Miracle-dz/UniFAR-FTUs huggingface dataset:
Electric Autonomous Mobility-on-Demand: Joint Optimization of Routing and Charging Infrastructure Siting

The advent of vehicle autonomy, connectivity and electric powertrains is expected to enable the deployment of Autonomous Mobility-on-Demand systems. Crucially, the routing and charging activities of these fleets are impacted by the design of the individual vehicles and the surrounding charging infrastructure which, in turn, should be designed to account for the intended fleet operation. This paper presents a modeling and optimization framework where we optimize the activities of the fleet jointly with the placement of the charging infrastructure. We adopt a mesoscopic planning perspective and devise a time-invariant model of the fleet activities in terms of routes and charging patterns, explicitly capturing the state of charge of the vehicles by resampling the road network as a digraph with iso-energy arcs. Then, we cast the problem as a mixed-integer linear program that guarantees global optimality and can be solved in less than 10 min. Finally, we showcase two case studies with real-world taxi data in Manhattan, NYC: The first one captures the optimal trade-off between charging infrastructure prevalence and the empty-mileage driven by the fleet. We observe that jointly optimizing the infrastructure siting significantly outperforms heuristic placement policies, and that increasing the number of stations is beneficial only up to a certain point. The second case focuses on vehicle design and shows that deploying vehicles equipped with a smaller battery results in the lowest energy consumption: Although necessitating more trips to the charging stations, such fleets require about 12% less energy than the vehicles with a larger battery capacity.
Coordinate-Based Seismic Interpolation in Irregular Land Survey: A Deep Internal Learning Approach

Physical and budget constraints often result in irregular sampling, which complicates accurate subsurface imaging. Preprocessing approaches, such as missing trace or shot interpolation, are typically employed to enhance seismic data in such cases. Recently, deep learning has been used to address the trace interpolation problem at the expense of large amounts of training data to adequately represent typical seismic events. Nonetheless, most research in this area has focused on trace reconstruction, with little attention having been devoted to shot interpolation. Furthermore, existing methods assume regularly spaced receivers/sources failing in approximating seismic data from real (irregular) surveys. This work presents a novel shot gather interpolation approach which uses a continuous coordinate-based representation of the acquired seismic wavefield parameterized by a neural network. The proposed unsupervised approach, which we call coordinate-based seismic interpolation (CoBSI), enables the prediction of specific seismic characteristics in irregular land surveys without using external data during neural network training. Experimental results on real and synthetic 3-D data validate the ability of the proposed method to estimate continuous smooth seismic events in the time-space and frequency-wavenumber domains, improving sparsity or low-rank-based interpolation methods.
The muon anomalous magnetic moment: is the lattice spacing small enough?

We present new results for the light-quark connected part of the leading order hadronic-vacuum-polarization (HVP) contribution to the muon anomalous magnetic moment, using 2 + 1 + 1 staggered fermions. We have collected more statistics on previous ensembles, and we added two new ensembles. This allows us to reduce statistical errors on the HVP contribution and related window quantities signiﬁcantly. We also calculated the current-current correlator to next-to-next-to-leading order (NNLO) in staggered chiral perturbation theory, so that we can correct to NNLO for ﬁnite-volume, pion-mass mistuning and taste-breaking eﬀects. We discuss the applicability of NNLO chiral perturbation theory, emphasizing that it provides a systematic EFT approach to the HVP contribution, but not to short- or intermediate-distance window quantities. This makes it diﬃcult to assess systematic errors on the standard intermediate-distance window quantity that is now widely considered in the literature. In view of this, we investigate a longer-distance window, for which EFT methods should be more reliable. Our most important conclusion is that new high-statistics computations at lattice spacings signiﬁcantly smaller than 0.06 fm are indispensable. The ensembles we use have been generously provided by MILC and CalLat.
Differentiable Fuzzy $\mathcal{ALC}$: A Neural-Symbolic Representation Language for Symbol Grounding

