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Diffusion Models (DMs) benefit from large and diverse datasets for their training. Since this data is often scraped from the Internet without permission from the data owners, this raises concerns about copyright and intellectual property protections. While (illicit) use of data is easily detected for training samples perfectly re-created by a DM at inference time, it is much harder for data owners to verify if their data was used for training when the outputs from the suspect DM are not close replicas. Conceptually, membership inference attacks (MIAs), which detect if a given data point was used during training, present themselves as a suitable tool to address this challenge. However, we demonstrate that existing MIAs are not strong enough to reliably determine the membership of individual images in large, state-of-the-art DMs. To overcome this limitation, we propose CDI, a framework for data owners to identify whether their dataset was used to train a given DM. CDI relies on dataset inference techniques, i.e., instead of using the membership signal from a single data point, CDI leverages the fact that most data owners, such as providers of stock photography, visual media companies, or even individual artists, own datasets with multiple publicly exposed data points which might all be included in the training of a given DM. By selectively aggregating signals from existing MIAs and using new handcrafted methods to extract features for these datasets, feeding them to a scoring model, and applying rigorous statistical testing, CDI allows data owners with as little as 70 data points to identify with a confidence of more than 99% whether their data was used to train a given DM. Thereby, CDI represents a valuable tool for data owners to claim illegitimate use of their copyrighted data., Comment: Coda available at https://github.com/sprintml/copyrighted_data_identification

Risk-based approaches to governance bear an ambiguous stance regarding the Research and Development stages of AI, for they the possibility of explicit risks before they are posed by a given finalised product. In this context, Institutional Review Boards (IRBs) stand as unique governance mechanisms, capable of addressing the step from general research to concrete product development. However, IRBs face several challenges in governing AI-based medical products, including: (a) achieving consistency, (b) being exhaustive, (c) ensuring process transparency, and (d) reducing the existing capacity and knowledge asymmetry between different stakeholders. This article explores four governance levers that can be used to effect change, four governance entry-points throughout a product's lifecycle, and five different behaviours that IRBs should try to advance to ensure the effective governance of the R&D stages of AI-based medical projects. In doing so, IRBs can seize the unique opportunity they present to bring principles into practice, increase research quality, reduce governance costs, and bridge the knowledge gap between stakeholders., Comment: 10 pages, 1 figure, paper published by AIRe after peer review

High-frequency combustion instabilities can lead to significant fluctuations in chamber pressure, affecting the structural integrity and performance of solid rocket motors. Since these instabilities manifest as acoustic oscillations during combustion, a mathematical model has been developed to calculate the chamber modes, providing an accurate method for predicting the acoustic behaviour of the combustion chamber. A novel method for discretising the Laplacian operator is introduced, which allows the calculation of the acoustic modes of complex chamber geometries. This approach uses an efficient numerical algorithm designed for unstructured mesh configurations. Several computational examples are presented to demonstrate the application of the model to complex geometries typical of solid rocket motors. In addition, an analytical procedure is developed in these examples to aid the design process, providing engineers with critical data to optimise the stability and performance of propulsion systems. This research provides valuable insights into the analysis of combustion instabilities, with broad implications for the design and optimisation of propulsion systems in aerospace engineering., Comment: 27 pages, 24 figures

Applications like program synthesis sometimes require proving that a property holds for all of the infinitely many programs described by a grammar - i.e., an inductively defined set of programs. Current verification frameworks overapproximate programs' behavior when sets of programs contain loops, including two Hoare-style logics that fail to be relatively complete when loops are allowed. In this work, we prove that compositionally verifying simple properties for infinite sets of programs requires tracking distinct program behaviors over unboundedly many executions. Tracking this information is both necessary and sufficient for verification. We prove this fact in a general, reusable theory of denotational semantics that can model the expressivity and compositionality of verification techniques over infinite sets of programs. We construct the minimal compositional semantics that captures simple properties of sets of programs and use it to derive the first sound and relatively complete Hoare-style logic for infinite sets of programs. Thus, our methods can be used to design minimally complex, compositional verification techniques for sets of programs., Comment: 44 pages, 8 Figures

Stellar mass black holes are formed from the terminal collapse of massive stars if the ensuing neutrino shock is unable to eject the stellar envelope. Direct observations of black hole formation remain inconclusive. We report observations of M31-2014-DS1, a massive, hydrogen-depleted supergiant in the Andromeda galaxy identified via a mid-infrared brightening in 2014. Its total luminosity remained nearly constant for the subsequent thousand days, before fading dramatically over the next thousand days by $\gtrsim 10\times$ and $\gtrsim 10^4\times$ in total and visible light, respectively. Together with the lack of a detected optical outburst, the observations are explained by the fallback of the stellar envelope into a newly formed black hole, moderated by the injection of a $\sim 10^{48}$ erg shock. Unifying these observations with a candidate in NGC 6946, we present a concordant picture for the birth of stellar mass black holes from stripped massive stars., Comment: Submitted for review

