Miracle-dz/UniFAR-FTUs huggingface dataset:
Equivariant and Invariant Grounding for Video Question Answering

Video Question Answering (VideoQA) is the task of answering the natural language questions about a video. Producing an answer requires understanding the interplay across visual scenes in video and linguistic semantics in question. However, most leading VideoQA models work as black boxes, which make the visual-linguistic alignment behind the answering process obscure. Such black-box nature calls for visual explainability that reveals "What part of the video should the model look at to answer the question? ". Only a few works present the visual explanations in a post-hoc fashion, which emulates the target model's answering process via an additional method. Instead of post-hoc explainability, we focus on intrinsic interpretability to make the answering process transparent. At its core is grounding the question-critical cues as the causal scene to yield answers, while rolling out the question-irrelevant information as the environment scene. Taking a causal look at VideoQA, we devise a self-interpretable framework, Equivariant and Invariant Grounding for Interpretable VideoQA (EIGV). Specifically, the equivariant grounding encourages the answering to be sensitive to the semantic changes in the causal scene and question; in contrast, the invariant grounding enforces the answering to be insensitive to the changes in the environment scene. By imposing them on the answering process, EIGV is able to distinguish the causal scene from the environment information, and explicitly present the visual-linguistic alignment. Extensive experiments on three benchmark datasets justify the superiority of EIGV in terms of accuracy and visual interpretability over the leading baselines.
Topology of Almost Complex Structures on Six-Manifolds

We study the space of (orthogonal) almost complex structures on closed six-dimensional manifolds as the space of sections of the twistor space for a given metric. For a connected six-manifold with vanishing first Betti number, we express the space of almost complex structures as a quotient of the space of sections of a seven-sphere bundle over the manifold by a circle action, and then use this description to compute the rational homotopy theoretic minimal model of the components that satisfy a certain Chern number condition. We further obtain a formula for the homological intersection number of two sections of the twistor space in terms of the Chern classes of the corresponding almost complex structures.
Future-Dependent Value-Based Off-Policy Evaluation in POMDPs

We study off-policy evaluation (OPE) for partially observable MDPs (POMDPs) with general function approximation. Existing methods such as sequential importance sampling estimators and fitted-Q evaluation suffer from the curse of horizon in POMDPs. To circumvent this problem, we develop a novel model-free OPE method by introducing future-dependent value functions that take future proxies as inputs. Future-dependent value functions play similar roles as classical value functions in fully-observable MDPs. We derive a new Bellman equation for future-dependent value functions as conditional moment equations that use history proxies as instrumental variables. We further propose a minimax learning method to learn future-dependent value functions using the new Bellman equation. We obtain the PAC result, which implies our OPE estimator is consistent as long as futures and histories contain sufficient information about latent states, and the Bellman completeness. Finally, we extend our methods to learning of dynamics and establish the connection between our approach and the well-known spectral learning methods in POMDPs.
The Gaia-ESO survey: A spectroscopic study of the young open cluster NGC 3293

We present a spectroscopic analysis of the GIRAFFE and UVES data collected by the Gaia-ESO survey for the young open cluster NGC 3293. Archive spectra from the same instruments obtained in the framework of the `VLT-FLAMES survey of massive stars' are also analysed. Atmospheric parameters, non-LTE chemical abundances for six elements, or variability information are reported for a total of about 160 B stars spanning a wide range in terms of spectral types (B1 to B9.5) and rotation rate (up to 350 km/s). We take advantage of the multi-epoch observations to detect several binary systems or intrinsically line-profile variables. A deconvolution algorithm is used to infer the current, true (deprojected) rotational velocity distribution. We find a broad, Gaussian-like distribution peaking around 200-250 km/s. Although some stars populate the high-velocity tail, most stars in the cluster appear to rotate far from critical. We discuss the chemical properties of the cluster, including the low occurrence of abundance peculiarities in the late B stars and the paucity of objects showing CN-cycle burning products at their surface. We argue that the former result can largely be explained by the inhibition of diffusion effects because of fast rotation, while the latter is generally in accord with the predictions of single-star evolutionary models under the assumption of a wide range of initial spin rates at the onset of main-sequence evolution. However, we find some evidence for a less efficient mixing in two quite rapidly rotating stars that are among the most massive objects in our sample. Finally, we obtain a cluster age of ~20 Myrs through a detailed, star-to-star correction of our results for the effect of stellar rotation. This is significantly older than previous estimates from turn-off fitting that fully relied on classical, non-rotating isochrones. [abridged]
A New Causal Decomposition Paradigm towards Health Equity

