Miracle-dz/UniFAR-FTUs huggingface dataset:
Grouping-matrix based Graph Pooling with Adaptive Number of Clusters

Graph pooling is a crucial operation for encoding hierarchical structures within graphs. Most existing graph pooling approaches formulate the problem as a node clustering task which effectively captures the graph topology. Conventional methods ask users to specify an appropriate number of clusters as a hyperparameter, then assuming that all input graphs share the same number of clusters. In inductive settings where the number of clusters could vary, however, the model should be able to represent this variation in its pooling layers in order to learn suitable clusters. Thus we propose GMPool, a novel differentiable graph pooling architecture that automatically determines the appropriate number of clusters based on the input data. The main intuition involves a grouping matrix defined as a quadratic form of the pooling operator, which induces use of binary classification probabilities of pairwise combinations of nodes. GMPool obtains the pooling operator by first computing the grouping matrix, then decomposing it. Extensive evaluations on molecular property prediction tasks demonstrate that our method outperforms conventional methods.
Security and Privacy of IP-ICN Coexistence: A Comprehensive Survey

Today’s Internet is experiencing a massive number of users with a continuously increasing need for data, which is the leading cause of introduced limitations among security and privacy issues. To overcome these limitations, a shift from host-centric to data-centric is proposed, and in this context, Information-Centric Networking (ICN) represents a promising solution. Nevertheless, unsettling the current Internet’s network layer – i.e., Internet Protocol (IP) – with ICN is a challenging, expensive task since it requires worldwide coordination among Internet Service Providers (ISPs), backbone, and Autonomous Services (AS). Therefore, researchers foresee that the replacement process of the current Internet will transition through the coexistence of IP and ICN. In this perspective, novel architectures combine IP and ICN protocols. However, only a few of the proposed architectures place the security-by-design feature. Therefore, this article provides the first comprehensive Security and Privacy (SP) analysis of the state-of-the-art IP-ICN coexistence architectures by horizontally comparing the SP features among three deployment approaches – i.e., overlay, underlay, and hybrid – and vertically comparing among the ten considered SP features. Lastly, the article sheds light on the open issues and possible future directions for IP-ICN coexistence. Our analysis shows that most architectures fail to provide several SP features, including data and traffic flow confidentiality, availability, and anonymity of communication. Thus, this article shows the secure combination of current and future protocol stacks during the coexistence phase that the Internet will definitely walk across.
Can GAN-induced Attribute Manipulations Impact Face Recognition?

Impact due to demographic factors such as age, sex, race, etc., has been studied extensively in automated face recognition systems. However, the impact of \textit{digitally modified} demographic and facial attributes on face recognition is relatively under-explored. In this work, we study the effect of attribute manipulations induced via generative adversarial networks (GANs) on face recognition performance. We conduct experiments on the CelebA dataset by intentionally modifying thirteen attributes using AttGAN and STGAN and evaluating their impact on two deep learning-based face verification methods, ArcFace and VGGFace. Our findings indicate that some attribute manipulations involving eyeglasses and digital alteration of sex cues can significantly impair face recognition by up to 73% and need further analysis.
Semi-supervised Invertible DeepONets for Bayesian Inverse Problems

Deep Operator Networks (DeepONets) oﬀer a powerful, data-driven tool for solving parametric PDEs by learning operators, i.e. maps between inﬁnite-dimensional function spaces. In this work, we employ physics-informed DeepONets in the context of high-dimensional, Bayesian inverse problems. Traditional solution strategies necessitate an enormous, and frequently infeasible, number of forward model solves, as well as the computation of parametric derivatives. In order to enable eﬃcient solutions, we extend DeepONets by employing a realNVP architecture which yields an invertible and diﬀerentiable map between the parametric input and the branch net output. This allows us to construct accurate approximations of the full posterior which can be readily adapted irrespective of the number of observations and the magnitude of the observation noise. As a result, no additional forward solves are required, nor is there any need for costly sampling pro-cedures. We demonstrate the eﬃcacy and accuracy of the proposed methodology in the context of inverse problems based on a anti-derivative, a reaction-diﬀusion and a Darcy-ﬂow equation.
Auto-TransRL: Autonomous Composition of Vision Pipelines for Robotic Perception

—Creating a vision pipeline for different datasets to solve a computer vision task is a complex and time consuming process. Currently, these pipelines are developed with the help of domain experts. Moreover, there is no systematic structure to construct a vision pipeline apart from relying on experience, trial and error or using template-based approaches. As the search space for choosing suitable algorithms for achieving a particular vision task is large, human exploration for ﬁnding a good solution requires time and effort. To address the following issues, we propose a dynamic and data-driven way to identify an appropriate set of algorithms that would be ﬁt for building the vision pipeline in order to achieve the goal task. We introduce a Transformer Architecture complemented with Deep Reinforcement Learning to recommend algorithms that can be incorporated at different stages of the vision workﬂow. This system is both robust and adaptive to dynamic changes in the environment. Experimental results further show that our method also generalizes well to recommend algorithms that have not been used while training and hence alleviates the need of retraining the system on a new set of algorithms introduced during test time.
From Human Walking to Bipedal Robot Locomotion: Reflex Inspired Compensation on Planned and Unplanned Downsteps