We characterize group representations that factor through monomial representations, respectively, block-triangular representations with monomial diagonal blocks, by arithmetic properties. Similar results are obtained for semigroup representations by invertible transformations. The characterizations use results on unit equations from Diophantine number theory (by Evertse, van der Poorten, and Schlickewei in characteristic zero, and by Derksen and Masser in positive characteristic). Specialized to finitely generated groups in characteristic zero, one of our main theorems recovers an improvement of a very recent similar characterization by Corvaja, Demeio, Rapinchuk, Ren, and Zannier that was motivated by the study of the bounded generation (BG) property. In positive characteristic, we get a characterization of linear BG groups, recovering a theorem of Ab\'ert, Lubotzky, and Pyber from 2003. Our motivation comes from weighted finite automata (WFA) over a field. For invertible WFA we show that $M$-ambiguity, finite ambiguity, and polynomial ambiguity are characterized by arithmetic properties. We discover a full correspondence between arithmetic properties and a complexity hierarchy for WFA based on ambiguity. In the invertible case, this is a far-reaching generalization of a recent result by Bell and the second author, characterizing unambiguous WFA, that resolved a 1979 conjecture of Reutenauer. As a consequence, using the computability of the (linear) Zariski closure of a finitely generated matrix semigroup, the $M$-ambiguity, finite ambiguity, and polynomial ambiguity properties are algorithmically decidable for invertible WFA.

Denoising is one of the fundamental steps of the processing pipeline that converts data captured by a camera sensor into a display-ready image or video. It is generally performed early in the pipeline, usually before demosaicking, although studies swapping their order or even conducting them jointly have been proposed. With the advent of deep learning, the quality of denoising algorithms has steadily increased. Even so, modern neural networks still have a hard time adapting to new noise levels and scenes, which is indispensable for real-world applications. With those in mind, we propose a self-similarity-based denoising scheme that weights both a pre- and a post-demosaicking denoiser for Bayer-patterned CFA video data. We show that a balance between the two leads to better image quality, and we empirically find that higher noise levels benefit from a higher influence pre-demosaicking. We also integrate temporal trajectory prefiltering steps before each denoiser, which further improve texture reconstruction. The proposed method only requires an estimation of the noise model at the sensor, accurately adapts to any noise level, and is competitive with the state of the art, making it suitable for real-world videography., Comment: 16 pages, 9 figures

We discuss production of $D^0$ and ${\bar D}^0$ mesons in proton-nucleus collisions in fixed target experiments. We include gluon-gluon fusion, intrinsic charm and perturbative recombination. We compare rapidity and transverse momentum distributions obtained within our approach with recent fixed target LHCb experimental data. All the mechanisms seem important for inclusive production of $D^0 + {\bar D}^0$ mesons. The recombination mechanism seems crucial to understand asymmetry in production of $D^0$ and ${\bar D}^0$ mesons., Comment: 6 pages, 4 figures, talk given by A. Szczurek at 42nd International Conference on High Energy Physics (ICHEP2024), 18-24 July 2024, Prague, Czech Republic

Objective: To propose a mathematical model for applying Ionization Detail (ID), the detailed spatial distribution of ionization along a particle track, to proton and ion beam radiotherapy treatment planning (RTP). Approach: Our model provides for selection of preferred ID parameters (I_p) for RTP, that associate closest to biological effects. Cluster dose is proposed to bridge the large gap between nanoscopic I_p and macroscopic RTP. Selection of I_p is demonstrated using published cell survival measurements for protons through argon, comparing results for nineteen Ip: N_k; k = 2,3,...,10, the number of ionizations in clusters of k or more per particle, and F_k; k = 1,2,...,10, the number of clusters of k or more per particle. We then describe application of the model to ID-based RTP and propose a path to clinical translation. Main results: The preferred I_p were N_4 and F_5 for aerobic cells, N_5 and F_7 for hypoxic cells. Signifcant differences were found in cell survival for beams having the same LET or the preferred N_k. Conversely, there was no signi?cant difference for F_5 for aerobic cells and F_7 for hypoxic cells, regardless of ion beam atomic number or energy. Further, cells irradiated with the same cluster dose for these I_p had the same cell survival. Based on these preliminary results and other compelling results in nanodosimetry, it is reasonable to assert that I_p exist that are more closely associated with biological effects than current LET-based approaches and microdosimetric RBE-based models used in particle RTP. However, more biological variables such as cell line and cycle phase, as well as ion beam pulse structure and rate still need investigation. Signifcance: Our model provides a practical means to select preferred I_p from radiobiological data, and to convert I_p to the macroscopic cluster dose for particle RTP., Comment: 31 pages, 12 figures

Machine learning (ML) is increasingly used in high-stakes settings, yet multiplicity -- the existence of multiple good models -- means that some predictions are essentially arbitrary. ML researchers and philosophers posit that multiplicity poses a fairness risk, but no studies have investigated whether stakeholders agree. In this work, we conduct a survey to see how the presence of multiplicity impacts lay stakeholders' -- i.e., decision subjects' -- perceptions of ML fairness, and which approaches to address multiplicity they prefer. We investigate how these perceptions are modulated by task characteristics (e.g., stakes and uncertainty). Survey respondents think that multiplicity lowers distributional, but not procedural, fairness, even though existing work suggests the opposite. Participants are strongly against resolving multiplicity by using a single good model (effectively ignoring multiplicity) or by randomizing over possible outcomes. Our results indicate that model developers should be intentional about dealing with multiplicity in order to maintain fairness., Comment: 30 pages, 3 figures

Contemporary deep learning architectures lack principled means for capturing and handling fundamental visual concepts, like objects, shapes, geometric transforms, and other higher-level structures. We propose a neurosymbolic architecture that uses a domain-specific language to capture selected priors of image formation, including object shape, appearance, categorization, and geometric transforms. We express template programs in that language and learn their parameterization with features extracted from the scene by a convolutional neural network. When executed, the parameterized program produces geometric primitives which are rendered and assessed for correspondence with the scene content and trained via auto-association with gradient. We confront our approach with a baseline method on a synthetic benchmark and demonstrate its capacity to disentangle selected aspects of the image formation process, learn from small data, correct inference in the presence of noise, and out-of-sample generalization.

