Miracle-dz/UniFAR-FTUs huggingface dataset:
Causal Estimation for Text Data with (Apparent) Overlap Violations

Consider the problem of estimating the causal effect of some attribute of a text document; for example: what effect does writing a polite vs. rude email have on response time? To estimate a causal effect from observational data, we need to adjust for confounding aspects of the text that affect both the treatment and outcome -- e.g., the topic or writing level of the text. These confounding aspects are unknown a priori, so it seems natural to adjust for the entirety of the text (e.g., using a transformer). However, causal identification and estimation procedures rely on the assumption of overlap: for all levels of the adjustment variables, there is randomness leftover so that every unit could have (not) received treatment. Since the treatment here is itself an attribute of the text, it is perfectly determined, and overlap is apparently violated. The purpose of this paper is to show how to handle causal identification and obtain robust causal estimation in the presence of apparent overlap violations. In brief, the idea is to use supervised representation learning to produce a data representation that preserves confounding information while eliminating information that is only predictive of the treatment. This representation then suffices for adjustment and can satisfy overlap. Adapting results on non-parametric estimation, we find that this procedure is robust to conditional outcome misestimation, yielding a low-bias estimator with valid uncertainty quantification under weak conditions. Empirical results show strong improvements in bias and uncertainty quantification relative to the natural baseline.
Some pointwise and decidable properties of non-uniform cellular automata

For non-uniform cellular automata (NUCA) with ﬁnite memory over an arbitrary universe with multiple local transition rules, we show that pointwise nilpotency, pointwise periodicity, and pointwise eventual periodicity properties are respectively equivalent to nilpotency, periodicity, and eventual periodicity. Moreover, we prove that every linear NUCA which satisﬁes pointwise a polynomial equation (which may depend on the conﬁguration) must be an eventually periodic linear NUCA. Generalizing results for higher dimensional group and linear CA, we also establish the decidability results of the above dynamical properties as well as the injectivity for arbitrary NUCA with ﬁnite memory which are local perturbations of higher dimensional linear and group CA. Some generalizations to the case of sparse global perturbations of higher dimensional linear and group CA are also obtained.
HyperHawkes: Hypernetwork based Neural Temporal Point Process

Temporal point process serves as an essential tool for modeling time-to-event data in continuous time space. Despite having massive amounts of event sequence data from various domains like social media, healthcare etc., real world application of temporal point process faces two major challenges: 1) it is not generalizable to predict events from unseen sequences in dynamic environment 2) they are not capable of thriving in continually evolving environment with minimal supervision while retaining previously learnt knowledge. To tackle these issues, we propose \textit{HyperHawkes}, a hypernetwork based temporal point process framework which is capable of modeling time of occurrence of events for unseen sequences. Thereby, we solve the problem of zero-shot learning for time-to-event modeling. We also develop a hypernetwork based continually learning temporal point process for continuous modeling of time-to-event sequences with minimal forgetting. In this way, \textit{HyperHawkes} augments the temporal point process with zero-shot modeling and continual learning capabilities. We demonstrate the application of the proposed framework through our experiments on two real-world datasets. Our results show the efficacy of the proposed approach in terms of predicting future events under zero-shot regime for unseen event sequences. We also show that the proposed model is able to predict sequences continually while retaining information from previous event sequences, hence mitigating catastrophic forgetting for time-to-event data.
Unsupervised Multi-View Object Segmentation Using Radiance Field Propagation