Neural-symbolic computing aims at integrating robust neural learning and sound symbolic reasoning into a single framework, so as to leverage the complementary strengths of both of these, seemingly unrelated (maybe even contradictory) AI paradigms. The central challenge in neural-symbolic computing is to unify the formulation of neural learning and symbolic reasoning into a single framework with common semantics, that is, to seek a joint representation between a neural model and a logical theory that can support the basic grounding learned by the neural model and also stick to the semantics of the logical theory. In this paper, we propose differentiable fuzzy $\mathcal{ALC}$ (DF-$\mathcal{ALC}$) for this role, as a neural-symbolic representation language with the desired semantics. DF-$\mathcal{ALC}$ unifies the description logic $\mathcal{ALC}$ and neural models for symbol grounding; in particular, it infuses an $\mathcal{ALC}$ knowledge base into neural models through differentiable concept and role embeddings. We define a hierarchical loss to the constraint that the grounding learned by neural models must be semantically consistent with $\mathcal{ALC}$ knowledge bases. And we find that capturing the semantics in grounding solely by maximizing satisfiability cannot revise grounding rationally. We further define a rule-based loss for DF adapting to symbol grounding problems. The experiment results show that DF-$\mathcal{ALC}$ with rule-based loss can improve the performance of image object detectors in an unsupervised learning way, even in low-resource situations.
Towards Human-Interpretable Prototypes for Visual Assessment of Image Classification Models

Explaining black-box Artificial Intelligence (AI) models is a cornerstone for trustworthy AI and a prerequisite for its use in safety critical applications such that AI models can reliably assist humans in critical decisions. However, instead of trying to explain our models post-hoc, we need models which are interpretable-by-design built on a reasoning process similar to humans that exploits meaningful high-level concepts such as shapes, texture or object parts. Learning such concepts is often hindered by its need for explicit specification and annotation up front. Instead, prototype-based learning approaches such as ProtoPNet claim to discover visually meaningful prototypes in an unsupervised way. In this work, we propose a set of properties that those prototypes have to fulfill to enable human analysis, e.g. as part of a reliable model assessment case, and analyse such existing methods in the light of these properties. Given a 'Guess who? ' game, we find that these prototypes still have a long way ahead towards definite explanations. We quantitatively validate our findings by conducting a user study indicating that many of the learnt prototypes are not considered useful towards human understanding. We discuss about the missing links in the existing methods and present a potential real-world application motivating the need to progress towards truly human-interpretable prototypes.
Quality Analysis of Battery Degradation Models with Real Battery Aging Experiment Data

The installation capacity of energy storage system, especially the battery energy storage system (BESS), has increased significantly in recent years, which is mainly applied to mitigate the fluctuation caused by renewable energy sources (RES) due to the fast response and high round-trip energy efficiency of BESS. The main components of majority of BESSs are lithium-ion batteries, which will degrade during the BESS daily operation. Heuristic battery degradation models are proposed to consider the battery degradation in the operations of energy systems to optimize the scheduling. However, those heuristic models are not evaluated or demonstrated with real battery degradation data. Thus, this paper will perform a quality analysis on the popular heuristic battery degradation models using the real battery aging experiment data to evaluate their performance. A benchmark model is also proposed to represent the real battery degradation value based on the averaged cycle value of the experimental data.
Superconductivity in monolayer and few-layer graphene. II. Topological edge states and Chern numbers

We study the emergence of electronic edge states in superconducting (SC) monolayer, bilayer, and trilayer graphene for both spin-singlet and spin-triplet SC order parameters. We focus mostly on the gapped chiral $p+ip'$- and $d+id'$-wave SC states that show a non-zero Chern number and a corresponding number of edge states. For the $p+ip'$-wave state, we observe a rich Chern phase diagram when tuning the chemical potential and the SC order parameter amplitudes, which depends strongly on the number of layers and their stacking, and is also modified by trigonal warping. At small parameter values we observe a region whose Chern number is unique to rhombohedrally stacked graphene, and is independent of the number of layers. Our results can be understood in relation not only to the SC order parameter winding as expected, but also to the normal state band structure. This observation establishes the importance of the normal state characteristics for understanding the topology in SC graphene systems.
The effective reproduction number: Convexity, concavity and invariance

