Miracle-dz/UniFAR-FTUs huggingface dataset:
Singularity models in the three-dimensional Ricci flow

The Ricci ﬂow is a natural evolution equation for Riemannian metrics on a given manifold. The main goal is to understand singularity formation. In his spectacular 2002 breakthrough, Perelman achieved a qualitative understanding of singularity formation in dimension 3. More precisely, Perelman showed that every ﬁnite-time singularity to the Ricci ﬂow in dimension 3 is modeled on an ancient κ -solution. Moreover, Perelman proved a structure theorem for ancient κ -solutions in dimension 3. In this survey, we discuss recent developments which have led to a complete classiﬁcation of all the singularity models in dimension 3. Moreover, we give an alternative proof of the classiﬁcation of noncollapsed steady gradient Ricci solitons in dimension 3 (originally proved by the author in 2012).
The regulator dominates the rank

After noticing that the regulator of a number field dominates the rank of its group of units, we bound from below the regulator of the Mordell-Weil group of elliptic curves over global function fields of characteristic $p\geq5$. The lower bound is an increasing function of the rank and of the height.
Data-Efficient Learning of High-Quality Controls for Kinodynamic Planning used in Vehicular Navigation

This paper aims to improve the path quality and computational efficiency of kinodynamic planners used for vehicular systems. It proposes a learning framework for identifying promising controls during the expansion process of sampling-based motion planners for systems with dynamics. Offline, the learning process is trained to return the highest-quality control that reaches a local goal state (i.e., a waypoint) in the absence of obstacles from an input difference vector between its current state and a local goal state. The data generation scheme provides bounds on the target dispersion and uses state space pruning to ensure high-quality controls. By focusing on the system's dynamics, this process is data efficient and takes place once for a dynamical system, so that it can be used for different environments with modular expansion functions. This work integrates the proposed learning process with a) an exploratory expansion function that generates waypoints with biased coverage over the reachable space, and b) proposes an exploitative expansion function for mobile robots, which generates waypoints using medial axis information. This paper evaluates the learning process and the corresponding planners for a first and second-order differential drive systems. The results show that the proposed integration of learning and planning can produce better quality paths than kinodynamic planning with random controls in fewer iterations and computation time.
On the Effectiveness of Sampled Softmax Loss for Item Recommendation

The learning objective plays a fundamental role to build a recommender system. Most methods routinely adopt either pointwise (e.g., binary cross-entropy) or pairwise (e.g., BPR) loss to train the model parameters, while rarely pay attention to softmax loss, which assumes the probabilities of all classes sum up to 1, due to its computational complexity when scaling up to large datasets or intractability for streaming data where the complete item space is not always available. The sampled softmax (SSM) loss emerges as an efficient substitute for softmax loss. Its special case, InfoNCE loss, has been widely used in self-supervised learning and exhibited remarkable performance for contrastive learning. Nonetheless, limited recommendation work uses the SSM loss as the learning objective. Worse still, none of them explores its properties thoroughly and answers “Does SSM loss suit for item recommendation? ” and “What are the conceptual advantages of SSM loss, as compared with the prevalent losses? ”, to the best of our knowledge. In this work, we aim to offer a better understanding of SSM for item recommendation. Specifically, we first theoretically reveal three model-agnostic advantages: (1) mitigating popularity bias, which is beneficial to long-tail recommendation; (2) mining hard negative samples, which offers informative gradients to optimize model parameters; and (3) maximizing the ranking metric, which facilitates top-K performance. However, based on our empirical studies, we recognize that the default choice of cosine similarity function in SSM limits its ability in learning the magnitudes of representation vectors. As such, the combinations of SSM with the models that also fall short in adjusting magnitudes (e.g., matrix factorization) may result in poor representations. One step further, we provide mathematical proof that message passing schemes in graph convolution networks can adjust representation magnitude according to node degree, which naturally compensates for the shortcoming of SSM. Extensive experiments on four benchmark datasets justify our analyses, demonstrating the superiority of SSM for item recommendation. Our implementations are available in both TensorFlow and PyTorch.
Churn prediction in online gambling