We present radiance field propagation (RFP), a novel approach to segmenting objects in 3D during reconstruction given only unlabeled multi-view images of a scene. RFP is derived from emerging neural radiance field-based techniques, which jointly encodes semantics with appearance and geometry. The core of our method is a novel propagation strategy for individual objects' radiance fields with a bidirectional photometric loss, enabling an unsupervised partitioning of a scene into salient or meaningful regions corresponding to different object instances. To better handle complex scenes with multiple objects and occlusions, we further propose an iterative expectation-maximization algorithm to refine object masks. RFP is one of the first unsupervised approach for tackling 3D real scene object segmentation for neural radiance field (NeRF) without any supervision, annotations, or other cues such as 3D bounding boxes and prior knowledge of object class. Experiments demonstrate that RFP achieves feasible segmentation results that are more accurate than previous unsupervised image/scene segmentation approaches, and are comparable to existing supervised NeRF-based methods. The segmented object representations enable individual 3D object editing operations.
iCTGAN–An Attack Mitigation Technique for Random-vector Attack on Accelerometer-based Gait Authentication Systems

A recent study showed that commonly (vanilla) studied implementations of accelerometer-based gait authentication systems (vABGait) are susceptible to random-vector attack. The same study proposed a beta noise-assisted implementation (βABGait) to mitigate the attack. In this paper, we assess the effectiveness of the random-vector attack on both vABGait and (βABGait using three accelerometer-based gait datasets. In addition, we propose iABGait, an alternative implementation of ABGait, which uses a Conditional Tabular Generative Adversarial Network. Then we evaluate iABGait's resilience against the traditional zero-effort and random-vector attacks. The results show that iABGait mitigates the impact of the random-vector attack to a reasonable extent and outperforms βABGait in most experimental settings.
Benign Autoencoders

Recent progress in Generative Artificial Intelligence (AI) relies on efficient data representations, often featuring encoder-decoder architectures. We formalize the mathematical problem of finding the optimal encoder-decoder pair and characterize its solution, which we name the"benign autoencoder"(BAE). We prove that BAE projects data onto a manifold whose dimension is the optimal compressibility dimension of the generative problem. We highlight surprising connections between BAE and several recent developments in AI, such as conditional GANs, context encoders, stable diffusion, stacked autoencoders, and the learning capabilities of generative models. As an illustration, we show how BAE can find optimal, low-dimensional latent representations that improve the performance of a discriminator under a distribution shift. By compressing"malignant"data dimensions, BAE leads to smoother and more stable gradients.
Doing data science with platforms crumbs: an investigation into fakes views on YouTube

This paper contributes to the ongoing discussions on the scholarly access to social media data, discussing a case where this access is barred despite its value for understanding and countering online disinformation and despite the absence of privacy or copyright issues. Our study concerns YouTube's engagement metrics and, more specifically, the way in which the platform removes"fake views"(i.e., views considered as artificial or illegitimate by the platform). Working with one and a half year of data extracted from a thousand French YouTube channels, we show the massive extent of this phenomenon, which concerns the large majority of the channels and more than half the videos in our corpus. Our analysis indicates that most fakes news are corrected relatively late in the life of the videos and that the final view counts of the videos are not independent from the fake views they received. We discuss the potential harm that delays in corrections could produce in content diffusion: by inflating views counts, illegitimate views could make a video appear more popular than it is and unwarrantedly encourage its human and algorithmic recommendation. Unfortunately, we cannot offer a definitive assessment of this phenomenon, because YouTube provides no information on fake views in its API or interface. This paper is, therefore, also a call for greater transparency by YouTube and other online platforms about information that can have crucial implications for the quality of online public debate.
Local and Global Expansion in Random Geometric Graphs

Consider a random geometric 2-dimensional simplicial complex X sampled as follows: first, sample n vectors u1,…,un uniformly at random on Sd−1; then, for each triple i,j,k ∈ [n], add {i,j,k} and all of its subsets to X if and only if ⟨ ui,uj ⟩ ≥ τ, ⟨ ui,uk ⟩ ≥ τ, and ⟨ uj, uk ⟩ ≥ τ. We prove that for every ε > 0, there exists a choice of d = Θ(logn) and τ = τ(ε,d) so that with high probability, X is a high-dimensional expander of average degree nε in which each 1-link has spectral gap bounded away from 1/2. To our knowledge, this is the first demonstration of a natural distribution over 2-dimensional expanders of arbitrarily small polynomial average degree and spectral link expansion better than 1/2. All previously known constructions are algebraic. This distribution also furnishes an example of simplicial complexes for which the trickle-down theorem is nearly tight. En route, we prove general bounds on the spectral expansion of random induced subgraphs of arbitrary vertex transitive graphs, which may be of independent interest. For example, one consequence is an almost-sharp bound on the second eigenvalue of random n-vertex geometric graphs on Sd−1, which was previously unknown for most n,d pairs. The full version of this paper can be found here.
Motion-inductive Self-supervised Object Discovery in Videos

