Miracle-dz/UniFAR-FTUs huggingface dataset:
Innovation-Based Remote State Estimation Secrecy with no Acknowledgments

Secrecy encoding for remote state estimation in the presence of adversarial eavesdroppers is a well studied problem. Typical existing secrecy encoding schemes rely on the transmitter's knowledge of the remote estimator's current performance. This performance measure is often shared via packet receipt acknowledgments. However, in practical situations the acknowledgment channel may be susceptible to interference from an active adversary, resulting in the secrecy encoding scheme failing. Aiming to achieve a reliable state estimate for a legitimate estimator while ensuring secrecy, we propose a secrecy encoding scheme without the need for packet receipt acknowledgments. Our encoding scheme uses a pre-arranged scheduling sequence established at the transmitter and legitimate receiver. We transmit a packet containing either the state measurement or encoded information for the legitimate user. The encoding makes the packet appear to be the state but is designed to damage an eavesdropper's estimate. The pre-arranged scheduling sequence and encoding is chosen psuedo-random. We analyze the performance of our encoding scheme against a class of eavesdropper, and show conditions to force the eavesdropper to have an unbounded estimation performance. Further, we provide a numerical illustration and apply our encoding scheme to an application in power systems.
Lessons learned from the evaluation of Spanish Language Models

Given the impact of language models on the field of Natural Language Processing, a number of Spanish encoder-only masked language models (aka BERTs) have been trained and released. These models were developed either within large projects using very large private corpora or by means of smaller scale academic efforts leveraging freely available data. In this paper we present a comprehensive head-to-head comparison of language models for Spanish with the following results: (i) Previously ignored multilingual models from large companies fare better than monolingual models, substantially changing the evaluation landscape of language models in Spanish; (ii) Results across the monolingual models are not conclusive, with supposedly smaller and inferior models performing competitively. Based on these empirical results, we argue for the need of more research to understand the factors underlying them. In this sense, the effect of corpus size, quality and pre-training techniques need to be further investigated to be able to obtain Spanish monolingual models significantly better than the multilingual ones released by large private companies, specially in the face of rapid ongoing progress in the field. The recent activity in the development of language technology for Spanish is to be welcomed, but our results show that building language models remains an open, resource-heavy problem which requires to marry resources (monetary and/or computational) with the best research expertise and practice.
Assessing the Impact of Sequence Length Learning on Classification Tasks for Transformer Encoder Models

Classification algorithms using Transformer architectures can be affected by the sequence length learning problem whenever observations from different classes have a different length distribution. This problem causes models to use sequence length as a predictive feature instead of relying on important textual information. Although most public datasets are not affected by this problem, privately owned corpora for fields such as medicine and insurance may carry this data bias. The exploitation of this sequence length feature poses challenges throughout the value chain as these machine learning models can be used in critical applications. In this paper, we empirically expose this problem and present approaches to minimize its impacts.
ReCo: Reliable Causal Chain Reasoning via Structural Causal Recurrent Neural Networks

Causal chain reasoning (CCR) is an essential ability for many decision-making AI systems, which requires the model to build reliable causal chains by connecting causal pairs. However, CCR suffers from two main transitive problems: threshold effect and scene drift. In other words, the causal pairs to be spliced may have a conflicting threshold boundary or scenario.To address these issues, we propose a novel Reliable Causal chain reasoning framework (ReCo), which introduces exogenous variables to represent the threshold and scene factors of each causal pair within the causal chain, and estimates the threshold and scene contradictions across exogenous variables via structural causal recurrent neural networks (SRNN). Experiments show that ReCo outperforms a series of strong baselines on both Chinese and English CCR datasets. Moreover, by injecting reliable causal chain knowledge distilled by ReCo, BERT can achieve better performances on four downstream causal-related tasks than BERT models enhanced by other kinds of knowledge.
Evaluation of Synthetic Datasets for Conversational Recommender Systems

For researchers leveraging Large-Language Models (LLMs) in the generation of training datasets, especially for conversational recommender systems - the absence of robust evaluation frameworks has been a long-standing problem. The efficiency brought about by LLMs in the data generation phase is impeded during the process of evaluation of the generated data, since it generally requires human-raters to ensure that the data generated is of high quality and has sufficient diversity. Since the quality of training data is critical for downstream applications, it is important to develop metrics that evaluate the quality holistically and identify biases. In this paper, we present a framework that takes a multi-faceted approach towards evaluating datasets produced by generative models and discuss the advantages and limitations of various evaluation methods.
POTATO: The Portable Text Annotation Tool

