Miracle-dz/UniFAR-FTUs huggingface dataset:
Identifying the Key Attributes in an Unlabeled Event Log for Automated Process Discovery

Process mining discovers and analyzes a process model from historical event logs. The prior art methods use the key attributes of case-id, activity, and timestamp hidden in an event log as clues to discover a process model. However, a user needs to specify them manually, and this can be an exhaustive task. In this article, we propose a two-stage key attribute identification method to avoid such a manual investigation, and thus this is a step toward fully automated process discovery. One of the challenging tasks is how to avoid exhaustive computation due to combinatorial explosion. For this, we narrow down candidates for each key attribute by using supervised machine learning in the first stage and identify the best combination of the key attributes by discovering process models and evaluating them in the second stage. Our computational complexity can be reduced from <inline-formula><tex-math notation="LaTeX">$\mathcal {O}(N^{3})$</tex-math><alternatives><mml:math><mml:mrow><mml:mi mathvariant="script">O</mml:mi><mml:mo>(</mml:mo><mml:msup><mml:mi>N</mml:mi><mml:mn>3</mml:mn></mml:msup><mml:mo>)</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="toyoda-ieq1-3330175.gif"/></alternatives></inline-formula> to <inline-formula><tex-math notation="LaTeX">$\mathcal {O}(k^{3})$</tex-math><alternatives><mml:math><mml:mrow><mml:mi mathvariant="script">O</mml:mi><mml:mo>(</mml:mo><mml:msup><mml:mi>k</mml:mi><mml:mn>3</mml:mn></mml:msup><mml:mo>)</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="toyoda-ieq2-3330175.gif"/></alternatives></inline-formula> where <inline-formula><tex-math notation="LaTeX">$N$</tex-math><alternatives><mml:math><mml:mi>N</mml:mi></mml:math><inline-graphic xlink:href="toyoda-ieq3-3330175.gif"/></alternatives></inline-formula> and <inline-formula><tex-math notation="LaTeX">$k$</tex-math><alternatives><mml:math><mml:mi>k</mml:mi></mml:math><inline-graphic xlink:href="toyoda-ieq4-3330175.gif"/></alternatives></inline-formula> are the numbers of columns and candidates we keep in the first stage, respectively, and usually <inline-formula><tex-math notation="LaTeX">$k$</tex-math><alternatives><mml:math><mml:mi>k</mml:mi></mml:math><inline-graphic xlink:href="toyoda-ieq5-3330175.gif"/></alternatives></inline-formula> is much smaller than <inline-formula><tex-math notation="LaTeX">$N$</tex-math><alternatives><mml:math><mml:mi>N</mml:mi></mml:math><inline-graphic xlink:href="toyoda-ieq6-3330175.gif"/></alternatives></inline-formula>. We evaluated our method with 14 open datasets and showed that our method could identify the key attributes even with <inline-formula><tex-math notation="LaTeX">$k = 2$</tex-math><alternatives><mml:math><mml:mrow><mml:mi>k</mml:mi><mml:mo>=</mml:mo><mml:mn>2</mml:mn></mml:mrow></mml:math><inline-graphic xlink:href="toyoda-ieq7-3330175.gif"/></alternatives></inline-formula> for about 20 seconds for many datasets.
Guided Deep Reinforcement Learning for Articulated Swimming Robots

Deep reinforcement learning has recently been applied to a variety of robotics applications, but learning locomotion for robots with unconventional configurations is still limited. Prior work has shown that, despite the simple modeling of articulated swimmer robots, such systems struggle to find effective gaits using reinforcement learning due to the heterogeneity of the search space. In this work, we leverage insight from geometric models of these robots in order to focus on promising regions of the space and guide the learning process. We demonstrate that our augmented learning technique is able to produce gaits for different learning goals for swimmer robots in both low and high Reynolds number fluids.
Approximating DTW with a convolutional neural network on EEG data