In this paper, we consider the task of unsupervised object discovery in videos. Previous works have shown promising results via processing optical flows to segment objects. However, taking flow as input brings about two drawbacks. First, flow cannot capture sufficient cues when objects re-main static or partially occluded. Second, it is challeng-ing to establish temporal coherency from flow-only input, due to the missing texture information. To tackle these limitations, we propose a model for directly processing consecutive RGB frames, and infer the optical flow between any pair of frames using a layered representation, with the opacity channels being treated as the segmentation. Ad-ditionally, to enforce object permanence, we apply temporal consistency loss on the inferred masks from randomly-paired frames, which refer to the motions at different paces, and encourage the model to segment the objects even if they may not move at the current time point. Experimentally, we demonstrate superior performance over previous state-of-the-art methods on three public video segmentation datasets (DAVIS2016, SegTrackv2, and FBMS-59), while being computationally efficient by avoiding the overhead of computing optical flow as input.
Going In Blind: Object Motion Classification using Distributed Tactile Sensing for Safe Reaching in Clutter

Robotic manipulators navigating cluttered shelves or cabinets may find it challenging to avoid contact with obstacles. Indeed, rearranging obstacles may be necessary to access a target. Rather than planning explicit motions that place obstacles into a desired pose, we suggest allowing incidental contacts to rearrange obstacles while monitoring contacts for safety. Bypassing object identification, we present a method for categorizing object motions from tactile data collected from incidental contacts with a capacitive tactile skin on an Allegro Hand. We formalize tactile cues associated with categories of object motion, demonstrating that they can determine with > 90% accuracy whether an object is movable and whether a contact is causing the object to slide stably (safe contact) or tip (unsafe).
Improved Stein Variational Gradient Descent with Importance Weights

Stein Variational Gradient Descent ( SVGD ) is a popular sampling algorithm used in various machine learning tasks. It is well known that SVGD arises from a discretization of the kernelized gradient ﬂow of the Kullback-Leibler divergence D KL ( · | π ) , where π is the target distribution. In this work, we propose to enhance SVGD via the introduction of importance weights , which leads to a new method for which we coin the name β -SVGD . In the continuous time and inﬁnite particles regime, the time for this ﬂow to converge to the equilibrium distribution π , quantiﬁed by the Stein Fisher information, depends on ρ 0 and π very weakly. This is very different from the kernelized gradient ﬂow of Kullback-Leibler divergence, whose time complexity depends on D KL ( ρ 0 | π ) . Under certain assumptions, we provide a descent lemma for the population limit β -SVGD , which covers the descent lemma for the population limit SVGD when β → 0 . We also illustrate the advantages of β -SVGD over SVGD by simple experiments.
High-energy Photon Opacity in the Twisted Magnetospheres of Magnetars

