Miracle-dz/UniFAR-FTUs huggingface dataset:
LISTER: Neighbor Decoding for Length-Insensitive Scene Text Recognition

The diversity in length constitutes a significant characteristic of text. Due to the long-tail distribution of text lengths, most existing methods for scene text recognition (STR) only work well on short or seen-length text, lacking the capability of recognizing longer text or performing length extrapolation. This is a crucial issue, since the lengths of the text to be recognized are usually not given in advance in real-world applications, but it has not been adequately investigated in previous works. Therefore, we propose in this paper a method called Length-Insensitive Scene TExt Recognizer (LISTER), which remedies the limitation regarding the robustness to various text lengths. Specifically, a Neighbor Decoder is proposed to obtain accurate character attention maps with the assistance of a novel neighbor matrix regardless of the text lengths. Besides, a Feature Enhancement Module is devised to model the long-range dependency with low computation cost, which is able to perform iterations with the neighbor decoder to enhance the feature map progressively. To the best of our knowledge, we are the first to achieve effective length-insensitive scene text recognition. Extensive experiments demonstrate that the proposed LISTER algorithm exhibits obvious superiority on long text recognition and the ability for length extrapolation, while comparing favourably with the previous state-of-the-art methods on standard benchmarks for STR (mainly short text)1.
Auto-weighted Bayesian Physics-Informed Neural Networks and robust estimations for multitask inverse problems in pore-scale imaging of dissolution

In this article, we present a novel data assimilation strategy in pore-scale imaging and demonstrate that this makes it possible to robustly address reactive inverse problems incorporating Uncertainty Quantification (UQ). Pore-scale modeling of reactive flow offers a valuable opportunity to investigate the evolution of macro-scale properties subject to dynamic processes. Yet, they suffer from imaging limitations arising from the associated X-ray microtomography (X-ray microCT) process, which induces discrepancies in the properties estimates. Assessment of the kinetic parameters also raises challenges, as reactive coefficients are critical parameters that can cover a wide range of values. We account for these two issues and ensure reliable calibration of pore-scale modeling, based on dynamical microCT images, by integrating uncertainty quantification in the workflow. The present method is based on a multitasking formulation of reactive inverse problems combining data-driven and physics-informed techniques in calcite dissolution. This allows quantifying morphological uncertainties on the porosity field and estimating reactive parameter ranges through prescribed PDE models with a latent concentration field and dynamical microCT. The data assimilation strategy relies on sequential reinforcement incorporating successively additional PDE constraints. We guarantee robust and unbiased uncertainty quantification by straightforward adaptive weighting of Bayesian Physics-Informed Neural Networks (BPINNs), ensuring reliable micro-porosity changes during geochemical transformations. We demonstrate successful Bayesian Inference in 1D+Time and 2D+Time calcite dissolution based on synthetic microCT images with meaningful posterior distribution on the reactive parameters and dimensionless numbers.
Prediction without Preclusion: Recourse Verification with Reachable Sets

Machine learning models are often used to decide who will receive a loan, a job interview, or a public benefit. Standard techniques to build these models use features about people but overlook their actionability. In turn, models can assign predictions that are fixed, meaning that consumers who are denied loans, interviews, or benefits may be permanently locked out from access to credit, employment, or assistance. In this work, we introduce a formal testing procedure to flag models that assign fixed predictions that we call recourse verification. We develop machinery to reliably determine if a given model can provide recourse to its decision subjects from a set of user-specified actionability constraints. We demonstrate how our tools can ensure recourse and adversarial robustness in real-world datasets and use them to study the infeasibility of recourse in real-world lending datasets. Our results highlight how models can inadvertently assign fixed predictions that permanently bar access, and we provide tools to design algorithms that account for actionability when developing models.
Compact convergence, deformation of the $L^2$-$\overline{\partial}$-complex and canonical $K$-homology classes

