Miracle-dz/UniFAR-FTUs huggingface dataset:
Revealing intra-urban spatial structure through an exploratory analysis by combining road network abstraction model and taxi trajectory data

The unprecedented urbanization in China has dramatically changed the urban spatial structure of cities. With the proliferation of individual-level geospatial big data, previous studies have widely used the network abstraction model to reveal the underlying urban spatial structure. However, the construction of network abstraction models primarily focuses on the topology of the road network without considering individual travel flows along with the road networks. Individual travel flows reflect the urban dynamics, which can further help understand the underlying spatial structure. This study therefore aims to reveal the intra-urban spatial structure by integrating the road network abstraction model and individual travel flows. To achieve this goal, we 1) quantify the spatial interaction relatedness of road segments based on the Word2Vec model using large volumes of taxi trip data, then 2) characterize the road abstraction network model according to the identified spatial interaction relatedness, and 3) implement a community detection algorithm to reveal sub-regions of a city. Our results reveal three levels of hierarchical spatial structures in the Wuhan metropolitan area. This study provides a data-driven approach to the investigation of urban spatial structure via identifying traffic interaction patterns on the road network, offering insights to urban planning practice and transportation management.
Preliminary Bias Results in Search Engines

This report aims to report my thesis progress so far. My work attempts to show the differences in the perspectives of two search engines, Bing and Google on several selected controversial topics. In this work, we try to make a distinction on the viewpoints of Bing \&Google by using sentiment as well as the ranking of the document returned from these two search engines on the same queries, these queries are related mainly to controversial topics. You can find the methods we used with experimental results below.
Directed Acyclic Graph Factorization Machines for CTR Prediction via Knowledge Distillation

With the growth of high-dimensional sparse data in web-scale recommender systems, the computational cost to learn high-order feature interaction in CTR prediction task largely increases, which limits the use of high-order interaction models in real industrial applications. Some recent knowledge distillation based methods transfer knowledge from complex teacher models to shallow student models for accelerating the online model inference. However, they suffer from the degradation of model accuracy in knowledge distillation process. It is challenging to balance the efficiency and effectiveness of the shallow student models. To address this problem, we propose a Directed Acyclic Graph Factorization Machine (KD-DAGFM) to learn the high-order feature interactions from existing complex interaction models for CTR prediction via Knowledge Distillation. The proposed lightweight student model DAGFM can learn arbitrary explicit feature interactions from teacher networks, which achieves approximately lossless performance and is proved by a dynamic programming algorithm. Besides, an improved general model KD-DAGFM+ is shown to be effective in distilling both explicit and implicit feature interactions from any complex teacher model. Extensive experiments are conducted on four real-world datasets, including a large-scale industrial dataset from WeChat platform with billions of feature dimensions. KD-DAGFM achieves the best performance with less than 21.5% FLOPs of the state-of-the-art method on both online and offline experiments, showing the superiority of DAGFM to deal with the industrial scale data in CTR prediction task.
Simplicity condition and boundary-bulk duality

In the first-order formulation, general relativity could be formally viewed as the topological $BF$ theory with a specific constraint, the Plebanski constraint. $BF$ theory is expected to be the classical limit of the Crane-Yetter~(CY) topological state sum. In the Euclidean case, the Plebanski constraint could be lifted in an elegant way to a quantum version in the CY state sum, called the simplicity condition. The constrained state sum is known as the Barrett-Crane~(BC) model. In this note we investigate this condition from the topological field theory side. Since the condition is in fact imposed on the faces, we want to understand it from the viewpoint of the surface theory. Essentially this condition could be thought of as resulting from the boundary-bulk dualtiy, or more precisely from the recent "bulk=center" proposal. In the language of topological phases, it corresponds to the diagonal anyon condensation, with the BC model being the condensed phase. The CY state sum, and correspondingly the BC model, is usually constructed with a modular tensor category~(MTC) from representations of a quantum group at roots of unity. The category of representations of quantum group and that of modules of the corresponding affine Lie algebra are known to be equivalent as MTCs. This equivalence, together with the simplicity condition in the BC model, guarantees the construction of a full 2d rational conformal field theory through the Fuchs-Runkel-Schweigert formalism. We thus obtain a full-fledged 4d quantum geometry, which we name as "stringy quantum geometry". Some attracting features of such a new geometry are briefly discussed.
The $d^{*}$-space

