Miracle-dz/UniFAR-FTUs huggingface dataset:
Online Metro Origin-Destination Prediction via Heterogeneous Information Aggregation

Metro origin-destination prediction is a crucial yet challenging time-series analysis task in intelligent transportation systems, which aims to accurately forecast two specific types of cross-station ridership, i.e., Origin-Destination (OD) one and Destination-Origin (DO) one. However, complete OD matrices of previous time intervals can not be obtained immediately in online metro systems, and conventional methods only used limited information to forecast the future OD and DO ridership separately. In this work, we proposed a novel neural network module termed Heterogeneous Information Aggregation Machine (HIAM), which fully exploits heterogeneous information of historical data (e.g., incomplete OD matrices, unfinished order vectors, and DO matrices) to jointly learn the evolutionary patterns of OD and DO ridership. Specifically, an OD modeling branch estimates the potential destinations of unfinished orders explicitly to complement the information of incomplete OD matrices, while a DO modeling branch takes DO matrices as input to capture the spatial-temporal distribution of DO ridership. Moreover, a Dual Information Transformer is introduced to propagate the mutual information among OD features and DO features for modeling the OD-DO causality and correlation. Based on the proposed HIAM, we develop a unified Seq2Seq network to forecast the future OD and DO ridership simultaneously. Extensive experiments conducted on two large-scale benchmarks demonstrate the effectiveness of our method for online metro origin-destination prediction. Our code is resealed at https://github.com/HCPLab-SYSU/HIAM.
An Investigation of the (In)effectiveness of Counterfactually Augmented Data

While pretrained language models achieve excellent performance on natural language understanding benchmarks, they tend to rely on spurious correlations and generalize poorly to out-of-distribution (OOD) data. Recent work has explored using counterfactually-augmented data (CAD)—data generated by minimally perturbing examples to flip the ground-truth label—to identify robust features that are invariant under distribution shift. However, empirical results using CAD during training for OOD generalization have been mixed. To explain this discrepancy, through a toy theoretical example and empirical analysis on two crowdsourced CAD datasets, we show that: (a) while features perturbed in CAD are indeed robust features, it may prevent the model from learning unperturbed robust features; and (b) CAD may exacerbate existing spurious correlations in the data. Our results thus show that the lack of perturbation diversity limits CAD’s effectiveness on OOD generalization, calling for innovative crowdsourcing procedures to elicit diverse perturbation of examples.
Polarized Self-Attention: Towards High-quality Pixel-wise Regression

Pixel-wise regression is probably the most common problem in fine-grained computer vision tasks, such as estimating keypoint heatmaps and segmentation masks. These regression problems are very challenging particularly because they require, at low computation overheads, modeling long-range dependencies on high-resolution inputs/outputs to estimate the highly nonlinear pixel-wise semantics. While attention mechanisms in Deep Convolutional Neural Networks(DCNNs) has become popular for boosting long-range dependencies, element-specific attention, such as Nonlocal blocks, is highly complex and noise-sensitive to learn, and most of simplified attention hybrids try to reach the best compromise among multiple types of tasks. In this paper, we present the Polarized Self-Attention(PSA) block that incorporates two critical designs towards high-quality pixel-wise regression: (1) Polarized filtering: keeping high internal resolution in both channel and spatial attention computation while completely collapsing input tensors along their counterpart dimensions. (2) Enhancement: composing non-linearity that directly fits the output distribution of typical fine-grained regression, such as the 2D Gaussian distribution (keypoint heatmaps), or the 2D Binormial distribution (binary segmentation masks). PSA appears to have exhausted the representation capacity within its channel-only and spatial-only branches, such that there is only marginal metric differences between its sequential and parallel layouts. Experimental results show that PSA boosts standard baselines by $2-4$ points, and boosts state-of-the-arts by $1-2$ points on 2D pose estimation and semantic segmentation benchmarks.
Optimal preconditioners for a Nitsche stabilized fictitious domain finite element method