Causal decomposition has provided a powerful tool to analyze health disparity problems, by assessing the proportion of disparity caused by each mediator. However, most of these methods lack \emph{policy implications}, as they fail to account for all sources of disparities caused by the mediator. Besides, their estimations \emph{pre-specified} some covariates set (\emph{a.k.a}, admissible set) for the strong ignorability condition to hold, which can be problematic as some variables in this set may induce new spurious features. To resolve these issues, under the framework of the structural causal model, we propose to decompose the total effect into adjusted and unadjusted effects, with the former being able to include all types of disparity by adjusting each mediator's distribution from the disadvantaged group to the advantaged ones. Besides, equipped with maximal ancestral graph and context variables, we can automatically identify the admissible set, followed by an efficient algorithm for estimation. Theoretical correctness and the efficacy of our method are demonstrated on a synthetic dataset and a spine disease dataset.
EEG-Based Detection of Braking Intention During Simulated Driving

Accurately detecting and identifying drivers’ braking intention is the basis of man-machine driving. This paper proposed an electroencephalographic (EEG)-based braking intention measurement strategy. We used the Car Learning to Act (Carla) platform to build the simulated driving environment. 11 subjects participated in our study, and each subject drove a simulated vehicle to complete emergency braking and normal braking tasks. We compared the EEG topographic maps in different braking situations and used three different classifiers to predict the subjects’ braking intention through EEG signals. The results of the experiment showed that the grand-average response time of all subjects in emergency braking was 762 ms; emergency braking and no braking can be well distinguished, while normal braking and no braking were not easy to be classified; for the two different types of braking, emergency braking and normal braking had obvious differences in EEG topographic maps, and the classification results also showed that the two were highly distinguishable. This study provides a user-centered driver-assistance system and a good framework to combine with advanced shared control algorithms, which has the potential to be applied to achieve a more friendly interaction between the driver and vehicle in real driving environment. An earlier version of this work Liang et al. (2022) has been presented as preprint in arXiv (https://arxiv.org/abs/2207.12669).
A Randomized Algorithm for Tensor Singular Value Decomposition Using an Arbitrary Number of Passes

Efficient and fast computation of a tensor singular value decomposition (t-SVD) with a few passes over the underlying data tensor is crucial because of its many potential applications. The current/existing subspace randomized algorithms need $$(2q+2)$$ ( 2 q + 2 ) passes over the data tensor to compute a t-SVD, where q is a non-negative integer number (power iteration parameter). In this paper, we propose an efficient and flexible randomized algorithm that can handle any number of passes q , which not necessary need be even. The flexibility of the proposed algorithm in using fewer passes naturally leads to lower computational and communication costs. This advantage makes it particularly appropriate when our task calls for several tensor decompositions or when the data tensors are huge. The proposed algorithm is a generalization of the methods developed for matrices to tensors. The expected/average error bound of the proposed algorithm is derived. Extensive numerical experiments on random and real-world data sets are conducted, and the proposed algorithm is compared with some baseline algorithms. The extensive computer simulation experiments demonstrate that the proposed algorithm is practical, efficient, and in general outperforms the state of the arts algorithms. We also demonstrate how to use the proposed method to develop a fast algorithm for the tensor completion problem.
Magnetic field measurement in TMC-1C using 22.3 GHz CCS Zeeman splitting