The interaction between surface acoustic waves and magnetization offers an efficient route for electrically controlling magnetic states. Here, we demonstrate the excitation of magnetoacoustic waves in galfenol, a highly magnetostrictive alloy made of iron (72%) and gallium (28%). We quantify the amplitude of the induced magnetization oscillations using magnetic imaging in an X-ray photoelectron microscope and estimate the dynamic magnetoelastic constants through micromagnetic simulations. Our findings demonstrate the potential of galfenol for magnonic applications and reveal that, despite strong magnetoelastic coupling, magnetic interactions and spin-wave dispersion relations significantly influence the overall amplitude of magnetoacoustic waves., Comment: 6 pages, 4 figures

Craniofacial anomalies indicate early developmental disturbances and are usually linked to many genetic syndromes. Early diagnosis is critical, yet ultrasound (US) examinations often fail to identify these features. This study presents an AI-driven tool to assist clinicians in standardizing fetal facial axes/planes in 3D US, reducing sonographer workload and facilitating the facial evaluation. Our network, structured into three blocks-feature extractor, rotation and translation regression, and spatial transformer-processes three orthogonal 2D slices to estimate the necessary transformations for standardizing the facial planes in the 3D US. These transformations are applied to the original 3D US using a differentiable module (the spatial transformer block), yielding a standardized 3D US and the corresponding 2D facial standard planes. The dataset used consists of 1180 fetal facial 3D US images acquired between weeks 20 and 35 of gestation. Results show that our network considerably reduces inter-observer rotation variability in the test set, with a mean geodesic angle difference of 14.12$^{\circ}$ $\pm$ 18.27$^{\circ}$ and an Euclidean angle error of 7.45$^{\circ}$ $\pm$ 14.88$^{\circ}$. These findings demonstrate the network's ability to effectively standardize facial axes, crucial for consistent fetal facial assessments. In conclusion, the proposed network demonstrates potential for improving the consistency and accuracy of fetal facial assessments in clinical settings, facilitating early evaluation of craniofacial anomalies.

Multi-view crowd localization predicts the ground locations of all people in the scene. Typical methods usually estimate the crowd density maps on the ground plane first, and then obtain the crowd locations. However, the performance of existing methods is limited by the ambiguity of the density maps in crowded areas, where local peaks can be smoothed away. To mitigate the weakness of density map supervision, optimal transport-based point supervision methods have been proposed in the single-image crowd localization tasks, but have not been explored for multi-view crowd localization yet. Thus, in this paper, we propose a novel Mahalanobis distance-based multi-view optimal transport (M-MVOT) loss specifically designed for multi-view crowd localization. First, we replace the Euclidean-based transport cost with the Mahalanobis distance, which defines elliptical iso-contours in the cost function whose long-axis and short-axis directions are guided by the view ray direction. Second, the object-to-camera distance in each view is used to adjust the optimal transport cost of each location further, where the wrong predictions far away from the camera are more heavily penalized. Finally, we propose a strategy to consider all the input camera views in the model loss (M-MVOT) by computing the optimal transport cost for each ground-truth point based on its closest camera. Experiments demonstrate the advantage of the proposed method over density map-based or common Euclidean distance-based optimal transport loss on several multi-view crowd localization datasets. Project page: https://vcc.tech/research/2024/MVOT., Comment: ECCV 2024

Radio Access Network (RAN) disaggregation is emerging as a key trend in beyond 5G, as it offers new opportunities for more flexible deployments and intelligent network management. A relevant problem in disaggregated RAN is the functional split selection, which dynamically decides which baseband (BB) functions of a base station are kept close to the radio units and which ones are centralized. In this context, this paper firstly presents an architectural framework for supporting this concept relying on the O-RAN architecture. Then, the paper analyzes how the functional split can be optimized to adapt to the different load conditions while minimizing energy costs., Comment: 2024 19th International Symposium on Wireless Communication Systems (ISWCS)

In top-down enumeration for program synthesis, abstraction-based pruning uses an abstract domain to approximate the set of possible values that a partial program, when completed, can output on a given input. If the set does not contain the desired output, the partial program and all its possible completions can be pruned. In its general form, abstraction-based pruning requires manually designed, domain-specific abstract domains and semantics, and thus has only been used in domain-specific synthesizers. This paper provides sufficient conditions under which a form of abstraction-based pruning can be automated for arbitrary synthesis problems in the general-purpose Semantics-Guided Synthesis (SemGuS) framework without requiring manually-defined abstract domains. We show that if the semantics of the language for which we are synthesizing programs exhibits some monotonicity properties, one can obtain an abstract interval-based semantics for free from the concrete semantics of the programming language, and use such semantics to effectively prune the search space. We also identify a condition that ensures such abstract semantics can be used to compute a precise abstraction of the set of values that a program derivable from a given hole in a partial program can produce. These precise abstractions make abstraction-based pruning more effective. We implement our approach in a tool, Moito, which can tackle synthesis problems defined in the SemGuS framework. Moito can automate interval-based pruning without any a-priori knowledge of the problem domain, and solve synthesis problems that previously required domain-specific, abstraction-based synthesizers -- e.g., synthesis of regular expressions, CSV file schema, and imperative programs from examples.

