-cropped images as input features, diminishing speaker-specific appearance characteristics. Furthermore, a max-min mutual information regularization approach is proposed to capture speaker-insensitive latent representations. Experimental evaluations on public lip reading datasets demonstrate the effectiveness of the proposed approach under the intra-speaker and inter-speaker conditions.
What are the quantum commutation relations for the total angular momentum of light?

The total angular momentum of light has received attention for its application in a variety of phenomena such as optical communication, optical forces and sensing. However, the quantum behavior including the commutation relations have been relatively less explored. Here, we derive the correct commutation relation for the total angular momentum of light using both relativistic and non-relativistic approaches. An important outcome of our work is the proof that the widely-assumed quantum commutation relation for the total observable angular momentum of light is fundamentally incorrect. Our work will motivate experiments and leads to new insight on the quantum behavior of the angular momentum of light.
Port Forwarding Services Are Forwarding Security Risks

We conduct the first comprehensive security study on representative port forwarding services (PFS), which emerge in recent years and make the web services deployed in internal networks available on the Internet along with better usability but less complexity compared to traditional techniques (e.g., NAT traversal techniques). Our study is made possible through a set of novel methodologies, which are designed to uncover the technical mechanisms of PFS, experiment attack scenarios for PFS protocols, automatically discover and snapshot port-forwarded websites (PFWs) at scale, and classify PFWs into well-observed categories. Leveraging these methodologies, we have observed the widespread adoption of PFS with millions of PFWs distributed across tens of thousands of ISPs worldwide. Furthermore, 32.31% PFWs have been classified into website categories that serve access to critical data or infrastructure, such as, web consoles for industrial control systems, IoT controllers, code repositories, and office automation systems. And 18.57% PFWs didn't enforce any access control for external visitors. Also identified are two types of attacks inherent in the protocols of Oray (one well-adopted PFS provider), and the notable abuse of PFSes by malicious actors in activities such as malware distribution, botnet operation and phishing.
CBGT-Net: A Neuromimetic Architecture for Robust Classification of Streaming Data

This paper describes CBGT-Net, a neural network model inspired by the cortico-basal ganglia-thalamic (CBGT) circuits found in mammalian brains. Unlike traditional neural network models, which either generate an output for each provided input, or an output after a fixed sequence of inputs, the CBGT-Net learns to produce an output after a sufficient criteria for evidence is achieved from a stream of observed data. For each observation, the CBGT-Net generates a vector that explicitly represents the amount of evidence the observation provides for each potential decision, accumulates the evidence over time, and generates a decision when the accumulated evidence exceeds a pre-defined threshold. We evaluate the proposed model on two image classification tasks, where models need to predict image categories based on a stream of small patches extracted from the image. We show that the CBGT-Net provides improved accuracy and robustness compared to models trained to classify from a single patch, and models leveraging an LSTM layer to classify from a fixed sequence length of patches.
Digital Twin Assisted Intelligent Network Management for Vehicular Applications

The emerging data-driven methods based on artificial intelligence (AI) have paved the way for intelligent, flexible, and adaptive network management in vehicular applications. To enhance network management towards network automation, this article presents a digital twin (DT) assisted two-tier learning framework, which facilitates the automated life-cycle management of machine learning based intelligent network management functions (INMFs). Specifically, at a high tier, meta learning is employed to capture different levels of general features for the INMFs under nonstationary network conditions. At a low tier, individual learning models are customized for local networks based on fast model adaptation. Hierarchical DTs are deployed at the edge and cloud servers to assist the two-tier learning process, through closed-loop interactions with the physical network domain. Finally, a case study demonstrates the fast and accurate model adaptation ability of meta learning in comparison with benchmark schemes.
Emotion Recognition from the perspective of Activity Recognition

Applications of an efficient emotion recognition system can be found in several domains such as medicine, driver fatigue surveillance, social robotics, and human-computer interaction. Appraising human emotional states, behaviors, and reactions displayed in real-world settings can be accomplished using latent continuous dimensions. Continuous dimensional models of human affect, such as those based on valence and arousal are more accurate in describing a broad range of spontaneous everyday emotions than more traditional models of discrete stereotypical emotion categories (e.g. happiness, surprise). Most of the prior work on estimating valence and arousal considers laboratory settings and acted data. But, for emotion recognition systems to be deployed and integrated into real-world mobile and computing devices, we need to consider data collected in the world. Action recognition is a domain of Computer Vision that involves capturing complementary information on appearance from still frames and motion between frames. In this paper, we treat emotion recognition from the perspective of action recognition by exploring the application of deep learning architectures specifically designed for action recognition, for continuous affect recognition. We propose a novel three-stream end-to-end deep learning regression pipeline with an attention mechanism, which is an ensemble design based on sub-modules of multiple state-of-the-art action recognition systems. The pipeline constitutes a novel data pre-processing approach with a spatial self-attention mechanism to extract keyframes. The optical flow of high-attention regions of the face is extracted to capture temporal context. AFEW-VA in-the-wild dataset has been used to conduct comparative experiments. Quantitative analysis shows that the proposed model outperforms multiple standard baselines of both emotion recognition and action recognition models.
Uncovering Selective State Space Model's Capabilities in Lifelong Sequential Recommendation

Sequential Recommenders have been widely applied in various online services, aiming to model users' dynamic interests from their sequential interactions. With users increasingly engaging with online platforms, vast amounts of lifelong user behavioral sequences have been generated. However, existing sequential recommender models often struggle to handle such lifelong sequences. The primary challenges stem from computational complexity and the ability to capture long-range dependencies within the sequence. Recently, a state space model featuring a selective mechanism (i.e., Mamba) has emerged. In this work, we investigate the performance of Mamba for lifelong sequential recommendation (i.e., length>=2k). More specifically, we leverage the Mamba block to model lifelong user sequences selectively. We conduct extensive experiments to evaluate the performance of representative sequential recommendation models in the setting of lifelong sequences. Experiments on two real-world datasets demonstrate the superiority of Mamba. We found that RecMamba achieves performance comparable to the representative model while significantly reducing training duration by approximately 70% and memory costs by 80%. Codes and data are available at \url{https://github.com/nancheng58/RecMamba}.
Physics-informed and Unsupervised Riemannian Domain Adaptation for Machine Learning on Heterogeneous EEG Datasets

Combining electroencephalogram (EEG) datasets for supervised machine learning (ML) is challenging due to session, subject, and device variability. ML algorithms typically require identical features at train and test time, complicating analysis due to varying sensor numbers and positions across datasets. Simple channel selection discards valuable data, leading to poorer performance, especially with datasets sharing few channels. To address this, we propose an unsupervised approach leveraging EEG signal physics. We map EEG channels to fixed positions using field interpolation, facilitating source-free domain adaptation. Leveraging Riemannian geometry classification pipelines and transfer learning steps, our method demonstrates robust performance in brain-computer interface (BCI) tasks and potential biomarker applications. Comparative analysis against a statistical-based approach known as Dimensionality Transcending, a signal-based imputation called ComImp, source-dependent methods, as well as common channel selection and spherical spline interpolation, was conducted with leave-one-dataset-out validation on six public BCI datasets for a right-hand/left-hand classification task. Numerical experiments show that in the presence of few shared channels in train and test, the field interpolation consistently outperforms other methods, demonstrating enhanced classification performance across all datasets. When more channels are shared, field interpolation was found to be competitive with other methods and faster to compute than source-dependent methods.