We present POTATO, the Portable text annotation tool, a free, fully open-sourced annotation system that 1) supports labeling many types of text and multimodal data; 2) offers easy-to-configure features to maximize the productivity of both deployers and annotators (convenient templates for common ML/NLP tasks, active learning, keypress shortcuts, keyword highlights, tooltips); and 3) supports a high degree of customization (editable UI, inserting pre-screening questions, attention and qualification tests). Experiments over two annotation tasks suggest that POTATO improves labeling speed through its specially-designed productivity features, especially for long documents and complex tasks. POTATO is available at https://github.com/davidjurgens/potato and will continue to be updated.
Coreless vortices as direct signature of chiral 
d
-wave superconductivity

Chiral $d$-wave superconductivity has been proposed in a number of different materials, but characteristic experimental fingerprints have been largely lacking. We show that quadruply quantized coreless vortices are prone to form and offer distinctive signatures of the chiral $d$-wave state in both the local density of states and the total magnetic moment. Their dissimilarity in positive versus negative magnetic fields further leads to additional spontaneous symmetry breaking, producing clear evidence of time-reversal symmetry breaking, chiral superconductivity, and the Chern number.
Fast Rule-Based Decoding: Revisiting Syntactic Rules in Neural Constituency Parsing

Most recent studies on neural constituency parsing focus on encoder structures, while few developments are devoted to decoders. Previous research has demonstrated that proba-bilistic statistical methods based on syntactic rules are particularly effective in constituency parsing, whereas syntactic rules are not used during the training of neural models in prior work probably due to their enormous compu-tation requirements. In this paper, we ﬁrst implement a fast CKY decoding procedure harnessing GPU acceleration, based on which we further derive a syntactic rule-based (rule-constrained) CKY decoding. In the experiments, our method obtains 95.89 and 92.52 F1 on the datasets of PTB and CTB respectively, which shows signiﬁcant improvements compared with previous approaches. Besides, our parser achieves strong and competitive cross-domain performance in zero-shot settings.
Free-form 3D Scene Inpainting with Dual-stream GAN

Nowadays, the need for user editing in a 3D scene has rapidly increased due to the development of AR and VR technology. However, the existing 3D scene completion task (and datasets) cannot suit the need because the missing regions in scenes are generated by the sensor limitation or object occlusion. Thus, we present a novel task named free-form 3D scene inpainting. Unlike scenes in previous 3D completion datasets preserving most of the main structures and hints of detailed shapes around missing regions, the proposed inpainting dataset, FF-Matterport, contains large and diverse missing regions formed by our free-form 3D mask generation algorithm that can mimic human drawing trajectories in 3D space. Moreover, prior 3D completion methods cannot perform well on this challenging yet practical task, simply interpolating nearby geometry and color context. Thus, a tailored dual-stream GAN method is proposed. First, our dual-stream generator, fusing both geometry and color information, produces distinct semantic boundaries and solves the interpolation issue. To further enhance the details, our lightweight dual-stream discriminator regularizes the geometry and color edges of the predicted scenes to be realistic and sharp. We conducted experiments with the proposed FF-Matterport dataset. Qualitative and quantitative results validate the superiority of our approach over existing scene completion methods and the efficacy of all proposed components.
Dataflow Analysis-Inspired Deep Learning for Efficient Vulnerability Detection

Deep learning-based vulnerability detection has shown great performance and, in some studies, outperformed static analysis tools. However, the highest-performing approaches use token-based transformer models, which are not the most efficient to capture code semantics required for vulnerability detection. Classical program analysis techniques such as dataflow analysis can detect many types of bugs based on their root causes. In this paper, we propose to combine such causal-based vulnerability detection algorithms with deep learning, aiming to achieve more efficient and effective vulnerability detection. Specifically, we designed DeepDFA, a dataflow analysis-inspired graph learning framework and an embedding technique that enables graph learning to simulate dataflow computation. We show that DeepDFA is both performant and efficient. DeepDFA outperformed all non-transformer baselines. It was trained in 9 minutes, 75x faster than the highest-performing baseline model. When using only 50+ vulnerable and several hundreds of total examples as training data, the model retained the same performance as 100% of the dataset. DeepDFA also generalized to real-world vulnerabilities in DbgBench; it detected 8.7 out of 17 vulnerabilities on average across folds and was able to distinguish between patched and buggy versions, while the highest-performing baseline models did not detect any vulnerabilities. By combining DeepDFA with a large language model, we surpassed the state-of-the-art vulnerability detection performance on the Big-Vul dataset with 96.46 F1 score, 97.82 precision, and 95.14 recall. Our replication package is located at https://doi.org/10.6084/m9.figshare. 21225413 .
Enhancing Multi-modal Multi-hop Question Answering via Structured Knowledge and Unified Retrieval-Generation