In business retention, churn prevention has always been a major concern. This work contributes to this domain by formalizing the problem of churn prediction in the context of online gambling as a binary classification task. We also propose an algorithmic answer to this problem based on recurrent neural network. This algorithm is tested with online gambling data that have the form of time series, which can be efficiently processed by recurrent neural networks. To evaluate the performances of the trained models, standard machine learning metrics were used, such as accuracy, precision and recall. For this problem in particular, the conducted experiments allowed to assess that the choice of a specific architecture depends on the metric which is given the greatest importance. Architectures using nBRC favour precision, those using LSTM give better recall, while GRU-based architectures allow a higher accuracy and balance two other metrics. Moreover, further experiments showed that using only the more recent time-series histories to train the networks decreases the quality of the results. We also study the performances of models learned at a specific instant $t$, at other times $t^{\prime}>t$. The results show that the performances of the models learned at time $t$ remain good at the following instants $t^{\prime}>t$, suggesting that there is no need to refresh the models at a high rate. However, the performances of the models were subject to noticeable variance due to one-off events impacting the data.
Electric Vehicle Routing Problem with Spatio-temporal Varying Electricity Price and Incentive-aware Customers

This paper investigates the optimization problem of a fleet of electric vehicles (EVs) serving a set of time-specified customers, where the operator needs to optimize routing and charging problem jointly for each EV. In particular, regarding to the spatio-temporal varying electricity price, we consider incentive-aware customers and propose that the operator offers monetary incentives to exchange time flexibility of customers. In this manner, a win-win situation is achievable since time flexibility enables the fleet operator to obtain a routing and charging schedule with lower cost, whilst the customers receives monetary compensation. Specifically, we first devise a bi-level model whereby the fleet operator optimizes the routing and charging schedule jointly with a monetary incentive to reimburse the delivery time flexibility experienced by the customers. At the same time, the customers choose the optimal time flexibility by minimizing its own cost. Second, we tackle the complexity resulting from the bi-level and nonlinear problem with an equivalent transformation method consisting of Karush–Kuhn–Tucker (KKT) optimality condition, Big-M linearization method, and strong duality of convex optimization. Eventually, we reformulate the problem as a single-level optimization problem, which later is solved by proposed Benders dual decomposition method holding a faster convergence rate than the generalized Benders decomposition method. To evaluate the effectiveness of our framework and proposed Benders dual decomposition algorithm, we carry out extensive numerical experiments using VRP-REP data from Belgium.
Entanglement Entropy in Horndeski Gravity

In this work, we explore the holographic entanglement entropy with an infinite strip region of the boundary in Horndeski gravity. In our prescription we consider the spherically and planar topologies black holes in the AdS$_{4}$/CFT$_{3}$ scenario. In such framework, we show the behavior of the entanglement entropy in function of the Horndeski parameters. Such parameters modify the information store of subsystem A, especially when the parameter $\gamma$ increases the information about the subsystem will also increase or decrease when it decreases. Thus, with this scheme we compute the first law of entanglement thermodynamics in Horndeski gravity and we show that a very small subsystem obeys the analogous property of the first law of thermodynamics if we excite the system.
Video Summarization Based on Video-text Representation

Modern video summarization methods are based on deep neural networks which require a large amount of annotated data for training. However, existing datasets for video summarization are small-scale, easily leading to over-ﬁtting of the deep models. Considering that the annotation of large-scale datasets is time-consuming, we propose a multimodal self-supervised learning framework to obtain semantic representations of videos, which beneﬁts the video summarization task. Speciﬁcally, we explore the semantic consistency between the visual information and text information of videos, for the self-supervised pretraining of a multimodal encoder on a newly-collected dataset of video-text pairs. Additionally, we introduce a progressive video summarization method, where the important content in a video is pinpointed progressively to generate better summaries. Finally, an objective evaluation framework is proposed to measure the quality of video summaries based on video classiﬁcation. Extensive experiments have proved the effectiveness and superiority of our method in rank correlation coefﬁcients, F-score, and the proposed objective evaluation compared to the state of the art.
From Textual Experiments to Experimental Texts: Expressive Repetition in "Artificial Intelligence Literature"

Since the birth of artificial intelligence 70 years ago, attempts at literary “creation” with computers are present in the course of technological development, creating what one might call “artificial intelligence literature” (AI literature). Evolving from “textual experiments” conducted by technologists to “experimental texts” that explore the possibilities of conceptions of literature, AI literature integrates primitive problems including machine thinking, text generation, and machine creativity, which exhibits the two-way interaction between social ideas and technology. In the early stage, the mutual support between technological path and artistic ideas turned out to be a failure, while AI-driven expressive repetitions are made probable in the contemporary technological context, paving the way for the transformation of AI literature from proof for technical possibilities to self-verification of literary value.
A Transfer Learning Pipeline for Educational Resource Discovery with Application in Leading Paragraph Generation