Semantics-Guided Synthesis (SemGuS) provides a framework to specify synthesis problems in a solver-agnostic and domain-agnostic way, by allowing a user to provide both the syntax and semantics of the language in which the desired program should be synthesized. Because synthesis and verification are closely intertwined, the SemGuS framework raises the problem of how to verify programs in a solver and domain-agnostic way. We prove that the problem of verifying whether a program is a valid solution to a SemGuS problem can be reduced to proving validity of a query in the `CLP calculus, a fixed-point logic that generalizes Constrained Horn Clauses and co-Constrained Horn Clauses. Our encoding into `CLP allows us to further classify the SemGuS verification problems into ones that are reducible to validity of (i) first-order-logic formulas, (ii) Constrained Horn Clauses, (iii) co-Constrained Horn Clauses, and (iv) `CLP queries. Furthermore, our encoding shines light on some limitations of the SemGuS framework, such as its inability to model nondeterminism and reactive synthesis. We thus propose a modification to SemGuS that makes it more expressive, and for which verifying solutions is exactly equivalent to proving validity of a query in the `CLP calculus. Our implementation of SemGuS verifiers based on the above encoding can verify instances that were not even encodable in previous work. Furthermore, we use our SemGuS verifiers within an enumeration-based SemGuS solver to correctly synthesize solutions to SemGuS problems that no previous SemGuS synthesizer could solve.

Program verification and synthesis frameworks that allow one to customize the language in which one is interested typically require the user to provide a formally defined semantics for the language. Because writing a formal semantics can be a daunting and error-prone task, this requirement stands in the way of such frameworks being adopted by non-expert users. We present an algorithm that can automatically synthesize inductively defined syntax-directed semantics when given (i) a grammar describing the syntax of a language and (ii) an executable (closed-box) interpreter for computing the semantics of programs in the language of the grammar. Our algorithm synthesizes the semantics in the form of Constrained-Horn Clauses (CHCs), a natural, extensible, and formal logical framework for specifying inductively defined relations that has recently received widespread adoption in program verification and synthesis. The key innovation of our synthesis algorithm is a Counterexample-Guided Synthesis (CEGIS) approach that breaks the hard problem of synthesizing a set of constrained Horn clauses into small, tractable expression-synthesis problems that can be dispatched to existing SyGuS synthesizers. Our tool Synantic synthesized inductively-defined formal semantics from 14 interpreters for languages used in program-synthesis applications. When synthesizing formal semantics for one of our benchmarks, Synantic unveiled an inconsistency in the semantics computed by the interpreter for a language of regular expressions; fixing the inconsistency resulted in a more efficient semantics and, for some cases, in a 1.2x speedup for a synthesizer solving synthesis problems over such a language., Comment: Accepted at OOPSLA'24. Revised wording and added some references. Updated benchmark results in the appendix. Changed cite of paper "The SemGuS Toolkit" to the CAV'24 version. Revised comments

Specifications allow us to formally state and understand what programs are intended to do. To help one extract useful properties from code, Park et al. recently proposed a framework that given (i) a quantifier-free query posed about a set of function definitions, and (ii) a domain-specific language L in which each extracted property is to be expressed (we call properties in the language L-properties), synthesizes a set of L-properties such that each of the property is a strongest L-consequence for the query: the property is an over-approximation of query and there is no other L-property that over-approximates query and is strictly more precise than each property. The framework by Park et al. has two key limitations. First, it only supports quantifier-free query formulas and thus cannot synthesize specifications for queries involving nondeterminism, concurrency, etc. Second, it can only compute L-consequences, i.e., over-approximations of the program behavior. This paper addresses these two limitations and presents a framework, Loud, for synthesizing strongest L-consequences and weakest L-implicants (i.e., under-approximations of the query) for function definitions that can involve existential quantifiers. We implemented a solver, Aspire, for problems expressed in Loud which can be used to describe and identify sources of bugs in both deterministic and nondeterministic programs, extract properties from concurrent programs, and synthesize winning strategies in two-player games.

Canopy fuels and surface fuel models, topographic features and other canopy attributes such as stand height and canopy cover, provide the necessary spatial datasets required by various fire behaviour modelling simulators. This is a technical note reporting on a pan-European fuel map server, highlighting the methods for the production and validation of canopy features, more specifically canopy fuels, and surface fuel models created for the European Union Horizon 2020 FIRE-RES project, as well as other related data derived from earth observation. The aim was to deliver a fuel cartography in a findable, accessible, interoperable and replicable manner as per F.A.I.R. guiding principles for research data stewardship. We discuss the technology behind sharing large raster datasets via web-GIS technologies and highlight advances and novelty of the shared data. Uncertainty maps related to the canopy fuel variables are also available to give users the expected reliability of the data. Users can view, query and download single layers of interest, or download the whole pan-European dataset. All layers are in raster format and co-registered in the same reference system, extent and spatial resolution (100 m). Viewing and downloading is available at all NUTS scales, ranging from country level (NUTS0) to province level (NUTS3), thus facilitating data management and access. The system was implemented using R for part of the processing and Google Earth Engine. The final app is openly available to the public for accessing the data at various scales.