Dynamic Time Wrapping (DTW) is a widely used algorithm for measuring similarities between two time series. It is especially valuable in a wide variety of applications, such as clustering, anomaly detection, classiﬁcation, or video segmentation, where the time-series have different timescales, are irregularly sampled, or are shifted. However, it is not prone to be considered as a loss function in an end-to-end learning framework because of its non-differentiability and its quadratic temporal complexity. While differentiable variants of DTW have been introduced by the community, they still present some drawbacks: computing the distance is still expensive and this similarity tends to blur some differences in the time-series. In this paper, we propose a fast and differentiable approximation of DTW by comparing two architectures: the ﬁrst one for learning an embedding in which the Euclidean distance mimics the DTW, and the second one for directly predicting the DTW output using regression. We build the former by training a siamese neural network to regress the DTW value between two time-series. Depending on the nature of the activation function, this approximation naturally supports differentiation, and it is efﬁcient to compute. We show, in a time-series retrieval context on EEG datasets, that our methods achieve at least the same level of accuracy as other DTW main approximations with higher computational efﬁciency. We also show that it can be used to learn in an end-to-end setting on long time series by proposing generative models of EEGs.
Tackling Stackelberg Network Interdiction against a Boundedly Rational Adversary

This work studies Stackelberg network interdiction games -- an important class of games in which a defender first allocates (randomized) defense resources to a set of critical nodes on a graph while an adversary chooses its path to attack these nodes accordingly. We consider a boundedly rational adversary in which the adversary's response model is based on a dynamic form of classic logit-based discrete choice models. We show that the problem of finding an optimal interdiction strategy for the defender in the rational setting is NP-hard. The resulting optimization is in fact non-convex and additionally, involves complex terms that sum over exponentially many paths. We tackle these computational challenges by presenting new efficient approximation algorithms with bounded solution guarantees. First, we address the exponentially-many-path challenge by proposing a polynomial-time dynamic programming-based formulation. We then show that the gradient of the non-convex objective can also be computed in polynomial time, which allows us to use a gradient-based method to solve the problem efficiently. Second, we identify a restricted problem that is convex and hence gradient-based methods find the global optimal solution for this restricted problem. We further identify mild conditions under which this restricted problem provides a bounded approximation for the original problem.
Analyzing Robustness of the Deep Reinforcement Learning Algorithm in Ramp Metering Applications Considering False Data Injection Attack and Defense

Ramp metering is the act of controlling on-going vehicles to the highway mainlines. Decades of practices of ramp metering have proved that ramp metering can decrease total travel time, mitigate shockwaves, decrease rear-end collisions by smoothing the traffic interweaving process, etc. Besides traditional control algorithm like ALINEA, Deep Reinforcement Learning (DRL) algorithms have been introduced to build a finer control. However, two remaining challenges still hinder DRL from being implemented in the real world: (1) some assumptions of algorithms are hard to be matched in the real world; (2) the rich input states may make the model vulnerable to attacks and data noises. To investigate these issues, we propose a Deep Q-Learning algorithm using only loop detectors information as inputs in this study. Then, a set of False Data Injection attacks and random noise attack are designed to investigate the robustness of the model. The major benefit of the model is that it can be applied to almost any ramp metering sites regardless of the road geometries and layouts. Besides outcompeting the ALINEA method, the Deep Q-Learning method also shows a good robustness through training among very different demands and geometries. For example, during the testing case in I-24 near Murfreesboro, TN, the model shows its robustness as it still outperforms ALINEA algorithm under Fast Gradient Sign Method attacks. Unlike many previous studies, the model is trained and tested in completely different environments to show the capabilities of the model.
Einstein's Equations and the Entropy of pseudo-Riemannian Information Manifolds