Let $(X,\gamma)$ be a compact, irreducible Hermitian complex space of complex dimension $m$ and with $\mathrm{dim}(\mathrm{sing}(X))=0$. Let $(F,\tau)\rightarrow X$ be a Hermitian holomorphic vector bundle over $X$ and let us denote with $\overline{\eth}_{F,m,\mathrm{abs}}$ the rolled-up operator of the maximal $L^2$-$\overline{\partial}$ complex of $F$-valued $(m,\bullet)$-forms. Let $\pi:M\rightarrow X$ be a resolution of singularities, $g$ a metric on $M$, $E:=\pi^*F$ and $\rho:=\pi^*\tau$. In this paper, under quite general assumptions on $\tau$, we prove the following equality of analytic $K$-homology classes $[\overline{\eth}_{F,m,\mathrm{abs}}]=\pi_*[\overline{\eth}_{E,m}]$, with $\overline{\eth}_{E,m}$ the rolled-up operator of the $L^2$-$\overline{\partial}$ complex of $E$-valued $(m,\bullet)$-forms on $M$. Our proof is based on functional analytic techniques developed in \cite{KuSh} and provides an explicit homotopy between the even unbounded Fredholm modules induced by $\overline{\eth}_{F,m,\mathrm{abs}}$ and $\overline{\eth}_{E,m}$.
npm-follower: A Complete Dataset Tracking the NPM Ecosystem

Software developers typically rely upon a large network of dependencies to build their applications. For instance, the NPM package repository contains over 3 million packages and serves tens of billions of downloads weekly. Understanding the structure and nature of packages, dependencies, and published code requires datasets that provide researchers with easy access to metadata and code of packages. However, prior work on NPM dataset construction typically has two limitations: 1) only metadata is scraped, and 2) packages or versions that are deleted from NPM can not be scraped. Over 330,000 versions of packages were deleted from NPM between July 2022 and May 2023. This data is critical for researchers as it often pertains to important questions of security and malware. We present npm-follower, a dataset and crawling architecture which archives metadata and code of all packages and versions as they are published, and is thus able to retain data which is later deleted. The dataset currently includes over 35 million versions of packages, and grows at a rate of about 1 million versions per month. The dataset is designed to be easily used by researchers answering questions involving either metadata or program analysis. Both the code and dataset are available at https://dependencies.science.
Handle element on nearly Frobenius algebras

In this article the concept of handle element of Frobenius algebras, as in [16], will be extended to nearly Frobenius algebras. The main properties of this element will be analyzed in this case and many examples will be constructed. Also the Casimir and Schur elements of symmetric algebras will be considered ([8]) and generalized for Frobenius and nearly Frobenius algebras showing which results still hold in this framework and which ones fail.
Certifiably Optimal Rotation and Pose Estimation Based on the Cayley Map

We present novel, tight, convex relaxations for rotation and pose estimation problems that can guarantee global optimality via strong Lagrangian duality. Some such relaxations exist in the literature for specific problem setups that assume the matrix von Mises-Fisher distribution (a.k.a., matrix Langevin distribution or chordal distance) for isotropic rotational uncertainty. However, another common way to represent uncertainty for rotations and poses is to define anisotropic noise in the associated Lie algebra. Starting from a noise model based on the Cayley map, we define our estimation problems, convert them to Quadratically Constrained Quadratic Programs (QCQPs), then relax them to Semidefinite Programs (SDPs), which can be solved using standard interior-point optimization methods. We first show how to carry out basic rotation and pose averaging. We then turn to the more complex problem of trajectory estimation, which involves many pose variables with both individual and inter-pose measurements (or motion priors). Our contribution is to formulate SDP relaxations for all these problems, including the identification of sufficient redundant constraints to make them tight. We hope our results can add to the catalogue of useful estimation problems whose global optimality can be guaranteed.
Dynamique océanique dans les golfes Persique et d'Oman