Magnetars are neutron stars characterized by strong surface magnetic fields generally exceeding the quantum critical value of 44.1 TG. High-energy photons propagating in their magnetospheres can be attenuated by QED processes like photon splitting and magnetic pair creation. In this paper, we compute the opacities due to photon splitting and pair creation by photons emitted anywhere in the magnetosphere of a magnetar. Axisymmetric, twisted dipole field configurations embedded in the Schwarzschild metric are treated. The paper computes the maximum energies for photon transparency that permit propagation to infinity in curved spacetime. Special emphasis is given to cases where photons are generated along magnetic field loops and/or in polar regions; these cases directly relate to resonant inverse Compton scattering models for the hard X-ray emission from magnetars and Comptonized soft gamma-ray emission from giant flares. We find that increases in magnetospheric twists raise or lower photon opacities, depending on both the emission locale and the competition between field-line straightening and field strength enhancement. Consequently, given the implicit spectral transparency of hard X-ray bursts and persistent “tail” emission of magnetars, photon splitting considerations constrain their emission region locales and the twist angle of the magnetosphere; these constraints can be probed by future soft gamma-ray telescopes such as COSI and AMEGO. The inclusion of twists generally increases the opaque volume of pair creation by photons above its threshold, except when photons are emitted in polar regions and approximately parallel to the field.
Learning Spatially-Adaptive Squeeze-Excitation Networks for Image Synthesis and Image Recognition

Learning light-weight yet expressive deep networks in both image synthesis and image recognition remains a challenging problem. Inspired by a more recent observation that it is the data-specificity that makes the multi-head self-attention (MHSA) in the Transformer model so powerful, this paper proposes to extend the widely adopted light-weight Squeeze-Excitation (SE) module to be spatially-adaptive to reinforce its data specificity, as a convolutional alternative of the MHSA, while retaining the efficiency of SE and the inductive basis of convolution. It presents two designs of spatially-adaptive squeeze-excitation (SASE) modules for image synthesis and image recognition respectively. For image synthesis tasks, the proposed SASE is tested in both low-shot and one-shot learning tasks. It shows better performance than prior arts. For image recognition tasks, the proposed SASE is used as a drop-in replacement for convolution layers in ResNets and achieves much better accuracy than the vanilla ResNets, and slightly better than the MHSA counterparts such as the Swin-Transformer and Pyramid-Transformer in the ImageNet-1000 dataset, with significantly smaller models.
Limiting Dynamics for Q-Learning with Memory One in Symmetric Two-Player, Two-Action Games

We develop a method based on computer algebra systems to represent the mutual pure strategy best-response dynamics of symmetric two-player, two-action repeated games played by players with a one-period memory. We apply this method to the iterated prisoner’s dilemma, stag hunt, and hawk-dove games and identify all possible equilibrium strategy pairs and the conditions for their existence. The only equilibrium strategy pair that is possible in all three games is the win-stay, lose-shift strategy. Lastly, we show that the mutual best-response dynamics are realized by a sample batch Q-learning algorithm in the infinite batch size limit.
Implicit Neural Deformation for Sparse‐View Face Reconstruction

In this work, we present a new method for 3D face reconstruction from sparse‐view RGB images. Unlike previous methods which are built upon 3D morphable models (3DMMs) with limited details, we leverage an implicit representation to encode rich geometric features. Our overall pipeline consists of two major components, including a geometry network, which learns a deformable neural signed distance function (SDF) as the 3D face representation, and a rendering network, which learns to render on‐surface points of the neural SDF to match the input images via self‐supervised optimization. To handle in‐the‐wild sparse‐view input of the same target with different expressions at test time, we propose residual latent code to effectively expand the shape space of the learned implicit face representation as well as a novel view‐switch loss to enforce consistency among different views. Our experimental results on several benchmark datasets demonstrate that our approach outperforms alternative baselines and achieves superior face reconstruction results compared to state‐of‐the‐art methods.
Graph Representation Learning Through Recoverability