Large-scale vision models have become integral in many applications due to their unprecedented performance and versatility across downstream tasks. However, the robustness of these foundation models has primarily been explored for a single task, namely image classification. The vulnerability of other common vision tasks, such as semantic segmentation and depth estimation, remains largely unknown. We present a comprehensive empirical evaluation of the adversarial robustness of self-supervised vision encoders across multiple downstream tasks. Our attacks operate in the encoder embedding space and at the downstream task output level. In both cases, current state-of-the-art adversarial fine-tuning techniques tested only for classification significantly degrade clean and robust performance on other tasks. Since the purpose of a foundation model is to cater to multiple applications at once, our findings reveal the need to enhance encoder robustness more broadly. Our code is available at ${github.com/layer6ai-labs/ssl-robustness}$., Comment: Accepted at the ICML 2024 Workshop on Foundation Models in the Wild

Motivation is essential in the learning process of university students, and teachers should have a wide range of strategies to address this issue. The emergence of social technologies has had a considerable influence in e-learning systems, and a number of experts state that their use is a good method to motivate students and to increase their participation in activities. This study attempts to determine whether social networks and social applications should be viewed as many other tools or whether they can actually provide extra motivation for students to participate. The study compared the percentage of student participation in tasks of self-assessment. The experiments covered three traditional strategies of student motivation and another one in which social networks were used to introduce, explain and deliver the self-assessment tasks. The case with a higher participation was the one in which students obtained a reward from the completion of the activity. Despite this result, the statistical analysis indicated that the use of social networks obtained similar results as a strategy of continuous and regular motivational speeches.

Driver drowsiness electroencephalography (EEG) signal monitoring can timely alert drivers of their drowsiness status, thereby reducing the probability of traffic accidents. Graph convolutional networks (GCNs) have shown significant advancements in processing the non-stationary, time-varying, and non-Euclidean nature of EEG signals. However, the existing single-channel EEG adjacency graph construction process lacks interpretability, which hinders the ability of GCNs to effectively extract adjacency graph features, thus affecting the performance of drowsiness monitoring. To address this issue, we propose an edge-end lightweight dual graph convolutional network (LDGCN). Specifically, we are the first to incorporate neurophysiological knowledge to design a Baseline Drowsiness Status Adjacency Graph (BDSAG), which characterizes driver drowsiness status. Additionally, to express more features within limited EEG data, we introduce the Augmented Graph-level Module (AGM). This module captures global and local information at the graph level, ensuring that BDSAG features remain intact while enhancing effective feature expression capability. Furthermore, to deploy our method on the fourth-generation Raspberry Pi, we utilize Adaptive Pruning Optimization (APO) on both channels and neurons, reducing inference latency by almost half. Experiments on benchmark datasets demonstrate that LDGCN offers the best trade-off between monitoring performance and hardware resource utilization compared to existing state-of-the-art algorithms. All our source code can be found at https://github.com/BryantDom/Driver-Drowsiness-Monitoring.

The aim of this study was to see whether student participation increased when mobile devices were used in the classroom. We measured the amount of student participative actions when the Socrative tool was used and when it was not used. Our experiment involved a total of 192 students, corresponding to 4 different subjects of Computer Engineering at the Universitat de les Illes Balears, during 2012/2013 and 2013/2014 courses. An independent paired t-test was performed on the measurements. The analysis results show that student participation increases with the use of mobile devices for theory classes and students are willing to participate in class activities and share their own results., Comment: in Spanish language

We examine subgroups of locally compact groups that are continuous homomorphic images of connected Lie groups and we give a criterion for being such an image. We also provide a new characterisation of Lie groups and a characterisation of groups that are images of connected locally compact groups., Comment: 9 pages

Neural networks used in computations with more advanced algebras than real numbers perform better in some applications. However, there is no general framework for constructing hypercomplex neural networks. We propose a library integrated with Keras that can do computations within TensorFlow and PyTorch. It provides Dense and Convolutional 1D, 2D, and 3D layers architectures.

Fully tensorial theory of hypercomplex neural networks is given. It allows neural networks to use arithmetic based on arbitrary algebras. The key point is to observe that algebra multiplication can be represented as a rank three tensor and use this tensor in every algebraic operation. This approach is attractive for neural network libraries that support effective tensorial operations. It agrees with previous implementations for four-dimensional algebras., Comment: 13 pages

We study for a dynamical system $f:X\longrightarrow X$ some of the principal topological recurrence-kind properties with respect to the induced maps $\overline{f}:\mathcal{K}(X)\longrightarrow\mathcal{K}(X)$, on the hyperspace of non-empty compact subsets of $X$, and $\hat{f}:\mathcal{F}(X)\longrightarrow\mathcal{F}(X)$, on the space of normal fuzzy sets consisting of the upper-semicontinuous functions $u:X\longrightarrow [0,1]$ with compact support and such that $u^{-1}(\{1\})\neq\varnothing$. In particular, we characterize the properties of topological and multiple recurrence for the extended systems $(\mathcal{K}(X),\overline{f})$ and $(\mathcal{F}(X),\hat{f})$. Special attention is given to the case where the underlying space is completely metrizable, for which we obtain some stronger point-recurrence equivalences., Comment: 17 pages

We have studied the gradient-flow equations in information geometry from a point-particle perspective. Based on the motion of a null (or light-like) particle in a curved space, we have rederived the Hamiltonians which describe the gradient-flows in information geometry., Comment: 14 pages, 1 figure, accepted to EPJB

