Miracle-dz/UniFAR-FTUs huggingface dataset:
A Simple Long-Tailed Recognition Baseline via Vision-Language Model

The visual world naturally exhibits a long-tailed distribution of open classes, which poses great challenges to modern visual systems. Existing approaches either perform class re-balancing strategies or directly improve network modules to address the problem. However, they still train models with a finite set of predefined labels, limiting their supervision information and restricting their transferability to novel instances. Recent advances in large-scale contrastive visual-language pretraining shed light on a new pathway for visual recognition. With open-vocabulary supervisions, pretrained contrastive vision-language models learn powerful multimodal representations that are promising to handle data deficiency and unseen concepts. By calculating the semantic similarity between visual and text inputs, visual recognition is converted to a vision-language matching problem. Inspired by this, we propose BALLAD to leverage contrastive vision-language models for long-tailed recognition. We first continue pretraining the vision-language backbone through contrastive learning on a specific long-tailed target dataset. Afterward, we freeze the backbone and further employ an additional adapter layer to enhance the representations of tail classes on balanced training samples built with re-sampling strategies. Extensive experiments have been conducted on three popular long-tailed recognition benchmarks. As a result, our simple and effective approach sets the new state-of-the-art performances and outperforms competitive baselines with a large margin. Code is released at https://github.com/gaopengcuhk/BALLAD.
SAGCI-System: Towards Sample-Efficient, Generalizable, Compositional, and Incremental Robot Learning

Building general-purpose robots to perform a diverse range of tasks in a large variety of environments in the physical world at the human level is extremely challenging. According to [1], it requires the robot learning to be sample-efficient, generalizable, compositional, and incremental. In this work, we introduce a systematic learning framework called SAGCI-system towards achieving these above four requirements. Our system first takes the raw point clouds gathered by the camera mounted on the robot's wrist as the inputs and produces initial modeling of the surrounding environment represented as a file of Unified Robot Description Format (URDF). Our system adopts a learning-augmented differentiable simulation that loads the URDF. The robot then utilizes the interactive perception to interact with the environment to online verify and modify the URDF. Leveraging the differentiable simulation, we propose a model-based learning algorithm combining object-centric and robot-centric stages to efficiently produce policies to accomplish manipulation tasks. We apply our system to perform articulated object manipulation tasks, both in the simulation and the real world. Extensive experiments demonstrate the effectiveness of our proposed learning framework. Supplemental materials and videos are available on our project webpage https://sites.google. com/view/egci.
Multidisciplinary conceptual design optimization of aircraft using a sound-matching-based objective function

The article presents a novel approach to include community noise considerations based on sound quality in the Multidisciplinary Conceptual Design Optimization (MCDO) of civil transportation aircraft. The novelty stems from the use of an unconventional objective function, defined as a measure of the difference between the noise emission of the aircraft under analysis and a reference ‘weakly annoying’ noise, the target sound. The minimization of such a merit factor yields an aircraft concept with a noise signature as close as possible to the given target. The reference sound is one of the outcomes of the European Research Project SEFA (Sound Engineering For Aircraft, VI Framework Programme, 2004-2007), and used here as an external input. The aim of the present work is to address the definition and the inclusion of the sound-matching-based objective function in the MCDO of aircraft.
Concavity properties for solutions to p-Laplace equations with concave nonlinearities

Abstract We obtain new concavity results, up to a suitable transformation, for a class of quasi-linear equations in a convex domain involving the p-Laplace operator and a general nonlinearity satisfying concavity-type assumptions. This provides an extension of results previously known in the literature only for the torsion and the first eigenvalue equations. In the semilinear case p = 2 {p=2} the results are already new since they include new admissible nonlinearities.
Leading UK housebuilders' utilization of offsite construction methods