Self-supervised learning methods became a popular approach for graph representation learning because they do not rely on manual labels and offer better generalization. Contrastive methods based on mutual information maximization between augmented instances of the same object are widely used in self-supervised learning of representations. For graph-structured data, however, there are two obstacles to successfully utilizing these methods: the data augmentation strategy and training decoder for mutual information estimation between augmented representations of nodes, sub-graphs, or graphs. In this work, we propose a self-supervised graph representation learning algorithm, Graph Information Representation Learning (GIRL). GIRL does not require augmentations or a decoder for mutual information estimation. The algorithm is based on an alternative information metric, recoverability , which is tightly related to mutual information but is less complicated when estimating. Our self-supervised algorithm consistently outperforms existing state-of-the-art contrast-based self-supervised methods by a large margin on a variety of datasets. In addition, we show how the recoverability can be used in a supervised setting to alleviate the effect of over-smoothing/squashing in deeper graph neural networks. The code to reproduce our experiments is available at https://github.com/Anonymous1252022/Recoverability .
Ranking Models for the Temporal Dimension of Text

Temporal features of text have been shown to improve clustering and organization of documents, text classification, visualization, and ranking. Temporal ranking models consider the temporal expressions found in text (e.g., “in 2021” or “last year”) as time units, rather than as keywords, to define a temporal relevance and improve ranking. This article introduces a new class of ranking models called Temporal Metric Space Models (TMSM), based on a new domain for representing temporal information found in documents and queries, where each temporal expression is represented as a time interval. Furthermore, we introduce a new frequency-based baseline called Temporal BM25 (TBM25). We evaluate the effectiveness of each proposed metric against a purely textual baseline, as well as several variations of the metrics themselves, where we change the aggregate function, the time granularity and the combination weight. Our extensive experiments on five test collections show statistically significant improvements of TMSM and TBM25 over state-of-the-art temporal ranking models. Combining the temporal similarity scores with the text similarity scores always improves the results, when the combination weight is between 2% and 6% for the temporal scores. This is true also for test collections where only 5% of queries contain explicit temporal expressions.
Detector configuration of KAGRA–the Japanese cryogenic gravitational-wave detector

The construction of the Japanese second-generation gravitational-wave detector KAGRA (previously called LCGT) has been started. In the next 6–7 years, we will be able to observe the spacetime ripple from faraway galaxies. KAGRA is equipped with the latest advanced technologies. The entire 3 km long detector is located in the underground to be isolated from the seismic motion, the core optics are cooled down to 20 K to reduce thermal fluctuations and quantum non-demolition techniques are used to decrease quantum noise. In this paper, we introduce the detector configuration of KAGRA, its design, strategy and downselection of parameters.
STHAN: Transportation Demand Forecasting with Compound Spatio-Temporal Relationships

Transportation demand forecasting is a critical precondition of optimal online transportation dispatch, which will greatly reduce drivers’ wasted mileage and customers’ waiting time, contributing to economic and environmental sustainability. Though various methods have been developed, the core spatio-temporal complexity remains challenging from three perspectives: (1) Compound spatial relationships. According to our empirical analysis, these relationships widely exist. Previous studies focus on capturing different spatial relationships using multi-homogeneous graphs. However, the information flow across various spatial relationships is not modeled explicitly. (2) Heterogeneity in spatial relationships. A region’s neighbors under the same spatial relationship may have different weights for this region. Meanwhile, different relationships may also weigh differently. (3) Synchronicity between compound spatial relationships and temporal relationships. Previous research considers synchronous influences from spatial and temporal relationships in a homogeneous fashion while compound spatial relationships are not captured for this synchronicity. To address the aforementioned perspectives, we propose the Spatio-Temporal Heterogeneous graph Attention Network (STHAN), where the key intuition is capturing the compound spatial relationships via meta-paths explicitly. We first construct a spatio-temporal heterogeneous graph including multiple spatial relationships and temporal relationships and use meta-paths to depict compound spatial relationships. To capture the heterogeneity, we use hierarchical attention, which contains node level attention and meta-path level attention. The synchronicity between temporal relationships and spatial relationships, including compound ones, is modeled in meta-path-level attention. Our framework outperforms state-of-the-art models by reducing 6.58%, 4.57%, and 4.20% of WMAPE in experiments on three real-world datasets, respectively.