In this paper we consider a class of fictitious domain finite element methods known from the literature. These methods use standard finite element spaces on a fixed unfitted triangulation combined with the Nitsche technique and a ghost penalty stabilization. As a model problem we consider the application of such a method to the Poisson equation. We introduce and analyze a new class of preconditioners that is based on a subspace decomposition approach. The finite element space is split into two subspaces, where one subspace is spanned by all nodal basis functions corresponding to nodes on the boundary of the fictitious domain and the other space is spanned by all remaining nodal basis functions. We will show that this splitting is stable, uniformly in the discretization parameter and in the location of the problem boundary in the triangulation. We also prove that the Galerkin discretization in the first subspace leads to a uniformly well-conditioned matrix and that the Galerkin discretization in the second subspace is uniformly equivalent to a standard finite element discretization of a Poisson equation on the fictitious domain with homogeneous Dirichlet boundary conditions. Results of numerical experiments that illustrate optimality of such a preconditioner are included.
Google COVID-19 Vaccination Search Insights: Anonymization Process Description

This report describes the aggregation and anonymization process applied to the COVID-19 Vaccination Search Insights (published at http://goo.gle/covid19vaccinationinsights), a publicly available dataset showing aggregated and anonymized trends in Google searches related to COVID-19 vaccination. The applied anonymization techniques protect every user's daily search activity related to COVID-19 vaccinations with $(\varepsilon, \delta)$-differential privacy for $\varepsilon = 2.19$ and $\delta = 10^{-5}$.
The fixed angle conjecture for QAOA on regular MaxCut graphs

The quantum approximate optimization algorithm (QAOA) is a near-term combinatorial optimization algorithm suitable for noisy quantum devices. However, little is known about performance guarantees for $p>2$. A recent work \cite{Wurtz_guarantee} computing MaxCut performance guarantees for 3-regular graphs conjectures that any $d$-regular graph evaluated at particular fixed angles has an approximation ratio greater than some worst-case guarantee. In this work, we provide numerical evidence for this fixed angle conjecture for $p<12$. We compute and provide these angles via numerical optimization and tensor networks. These fixed angles serve for an optimization-free version of QAOA, and have universally good performance on any 3 regular graph. Heuristic evidence is presented for the fixed angle conjecture on graph ensembles, which suggests that these fixed angles are ``close"to global optimum. Under the fixed angle conjecture, QAOA has a larger performance guarantee than the Goemans Williamson algorithm on 3-regular graphs for $p\geq 11$.
Locally octahedral and locally almost square K\"othe-Bochner spaces

It has been proved in [19] that a Köthe-Bochner space E(X) is locally octahedral/locally almost square if X has the respective property and the simple functions are dense in E(X). Here we show that the result still holds true without the density assumption. The proof makes use of the Kuratowski-Ryll-Nardzewski Theorem on measurable selections.
Learned Token Pruning for Transformers

Efficient deployment of transformer models in practice is challenging due to their inference cost including memory footprint, latency, and power consumption, which scales quadratically with input sequence length. To address this, we present a novel token reduction method dubbed Learned Token Pruning (LTP) which adaptively removes unimportant tokens as an input sequence passes through transformer layers. In particular, LTP prunes tokens with an attention score below a threshold, whose value is learned for each layer during training. Our threshold-based method allows the length of the pruned sequence to vary adaptively based on the input sequence, and avoids algorithmically expensive operations such as top-k token selection. We extensively test the performance of LTP on GLUE and SQuAD tasks and show that our method outperforms the prior state-of-the-art token pruning methods by up to ∽2.5% higher accuracy with the same amount of FLOPs. In particular, LTP achieves up to 2.1× FLOPs reduction with less than 1% accuracy drop, which results in up to 1.9× and 2.0× throughput improvement on Intel Haswell CPUs and NVIDIA V100 GPUs. Furthermore, we demonstrate that LTP is more robust than prior methods to variations in input sequence lengths. Our code has been developed in PyTorch and open-sourced
Exploiting Positional Information for Session-Based Recommendation