We present a mean-field study of caloric effects driven by primary and secondary fields near tricritical points, which are fields thermodynamically conjugated to the main and secondary order parameters, respectively. General features, such as critical exponents and their crossover from critical to tricritical behaviours, are studied by means of a generic free energy Landau expansion. To deal with specific materials, we propose a model that combines the Blume-Emery-Griffiths prototype to study tricritical points with the Bean-Rodbell approach to include magnetovolume effects. In this model the primary field is the magnetic field, while chemical and mechanical pressures are secondary fields. In spite of the scarcity of experimental data, we have shown that results for the La(Fe$_{x}$Si$_{1-x}$)$_{13}$ and MnSi compounds are in good agreement with our predictions. We expect that our results will motivate and guide new experimental research aiming at optimizing caloric materials., Comment: 13 pages, 6 figures, one appendix

Recent results indicate that charm quarks are intrinsic components of the proton wave function, and that the charm and anticharm distributions for a given value of the Bjorken - $x$ variable can be different. In this paper, we will investigate the impact of this asymmetric intrinsic charm on the production of $D^0$ and ${\bar D}^0$ mesons for fixed target $p \!+ ^{20}\!\!Ne$ collisions at the LHCb. In our calculations, we include the contribution of the gluon-gluon fusion, gluon - charm and recombination processes and assume distinct models for the treatment of the intrinsic charm component. We demonstrate that the presence of an intrinsic charm improves the description of the current data for the rapidity and transverse momentum distributions of $D$ mesons. However, such models are not able to describe the LHCb data for the $D^0$-${\bar D}^0$ asymmetry at large transverse momentum, which point out that the description of the intrinsic charm needs to be improved and/or new effects should be taken into account in the production of heavy mesons at forward rapidities in fixed - target collisions., Comment: 23 pages, 11 pages. arXiv admin note: text overlap with arXiv:2206.02750

Semantics-Guided Synthesis (SemGuS) is a programmable framework for defining synthesis problems in a domain- and solver-agnostic way. This paper presents the standardized SemGuS format, together with an open-source toolkit that provides a parser, a verifier, and enumerative SemGuS solvers. The paper also describes an initial set of SemGuS benchmarks, which form the basis for comparing SemGuS solvers, and presents an evaluation of the baseline enumerative solvers.

Large Language Models (LLMs) struggle with reliably generating highly structured outputs, such as program code, mathematical formulas, or well-formed markup. Constrained decoding approaches mitigate this problem by greedily restricting what tokens an LLM can output at each step to guarantee that the output matches a given constraint. Specifically, in grammar-constrained decoding (GCD), the LLM's output must follow a given grammar. In this paper, we demonstrate that GCD techniques (and in general constrained decoding techniques) can distort the LLM's distribution, leading to outputs that are grammatical but appear with likelihoods that are not proportional to the ones given by the LLM, and so ultimately are low-quality. We call the problem of aligning sampling with a grammar constraint, grammar-aligned decoding (GAD), and propose adaptive sampling with approximate expected futures (ASAp), a decoding algorithm that guarantees the output to be grammatical while provably producing outputs that match the conditional probability of the LLM's distribution conditioned on the given grammar constraint. Our algorithm uses prior sample outputs to soundly overapproximate the future grammaticality of different output prefixes. Our evaluation on code generation and structured NLP tasks shows how ASAp often produces outputs with higher likelihood (according to the LLM's distribution) than existing GCD techniques, while still enforcing the desired grammatical constraints., Comment: Accepted to NeurIPS 2024

Recent deep learning-based multi-view people detection (MVD) methods have shown promising results on existing datasets. However, current methods are mainly trained and evaluated on small, single scenes with a limited number of multi-view frames and fixed camera views. As a result, these methods may not be practical for detecting people in larger, more complex scenes with severe occlusions and camera calibration errors. This paper focuses on improving multi-view people detection by developing a supervised view-wise contribution weighting approach that better fuses multi-camera information under large scenes. Besides, a large synthetic dataset is adopted to enhance the model's generalization ability and enable more practical evaluation and comparison. The model's performance on new testing scenes is further improved with a simple domain adaptation technique. Experimental results demonstrate the effectiveness of our approach in achieving promising cross-scene multi-view people detection performance. See code here: https://vcc.tech/research/2024/MVD., Comment: AAAI 2024

This paper reveals a key insight that a one-layer decoder-only Transformer is equivalent to a two-layer Recurrent Neural Network (RNN). Building on this insight, we propose ARC-Tran, a novel approach for verifying the robustness of decoder-only Transformers against arbitrary perturbation spaces. Compared to ARC-Tran, current robustness verification techniques are limited either to specific and length-preserving perturbations like word substitutions or to recursive models like LSTMs. ARC-Tran addresses these limitations by meticulously managing position encoding to prevent mismatches and by utilizing our key insight to achieve precise and scalable verification. Our evaluation shows that ARC-Tran (1) trains models more robust to arbitrary perturbation spaces than those produced by existing techniques and (2) shows high certification accuracy of the resulting models.