Measurement of magnetic fields in dense molecular clouds is essential for understanding the fragmentation process prior to star formation. Radio interferometric observations of CCS 22.3 GHz emission, from the starless core TMC-1C, have been carried out with the Karl G. Jansky Very Large Array to search for Zeeman splitting of the line in order to constrain the magnetic field strength. Toward a region offset from the dust peak, we report a detection of the Zeeman splitting of the CCS 21 - 10 transition, with an inferred magnetic field of ∼2 mG. If we interpret the dust peak to be the core of TMC-1C, and the region where we have made a detection of the magnetic field to be the envelope, then our observed value for the magnetic field is consistent with a subcritical mass-to-flux ratio envelope around a core with supercritical mass-to-flux ratio. The ambipolar diffusion timescale for the formation of the core is consistent with the relevant timescale based on chemical modeling of the TMC-1C core. This work demonstrates the potential of deep CCS observation to carry out future measurements of magnetic field strengths in dense molecular clouds and, in turn, understand the role of the magnetic field in star formation.
Simple supercuspidal L-packets of symplectic groups over dyadic fields

We consider the symplectic group Sp 2 n deﬁned over a p -adic ﬁeld F , where p = 2. We prove that every simple supercuspidal representation (in the sense of Gross–Reeder) of Sp 2 n ( F ) corresponds to an irreducible L - parameter under the local Langlands correspondence for Sp 2 n established by Arthur.
Vortex solutions in a binary immiscible Bose-Einstein condensate

We consider the mean-field vortex solutions and their stability within a two-component Bose Einstein condensate in the immiscible limit. A variational approach is employed to study a system consisting of a majority component which contains a single quantised vortex and a minority component which fills the vortex core. We show that a super-Gaussian function is a good approximation to the two-component vortex solution for a range of atom numbers of the in-filling component, by comparing the variational solutions to the full numerical solutions of the coupled Gross-Pitaevskii equations. We subsequently examine the stability of the vortex solutions by perturbing the in-filling component away from the centre of the vortex core, thereby demonstrating their stability to small perturbations.
Live Stream Temporally Embedded 3D Human Body Pose and Shape Estimation

3D Human body pose and shape estimation within a temporal sequence can be quite critical for understanding human behavior. Despite the significant progress in human pose estimation in the recent years, which are often based on single images or videos, human motion estimation on live stream videos is still a rarely-touched area considering its special requirements for real-time output and temporal consistency. To address this problem, we present a temporally embedded 3D human body pose and shape estimation (TePose) method to improve the accuracy and temporal consistency of pose estimation in live stream videos. TePose uses previous predictions as a bridge to feedback the error for better estimation in the current frame and to learn the correspondence between data frames and predictions in the history. A multi-scale spatio-temporal graph convolutional network is presented as the motion discriminator for adversarial training using datasets without any 3D labeling. We propose a sequential data loading strategy to meet the special start-to-end data processing requirement of live stream. We demonstrate the importance of each proposed module with extensive experiments. The results show the effectiveness of TePose on widely-used human pose benchmarks with state-of-the-art performance.
Pinned Distance Sets Using Effective Dimension

In this paper, we use algorithmic tools, effective dimension and Kolmogorov complexity, to study the fractal dimension of distance sets. We show that, for any analytic set $E\subseteq\R^2$ of Hausdorff dimension strictly greater than one, the \textit{pinned distance set} of $E$, $\Delta_x E$, has Hausdorff dimension of at least $\frac{3}{4}$, for all points $x$ outside a set of Hausdorff dimension at most one. This improves the best known bounds when the dimension of $E$ is close to one.
Fox-Neuwirth cells, quantum shuffle algebras, and the homology of type-B Artin groups