For present e-commerce platforms, it is important to accurately predict users’ preference for a timely next-item recommendation. To achieve this goal, session-based recommender systems are developed, which are based on a sequence of the most recent user-item interactions to avoid the influence raised from outdated historical records. Although a session can usually reflect a user’s current preference, a local shift of the user’s intention within the session may still exist. Specifically, the interactions that take place in the early positions within a session generally indicate the user’s initial intention, while later interactions are more likely to represent the latest intention. Such positional information has been rarely considered in existing methods, which restricts their ability to capture the significance of interactions at different positions. To thoroughly exploit the positional information within a session, a theoretical framework is developed in this paper to provide an in-depth analysis of the positional information. We formally define the properties of forward-awareness and backward-awareness to evaluate the ability of positional encoding schemes in capturing the initial and the latest intention. According to our analysis, existing positional encoding schemes are generally forward-aware only, which can hardly represent the dynamics of the intention in a session. To enhance the positional encoding scheme for the session-based recommendation, a dual positional encoding (DPE) is proposed to account for both forward-awareness and backward-awareness. Based on DPE, we propose a novel Positional Recommender (PosRec) model with a well-designed Position-aware Gated Graph Neural Network module to fully exploit the positional information for session-based recommendation tasks. Extensive experiments are conducted on two e-commerce benchmark datasets, Yoochoose and Diginetica and the experimental results show the superiority of the PosRec by comparing it with the state-of-the-art session-based recommender models.
Network of Star Formation: Fragmentation Controlled by Scale-dependent Turbulent Pressure and Accretion onto the Massive Cores Revealed in the Cygnus-X GMC Complex

Molecular clouds have complex density structures produced by processes including turbulence and gravity. We propose a triangulation-based method to dissect the density structure of a molecular cloud and study the interactions between dense cores and their environments. In our approach, a Delaunay triangulation is constructed, which consists of edges connecting these cores. Starting from this construction, we study the physical connections between neighboring dense cores and the ambient environment in a systematic fashion. We apply our method to the Cygnus-X massive GMC complex and find that the core separation is related to the mean surface density by Σedge∝lcore−0.28 , which can be explained by fragmentation controlled by a scale-dependent turbulent pressure (where the pressure is a function of scale, e.g., p ∼ l 2/3). We also find that the masses of low-mass cores ( Mcore<10M⊙ ) are determined by fragmentation, whereas massive cores ( Mcore>10M⊙ ) grow mostly through accretion. The transition from fragmentation to accretion coincides with the transition from a log-normal core mass function (CMF) to a power-law CMF. By constructing surface density profiles measured along edges that connect neighboring cores, we find evidence that the massive cores have accreted a significant fraction of gas from their surroundings and thus depleted the gas reservoir. Our analysis reveals a picture where cores form through fragmentation controlled by scale-dependent turbulent pressure support, followed by accretion onto the massive cores, and the method can be applied to different regions to achieve deeper understandings in the future.
Twist-bend nematics and heliconical cholesterics: a physico-chemical analysis of phase transitions and related specific properties

ABSTRACT Certain nematic liquid crystals, for example, those formed by banana-shaped molecules, can exhibit a low-temperature twist-bend nematic (Ntb) phase. Upon addition of chiral dopants, a chiral version of the twist-bend phase (N*tb) can be observed below the conventional chiral nematic (N*) phase, while under electric field the N* phase is transformed into a specific state known as ‘oblique helicoidal cholesteric’. In this work, we have studied the well-known Ntb-forming system CB7CB:CB6OCB with addition of 5CB and chiral dopants (ChDs) using optical microscopy, differential scanning calorimetry (DSC), and measurements of temperature-dependent optical transmission using optical microscopy. Effects of 5CB and different chiral dopants (of steroid or non-steroid nature) upon thermal stability of Ntb phase were analysed, and selective reflection in the visible range could be observed in the N* phase with a sufficiently high ChD content. As a peculiar feature, sharp unwinding of the cholesteric helix was observed at lower temperatures when approaching the N*tb phase, which appears to be similar to the helix unwinding close to the smectic-A transition. Graphical abstract
ResIST: Layer-Wise Decomposition of ResNets for Distributed Training