In recent years the construction industry has been exhorted to increase its utilization of offsite technologies, or ‘Modern Methods of Construction’ (MMC), in order to address the under-supply and poor build quality of housing. Despite the well-rehearsed benefits of such technologies, the take-up within the industry has been slow. This paper reports on research that examined housebuilders' practices and strategies regarding the use of offsite-MMC. A questionnaire survey of the top 100 housebuilders in the UK and a series of interviews were used to reveal the extent to which such technologies are being utilized and the factors which impinge on their popularity. The findings suggest that current offsite-MMC usage in large housebuilders is low, but that the level is likely to increase, given the pressures to improve quality, time, cost, productivity, and health and safety. The wider take-up of offsite-MMC is, however, inhibited by perceived higher capital costs, interfacing problems, long lead-in time, delayed planning process, and current manufacturing capacity. Based on these findings, the paper provides a set of strategies for improving offsite-MMC practices amongst housebuilders. It is hoped that will help deliver an improvement of housing supply in the UK. Au cours de ces dernières années, l'industrie de la construction a été vivement incitée à augmenter son utilisation des technologies hors site, c'est-à-dire les méthodes modernes de construction (MMC) pour régler les problèmes de pénurie d'approvisionnement et de qualité médiocre des logements construits. Malgré les avantages bien présentés de ces technologies, l'industrie a été lente à en prendre conscience. Cette communication rend compte des recherches concernant ces pratiques et les stratégies des constructeurs interrogés sur les MMC-hors site. Un questionnaire portant sur les 100 premières entreprises de construction du Royaume-Uni et une série d'entretien ont servi à révéler la mesure dans laquelle ces technologies sont utilisées et à exposer les facteurs qui ont un impact sur leur popularité. Les résultats laissent à penser que l'usage actuel des MMC-hors site par les grands industriels de la construction est peu important mais que le niveau devrait augmenter, compte tenu des pressions qui s'exercent pour améliorer la qualité, les délais, les coûts, la productivité ainsi que l'hygiène et la sécurité. Un plus grand usage des méthodes MMC-hors site est toutefois ralenti par la perception de coûts en investissements plus élevés, de problèmes d'interfaces, de longs délais de livraison, de plannings retardés et par la capacité de production actuelle. Sur la base de ces résultats, cet article présente un ensemble de stratégies qui pourraient améliorer les pratiques MMC-hors site chez les constructeurs de maisons. Il faut espérer que cela aidera à proposer une amélioration de l'offre de logements au Royaume-Uni. construction;construction de maisons;innovation;fabrication;méthodes modernes de construction (MMC);hors site;Royaume-Uni
Language models in word sense disambiguation for Polish

In the paper, we test two different approaches to the unsupervised word sense disambiguation task for Polish. In both methods, we use neural language models to predict words similar to those being disambiguated and, on the basis of these words, we predict the partition of word senses in different ways. In the first method, we cluster selected similar words, while in the second, we cluster vectors representing their subsets. The evaluation was carried out on texts annotated with plWordNet senses and provided a relatively good result (F1=0.68 for all ambiguous words). The results are significantly better than those obtained for the neural model-based unsupervised method proposed in (Wawer and Mykowiecka, 2017) and are at the level of the supervised method presented there. The proposed method may be a way of solving word sense disambiguation problem for languages which lack sense annotated data.
A theory of meta-factorization

We introduce meta-factorization, a theory that describes matrix decompositions as solutions of linear matrix equations: the projector and the reconstruction equation. Meta-factorization reconstructs known factorizations, reveals their internal structures, and allows for introducing modifications, as illustrated with SVD, QR, and UTV factorizations. The prospect of meta-factorization also provides insights into computational aspects of generalized matrix inverses and randomized linear algebra algorithms. The relations between the Moore-Penrose pseudoinverse, generalized Nystr\"{o}m method, and the CUR decomposition are revealed here as an illustration. Finally, meta-factorization offers hints on the structure of new factorizations and provides the potential of creating them.
Enabling Reusable Physical Design Flows with Modular Flow Generators