In this paper, we introduce the concept of $d^{\ast}$-spaces. We find that strong $d$-spaces are $d^{\ast}$-spaces, but the converse does not hold. We give a characterization for a topological space to be a $d^{\ast}$-space. We prove that the retract of a $d^{\ast}$-space is a $d^{\ast}$-space. We obtain the result that for any $T_{0}$ space $X$ and $Y$, if the function space $TOP(X,Y)$ endowed with the Isbell topology is a $d^{\ast}$-space, then $Y$ is a $d^{\ast}$-space. We also show that for any $T_{0}$ space $X$, if the Smyth power space $Q_{v}(X)$ is a $d^{\ast}$-space, then $X$ is a $d^{\ast}$-space. Meanwhile, we give a counterexample to illustrate that conversely, for a $d^{\ast}$-space $X$, the Smyth power space $Q_{v}(X)$ may not be a $d^{\ast}$-space.
Diversity of Dust Properties in External Galaxies Confirmed by Polarization Signals from Type II Supernovae

Investigating interstellar (IS) dust properties in external galaxies is important not only to infer the intrinsic properties of astronomical objects but also to understand the star/planet formation in the galaxies. From the non–Milky Way–like extinction and interstellar polarization (ISP) observed in reddened Type Ia supernovae (SNe), it has been suggested that their host galaxies contain dust grains whose properties are substantially different from the Milky Way (MW) dust. It is important to investigate the universality of such non-MW-like dust in the universe. Here we report spectropolarimetry of two highly extinguished Type II SNe (SN 2022aau and SN 2022ame). SN 2022aau shows a polarization maximum at a shorter wavelength than MW stars, which is also observed in some Type Ia SNe. This is clear evidence for the existence of non-MW-like dust in its host galaxy (i.e., NGC 1672). This fact implies that such non-MW-like dust might be more common in some environments than expected, and thus it might affect the picture of the star/planet formation. On the other hand, SN 2022ame shows MW-like ISP, implying the presence of MW-like dust in its host galaxy (i.e., NGC 1255). Our findings confirm that dust properties of galaxies are diverse, either locally or globally. The present work demonstrates that further investigation of IS dust properties in external galaxies using polarimetry of highly reddened SNe is promising, providing a great opportunity to study the universality of such non-MW-like dust grains in the universe.
Formal Abstractions for Packet Scheduling

Early programming models for software-defined networking (SDN) focused on basic features for controlling network-wide forwarding paths, but more recent work has considered richer features, such as packet scheduling and queueing, that affect performance. In particular, PIFO trees, proposed by Sivaraman et al., offer a flexible and efficient primitive for programmable packet scheduling. Prior work has shown that PIFO trees can express a wide range of practical algorithms including strict priority, weighted fair queueing, and hierarchical schemes. However, the semantic properties of PIFO trees are not well understood. This paper studies PIFO trees from a programming language perspective. We formalize the syntax and semantics of PIFO trees in an operational model that decouples the scheduling policy running on a tree from the topology of the tree. Building on this formalization, we develop compilation algorithms that allow the behavior of a PIFO tree written against one topology to be realized using a tree with a different topology. Such a compiler could be used to optimize an implementation of PIFO trees, or realize a logical PIFO tree on a target with a fixed topology baked into the hardware. To support experimentation, we develop a software simulator for PIFO trees, and we present case studies illustrating its behavior on standard and custom algorithms.
Deep Composite Face Image Attacks: Generation, Vulnerability and Detection

Face manipulation attacks have drawn the attention of biometric researchers because of their vulnerability to Face Recognition Systems (FRS). This paper proposes a novel scheme to generate Composite Face Image Attacks (CFIA) based on facial attributes using Generative Adversarial Networks (GANs). Given the face images corresponding to two unique data subjects, the proposed CFIA method will independently generate the segmented facial attributes, then blend them using transparent masks to generate the CFIA samples. We generate 526 unique CFIA combinations of facial attributes for each pair of contributory data subjects. Extensive experiments are carried out on our newly generated CFIA dataset consisting of 1000 unique identities with 2000 bona fide samples and 526000 CFIA samples, thus resulting in an overall 528000 face image samples. We present a sequence of experiments to benchmark the attack potential of CFIA samples using four different automatic FRS. We introduced a new metric named Generalized Morphing Attack Potential (G-MAP) to benchmark the vulnerability of generated attacks on FRS effectively. Additional experiments are performed on the representative subset of the CFIA dataset to benchmark both perceptual quality and human observer response. Finally, the CFIA detection performance is benchmarked using three different single image based face Morphing Attack Detection (MAD) algorithms. The source code of the proposed method together with CFIA dataset will be made publicly available: https://github.com/jagmohaniiit/LatentCompositionCode
Convolutional Filtering on Sampled Manifolds