Gravity measurements provide valuable information on the mass distribution below the earth surface relevant to various areas of geosciences such as hydrology, geodesy, geophysics, volcanology, and natural resources management. During the past decades, the needs for sensitivity, robustness, compactness, and transportability of instruments measuring the gravitational acceleration have constantly increased. Today, applications typically call for 1 $\mu \mathrm{Gal} = 10 \mathrm{nm.s}^{-2}$ ~ $10^{-9}$ g resolution on time scales ranging from minutes to years. Absolute Quantum Gravimeters (AQGs) based on matter-wave interferometry with laser-cooled atoms address all these challenges at once, even in uncontrolled environments. Furthermore, to date, quantum gravimeters are the only technology capable of providing continuous absolute gravity data over long measurement durations (~1 day to months or more). In this paper we recall the AQG working principle and present the reproducible high performance at the {\mu}Gal level on all 16 units fabricated so far. We also describe recent progress on the Differential Quantum Gravimeter (DQG) which measures simultaneously the mean gravitational acceleration and its vertical gradient at the level of $10 \mathrm{nm.s}^{-2}$ and $1$ E (1 E[eotvos] = $10^{-9}$ s$^{-2}$) respectively., Comment: 10 pages, 5 figures

In safety-critical applications such as medical imaging and autonomous driving, where decisions have profound implications for patient health and road safety, it is imperative to maintain both high adversarial robustness to protect against potential adversarial attacks and reliable uncertainty quantification in decision-making. With extensive research focused on enhancing adversarial robustness through various forms of adversarial training (AT), a notable knowledge gap remains concerning the uncertainty inherent in adversarially trained models. To address this gap, this study investigates the uncertainty of deep learning models by examining the performance of conformal prediction (CP) in the context of standard adversarial attacks within the adversarial defense community. It is first unveiled that existing CP methods do not produce informative prediction sets under the commonly used $l_{\infty}$-norm bounded attack if the model is not adversarially trained, which underpins the importance of adversarial training for CP. Our paper next demonstrates that the prediction set size (PSS) of CP using adversarially trained models with AT variants is often worse than using standard AT, inspiring us to research into CP-efficient AT for improved PSS. We propose to optimize a Beta-weighting loss with an entropy minimization regularizer during AT to improve CP-efficiency, where the Beta-weighting loss is shown to be an upper bound of PSS at the population level by our theoretical analysis. Moreover, our empirical study on four image classification datasets across three popular AT baselines validates the effectiveness of the proposed Uncertainty-Reducing AT (AT-UR)., Comment: ICML2024

The increasing reliance on Deep Learning models, combined with their inherent lack of transparency, has spurred the development of a novel field of study known as eXplainable AI (XAI) methods. These methods seek to enhance the trust of end-users in automated systems by providing insights into the rationale behind their decisions. This paper presents a novel approach for measuring user trust in XAI systems, allowing their refinement. Our proposed metric combines both performance metrics and trust indicators from an objective perspective. To validate this novel methodology, we conducted a case study in a realistic medical scenario: the usage of XAI system for the detection of pneumonia from x-ray images.

Detecting binary black hole (BBH) mergers with quantifiable orbital eccentricity would confirm the existence of a dynamical formation channel for these binaries. The current state-of-the-art gravitational wave searches of LIGO-Virgo-KAGRA strain data focus more on quasicircular mergers due to increased dimensionality and lack of efficient eccentric waveform models. In this work, we compare the sensitivities of two search pipelines, the matched filter-based \texttt{PyCBC} and the unmodelled coherent Wave Burst (\texttt{cWB}) algorithms towards the spinning eccentric BBH mergers, using a multipolar nonprecessing-spin eccentric signal model, \texttt{SEOBNRv4EHM}. Our findings show that neglecting eccentricity leads to missed opportunities for detecting eccentric BBH mergers, with \texttt{PyCBC} exhibiting a $10-20\, \%$ sensitivity loss for eccentricities exceeding $0.2$ defined at $10$ Hz. In contrast, \texttt{cWB} is resilient, with a $10\, \%$ sensitivity increase for heavier ($\mathcal{M} \ge 30 \, \text{M}_{\odot}$) eccentric BBH mergers, but is significantly less sensitive than \texttt{PyCBC} for lighter BBH mergers. Our fitting factor study confirmed that neglecting eccentricity biases the estimation of chirp mass, mass ratio, and effective spin parameter, skewing our understanding of astrophysical BBH populations, fundamental physics, and precision cosmology. Our results demonstrate that the current search pipelines are not sufficiently sensitive to eccentric BBH mergers, necessitating the development of a dedicated matched-filter search for these binaries. Whereas, burst searches should be optimized to detect lower chirp mass BBH mergers as eccentricity does not affect their search sensitivity significantly., Comment: 15 pages, 22 figures

We theoretically investigate the performance of coherent beam combination of two light beams under relative phase fluctuations in the photon starved regime. We apply a first-principles approach using the optimal Bayesian phase correction protocol. We analyze the efficiency of beam combination as a function of the phase fluctuations strength., Comment: 10 pages, 4 figures

We detail several scientific and engineering innovations which enable the controlled spalling of 10 - 50 micron thick films of single crystal 4H silicon carbide (4H-SiC) from bulk substrates. 4H-SiC's properties, including high thermal conductivity and a wide bandgap, make it an ideal candidate for high-temperature, high-voltage power electronic devices. Moreover, 4H-SiC has been shown to be an excellent host of solid-state atomic defect qubits for quantum computing and quantum networking. Because 4H-SiC single crystal substrates are expensive (due to long growth times and limited yield), techniques for removal and transfer of bulk-quality films in the tens-of-microns thickness range are highly desirable to allow for substrate reuse and integration of the separated films. In this work we utilize novel approaches for stressor layer thickness control and spalling crack initiation to demonstrate controlled spalling of 4H-SiC, the highest fracture toughness material spalled to date. Additionally, we demonstrate substrate re-use, bonding of the spalled films to carrier substrates, and explore the spin coherence of the spalled films. In preliminary studies we are able to achieve coherent spin control of neutral divacancy ($VV^{0}$) qubit ensembles and measure a quasi-bulk spin $T_{2}$ of 79.7 $\mu$s in such spalled films., Comment: 16 pages, 6 figures