Motivated by the corrected form of the entropy-area law, and with the help of von Neumann entropy of quantum matter, we construct an emergent spacetime by the virtue of the geometric language of statistical information manifolds. We discuss the link between Wald–Jacobson approaches of thermodynamic/gravity correspondence and Fisher pseudo –Riemannian metric of information manifold. We derive in detail Einstein’s ﬁeld equations in statistical information geometric forms. This results in ﬁnding a quantum origin of a positive cosmological constant that is founded on Fisher metric. This cosmological constant resembles those found in Lovelock’s theories in a de Sitter background as a result of using the complex extension of spacetime and the Gaussian exponential families of probability distributions, and we ﬁnd a time varying dynamical gravitational constant as a function of Fisher metric together with the corresponding Ryu–Takayanagi formula of such system. Consequently, we obtain a dynamical equation for the entropy in information manifold using Liouville– von Neumann equation from the Hamiltonian of the system. This Hamiltonian is suggested to be non–Hermitian, which corroborates the approaches that relate non– unitary conformal ﬁeld theories to information manifolds. This provides some insights on resolving “the problem of time”.
Supervised and Contrastive Self-Supervised In-Domain Representation Learning for Dense Prediction Problems in Remote Sensing

— In recent years Convolutional neural networks (CNN) have made significant progress in computer vision. These advancements have been applied to other areas, such as remote sensing and have shown satisfactory results. However, the lack of large labeled datasets and the inherent complexity of remote sensing problems have made it difficult to train deep CNNs for dense prediction problems. To solve this issue, ImageNet pre-trained weights have been used as a starting point in various dense predictions tasks. Although this type of transfer learning has led to improvements, the domain difference between natural and remote sensing images has also limited the performance of deep CNNs. On the other hand, self-supervised learning methods for learning visual representations from large unlabeled images have grown substantially over the past two years. Accordingly, in this paper we have explored the effectiveness of in-domain representations in both supervised and self-supervised forms to solve the domain difference between remote sensing and the ImageNet dataset. The obtained weights from remote sensing images are utilized as initial weights for solving semantic segmentation and object detection tasks and state-of-the-art results are obtained. For self-supervised pre-training, we have utilized the SimSiam algorithm as it is simple and does not need huge computational resources. One of the most influential factors in acquiring general visual representations from remote sensing images is the pre-training dataset. To examine the effect of the pre-training dataset, equal-sized remote sensing datasets are used for pre-training. Our results have demonstrated that using datasets with a high spatial resolution for self-supervised representation learning leads to high performance in downstream tasks.
DELTA: degradation-free fully test-time adaptation

Fully test-time adaptation aims at adapting a pre-trained model to the test stream during real-time inference, which is urgently required when the test distribution differs from the training distribution. Several efforts have been devoted to improving adaptation performance. However, we find that two unfavorable defects are concealed in the prevalent adaptation methodologies like test-time batch normalization (BN) and self-learning. First, we reveal that the normalization statistics in test-time BN are completely affected by the currently received test samples, resulting in inaccurate estimates. Second, we show that during test-time adaptation, the parameter update is biased towards some dominant classes. In addition to the extensively studied test stream with independent and class-balanced samples, we further observe that the defects can be exacerbated in more complicated test environments, such as (time) dependent or class-imbalanced data. We observe that previous approaches work well in certain scenarios while show performance degradation in others due to their faults. In this paper, we provide a plug-in solution called DELTA for Degradation-freE fuLly Test-time Adaptation, which consists of two components: (i) Test-time Batch Renormalization (TBR), introduced to improve the estimated normalization statistics. (ii) Dynamic Online re-weighTing (DOT), designed to address the class bias within optimization. We investigate various test-time adaptation methods on three commonly used datasets with four scenarios, and a newly introduced real-world dataset. DELTA can help them deal with all scenarios simultaneously, leading to SOTA performance.
Novelty Filtering with a Photorefractive Lithium-Niobate Crystal