Motivated by the question of optimal vaccine allocation strategies in heterogeneous population for epidemic models, we study various properties of the \emph{effective reproduction number}. In the simplest case, given a fixed, non-negative matrix $K$, this corresponds mathematically to the study of the spectral radius $R_e(\eta)$ of the matrix product $\mathrm{Diag}(\eta)K$, as a function of $\eta\in\mathbb{R}_+^n$. The matrix $K$ and the vector $\eta$ can be interpreted as a next-generation operator and a vaccination strategy. This can be generalized in an infinite dimensional case where the matrix $K$ is replaced by a positive integral compact operator, which is composed with a multiplication by a non-negative function $\eta$. We give sufficient conditions for the function $R_e$ to be convex or a concave. Eventually, we provide equivalence properties on models which ensure that the function $R_e$ is unchanged.
Sensitivity comparison of two-photon vs three-photon Rydberg electrometry

We investigate the sensitivity of co-linear three-photon electromagnetically induced transparency (EIT) in 133Cs Rydberg atoms to radio frequency electric fields and compare against the conventional two-photon system. Specifically, we model the 4-level and 5-level atomic systems and compare how the transmission of the probe changes with different laser powers and RF field strengths. In this model, we define a sensitivity metric that relates to the current best experimental implementation and assumes photon shot noise limited detection. We find that the three-photon system boasts much narrower linewidths compared to the conventional two-photon EIT. These narrow line features, however, do not align with the regions of the best sensitivity. In addition to this, we calculate the expected sensitivity for the two-photon Rydberg sensor and find that the best achievable sensitivity is over an order of magnitude better than the current measured values of 5 μVm−1Hz−1/2. However, by accounting for additional noise sources in the experiment and the quantum efficiency of the photo-detectors, the values are in good agreement.
High‐order asymptotics for the electromagnetic scattering by thin periodic layers

This work deals with the scattering of electromagnetic waves by a thin periodic layer made of an array of regularly spaced obstacles. The size of the obstacles and the spacing between two consecutive ones are of the same order δ, which is much smaller than the wavelength of the incident wave. We provide a complete description of the asymptotic behavior of the solution with respect to the small parameter δ: we use a method that combines matched asymptotic expansions and homogenization techniques. We pay particular attention to the construction of the near‐field terms. Indeed, they satisfy electrostatic problems posed in an infinite 3D strip, which require a careful analysis. Error estimates are carried out to justify the accuracy of our expansion. Copyright © 2014 John Wiley & Sons, Ltd.
Interpreting Neural Networks through the Polytope Lens

Mechanistic interpretability aims to explain what a neural network has learned at a nuts-and-bolts level. What are the fundamental primitives of neural network representations? Previous mechanistic descriptions have used individual neurons or their linear combinations to understand the representations a network has learned. But there are clues that neurons and their linear combinations are not the correct fundamental units of description: directions cannot describe how neural networks use nonlinearities to structure their representations. Moreover, many instances of individual neurons and their combinations are polysemantic (i.e. they have multiple unrelated meanings). Polysemanticity makes interpreting the network in terms of neurons or directions challenging since we can no longer assign a specific feature to a neural unit. In order to find a basic unit of description that does not suffer from these problems, we zoom in beyond just directions to study the way that piecewise linear activation functions (such as ReLU) partition the activation space into numerous discrete polytopes. We call this perspective the polytope lens. The polytope lens makes concrete predictions about the behavior of neural networks, which we evaluate through experiments on both convolutional image classifiers and language models. Specifically, we show that polytopes can be used to identify monosemantic regions of activation space (while directions are not in general monosemantic) and that the density of polytope boundaries reflect semantic boundaries. We also outline a vision for what mechanistic interpretability might look like through the polytope lens.
Evidence for $\eta_c$(2S)$\to\pipieta$ decay