Head avatars animated by visual signals have gained popularity, particularly in cross-driving synthesis where the driver differs from the animated character, a challenging but highly practical approach. The recently presented MegaPortraits model has demonstrated state-of-the-art results in this domain. We conduct a deep examination and evaluation of this model, with a particular focus on its latent space for facial expression descriptors, and uncover several limitations with its ability to express intense face motions. To address these limitations, we propose substantial changes in both training pipeline and model architecture, to introduce our EMOPortraits model, where we: Enhance the model's capability to faithfully support intense, asymmetric face expressions, setting a new state-of-the-art result in the emotion transfer task, surpassing previous methods in both metrics and quality. Incorporate speech-driven mode to our model, achieving top-tier performance in audio-driven facial animation, making it possible to drive source identity through diverse modalities, including visual signal, audio, or a blend of both. We propose a novel multi-view video dataset featuring a wide range of intense and asymmetric facial expressions, filling the gap with absence of such data in existing datasets.

Binary black holes (BBHs) in eccentric orbits produce distinct modulations the emitted gravitational waves (GWs). The measurement of orbital eccentricity can provide robust evidence for dynamical binary formation channels. We analyze 57 GW events from the first, second and third observing runs of the LIGO-Virgo-KAGRA (LVK) Collaboration using a multipolar aligned-spin inspiral-merger-ringdown waveform model with two eccentric parameters: eccentricity and relativistic anomaly. This is made computationally feasible with the machine-learning code DINGO which accelerates inference by 2-3 orders of magnitude compared to traditional inference. First, we find eccentric aligned-spin versus quasi-circular aligned-spin $\log_{10}$ Bayes factors of 1.84 to 4.75 (depending on the glitch mitigation) for GW200129, 3.0 for GW190701 and 1.77 for GW200208_22. We measure $e_{\text{gw}, 10Hz}$ to be $0.27_{-0.12}^{+0.10}$ to $0.17_{-0.13}^{+0.14}$ for GW200129, $0.35_{-0.11}^{+0.32}$ for GW190701 and $0.35_{-0.21}^{+0.18}$ for GW200208_22. Second, we find $\log_{10}$ Bayes factors between the eccentric aligned-spin versus quasi-circular precessing-spin hypothesis between 1.43 and 4.92 for GW200129, 2.61 for GW190701 and 1.23 for GW200208_22. Third, our analysis does not show evidence for eccentricity in GW190521, which has an eccentric aligned-spin against quasi-circular aligned-spin $\log_{10}$ Bayes factor of 0.04. Fourth, we estimate that if we neglect the spin-precession and use an astrophysical prior, the probability of one out of the 57 events being eccentric is greater than 99.5% or $(100 - 8.4 \times 10^{-4})$% (depending on the glitch mitigation). Fifth, we study the impact on parameter estimation when neglecting either eccentricity or higher modes in eccentric models. These results underscore the importance of including eccentric parameters in the characterization of BBHs for GW detectors., Comment: 36 pages, 14 figures

Precise image editing with text-to-image models has attracted increasing interest due to their remarkable generative capabilities and user-friendly nature. However, such attempts face the pivotal challenge of misalignment between the intended precise editing target regions and the broader area impacted by the guidance in practice. Despite excellent methods leveraging attention mechanisms that have been developed to refine the editing guidance, these approaches necessitate modifications through complex network architecture and are limited to specific editing tasks. In this work, we re-examine the diffusion process and misalignment problem from a frequency perspective, revealing that, due to the power law of natural images and the decaying noise schedule, the denoising network primarily recovers low-frequency image components during the earlier timesteps and thus brings excessive low-frequency signals for editing. Leveraging this insight, we introduce a novel fine-tuning free approach that employs progressive $\textbf{Fre}$qu$\textbf{e}$ncy truncation to refine the guidance of $\textbf{Diff}$usion models for universal editing tasks ($\textbf{FreeDiff}$). Our method achieves comparable results with state-of-the-art methods across a variety of editing tasks and on a diverse set of images, highlighting its potential as a versatile tool in image editing applications., Comment: Accepted by ECCV-2024

Existing deep trackers are typically trained with largescale video frames with annotated bounding boxes. However, these bounding boxes are expensive and time-consuming to annotate, in particular for large scale datasets. In this paper, we propose to learn tracking representations from single point annotations (i.e., 4.5x faster to annotate than the traditional bounding box) in a weakly supervised manner. Specifically, we propose a soft contrastive learning (SoCL) framework that incorporates target objectness prior into end-to-end contrastive learning. Our SoCL consists of adaptive positive and negative sample generation, which is memory-efficient and effective for learning tracking representations. We apply the learned representation of SoCL to visual tracking and show that our method can 1) achieve better performance than the fully supervised baseline trained with box annotations under the same annotation time cost; 2) achieve comparable performance of the fully supervised baseline by using the same number of training frames and meanwhile reducing annotation time cost by 78% and total fees by 85%; 3) be robust to annotation noise., Comment: Accept to CVPR2024-L3DIVU