La circulation dans les golfes Persique et d'Oman est un systeme complexe et mal connu qui resulte de l'interaction de nombreux forcages a differentes echelles de temps. A l'echelle de quelques heures , la maree joue un role important. A l'echelle saisonniere et inter-annuelle, une circulation thermohaline, typique d'un bassin de concentration, se met en place. Elle est caracterisee par une circulation cyclonique a forte variabilite saisonniere et par l'interaction de deux masses d'eaux : une eau dense formee dans le Golfe Persique (Persian Gulfe Water) qui s'ecoule sous la forme d'une veine dans le Golfe d'Oman et une eau plus legere en provenance des couches de surface de l'Ocean Indien (Indian Ocean surface water). L'analyse des donnees de la campagne GOGP99, realisee en automne 1999, montre l'existence des deux masses d'eaux PGW et IOSW, ainsi qu'une variabilite temporelle et spatiale importante des structures hydrologiques et courantologiques, l'evolution des caracteristiques de la veine d'eau PGW jusqu'en mer d'Arabie est precisee. Un modele realiste de la circulation dans les deux golfes est developpe pour comprendre les differents aspects de la dynamique. La circulation dans le golfe persique, les interactions entre les masses d'eau PGW et IOSW, la formation puis le developpement de l'eau PGW dans le Golfe d'Oman sont detailles et une variabilite importante est mise en evidence. Une etude de processus precise la part des differents forcages. L'importance de la parametrisation des flux de chaleur est mise en evidence. Finalement,une etude de sensibilite a la variation de certains forcages (fleuve, evaporation) et a la parametrisation de la turbulence est realisee.
Light propagation in the vicinity of the ModMax black hole

ModMax is a nonlinear electrodynamics theory with the same symmetries as Maxwell electrodynamics. Static spherically symmetric solutions have been derived by coupling ModMax electrodynamics with the Einstein equations, which can represent a black hole. In this paper, we analyze light propagation in the vicinity of the ModMax black hole. We determine birefringence, light trajectories, deflection, redshifts, as well as the shadow of the black hole using the effective or optical metric to determine the optical paths of light; comparison is done with the corresponding effects in the neighborhood of the Reissner-Nordstrom black hole, that is the solution to the Einstein-Maxwell equations.
IP Neo-colonialism: Geo-auditing RIR Address Registrations

Allocation of the global IP address space is under the purview of IANA, who distributes management responsibility among five geographically distinct Regional Internet Registries (RIRs). Each RIR is empowered to bridge technical (e.g., address uniqueness and aggregatability) and policy (e.g., contact information and IP scarcity) requirements unique to their region. While different RIRs have different policies for out-of-region address use, little prior systematic analysis has studied where addresses are used post-allocation. In this preliminary work, we e IPv4 prefix registrations across the five RIRs (50k total prefixes) and utilize the Atlas distributed active measurement infrastructure to geolocate prefixes at RIR-region granularity. We define a taxonomy of registration ``geo-consistency'' by comparing a prefixes' inferred physical location to the allocating RIR's coverage region as well as the registered organization's location. We then apply this methodology and taxonomy to audit the geo-consistency of 10k random IPv4 prefix allocations within each RIR (50k total prefixes). While we find registry information to largely be consistent with our geolocation inferences, we show that some RIRs have a non-trivial fraction of prefixes that are used both outside of the RIR's region and outside of the registered organization's region. A better understanding of such discrepancies can increase transparency for the community and inform ongoing discussions over in-region address use and policy.
CALM : A Multi-task Benchmark for Comprehensive Assessment of Language Model Bias