Achieving high code reuse in physical design flows is challenging but increasingly necessary to build complex systems. Unfortunately, existing approaches based on parameterized Tcl generators support very limited reuse and struggle to preserve reusable code as designers customize flows for specific designs and technologies. We present a vision and framework based on modular flow generators that encapsulates coarse-grain and fine-grain reusable code in modular nodes and assembles them into complete flows. The key feature is a flow consistency and instrumentation layer embedded in Python, which supports mechanisms for rapid and early feedback on inconsistent composition. The approach gradually types the Tcl language and allows both automatic and user-annotated static assertion checks. We evaluate the design flows of successive generations of silicon prototypes designed in TSMC16, TSMC28, TSMC40, SKY130, and IBM180 technologies, showing how our approach can enable significant code reuse in future flows.
Deep Learning with Multiple Data Set: A Weighted Goal Programming Approach

Large-scale data analysis is growing at an exponential rate as data proliferates in our societies. This abundance of data has the advantage of allowing the decision-maker to implement complex models in scenarios that were prohibitive before. At the same time, such an amount of data requires a distributed thinking approach. In fact, Deep Learning models require plenty of resources, and distributed training is needed. This paper presents a Multicriteria approach for distributed learning. Our approach uses the Weighted Goal Programming approach in its Chebyshev formulation to build an ensemble of decision rules that optimize aprioristically deﬁned performance metrics. Such a formulation is beneﬁcial because it is both model and metric agnostic and provides an interpretable output for the decision-maker. We test our approach by showing a practical application in electricity demand forecasting. Our results suggest that when we allow for dataset split overlapping, the performances of our methodology are consistently above the baseline model trained on the whole dataset.
Improving Experience Replay with Successor Representation

Prioritized experience replay is a reinforcement learning technique whereby agents speed up learning by replaying useful past experiences. This usefulness is quantified as the expected gain from replaying the experience, a quantity often approximated as the prediction error (TD-error). However, recent work in neuroscience suggests that, in biological organisms, replay is prioritized not only by gain, but also by"need"-- a quantity measuring the expected relevance of each experience with respect to the current situation. Importantly, this term is not currently considered in algorithms such as prioritized experience replay. In this paper we present a new approach for prioritizing experiences for replay that considers both gain and need. Our proposed algorithms show a significant increase in performance in benchmarks including the Dyna-Q maze and a selection of Atari games.
Active forces in confluent cell monolayers

We use a computational phase-field model together with analytical analysis to study how inter-cellular active forces can mediate individual cell morphology and collective motion in a confluent cell monolayer. Contractile inter-cellular interactions lead to cell elongation, nematic ordering and active turbulence, characterised by motile topological defects. Extensile interactions result in frustration, and perpendicular cell orientations become more prevalent. Furthermore, we show that contractile behaviour can change to extensile behaviour if anisotropic fluctuations in cell shape are considered.
Real-time Attention Span Tracking in Online Education

Over the last decade, e-learning has revolutionized how students learn by providing them access to quality education whenever and wherever they want. However, students often get distracted because of various reasons, which affect their learning capacity to a great extent. Many researchers have been trying to improve the quality of online education, but we need a holistic approach to address this issue. This paper intends to provide a mechanism that uses the camera feed and microphone input to monitor the real-time attention level of students during online classes. We explore various image processing techniques and machine learning algorithms throughout this study. We propose a system that uses five distinct non-verbal features to calculate the attention score of the student during computer-based tasks and generate real-time feedback for both students and the organization. We can use the generated feedback as a heuristic value to analyze the overall performance of students as well as the teaching standards of the lecturers.
Federated Gaussian Process: Convergence, Automatic Personalization and Multi-fidelity Modeling