Effective human learning depends on a wide selection of educational materials that align with the learner's current understanding of the topic. While the Internet has revolutionized human learning or education, a substantial resource accessibility barrier still exists. Namely, the excess of online information can make it challenging to navigate and discover high-quality learning materials. In this paper, we propose the educational resource discovery (ERD) pipeline that automates web resource discovery for novel domains. The pipeline consists of three main steps: data collection, feature extraction, and resource classification. We start with a known source domain and conduct resource discovery on two unseen target domains via transfer learning. We first collect frequent queries from a set of seed documents and search on the web to obtain candidate resources, such as lecture slides and introductory blog posts. Then we introduce a novel pretrained information retrieval deep neural network model, query-document masked language modeling (QD-MLM), to extract deep features of these candidate resources. We apply a tree-based classifier to decide whether the candidate is a positive learning resource. The pipeline achieves F1 scores of 0.94 and 0.82 when evaluated on two similar but novel target domains. Finally, we demonstrate how this pipeline can benefit an application: leading paragraph generation for surveys. This is the first study that considers various web resources for survey generation, to the best of our knowledge. We also release a corpus of 39,728 manually labeled web resources and 659 queries from NLP, Computer Vision (CV), and Statistics (STATS).
Solving Combinatorial Problems at Particle Colliders Using Machine Learning

High-multiplicity signatures at particle colliders can arise in Standard Model processes and beyond. With such signatures, diﬃculties often arise from the large dimensionality of the kinematic space. For ﬁnal states containing a single type of particle signature, this results in a combinatorial problem that hides underlying kinematic information. We explore using a neural network that in-cludes a Lorentz Layer to extract high-dimensional correlations. We use the case of squark decays in R -Parity-violating Supersymmetry as a benchmark, comparing the performance to that of classical methods. With this approach, we demonstrate signiﬁcant improvement over traditional methods.
iDECODe: In-distribution Equivariance for Conformal Out-of-distribution Detection

Machine learning methods such as deep neural networks (DNNs), despite their success across different domains, are known to often generate incorrect predictions with high confidence on inputs outside their training distribution. The deployment of DNNs in safety-critical domains requires detection of out-of-distribution (OOD) data so that DNNs can abstain from making predictions on those. A number of methods have been recently developed for OOD detection, but there is still room for improvement. We propose the new method iDECODe, leveraging in-distribution equivariance for conformal OOD detection. It relies on a novel base non-conformity measure and a new aggregation method, used in the inductive conformal anomaly detection framework, thereby guaranteeing a bounded false detection rate. We demonstrate the efficacy of iDECODe by experiments on image and audio datasets, obtaining state-of-the-art results. We also show that iDECODe can detect adversarial examples. Code, pre-trained models, and data are available at https://github.com/ramneetk/iDECODe.
Vertex finding in neutrino-nucleus interaction: a model architecture comparison

We compare different neural network architectures for machine learning algorithms designed to identify the neutrino interaction vertex position in the MINERvA detector. The architectures developed and optimized by hand are compared with the architectures developed in an automated way using the package “Multi-node Evolutionary Neural Networks for Deep Learning” (MENNDL), developed at Oak Ridge National Laboratory. While the domain-expert hand-tuned network was the best performer, the differences were negligible and the auto-generated networks performed as well. There is always a trade-off between human, and computer resources for network optimization and this work suggests that automated optimization, assuming resources are available, provides a compelling way to save significant expert time.
Developing Assistive Technology to Support Reminiscence Therapy: A User-Centered Study to Identify Caregivers' Needs

Reminiscence therapy is an inexpensive non-pharmacological therapy commonly used due to its therapeutic value for PwD, as it can be used to promote independence, positive moods and behavior, and improve their quality of life. Caregivers are one of the main pillars in the adoption of digital technologies for reminiscence therapy, as they are responsible for its administration. Despite their comprehensive understanding of the needs and difficulties associated with the therapy, their perspective has not been fully taken into account in the development of existing technological solutions. To inform the design of technological solutions within dementia care, we followed a user-centered design approach through worldwide surveys, follow-up semi-structured interviews, and focus groups. Seven hundred and seven informal and 52 formal caregivers participated in our study. Our findings show that technological solutions must provide mechanisms to carry out the therapy in a simple way, reducing the amount of work for caregivers when preparing and conducting therapy sessions. They should also diversify and personalize the current session (and following ones) based on both the biographical information of the PwD and their emotional reactions. This is particularly important since the PwD often become agitated, aggressive or angry, and caregivers might not know how to properly deal with this situation (in particular, the informal ones). Additionally, formal caregivers need an easy way to manage information of the different PwD they take care of, and consult the history of sessions performed (in particular, to identify images that triggered negative emotional reactions, and consult any notes taken about them). As a result, we present a list of validated functional requirements gathered for the PwD and both formal and informal caregivers, as well as the corresponding expected primary and secondary outcomes.
Neural calibration of hidden inhomogeneous Markov chains - Information decompression in life insurance