As language models (LMs) become increasingly powerful and widely used, it is important to quantify them for sociodemographic bias with potential for harm. Prior measures of bias are sensitive to perturbations in the templates designed to compare performance across social groups, due to factors such as low diversity or limited number of templates. Also, most previous work considers only one NLP task. We introduce Comprehensive Assessment of Language Models (CALM) for robust measurement of two types of universally relevant sociodemographic bias, gender and race. CALM integrates sixteen datasets for question-answering, sentiment analysis and natural language inference. Examples from each dataset are filtered to produce 224 templates with high diversity (e.g., length, vocabulary). We assemble 50 highly frequent person names for each of seven distinct demographic groups to generate 78,400 prompts covering the three NLP tasks. Our empirical evaluation shows that CALM bias scores are more robust and far less sensitive than previous bias measurements to perturbations in the templates, such as synonym substitution, or to random subset selection of templates. We apply CALM to 20 large language models, and find that for 2 language model series, larger parameter models tend to be more biased than smaller ones. The T0 series is the least biased model families, of the 20 LLMs investigated here. The code is available at https://github.com/vipulgupta1011/CALM.
A Huber Loss Minimization Approach to Byzantine Robust Federated Learning

Federated learning systems are susceptible to adversarial attacks. To combat this, we introduce a novel aggregator based on Huber loss minimization, and provide a comprehensive theoretical analysis. Under independent and identically distributed (i.i.d) assumption, our approach has several advantages compared to existing methods. Firstly, it has optimal dependence on epsilon, which stands for the ratio of attacked clients. Secondly, our approach does not need precise knowledge of epsilon. Thirdly, it allows different clients to have unequal data sizes. We then broaden our analysis to include non-i.i. d data, such that clients have slightly different distributions.
Can Linguistic Knowledge Improve Multimodal Alignment in Vision-Language Pretraining?

The multimedia community has shown a significant interest in perceiving and representing the physical world with multimodal pretrained neural network models, and among them, the visual-language pertaining (VLP) is, currently, the most captivating topic. However, there have been few endeavors dedicated to the exploration of 1) whether essential linguistic knowledge (e.g., semantics and syntax) can be extracted during VLP, and 2) how such linguistic knowledge impact or enhance the multimodal alignment. In response, here we aim to elucidate the impact of comprehensive linguistic knowledge, including semantic expression and syntactic structure, on multimodal alignment. Specifically, we design and release the SNARE, the first large-scale multimodal alignment probing benchmark, to detect the vital linguistic components, e.g., lexical, semantic, and syntax knowledge, containing four tasks: Semantic structure, Negation logic, Attribute ownership, and Relationship composition. Based on our proposed probing benchmarks, our holistic analyses of five advanced VLP models illustrate that the VLP model: i) shows insensitivity towards complex syntax structures and relies on content words for sentence comprehension; ii) demonstrates limited comprehension of combinations between sentences and negations; iii) faces challenges in determining the presence of actions or spatial relationships within visual information and struggles with verifying the correctness of triple combinations. We make our benchmark and code available at \url{https://github.com/WangFei-2019/SNARE/}.
FaceTouch: Detecting hand-to-face touch with supervised contrastive learning to assist in tracing infectious disease

Through our respiratory system, many viruses and diseases frequently spread and pass from one person to another. Covid-19 served as an example of how crucial it is to track down and cut back on contacts to stop its spread. There is a clear gap in finding automatic methods that can detect hand-to-face contact in complex urban scenes or indoors. In this paper, we introduce a computer vision framework, called FaceTouch, based on deep learning. It comprises deep sub-models to detect humans and analyse their actions. FaceTouch seeks to detect hand-to-face touches in the wild, such as through video chats, bus footage, or CCTV feeds. Despite partial occlusion of faces, the introduced system learns to detect face touches from the RGB representation of a given scene by utilising the representation of the body gestures such as arm movement. This has been demonstrated to be useful in complex urban scenarios beyond simply identifying hand movement and its closeness to faces. Relying on Supervised Contrastive Learning, the introduced model is trained on our collected dataset, given the absence of other benchmark datasets. The framework shows a strong validation in unseen datasets which opens the door for potential deployment.
‘Showing people part of your life’: service-user experiences of taking and sharing photographs in a DBT-informed emotional coping skills group