The increasing availability of geometric data has motivated the need for information processing over non-Euclidean domains modeled as manifolds. The building block for information processing architectures with desirable theoretical properties such as invariance and stability is convolutional filtering. Manifold convolutional filters are defined from the manifold diffusion sequence, constructed by successive applications of the Laplace-Beltrami operator to manifold signals. However, the continuous manifold model can only be accessed by sampling discrete points and building an approximate graph model from the sampled manifold. Effective linear information processing on the manifold requires quantifying the error incurred when approximating manifold convolutions with graph convolutions. In this paper, we derive a non-asymptotic error bound for this approximation, showing that convolutional filtering on the sampled manifold converges to continuous manifold filtering. Our findings are further demonstrated empirically on a problem of navigation control.
Hierarchically Modular Dynamical Neural Network Relaxing in a Warped Space: Basic Model and its Characteristics

We propose a hierarchically modular, dynamical neural network model whose architecture minimizes a specifically designed energy function and defines its temporal characteristics. The model has an internal and an external space that are connected with a layered internetwork that consists of a pair of forward and backward subnets composed of static neurons (with an instantaneous time-course). Dynamical neurons with large time constants in the internal space determine the overall time-course. The model offers a framework in which state variables in the network relax in a warped space, due to the cooperation between dynamic and static neurons. We assume that the system operates in either a learning or an association mode, depending on the presence or absence of feedback paths and input ports. In the learning mode, synaptic weights in the internetwork are modified by strong inputs corresponding to repetitive neuronal bursting, which represents sinusoidal or quasi-sinusoidal waves in the short-term average density of nerve impulses or in the membrane potential. A two-dimensional mapping relationship can be formed by employing signals with different frequencies based on the same mechanism as Lissajous curves. In the association mode, the speed of convergence to a goal point greatly varies with the mapping relationship of the previously trained internetwork, and owing to this property, the convergence trajectory in the two-dimensional model with the non-linear mapping internetwork cannot go straight but instead must curve. We further introduce a constrained association mode with a given target trajectory and elucidate that in the internal space, an output trajectory is generated, which is mapped from the external space according to the inverse of the mapping relationship of the forward subnet.
Contextual knowledge provided by a movie biases implicit perception of the protagonist

Abstract We are constantly categorizing other people as belonging to our in-group (‘one of us’) or out-group (‘one of them’). Such grouping occurs fast and automatically and can be based on others’ visible characteristics such as skin color or clothing style. Here we studied neural underpinnings of implicit social grouping not often visible on the face, male sexual orientation. A total of 14 homosexuals and 15 heterosexual males were scanned in functional magnetic resonance imaging while watching a movie about a homosexual man, whose face was also presented subliminally before (subjects did not know about the character’s sexual orientation) and after the movie. We discovered significantly stronger activation to the man’s face after seeing the movie in homosexual but not heterosexual subjects in medial prefrontal cortex, frontal pole, anterior cingulate cortex, right temporal parietal junction and bilateral superior frontal gyrus. In previous research, these brain areas have been connected to social perception, self-referential thinking, empathy, theory of mind and in-group perception. In line with previous studies showing biased perception of in-/out-group faces to be context dependent, our novel approach further demonstrates how complex contextual knowledge gained under naturalistic viewing can bias implicit social perception.
Towards Robust Dataset Learning

Adversarial training has been actively studied in re-cent computer vision research to improve the robustness of models. However, due to the huge computational cost of generating adversarial samples, adversarial training methods are often slow. In this paper, we study the problem of learning a robust dataset such that any classiﬁer naturally trained on the dataset is adversarially robust. Such a dataset beneﬁts the downstream tasks as the natural training is much faster than adversarial training, and demon-strates that the desired property of robustness is transferable between models and data. In this work, we propose a principled tri-level optimization to formulate the robust dataset learning problem. We show that, under an abstraction model that characterizes robust vs. non-robust features, the proposed method provably learns a robust dataset. Extensive experiments on benchmark datasets demonstrate the effectiveness of our new algorithm with different network initializations and architectures.
The Magnetospheric Sash and the Cross‐Tail S

As revealed in MHD simulation, the magnetospheric sash is a band of weak magnetic field that, for the usual case in which the IMF is approximately perpendicular to the geomagnetic dipole, runs tailward along the high‐latitude magnetopause flanks from one dayside cusp to the other, closing via the cross‐tail neutral sheet. On the magnetopause flanks, it contains the magnetic separator line, at which all three topological types of field lines meet. Seen in a cross‐sectional plane through the near‐Earth tail, the magnetospheric sash takes the form of the cross‐tail S, a weak‐field feature comprised of the tail neutral sheet with diagonally symmetric extensions along the magnetopause flanks connecting it to the separator line. The cross‐tail S is evident in the MHD results and in cross‐sectional maps based on IMP 8 data. The magnetopause expression of the sash is latent in prior works that described the geometry of antiparallel fields across the magnetopause and the consequent cancellation of the fields within the magnetopause layer. The sash picture bears a strong resemblance to antiparallel merging geometry.
Spacetime metric for pedestrian movement