Humans are able to negotiate downstep behaviors-both planned and unplanned-with remarkable agility and ease. The goal of this paper is to systematically study the translation of this human behavior to bipedal walking robots, even if the morphology is inherently different. Concretely, we begin with human data wherein planned and unplanned downsteps are taken. We analyze this data from the perspective of reduced-order modelling of the human, encoding the center of mass (CoM) kinematics and contact forces, which allows for the translation of these behaviors into the corresponding reduced-order model of a bipedal robot. We embed the resulting behaviors into the full-order dynamics of a bipedal robot via nonlinear optimization-based controllers. The end result is the demonstration of planned and unplanned downsteps in simulation on an underactuated walking robot.
Feasibility of a pulsed ponderomotive phase plate for electron beams

We propose a scheme for constructing a phase plate for use in an ultrafast Zernike-type phase contrast electron microscope, based on the interaction of the electron beam with a strongly focused, high-power femtosecond laser pulse and a pulsed electron beam. Analytical expressions for the phase shift using the time-averaged ponderomotive potential and a paraxial approximation for the focused laser beam are presented, as well as more rigorous quasiclassical simulations based on the quantum phase integral along classical, relativistic electron trajectories in an accurate, non-paraxial description of the laser beam. The results are shown to agree well unless the laser beam is focused to a waist size below a wavelength. For realistic (off-the-shelf) laser parameters the optimum phase shift of −π/2 is shown to be achievable. When combined with RF-cavity based electron chopping and compression techniques to produce electron pulses, a femtosecond regime pulsed phase contrast microscope can be constructed. The feasibility and robustness of the scheme are further investigated using the simulations, leading to motivated choices for design parameters such as wavelength, focus size and polarization.
3D Textured Shape Recovery with Learned Geometric Priors

3D textured shape recovery from partial scans is crucial for many real-world applications. Existing approaches have demonstrated the efficacy of implicit function representation, but they suffer from partial inputs with severe occlusions and varying object types, which greatly hinders their application value in the real world. This technical report presents our approach to address these limitations by incorporating learned geometric priors. To this end, we generate a SMPL model from learned pose prediction and fuse it into the partial input to add prior knowledge of human bodies. We also propose a novel completeness-aware bounding box adaptation for handling different levels of scales and partialness of partial scans.
Wavelength-aware 2D Convolutions for Hyperspectral Imaging

Deep Learning could drastically boost the classification accuracy for Hyperspectral Imaging (HSI). Still, the training on the mostly small hyperspectral data sets is not trivial. Two key challenges are the large channel dimension of the recordings and the incompatibility between cameras of different manufacturers.By introducing a suitable model bias and continuously defining the channel dimension, we propose a 2D convolution optimized for these challenges of Hyperspectral Imaging. We evaluate the method based on two different hyperspectral applications (inline inspection and remote sensing). Besides the shown superiority of the model, the modification adds additional explanatory power.In addition, the model learns the necessary camera filters in a data-driven manner. Based on these camera filters, an optimal camera can be designed.
Adam Mickiewicz University at WMT 2022: NER-Assisted and Quality-Aware Neural Machine Translation

This paper presents Adam Mickiewicz University’s (AMU) submissions to the constrained track of the WMT 2022 General MT Task. We participated in the Ukrainian ↔ Czech translation directions. The systems are a weighted ensemble of four models based on the Transformer (big) architecture. The models use source factors to utilize the information about named entities present in the input. Each of the models in the ensemble was trained using only the data provided by the shared task organizers. A noisy back-translation technique was used to augment the training corpora. One of the models in the ensemble is a document-level model, trained on parallel and synthetic longer sequences. During the sentence-level decoding process, the ensemble generated the n-best list. The n-best list was merged with the n-best list generated by a single document-level model which translated multiple sentences at a time. Finally, existing quality estimation models and minimum Bayes risk decoding were used to rerank the n-best list so that the best hypothesis was chosen according to the COMET evaluation metric. According to the automatic evaluation results, our systems rank first in both translation directions.
GPU implementation of a ray-surface intersection algorithm in CUDA (Compute Unified Device Architecture)