Markov chains play a key role in a vast number of areas, including life insurance mathematics. Standard actuarial quantities as the premium value can be interpreted as compressed, lossy information about the underlying Markov process. We introduce a method to reconstruct the underlying Markov chain given collective information of a portfolio of contracts. Our neural architecture explainably characterizes the process by explicitly providing one-step transition probabilities. Further, we provide an intrinsic, economic model validation to inspect the quality of the information decompression. Lastly, our methodology is successfully tested for a realistic data set of German term life insurance contracts.
An Unsupervised Masking Objective for Abstractive Multi-Document News Summarization

We show that a simple unsupervised masking objective can approach near supervised performance on abstractive multi-document news summarization. Our method trains a state-of-the-art neural summarization model to predict the masked out source document with highest lexical centrality relative to the multi-document group. In experiments on the Multi-News dataset, our masked training objective yields a system that outperforms past unsupervised methods and, in human evaluation, surpasses the best supervised method without requiring access to any ground-truth summaries. Further, we evaluate how different measures of lexical centrality, inspired by past work on extractive summarization, affect final performance.
Delay Alignment Modulation: Enabling Equalization-Free Single-Carrier Communication

This letter proposes a novel broadband transmission technology, termed delay alignment modulation (DAM), which enables the low-complexity equalization-free single-carrier communication, yet without suffering from inter-symbol interference (ISI). The key idea of DAM is to deliberately introduce appropriate delays for information-bearing symbols at the transmitter side, so that after propagating over the time-dispersive channel, all multi-path signal components will arrive at the receiver simultaneously and constructively. We first show that by applying DAM for the basic multiple-input single-output (MISO) communication system, an ISI-free additive white Gaussian noise (AWGN) system can be obtained with the simple zero-forcing (ZF) beamforming. Furthermore, the more general DAM schemes are studied with the ISI-maximal-ratio transmission (MRT) and the ISI-minimum mean-square error (MMSE) beamforming. Simulation results are provided to show that when the channel is sparse and/or the antenna dimension is large, DAM not only resolves the notorious practical issues suffered by orthogonal frequency-division multiplexing (OFDM), such as high peak-to-average-power ratio (PAPR) and vulnerability to carrier frequency offset (CFO), but also achieves higher spectral efficiency due to the saving of guard interval overhead.
Are parametrized tests of general relativity with gravitational waves robust to unknown higher post-Newtonian order effects?

Gravitational wave observations have great potential to reveal new information about the fundamental nature of gravity, but extracting that information can be difficult. One popular technique is the parametrized inspiral test of general relativity (a realization of the parametrized post-Einsteinian framework), where the gravitational waveform, as calculated in Einstein's theory as a series expansion in the orbital velocity, is parametrically deformed at a given set of orders in velocity. However, most current approaches usually only analyze the data while considering a single, specific modification at a time. Are then constraints placed with a single modification robust to our ignorance of higher post-Newtonian order corrections? We show here that for a wide class of theories, specifically those that admit a post-Newtonian expansion, single-parameter tests are indeed robust. In particular, through a series of full Bayesian parameter estimation studies on several different sets of synthetic data, we show that single-parameter constraints are not degraded but rather are improved by the inclusion of multiple parameters, provided one includes information about the mathematical structure of the series. We then exemplify this with a specific theory of gravity, shift-symmetric scalar Gauss-Bonnet theory, where the waveform has been calculated to higher post-Newtonian orders than leading. We show that the inclusion of these higher order terms strengthens single-parameter constraints, instead of weakening them, and that the strengthening is very mild. This analysis therefore provides strong evidence that single-parameter post-Einsteinian tests of general relativity are robust to ignorance of high post-Newtonian order terms in the general relativistic deformations.