In this letter we present a technique which employs a photorefractive lithium–niobate crystal for novelty filtering. Due to the minimal trail formation exhibited by this novelty filter, it can be used for reliable quantitative phase measurement for time intervals of the order of a few hours. We present a simplified theoretical description of this filter based on a coupled wave theory [N. V. Kukhtarev, V. B. Markov, S. G. Odulov, M. S. Soskin, and V. L. Vinetskii, Ferroelectrics 22, 949 (1979); N. V. Kukhtarev, V. B. Markov, S. G. Odulov, M. S. Soskin, and V. L. Vinetskii, Ferroelectrics22, 961 (1979)]. We also demonstrate the first experimental results of employing this device in the field of microfluid dynamics for measuring the concentration changes produced due to the mixing of two transparent liquids in a microchannel.
Credible, Optimal Auctions via Blockchains

Akbarpour and Li (2020) formalized credibility as an auction desideratum where the auctioneer cannot benefit by implementing undetectable deviations from the promised auction and showed that, in the plain model, the ascending price auction with reserves is the only credible, strategyproof, revenue-optimal auction. Ferreira and Weinberg (2020) proposed the Deferred Revelation Auction (DRA) as a communication efficient auction that avoids the uniqueness results from Akbarpour and Li (2020) assuming the existence of cryptographic commitments and as long as bidder valuations are MHR. They also showed DRA is not credible in settings where bidder valuations are $\alpha$-strongly regular unless $\alpha>1$. In this paper, we ask if blockchains allow us to design a larger class of credible auctions. We answer this question positively, by showing that DRA is credible even for $\alpha$-strongly regular distributions for all $\alpha>0$ if implemented over a secure and censorship-resistant blockchain. We argue ledgers provide two properties that limit deviations from a self-interested auctioneer. First, the existence of smart contracts allows one to extend the concept of credibility to settings where the auctioneer does not have a reputation -- one of the main limitations for the definition of credibility from Akbarpour and Li (2020). Second, blockchains allow us to implement mechanisms over a public broadcast channel, removing the adaptive undetectable deviations driving the negative results of Ferreira and Weinberg (2020).
Transverse spin angular momentum of a space-time surface plasmon polariton wave packet

In addition to longitudinal spin angular momentum (SAM) along the axis of propagation of light, spatially structured electromagnetic fields such as evanescent waves and focused beams have recently been found to possess transverse SAM in the direction perpendicular to the axis of propagation. In particular, the SAM of SPPs with spatial structure has been extensively studied in the last decade after it became clear that evanescent fields with spatially structured energy flow generate threedimensional spin texture. Here we present numerical calculations of the space-time surface plasmon polariton (ST-SPP) wave packet, a plasmonic bullet that propagates at an arbitrary group velocity while maintaining its spatial distribution. ST-SPP wave packets with complex spatial structure and energy flow density distribution determined by the group velocity are found to propagate with accompanying three-dimensional spin texture and finite topological charge density. Furthermore, the spatial distribution of the spin texture and topological charge density determined by the spatial structure of the SPP is controllable, and the deformation associated with propagation is negligible. ST-SPP wave packets, which can stably transport customizable three-dimensional spin textures and topological charge densities, can be excellent subjects of observation in studies of spinphotonics and optical topological materials.
Traffic Prediction in Cellular Networks using Graph Neural Networks

Cellular networks are ubiquitous entities that provide major means of communication all over the world. One major challenge in cellular networks is a dynamic change in the number of users and their usage of telecommunication service which results in overloading at certain base stations. One class of solution to deal with this overloading issue is the deployment of drones that can act as temporary base stations and offload the traffic from the overloaded base station. There are two main challenges in the development of this solution. Firstly, the drone is expected to be present around the base station where an overload would occur in the future thus requiring a prediction of traffic overload. Secondly, drones are highly constrained in their resources and can only fly for a few minutes. If the affected base station is really far, drones can never reach there. This requires the initial placement of drones in sectors where overloading can occur thus again requiring a traffic forecast but at a different spatial scale. It must be noted that the spatial extent of the region that the problem poses and the extremely limited power resources available to the drone pose a great challenge that is hard to overcome without deploying the drones in strategic positions to reduce the time to fly to the required high-demand zone. Moreover, since drone fly at a finite speed, it is important that a predictive solution that can forecast traffic surges is adopted so that drones are available to offload the overload before it actually happens. Both these goals require analysis and forecast of cellular network traffic which is the main goal of this project
Exploring Image Augmentations for Siamese Representation Learning with Chest X-Rays