In this paper, we will develop a family of braid representations of Artin groups of type B from braided vector spaces, and identify the homology of these groups with these coefficients with the cohomology of a specific bimodule over a quantum shuffle algebra. As an application, we give a complete characterization of the homology of type-B Artin groups with coefficients in one-dimensional braid representations over a field of characteristic 0. We will also discuss two different approaches to this computation: the first method extends a computation of the homology of braid groups due to Ellenberg-Tran-Westerland by means of induced representation, while the second method involves constructing a cellular stratification for configuration spaces of the punctured complex plane.
Data-Aided Active User Detection With False Alarm Correction in Grant-Free Transmission

In most existing grant-free (GF) studies, the two key tasks, namely active user detection (AUD) and payload data decoding, are handled separately. In this letter, a two-step data-aided AUD scheme is proposed, namely the initial AUD step and the false alarm correction step respectively. To implement the initial AUD step, an embedded low-density-signature (LDS) based preamble pool is constructed. In addition, two message passing algorithm (MPA) based initial estimators are developed. In the false alarm correction step, a redundant factor graph is constructed based on the initial active user set, on which MPA is employed for data decoding. The remaining false detected inactive users will be further detected by the false alarm corrector with the aid of decoded data symbols. Simulation results reveal that both the data decoding performance and the AUD performance are significantly enhanced by more than 1.5 dB at the target accuracy of $10^{-3}$ compared with the traditional compressed sensing (CS) based counterparts.
D = 11 dS 5 backgrounds with enhanced supersymmetry

We classify all warped dS 5 backgrounds in D = 11 supergravity with enhanced supersymmetry. We show that backgrounds preserving N = 16 supersymmetries consist of either a stack of M5 branes with transverse space R5 , or a generalized M5-brane configuration with transverse space R×N4 , where N 4 is a hyper-Kähler manifold and the M5-brane harmonic function is determined by a hyper-Kähler potential on N 4. Moreover, we find that there are no backgrounds preserving exactly N = 24 supersymmetries. Backgrounds preserving N = 32 supersymmetries correspond to either R1,10 or AdS7×S4 .
Eliciting Multimodal Gesture+Speech Interactions in a Multi-Object Augmented Reality Environment

As augmented reality (AR) technology and hardware become more mature and affordable, researchers have been exploring more intuitive and discoverable interaction techniques for immersive environments. This paper investigates multimodal interaction for 3D object manipulation in a multi-object AR environment. To identify the user-defined gestures, we conducted an elicitation study involving 24 participants and 22 referents using an augmented reality headset. It yielded 528 proposals and generated a winning gesture set with 25 gestures after binning and ranking all gesture proposals. We found that for the same task, the same gesture was preferred for both one and two-object manipulation, although both hands were used in the two-object scenario. We present the gestures and speech results, and the differences compared to similar studies in a single object AR environment. The study also explored the association between speech expressions and gesture stroke during object manipulation, which could improve the recognizer efficiency in augmented reality headsets.
Averages of long Dirichlet polynomials with modular coefficients

We study the moments of L‐functions associated with primitive cusp forms, in the weight aspect. In particular, we obtain an asymptotic formula for the twisted moments of a long Dirichlet polynomial with modular coefficients. This result, which is conditional on the Generalized Lindelöf Hypothesis, agrees with the prediction of the recipe by Conrey, Farmer, Keating, Rubinstein and Snaith.
Design and Simulation of RF Modulated Thermionic Electron Gun

We present design and simulation results on the electron source for 2856 MHz technological electron linac. This device is a combination of an RF gun and a DC diode gun. The designed gun consists of a thermionic cathode, which is mounted on the wall of the TM 010 cavity that is under negative high voltage potential relatively to an anode. The use of such an electron source makes it possible to exclude the high-voltage gun modulator and resonator for beam prebunching from the accelerator circuit. Duration and position of an electron beam pulse within a RF power supply pulse of the accelerating section can be adjusted with tailoring of RF pulse feeding the cavity. The paper describes the design and electrodynamics characteristics of the gun cavity and a filter, which allows to transfer microwave power to that cavity. The particles dynamics has been studied by simulation. It is shown that the beam parameters at the gun exit satisfy the stated requirements. At an average particle energy of 95 keV, the beam pulse current is 0.44 A, the emittance does not exceed 4.8 mm  mrad, and 70% of all electrons are concentrated in the 72° phase range.
Optimization Process Models of CHP and Renewable Energy Hybrid Systems in CES