This paper will present a model for pedestrian motion by defining a spacetime metric. This model considers the factors that are effective in the movement of pedestrians (such as obstacles, walls and other pedestrians) by defining a proper metric. In fact, the surrounding environment that affects the motion of pedestrians changes the flat(Euclidean) spacetime metric and, therefore, the shortest possible path to their destination. This change is such that pedestrians have a different feeling of time at any point and moment in the environment. Based on this feeling, they adjust their route to the destination so that they travel the shortest possible route in curved spacetime, which follows a geodesic. The contributions are :1. Defining spacetime metric for pedestrian movement.2. Visualizing the spacetime geometry for several timesteps.3. Introducing a parameter called the time factor and its physical meaning in terms of the pedestrian feeling of time. According to this paper formulation, pedestrians feel time differently at each position and moment, and the time factor measures this feeling of time. 4. Proposing a simple model with differential equations suitable for data assimilation methods while preserving the essential feature of collision avoidance behaviour.
NeuroSketch: Fast and Approximate Evaluation of Range Aggregate Queries with Neural Networks

Range aggregate queries (RAQs) are an integral part of many real-world applications, where, often, fast and approximate answers for the queries are desired. Recent work has studied answering RAQs using machine learning (ML) models, where a model of the data is learned to answer the queries. However, there is no theoretical understanding of why and when the ML based approaches perform well. Furthermore, since the ML approaches model the data, they fail to capitalize on any query specific information to improve performance in practice. In this paper, we focus on modeling "queries" rather than data and train neural networks to learn the query answers. This change of focus allows us to theoretically study our ML approach to provide a distribution and query dependent error bound for neural networks when answering RAQs. We confirm our theoretical results by developing NeuroSketch, a neural network framework to answer RAQs in practice. Extensive experimental study on real-world, TPC-benchmark and synthetic datasets show that NeuroSketch answers RAQs multiple orders of magnitude faster than state-of-the-art and with better accuracy.
Quantum tomography under perturbed Hamiltonian evolution and scrambling of errors - a quantum signature of chaos

How much can we trust quantum simulations or other quantum information protocols under noisy many-body chaotic dynamics that will lead to a rapid scrambling of quantum information as well as errors across the system? We employ continuous measurement quantum tomography as a paradigm to study this question. The measurement record is generated as a sequence of expectation values of a Hermitian observable evolving under the repeated application of the Floquet map of the quantum kicked top. Interestingly, we ﬁnd that the reconstruction ﬁdelity initially increases regardless of the degree of chaos or the strength of perturbations in the dynamics. For random states, when the measurement record is obtained from a random initial observable, the subsequent drop in the ﬁdelity obtained is inversely correlated to the degree of chaos in the dynamics. More importantly, this also gives us an operational interpretation of the Loschmidt echo for operators by connecting it to the performance of quantum tomography. We deﬁne a quantity to capture the scrambling of errors, an out-of-time-ordered correlator (OTOC) between two operators under perturbed and unperturbed system dynamics that serves as a signature of chaos and quantiﬁes the spread of errors. Our results demonstrate not only a fundamental link between Loschmidt echo and scrambling of errors as captured by OTOCs but that such a link can have operational consequences in quantum information processing.
Intelligence Processing Units Accelerate Neuromorphic Learning

—Spiking neural networks (SNNs) have achieved or- ders of magnitude improvement in terms of energy consumption and latency when performing inference with deep learning workloads. Error backpropagation is presently regarded as the most effective method for training SNNs, but in a twist of irony, when training on modern graphics processing units (GPUs) this becomes more expensive than non-spiking networks. The emergence of Graphcore’s Intelligence Processing Units (IPUs) balances the parallelized nature of deep learning workloads with the sequential, reusable, and sparsiﬁed nature of operations prevalent when training SNNs. IPUs adopt multi-instruction multi-data (MIMD) parallelism by running individual processing threads on smaller data blocks, which is a natural ﬁt for the sequential, non-vectorized steps required to solve spiking neuron dynamical state equations. We present an IPU-optimized release of our custom SNN Python package, snnTorch , which exploits ﬁne-grained parallelism by utilizing low-level, pre-compiled cus- tom operations to accelerate irregular and sparse data access patterns that are characteristic of training SNN workloads. We provide a rigorous performance assessment across a suite of commonly used spiking neuron models, and propose methods to further reduce training run-time via half-precision training. By amortizing the cost of sequential processing into vectorizable population codes, we ultimately demonstrate the potential for integrating domain-speciﬁc accelerators with the next generation of neural networks.