The decay $\eta_c(2S)\to\pipieta$ is searched for through the radiative transition $\psi(3686) \to\gamma\eta_c(2S)$ using 448 million $\psi$(3686) events accumulated at the BESIII detector. The first evidence of $\eta_c(2S)\to\pi^+\pi^-\eta$ is found with a statistical significance of 3.5$\sigma$. The product of the branching fractions of $\psi(3686)\to\gamma\eta_c(2S)$ and $\eta_c(2S)\to\pipieta$ is measured to be $Br(\psi(3686)\to\gamma\eta_c(2S))\times Br(\eta_c(2S)\to\pipieta)=(2.97\pm0.81\pm0.26)\times10^{-6}$, where the first uncertainty is statistical and the second one is systematic. The branching fraction of the decay $\eta_c(2S)\to\pipieta$ is determined to be $Br(\eta_c(2S)\to\pipieta)=(42.4\pm11.6\pm3.8\pm30.3)\times10^{-4}$, where the third uncertainty is transferred from the uncertainty of the branching fraction of $\psi(3686)\to\gamma\eta_c(2S)$.
A Statistical Algorithm for the Early Detection of Outbreaks of Infectious Disease

SUMMARY Outbreaks of infectious diseases must be detected early for effective control measures to be introduced. When dealing with large amounts of data, automated procedures can usefully supplement traditional surveillance methods, provided that the wide variety of patterns and frequencies of infections are taken into account. This paper describes a robust system developed to process weekly reports of infections received at the Communicable Disease Surveillance Centre. A simple regression algorithm is used to calculate suitable thresholds. Organisms exceeding their threshold are then flagged for further investigation.
Optimal control problems with mixed control-phase variable equality and inequality constraints

In this paper,' necessary conditions are obtained for optimal control problems containing equality constraints defined in terms of functions of the control and phase variables. The control system is assumed to be characterized by an ordinary differential equation, and more conventional constraints, including phase inequality constraints, are also assumed to be present. Because the first- mentioned equality constraint must be satisfied for all t (the independent variable of the differential equation) belonging to an arbitrary (prescribed) measurable set, this problem gives rise to infinite-dimensional equality constraints. To obtain the necessary conditions, which are in the form of a maximum principle, an implicit-function-type theorem in Banach spaces is derived.
The Restrain Food Database: validation of an open-source database of foods that should be eaten more or less as part of a healthy diet

Studies of food-related behaviours often involve measuring responses to pictorial stimuli of foods. Creating these can be burdensome, requiring a significant commitment of time, and with sharing of images for future research constrained by legal copyright restrictions. The Restrain Food Database is an open-source database of 626 images of foods that are categorized as those people could eat more or less of as part of a healthy diet. This paper describes the database and details how to navigate it using our purpose-built R Shiny tool and a pre-registered online validation of a sample of images. A total of 2150 participants provided appetitive ratings, perceptions of nutritional content and ratings of image quality for images from the database. We found support for differences between Food Category on appetitive ratings which were also moderated by state hunger ratings. Findings relating to individual differences in appetite ratings as well as differences between BMI weight categories are also reported. Our findings validate the food categorization in the Restrain Food Database and provide descriptive information for individual images within this investigation. This database should ease the burden of selecting and creating appropriate images for future studies.
C3PO: A Lightweight Copying Mechanism for Translating Pseudocode to Code