- In SS branch of Korea District Heating Corporation, Combined Heat & Power power plant with 99MW capacity and 98Gcal / h capacity is operated as a district electricity business. In this region, it is difficult to operate the generator due to the problem of surplus heat treatment between June and September due to the economic recession and the decrease in demand, so it is urgent to develop an economical energy new business model. Storage System). HOMER (Hybrid Optimization of Multiple Energy Resources) program was used to select the optimal model. As a result of the economic analysis, 99MW CHP combined cycle power generation is the most economical in terms of net present cost (NPC), but 99MW CHP in terms of carbon emission trading and renewable energy certificate And 5MW fuel cells, and 521kW of solar power to supply electricity and heat than the supply of electricity and heat by 99MW CHP cogeneration power, it was shown that it is economically up to 247.5 billion won. we confirmed the results of the improvement of the zone electricity business condition by introducing the fuel cell and the renewable energy hybrid system as the optimization process model.
A Pilot Study on The Impact of Stereoscopic Display Type on User Interactions Within A Immersive Analytics Environment

— Immersive Analytics (IA) and consumer adoption of augmented reality (AR) and virtual reality (VR) head-mounted displays (HMDs) are both rapidly growing. When used in conjunction, stereoscopic IA environments can offer improved user understanding and engagement; however, it is unclear how the choice of stereoscopic display impacts user interactions within an IA environment. This paper presents a pilot study that examines the impact of stereoscopic display type on object manipulation and environmental navigation using consumer-available AR and VR displays. This work ﬁnds that the display type can impact how users manipulate virtual content, how they navigate the environment, and how able they are to answer questions about the represented data.
A Survey of Explainable Graph Neural Networks: Taxonomy and Evaluation Metrics

Graph neural networks (GNNs) have demonstrated a significant boost in prediction performance on graph data. At the same time, the predictions made by these models are often hard to interpret. In that regard, many efforts have been made to explain the prediction mechanisms of these models from perspectives such as GNNExplainer, XGNN and PGExplainer. Although such works present systematic frameworks to interpret GNNs, a holistic review for explainable GNNs is unavailable. In this survey, we present a comprehensive review of explainability techniques developed for GNNs. We focus on explainable graph neural networks and categorize them based on the use of explainable methods. We further provide the common performance metrics for GNNs explanations and point out several future research directions.
NeuriCam: Video Super-Resolution and Colorization Using Key Frames

— We present NeuriCam, a key-frame video super-resolution and colorization based system, to achieve low-power video capture from dual-mode IOT cameras. Our idea is to design a dual-mode camera system where the ﬁrst mode is low power (1.1 mW) but only outputs gray-scale, low resolution and noisy video and the second mode consumes much higher power (100 mW) but outputs color and higher resolution images. To reduce total energy consumption, we heavily duty cycle the high power mode to output an image only once every second. The data from this camera system is then wirelessly streamed to a nearby plugged-in gateway, where we run our real-time neural network decoder to reconstruct a higher resolution color video. To achieve this, we introduce an attention feature ﬁlter mechanism that assigns different weights to different features, based on the correlation between the feature map and contents of the input frame at each spatial location. We design a wireless hardware prototype using off-the-shelf cameras and address practical issues including packet loss and perspective mismatch. Our evaluation shows that our dual-camera hardware reduces camera energy consumption while achieving an average gray-scale PSNR gain of 3.7 dB over prior video super resolution meth-ods and 5.6 dB RGB gain over existing color propagation methods.