In this paper, we propose FGPR: a Federated Gaussian process ( GP) regression framework that uses an averaging strategy for model aggregation and stochastic gradient descent for local computations. Notably, the resulting global model excels in personalization as FGPR jointly learns a shared prior across all devices. The predictive posterior is then obtained by exploiting this shared prior and conditioning on local data, which encodes personalized features from a specific dataset. Theoretically, we show that FGPR converges to a critical point of the full log-marginal likelihood function, subject to statistical errors. This result offers standalone value as it brings federated learning theoretical results to correlated paradigms. Through extensive case studies on several regression tasks, we show that FGPR excels in a wide range of applications and is a promising approach for privacy-preserving multi-fidelity data modeling.
Tapping BERT for Preposition Sense Disambiguation

Prepositions are frequently occurring polysemous words. Disambiguation of prepositions is crucial in tasks like semantic role labelling, question answering, text entailment, and noun compound paraphrasing. In this paper, we propose a novel methodology for preposition sense disambiguation (PSD), which does not use any linguistic tools. In a supervised setting, the machine learning model is presented with sentences wherein prepositions have been annotated with senses. These senses are IDs in what is called The Preposition Project (TPP). We use the hidden layer representations from pre-trained BERT and BERT variants. The latent representations are then classified into the correct sense ID using a Multi Layer Perceptron. The dataset used for this task is from SemEval-2007 Task-6. Our methodology gives an accuracy of 86.85% which is better than the state-of-the-art.
Totally real algebraic integers of arboreal height 2

In arXiv:1302.4423, Salez proved that every totally real algebraic integer is the eigenvalue of some tree. We define the"arboreal height"of a totally real algebraic integer $\lambda$ to be the minimal height of a rooted tree having $\lambda$ as an eigenvalue. In this paper, we prove several results about totally real algebraic integers of arboreal height $2$: We show that all real quadratic integers have arboreal height $\le 2$. We characterize the totally real cubic integers of arboreal height $2$. Finally, we prove that every totally irrational real number field is generated (as a ring over $\mathbb{Q}$) by some $\lambda$ of arboreal height $2$.
ROBIN : A Benchmark for Robustness to Individual Nuisancesin Real-World Out-of-Distribution Shifts

Enhancing the robustness in real-world scenarios has been proven very challenging. One reason is that existing robustness benchmarks are limited, as they either rely on synthetic data or they simply measure robustness as generalization between datasets and hence ignore the effects of individual nuisance factors. In this work, we introduce ROBIN , a benchmark dataset for diagnosing the robustness of vision algorithms to individual nuisances in real-world images. ROBIN builds on 10 rigid categories from the PAS-CAL VOC 2012 and ImageNet datasets, and includes out-of-distribution examples of the object’s 3D pose, shape, texture, context and the weather conditions. ROBIN is richly annotated to enable benchmark models for image classiﬁcation, object detection, and 3D pose estimation. We provide results for a number of popular baselines and make several interesting observations: 1) Some nuisance factors have a much stronger negative effect on the performance compared to others. Moreover, the negative effect of an OOD nuisance depends on the downstream vision task. 2) Current approaches to enhance OOD robustness using strong data augmentation have only marginal effects in real-world OOD scenarios, and sometimes even reduce the OOD performance. 3) We do not observe any signiﬁcant differences between convolutional and transformer architectures in terms of OOD robustness. We believe our dataset provides a rich testbed to study OOD robustness of vision algorithms and will help to signiﬁcantly push forward research in this
Mixing second- and third-order nonlinear interactions in nanophotonic lithium-niobate waveguides

In this paper, we investigate the interplay between the second and third-order nonlinearities in lithium-niobate waveguides with strong waveguide dispersion using uniform and linearly-chirped poling patterns at input powers in the pico-joule range. We implement the accurate unidirectional pulse propagation model to take into account all the possible nonlinear interactions inside these structures. In particular, the poling period is designed to quasi-phase-match single and multiple sum- and diﬀerence-frequency generation processes. We show how the poling period can be used as an additional degree of freedom to transform the output spectra of chip-based nonlinear waveguides in an unprecedented way.
ON THE q -ALGEBRA su q (2) AND ITS CONNECTION WITH THE QUANTUM THEORY OF ANGULAR MOMENTUM: A SURVEY