We propose ResIST, a novel distributed training protocol for Residual Networks (ResNets). ResIST randomly decomposes a global ResNet into several shallow sub-ResNets that are trained independently in a distributed manner for several local iterations, before having their updates synchronized and aggregated into the global model. In the next round, new sub-ResNets are randomly generated and the process repeats until convergence. By construction, per iteration, ResIST communicates only a small portion of network parameters to each machine and never uses the full model during training. Thus, ResIST reduces the per-iteration communication, memory, and time requirements of ResNet training to only a fraction of the requirements of full-model training. In comparison to common protocols, like data-parallel training and data-parallel training with local SGD, ResIST yields a decrease in communication and compute requirements, while being competitive with respect to model performance.
No-hair theorem in the wake of Event Horizon Telescope

Thanks to the release of the extraordinary EHT image of shadow attributed to the M87* supermassive black hole (SMBH), we have a novel window to assess the validity of fundamental physics in the strong-field regime. Motivated by this, we consider Johannsen & Psaltis metric parameterized by mass, spin, and an additional dimensionless hair parameter ϵ. This parametric framework in the high rotation regimes provides a well-behaved bed to the strong-gravity test of the no-hair theorem (NHT) using the EHT data. Incorporating the ϵ into the standard Kerr spacetime enrich it in the sense that, depending on setting the positive and negative values for that, we deal with alternative compact objects: deformed Kerr naked singularity and Kerr BH solutions, respectively. Shadows associated with these two possible solutions indicate that the deformation parameter ϵ affects the geometry shape of standard shadow such that it becomes more oblate and prolate with ϵ<0 and ϵ>0, respectively. By scanning the window associated with three shadow observables oblateness, deviation from circularity, and shadow diameter, we perform a numerical analysis within the range a*=0.9∓0.1 of the dimensionless rotation parameter, to find the constraints on the hair parameter ϵ in both possible solutions. For both possible signs of ϵ, we extract a variety of upper bounds that are in interplay with a*. Although by approaching the rotation parameters to the extreme limit, the allowable range of both hair parameters becomes narrower, the hairy Kerr BH solution is a more promising candidate to play the role of the alternative compact object instead of the standard Kerr BH. The lack of tension between hairy Kerr BH with the current observation of the EHT shadow of the M87* SMBH carries this message that there is the possibility of NHT violation.
Time and Evolution in Quantum and Classical Cosmology

We analyze the issue of dynamical evolution and time in quantum cosmology. We emphasize the problem of choice of phase space variables that can play the role of a time parameter in such a way that for expectation values of quantum operators the classical evolution is reproduced. We show that it is neither necessary nor sufficient for the Poisson bracket between the time variable and the super-Hamiltonian to be equal to unity in all of the phase space. We also discuss the question of switching between different internal times as well as the Montevideo interpretation of quantum theory.
Neural Network Layer Algebra: A Framework to Measure Capacity and Compression in Deep Learning

We present a new framework to measure the intrinsic properties of (deep) neural networks. While we focus on convolutional networks, our framework can be extrapolated to any network architecture. In particular, we evaluate two network properties, namely, capacity, which is related to expressivity, and compression, which is related to learnability. Both these properties depend only on the network structure and are independent of the network parameters. To this end, we propose two metrics: the first one, called layer complexity, captures the architectural complexity of any network layer; and, the second one, called layer intrinsic power, encodes how data are compressed along the network. The metrics are based on the concept of layer algebra, which is also introduced in this article. This concept is based on the idea that the global properties depend on the network topology, and the leaf nodes of any neural network can be approximated using local transfer functions, thereby allowing a simple computation of the global metrics. We show that our global complexity metric can be calculated and represented more conveniently than the widely used Vapnik-Chervonenkis (VC) dimension. We also compare the properties of various state-of-the-art architectures using our metrics and use the properties to analyze their accuracy on benchmark image classification datasets.
Effect of the reaction medium on hydrolysis of sodium borohydride