Writing computer programs is a skill that remains inaccessible to most due to the barrier of programming language (PL) syntax. While large language models (LLMs) have been proposed to translate natural language pseudocode to PL code, they are costly in terms of data and compute. We propose a lightweight alternative to LLMs that exploits the property of code wherein most tokens can be simply copied from the pseudocode. We divide the problem into three phases: Copy, Generate, and Combine. In the Copy Phase, a binary classifier is employed to determine and mask the pseudocode tokens that can be directly copied into the code. In the Generate Phase, a Sequence-to-Sequence model is used to generate the masked PL code equivalent. In the Combine Phase, the generated sequence is combined with the tokens that the Copy Phase had masked. We show that our C3PO models achieve similar performance to non-C3PO models while reducing the computational cost of training as well as the vocabulary sizes.
Towards Unsupervised Morphological Analysis of Polysynthetic Languages

Polysynthetic languages present a challenge for morphological analysis due to the complexity of their words and the lack of high-quality annotated datasets needed to build and/or evaluate computational models. The contribution of this work is twofold. First, using linguists’ help, we generate and contribute high-quality annotated data for two low-resource polysynthetic languages for two tasks: morphological segmentation and part-of-speech (POS) tagging. Second, we present the results of state-of-the-art unsupervised approaches for these two tasks on Adyghe and Inuktitut. Our findings show that for these polysynthetic languages, using linguistic priors helps the task of morphological segmentation and that using stems rather than words as the core unit of abstraction leads to superior performance on POS tagging.
To Train or Not to Train: Predicting the Performance of Massively Multilingual Models

Evaluating the performance of Massively Multilingual Language Models (MMLMs) is difficult due to the shortage of evaluation datasets in low-resource languages. Due to computational limitations evaluating MMLMs trained on all possible pivot configurations is not feasible. This paper describes our contribution to the SumEval 2022 shared task, which handles the crucial task of Performance prediction of MMLMs. We build upon Microsoft Research’s Project LITMUS and devise a method to further improve predictions. We develop various machine-learning approaches which outperform the baseline score provided by LITMUS. Our system ranked first with an RMSE score of 0.017 for the non-surprise and 0.109 for the surprise dataset.
Toward Building a Language Model for Understanding Temporal Commonsense

The ability to capture temporal commonsense relationships for time-related events expressed in text is a very important task in natural language understanding. On the other hand, pre-trained language models such as BERT, which have recently achieved great success in a wide range of natural language processing tasks, are still considered to have poor performance in temporal reasoning. In this paper, we focus on the development of language models for temporal commonsense inference over several pre-trained language models. Our model relies on multi-step fine-tuning using multiple corpora, and masked language modeling to predict masked temporal indicators that are crucial for temporal commonsense reasoning. We also experimented with multi-task learning and build a language model that can improve performance on multiple time-related tasks. In our experiments, multi-step fine-tuning using the general commonsense reading task as auxiliary task produced the best results. This result showed a significant improvement in accuracy over standard fine-tuning in the temporal commonsense inference task.
Exploring Nominal Coercion in Semantic Spaces with Static and Contextualized Word Embeddings

The distinction between mass nouns and count nouns has a long history in formal semantics, and linguists have been trying to identify the semantic properties defining the two classes. However, they also recognized that both can undergo meaning shifts and be used in contexts of a different type, via nominal coercion. In this paper, we present an approach to measure the meaning shift in count-mass coercion in English that makes use of static and contextualized word embedding distance. Our results show that the coercion shifts are detected only by a small subset of the traditional word embedding models, and that the shifts detected by the contextualized embedding of BERT are more pronounced for mass nouns.
Efficient and Robust Knowledge Graph Construction

Knowledge graph construction which aims to extract knowledge from the text corpus, has appealed to the NLP community researchers. Previous decades have witnessed the remarkable progress of knowledge graph construction on the basis of neural models; however, those models often cost massive computation or labeled data resources and suffer from unstable inference accounting for biased or adversarial samples. Recently, numerous approaches have been explored to mitigate the efficiency and robustness issues for knowledge graph construction, such as prompt learning and adversarial training. In this tutorial, we aim to bring interested NLP researchers up to speed on the recent and ongoing techniques for efficient and robust knowledge graph construction. Additionally, our goal is to provide a systematic and up-to-date overview of these methods and reveal new research opportunities to the audience.