In the present paper we review the q -analog of the Quantum Theory of Angular Momenta based on the q -algebra su q (2), with an special emphasis on the representation of the Clebsch-Gordan coeﬃcients in terms of q -hypergeometric series. This representation allows us to obtain several known properties of the Clebsch-Gordan coeﬃcients in an uniﬁed and simple way.
An axiomatic derivation of Condorcet-consistent social decision rules

A social decision rule (SDR) is any non empty set-valued map that associates any profile of individual preferences with the set of (winning) alternatives. An SDR is Condorcet-consistent if it selects the set of Condorcet winners whenever this later is non empty. We propose a characterization of Condorcet consistent SDRs with a set of minimal axioms. It appears that all these rules satisfy a weaker Condorcet principle the top consistency which is not explicitly based on majority comparisons while all scoring rules fail to meet it. We also propose an alternative characterization of this class of rules using Maskin monotonicity. Social decision rule Condorcet-consistency Top consistency Maskin monotonicity
Implementing Targeted Good Manufacturing Practices and Sanitation Procedures to Minimize Listeria Contamination of Smoked Seafood Products

SUMMARY The Smoked Seafood Working Group (SSWG), a collaboration of two national industry trade organizations, smoked seafood processors and academia, developed guidelines to minimize Listeria monocytogenes contamination of finished products in smoked seafood operations. The SSWG identified five elements required for a complete Listeria control program: (1) Listeria specific Good Manufacturing Practices (GMPs) and sanitation procedures, (2) employee training, (3) environmental microbiological monitoring and testing, (4) raw material controls, and (5) temperature controls for finished product. This manuscript describes specific GMPs and sanitation procedures to minimize Listeria contamination in smoked seafood operations. Targeted procedures that need to be implemented include GMPs to prevent cross contamination caused by improper design and layout of processing operations, the movement of people and equipment in the plant, and inadequate employee hygiene and food handling practices. In addition, cleaning and sanitation procedures for equipment and the processing plant environment that are designed to target Listeria contamination specifically need to be in place.
Incremental tensor regularized least squares with multiple right-hand sides

Solving linear discrete ill-posed problems for third order tensor equations based on a tensor t-product has attracted much attention. But when the data tensor is produced continuously, current algorithms are not time-saving. Here, we propose an incremental tensor regularized least squares (t-IRLS) algorithm with the t-product that incrementally computes the solution to the tensor regularized least squares (t-RLS) problem with multiple lateral slices on the right-hand side. More specifically, we update its solution by solving a t-RLS problem with a single lateral slice on the right-hand side whenever a new horizontal sample arrives, instead of solving the t-RLS problem from scratch. The t-IRLS algorithm is well suited for large data sets and real time operation. Numerical examples are presented to demonstrate the efficiency of our algorithm.
Heterogeneous Visible-Thermal and Visible-Infrared Face Recognition using Unit-Class Loss and Cross-Modality Discriminator

Visible-to-thermal face image matching is a challenging variate of cross-modality recognition. The challenge lies in the large modality gap and low correlation between visible and thermal modalities. Existing approaches employ image preprocessing, feature extraction, or common subspace projection, which are independent problems in themselves. In this paper, we propose an end-to-end framework for cross-modal face recognition. The proposed algorithm aims to learn identity-discriminative features from unprocessed facial images and identify cross-modal image pairs. A novel Unit-Class Loss is proposed for preserving identity information while discarding modality information. In addition, a Cross-Modality Discriminator block is proposed for integrating image-pair classification capability into the network. The proposed network can be used to extract modality-independent vector representations or a matching-pair classification for test images. Our cross-modality face recognition experiments on five independent databases demonstrate that the proposed method achieves marked improvement over existing state-of-the-art methods.