Abstract The added value of using photographs to explore experiences has been embraced in research methods such as photo-elicitation and Photovoice. Despite evidence that photographs aid communication and understanding, there is little research investigating photograph use within cognitive behavioural therapy. This project explores participant experiences of taking and sharing photographs within a dialectical behaviour therapy (DBT)-informed emotional coping skills group intervention. Five service users were recruited from groups that had implemented the taking and sharing of photographs as a feature of homework tasks and feedback. Semi-structured interviews were conducted, using the photographs taken for the group as prompts, and interpretive phenomenological analysis was used to develop themes. Group evaluation data were used to support the analysis. Participants were positive about the impact of the photographs, despite initially experiencing anxieties regarding sharing them. The task supported them to express themselves in ways that added value to verbal feedback, which had benefits for group relationships and for the therapeutic tasks such as mindful observing. Further research is indicated, given the small sample and participants all being white, working-age females. However, the findings suggest that using photographs as homework tasks could support communication and the exploration of experiences that may be difficult to express using words. Key learning aims (1) Photographs may be an excellent tool for the communication of meaning and emotion in therapy. (2) Photographs taken on smartphones are likely to be a user-friendly, familiar and effective addition to homework recording tools. (3) Sharing photographs may have particular benefit for service users who struggle to be open about or express their feelings. (4) Photographs can provide a prop to help service users share experiences in a non-verbal way, which may help when working with people who struggle to describe their emotional experiences. (5) Sharing photographs can lead to a deeper connection with others, although some negotiation about what types of images can be captured will need agreement in a group setting.
Efficacy of an innovative Herbst appliance with TADs for patients with hyperdivergent class II malocclusion: study protocol for a randomised controlled trial

Introduction Class II treatment with mandibular retrusion often involves the Herbst appliance due to its efficiency and low requirement of cooperation. Despite its advantages, it causes side effects concerning the occlusal plane and pogonion in terms of clockwise rotation that hinder the desired mandibular advancement for hyperdivergent patients. In this study, we will use a newly designed Herbst appliance, and a protocol that is accompanied by TADs for vertical control to avoid maxillary clockwise rotation. We hypothesise that the effect of the Herbst appliance with the vertical control approach will be beneficial for maintaining or even decreasing the skeletal divergence in hyperdivergent class II patients compared with conventional Herbst treatment. Methods and analysis This study is a randomised, parallel, prospective controlled trial that will study the efficacy of Herbst with or without vertical control in children with hyperdivergent skeletal class II malocclusion. A total of 44 patients will be enrolled and randomised in a ratio of 1:1 to either Herbst treatment or Herbst treatment with vertical control. Participants will be recruited at the Shanghai Stomatological Hospital, Shanghai, China. The primary endpoint is the change in the angle indicating the occlusal plane and Sella-Nasion plane from baseline (T0) to the primary endpoint (T2) on cephalometric measurements by lateral X-ray examination. Important secondary outcomes include the root length of the anterior teeth, and the assessment score of the Visual Analogue Scale Questionnaire, etc. Safety endpoints will also be evaluated. Ethics and dissemination This study has been approved by the ethics committee of the Shanghai Stomatological Hospital (Approval No. (2021) 012). Before enrolment, a qualified clinical research assistant will obtain written informed consent from both the participants and their guardians after full explanation of this study. The results will be presented at national or international conferences and published in peer-reviewed journals. Trial registration number ChiCTR2100049860, Chinese Clinical Trial Registry
2 . 3 The CRISP-DM Process CRISP -

In the last years there has been a huge growth and consolidation of the Data Mining field. Some efforts are being done that seek the establishment of standards in the area. Included on these efforts there can be enumerated SEMMA and CRISP-DM. Both grow as industrial standards and define a set of sequential steps that pretends to guide the implementation of data mining applications. The question of the existence of substantial differences between them and the traditional KDD process arose. In this paper, is pretended to establish a parallel between these and the KDD process as well as an understanding of the similarities between them.