Image augmentations are quintessential for effective visual representation learning across self-supervised learning techniques. While augmentation strategies for natural imaging have been studied extensively, medical images are vastly different from their natural counterparts. Thus, it is unknown whether common augmentation strategies employed in Siamese representation learning generalize to medical images and to what extent. To address this challenge, in this study, we systematically assess the effect of various augmentations on the quality and robustness of the learned representations. We train and evaluate Siamese Networks for abnormality detection on chest X-Rays across three large datasets (MIMIC-CXR, CheXpert and VinDR-CXR). We investigate the efficacy of the learned representations through experiments involving linear probing, fine-tuning, zero-shot transfer, and data efficiency. Finally, we identify a set of augmentations that yield robust representations that generalize well to both out-of-distribution data and diseases, while outperforming supervised baselines using just zero-shot transfer and linear probes by up to 20%. Our code is available at https://github.com/StanfordMIMI/siaug.
Human Cognition Surpasses the Nonlocality Tsirelson Bound: Is Mind Outside of Spacetime?

Recent experimental studies on human cognition, particularly where non-separable or entangled cognitive states have been found, show that in many such cases Bell or CHSH inequalities have been violated. The implications are that greater non-local correlations than allowed in quantum mechanics (often known as the Tsirelson bound), are found in human cognition. However, it is also evident that surpassing of Tsirelson limit within relativistic physical Spacetime seems impossible. Tsirelson limit is not guaranteed by no-signaling condition, but some deeper feature like indistinguishability of identical quantum particles might be related to such an upper bound in quantum physics, within relativistic Spacetime. We propose in the current paper that a non-local theory of mind is needed in order to account for the empirical findings. This requires a foundationally different approach than the extant 'quantum-like' approach to human mind. To account for the surpassing of the Tsirelson bound we propose abandoning the constraint of no-signaling that depends upon spacetime. Thus, we ask: 'Is mind outside spacetime? ' We discuss a candidate theory of quantum gravity based on nonlocality as fundamental that may accord with our proposal. We are led to suggest a new 6 - part ontological framework linking Mind, Matter, and Cosmos.
Efficient cooling of high-angular-momentum atoms

We propose a highly efficient and fast method of translational cooling for high-angular-momentum atoms. Optical pumping and stimulated transitions, combined with magnetic forces, can be used to compress phase-space density, and the efficiency of each compression step increases with the angular momentum. Entropy is removed by spontaneously emitted photons, and particle number is conserved. This method may be an attractive alternative to evaporative cooling of atoms and possibly molecules in order to produce quantum degenerate gases.
A Human Word Association based model for topic detection in social networks

With the widespread use of social networks, detecting the topics discussed in these networks has become a significant challenge. The current works are mainly based on frequent pattern mining or semantic relations, and the language structure is not considered. The meaning of language structural methods is to discover the relationship between words and how humans understand them. Therefore, this paper uses the Concept of the Imitation of the Mental Ability of Word Association to propose a topic detection framework in social networks. This framework is based on the Human Word Association method. A special extraction algorithm has also been designed for this purpose. The performance of this method is evaluated on the FA-CUP dataset. It is a benchmark dataset in the field of topic detection. The results show that the proposed method is a good improvement compared to other methods, based on the Topic-recall and the keyword F1 measure. Also, most of the previous works in the field of topic detection are limited to the English language, and the Persian language, especially microblogs written in this language, is considered a low-resource language. Therefore, a data set of Telegram posts in the Farsi language has been collected. Applying the proposed method to this dataset also shows that this method works better than other topic detection methods.