In this study, he effect of the reaction medium on the initial hydrogen decomposition of the sodium borohydride (SBH) hydrolysis reaction in the presence of the Co catalyst surface has been theoretically investigated using the Castep package program bottomed on DFT. The aim of the research is to distinguish the catalytic effects of fcc cobalt surfaces on the decomposition of hydrogen from SBH and to identify the effects of the environments made by the hydrolysis of H2O at the most efficient the surface on H decomposition. Therefore, the common surface types of the face-centered cobalt crystal were used to determine the effect of fcc surface structure on H extrication from SBH and H2O extrication. The activation energies required for H extrication from SBH on the catalyst surface with the lowest activation barrier were determined for each possible reaction medium containing different density of chemical species. According to our results, the value of activation energy increases as the ratio of OH radicals increases.
Electronic nature of chiral charge order in the kagome superconductor 
CsV3Sb5

Zhiwei Wang ,1,14,* Yu-Xiao Jiang,2,* Jia-Xin Yin ,2,*,† Yongkai Li,1,14,* Guan-Yong Wang,3 Hai-Li Huang,3 Shen Shao,4,5 Jinjin Liu,1,14 Peng Zhu,1,14 Nana Shumiya,2 Md Shafayat Hossain,2 Hongxiong Liu,6,7 Youguo Shi,6,7 Junxi Duan ,1,14 Xiang Li,1,14 Guoqing Chang ,8 Pengcheng Dai,9 Zijin Ye,10 Gang Xu,10 Yanchao Wang,4,5 Hao Zheng,3 Jinfeng Jia ,3 M. Zahid Hasan,2,11,12,13,‡ and Yugui Yao1,14,§ 1Centre for Quantum Physics, Key Laboratory of Advanced Optoelectronic Quantum Architecture and Measurement (MOE), School of Physics, Beijing Institute of Technology, Beijing 100081, China 2Laboratory for Topological Quantum Matter and Advanced Spectroscopy (B7), Department of Physics, Princeton University, Princeton, New Jersey 08544, USA 3School of Physics and Astronomy, Key Laboratory of Artificial Structures and Quantum Control (Ministry of Education), Shenyang National Laboratory for Materials Science, Tsung-Dao Lee Institute, Shanghai Jiao Tong University, Shanghai 200240, China 4State Key Laboratory of Superhard Materials, College of Physics, Jilin University, Changchun 130012, China 5International Center for Computational Method and Software, College of Physics, Jilin University, Changchun 130012, China 6Beijing National Laboratory for Condensed Matter Physics and Institute of Physics, Chinese Academy of Sciences, Beijing 100190, China 7University of Chinese Academy of Sciences, Beijing 100049, China 8Division of Physics and Applied Physics, School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore 9Department of Physics and Astronomy, Rice University, Houston, Texas 77005, USA 10Wuhan National High Magnetic Field Center, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China 11Princeton Institute for the Science and Technology of Materials, Princeton University, Princeton, New Jersey 08544, USA 12Materials Science Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA 13Quantum Science Center, Oak Ridge, Tennessee 37831, USA 14Beijing Key Lab of Nanophotonics and Ultrafine Optoelectronic Systems, Beijing Institute of Technology, Beijing 100081, China
On the Complexity of Symbolic Finite-State Automata

We revisit the complexity of procedures on SFAs (such as intersection, emptiness, etc.) and analyze them according to the measures we find suitable for symbolic automata: the number of states (n), the maximal number of transitions exiting a state (m) and the size of the most complex transition predicate (l). We pay attention to the special forms of SFAs: normalized SFAs and neat SFAs, as well as to SFAs over a monotonic effective Boolean algebra.
The effects of hepatitis B virus infection on natural and IVF pregnancy: A meta‐analysis study

Hepatitis B virus (HBV) has been considered a significant cause of human reproductive failure in different studies; however, there is a considerable disagreement on the true impacts of HBV on female reproduction. This study has evaluated the impact of HBV infection on pregnancy complications in natural pregnancy and also on pregnancy outcomes in women undergoing in vitro fertilization (IVF) treatment.