These notes accompany the open-source code published in GitHub which implements a GPU-based line-segment, surface-triangle intersection algorithm in CUDA. It mentions some relevant works and discusses issues specific to this implementation. The goal is to provide software documentation and greater clarity on collision buffer management which is sometimes omitted in online literature. For real-world applications, CPU-based implementations of the test are often deemed too slow to be useful. In contrast, the code described here targets Nvidia GPU devices and offers a solution that is vastly more efficient and scalable. The main API is also wrapped in Python. This geometry test is applied in various engineering problems, so the software developed can be reused in new situations.
Effect of nonhydrostatic pressure on the superconducting kagome metal CsV$_3$Sb$_5$

High-pressure single-crystal x-ray diffraction experiments reveal that the superconducting kagome metal CsV$_3$Sb$_5$ transforms from hexagonal ($P6/mmm$) to monoclinic ($C2/m$) symmetry above 10 GPa if nonhydrostatic pressure conditions are created in a diamond anvil cell with silicon oil as pressure-transmitting medium. This is contrary to the behavior of CsV$_3$Sb$_5$ under quasi-hydrostatic conditions in neon, with the hexagonal symmetry retained up to at least 20 GPa. Monoclinic distortion leaves the kagome planes almost unchanged but deforms honeycomb nets of the Sb atoms. While the onset of the distortion almost coincides with the reentrance of superconductivity, our \textit{ab initio} density-functional calculations reveal only minor changes in the electronic structure compared to the quasi-hydrostatic case. In particular, Fermi surface reconstruction driven by the formation of interlayer Sb-Sb bonds is observed in both monoclinic and hexagonal CsV$_3$Sb$_5$ structures at high pressures and comes out as the likely cause for the reentrant behavior.
Effects of Archive Size on Computation Time and Solution Quality for Multiobjective Optimization

An unbounded external archive has been used to store all nondominated solutions found by an evolutionary multiobjective optimization algorithm in some studies. It has been shown that a selected solution subset from the stored solutions is often better than the final population. However, the use of the unbounded archive is not always realistic. When the number of examined solutions is huge, we must prespecify the archive size. In this study, we examine the effects of the archive size on three aspects: 1) the quality of the selected final solution set; 2) the total computation time for the archive maintenance and the final solution set selection; and 3) the required memory size. Unsurprisingly, the increase of the archive size improves the final solution set quality. Interestingly, the total computation time of a medium-size archive is much larger than that of a small-size archive and a huge-size archive (e.g., an unbounded archive). To decrease the computation time, we examine two ideas: 1) periodical archive update and 2) archiving only in later generations. Compared with updating the archive at every generation, the first idea can obtain almost the same final solution set quality using a much shorter computation time at the cost of a slight increase of the memory size. The second idea drastically decreases the computation time at the cost of a slight deterioration of the final solution set quality. Based on our experimental results, some suggestions are given about how to appropriately choose an archiving strategy and an archive size.
Code Code Evolution: Understanding How People Change Data Science Notebooks Over Time

Sensemaking is the iterative process of identifying, extracting, and explaining insights from data, where each iteration is referred to as the “sensemaking loop. ” However, little is known about how sensemaking behavior evolves from exploration and explanation during this process. This gap limits our ability to understand the full scope of sensemaking, which in turn inhibits the design of tools that support the process. We contribute the first mixed-method to characterize how sensemaking evolves within computational notebooks. We study 2,574 Jupyter notebooks mined from GitHub by identifying data science notebooks that have undergone significant iterations, presenting a regression model that automatically characterizes sensemaking activity, and using this regression model to calculate and analyze shifts in activity across GitHub versions. Our results show that notebook authors participate in various sensemaking tasks over time, such as annotation, branching analysis, and documentation. We use our insights to recommend extensions to current notebook environments.
Bayesian and Frequentist Semantics for Common Variations of Differential Privacy: Applications to the 2020 Census

The purpose of this paper is to guide interpretation of the semantic privacy guarantees for some of the major variations of differential privacy, which include pure, approximate, Rényi, zero-concentrated, and f differential privacy. We interpret privacy-loss accounting parameters, frequentist semantics, and Bayesian semantics (including new results). The driving application is the interpretation of the confidentiality protections for the 2020 Census Public Law 94-171 Redistricting Data Summary File released August 12, 2021, which, for the first time, were produced with formal privacy guarantees.
First Principles of Cosmology

Part 1: Overview 1. Preliminaries. 2. Dynamics of the Universe. 3. Cosmological Models. 4. History of the Universe. Part 2: Cornerstones 5. Hubble Expansion. 6. Nucleosynthesis. 7. Cosmic Microwave Background Radiation. Part 3: Structure 8. Structure Formation. Beyond the Horizon.
RX J1856.5-3754: A Strange Star with Solid Quark Surface?

The featureless spectra of isolated 'neutron stars' may indicate that they are actually bare strange stars but a definitive conclusion on the nature of the compact objects cannot be reached until accurate and theoretically calculated spectra of the bare quark surface are known. However due to the complex nonlinearity of quantum chromodynamics it is almost impossible to present a definitive and accurate calculation of the density-dominated quark-gluon plasma from the first principles. Nevertheless it was suggested that cold quark matter with extremely high baryon density could be in a solid state. Within the realms of this possibility we have fitted the 500ks Chandra LETG/HRC data for the brightest isolated neutron star RX 51856.5-3754 with a phenomenological spectral model and found that electric conductivity of quark matter on the stellar surface is about 1.5 x 10(exp 16)/s.
Social media engagement and cryptocurrency performance

Cryptocurrencies are highly speculative assets with large price volatility. If one could forecast their behavior, this would make them more attractive to investors. In this work we study the problem of predicting the future performance of cryptocurrencies using social media data. We propose a new model to measure the engagement of users with topics discussed on social media based on interactions with social media posts. This model overcomes the limitations of previous volume and sentiment based approaches. We use this model to estimate engagement coefficients for 48 cryptocurrencies created between 2019 and 2021 using data from Twitter from the first month of the cryptocurrencies’ existence. We find that the future returns of the cryptocurrencies are dependent on the engagement coefficients. Cryptocurrencies whose engagement coefficients have extreme values have lower returns. Low engagement coefficients signal a lack of interest, while high engagement coefficients signal artificial activity which is likely from automated accounts known as bots. We measure the amount of bot posts for the cryptocurrencies and find that generally, cryptocurrencies with more bot posts have lower future returns. While future returns are dependent on both the bot activity and engagement coefficient, the dependence is strongest for the engagement coefficient, especially for short-term returns. We show that simple investment strategies which select cryptocurrencies with engagement coefficients exceeding a fixed threshold perform well for holding times of a few months.
Local Projection Inference in High Dimensions

In this paper, we estimate impulse responses by local projections in high-dimensional settings. We use the desparsified (de-biased) lasso to estimate the high-dimensional local projections, while leaving the impulse response parameter of interest unpenalized. We establish the uniform asymptotic normality of the proposed estimator under general conditions. Finally, we demonstrate small sample performance through a simulation study and consider two canonical applications in macroeconomic research on monetary policy and government spending.
Skeleton structure inherent in discrete-time quantum walks

In this paper, we demonstrate that a common underlying structure—a skeleton structure—is present behind discrete-time quantum walks on a one-dimensional lattice with a homogeneous coin matrix. More speciﬁcally, we examine the transition probabilities of random walks that replicate the probability distribution of quantum walks. We show that the transition probability contains a skeleton structure by considering the weak limit that excludes the oscillatory behavior. Remarkably, the skeleton structure does not depend on the coin matrix or the initial conditions of the quantum walk. Furthermore, we propose a random walk whose transition probabilities are deﬁned by the skeleton structure, which we call a “quantum-skeleton random walk. ” We demonstrate that the resultant properties of the walkers are similar to the original quantum walk.
Goldfish: No More Attacks on Ethereum?!

The LMD GHOST consensus protocol is a critical component of proof-of-stake Ethereum. In its current form, this protocol is brittle, as evidenced by recent attacks and patching attempts. We propose Goldfish, a new protocol that satisfies key properties required of a drop-in replacement for LMD GHOST: Goldfish is secure in the sleepy model, assuming a majority of the validators follows the protocol. Goldfish is reorg resilient so that honestly produced blocks are guaranteed inclusion in the ledger, and it supports fast confirmation with expected confirmation latency independent of the desired security level. Subsampling validators can improve the communication efficiency of Goldfish, and Goldfish is composable with finality/accountability gadgets. Crucially, Goldfish is structurally similar to LMD GHOST, providing a credible path to adoption in Ethereum. Attacks on LMD GHOST exploit lack of coordination among honest validators, typically provided by a locking mechanism in classical BFT protocols. However, locking requires votes from a quorum of all participants and is not compatible with fluctuating participation. Goldfish is powered by a novel coordination mechanism to synchronize the honest validators' actions. Experiments with our prototype implementation of Goldfish suggest practicality.
ASR2K: Speech Recognition for Around 2000 Languages without Audio

Most recent speech recognition models rely on large supervised datasets, which are unavailable for many low-resource languages. In this work, we present a speech recognition pipeline that does not require any audio for the target language. The only assumption is that we have access to raw text datasets or a set of n-gram statistics. Our speech pipeline consists of three components: acoustic, pronunciation, and language models. Unlike the standard pipeline, our acoustic and pronunciation models use multilingual models without any supervision. The language model is built using n-gram statistics or the raw text dataset. We build speech recognition for 1909 languages by combining it with Crubadan: a large endangered languages n-gram database. Furthermore, we test our approach on 129 languages across two datasets: Common Voice and CMU Wilderness dataset. We achieve 50% CER and 74% WER on the Wilderness dataset with Crubadan statistics only and improve them to 45% CER and 69% WER when using 10000 raw text utterances.