Multi-modal multi-hop question answering involves answering a question by reasoning over multiple input sources from different modalities. Existing methods often retrieve evidences separately and then use a language model to generate an answer based on the retrieved evidences, and thus do not adequately connect candidates and are unable to model the interdependent relations during retrieval. Moreover, the pipelined approaches of retrieval and generation might result in poor generation performance when retrieval performance is low. To address these issues, we propose a Structured Knowledge and Unified Retrieval-Generation (SKURG) approach. SKURG employs an Entity-centered Fusion Encoder to align sources from different modalities using shared entities. It then uses a unified Retrieval-Generation Decoder to integrate intermediate retrieval results for answer generation and also adaptively determine the number of retrieval steps. Extensive experiments on two representative multi-modal multi-hop QA datasets MultimodalQA and WebQA demonstrate that SKURG outperforms the state-of-the-art models in both source retrieval and answer generation performance with fewer parameters1.
Deep Learning Methods for Calibrated Photometric Stereo and Beyond: A Survey

Photometric stereo recovers the surface normals of an object from multiple images with varying shading cues, i.e., modeling the relationship between surface orientation and intensity at each pixel. Photometric stereo prevails in superior per-pixel resolution and fine reconstruction details. However, it is a complicated problem because of the non-linear relationship caused by non-Lambertian surface reflectance. Recently, various deep learning methods have shown a powerful ability in the context of photometric stereo against non-Lambertian surfaces. This paper provides a comprehensive review of existing deep learning-based calibrated photometric stereo methods. We first analyze these methods from different perspectives, including input processing, supervision, and network architecture. We summarize the performance of deep learning photometric stereo models on the most widely-used benchmark data set. This demonstrates the advanced performance of deep learning-based photometric stereo methods. Finally, we give suggestions and propose future research trends based on the limitations of existing models.
Aspects of the dark dimension in cosmology

It was recently understood that if the swampland conjectures are confronted to experiment they naturally point to a solution of the cosmological hierarchy problem in which the smallness of the dark energy is ascribed to an internal (dark) dimension with characteristic length-scale in the micron range. It was later inferred that the universal coupling of the Standard Model fields to the massive spin-2 Kaluza-Klein (KK) excitations of the graviton in the dark dimension leads to a dark matter candidate. Since the partial decay widths of KK gravitons into the visible sector must be relatively small to accommodate experiment, the model is particularly challenging to probe. We show that the model can accommodate neutrino masses associated to right-handed neutrinos propagating in the bulk of the dark dimension with an additional constraint imposed by neutrino oscillation data. After that, we study the impact of the KK tower in cosmology. We show that the modulation of redshifted 21-cm lines driven by ${\rm KK} \to \gamma \gamma$ could be within the reach of next generation experiments (e.g. SKA and FARSIDE). We also show that indirect dark matter searches could uncover the ${\rm KK} \to \gamma \gamma$ signal. These two observations combined have the potential for model identification. Finally, we explore the global structure of the inflationary phase and demonstrate that the model parameters required for a successful uniform inflation driven by a 5-dimensional cosmological constant (corresponding to a flat region of the 5-dimensional potential) are natural.
Semi-Siamese Network for Robust Change Detection Across Different Domains with Applications to 3D Printing

Automatic defect detection for 3D printing processes, which shares many characteristics with change detection problems, is a vital step for quality control of 3D printed products. However, there are some critical challenges in the current state of practice. First, existing methods for computer vision-based process monitoring typically work well only under specific camera viewpoints and lighting situations, requiring expensive pre-processing, alignment, and camera setups. Second, many defect detection techniques are specific to pre-defined defect patterns and/or print schematics. In this work, we approach the defect detection problem using a novel Semi-Siamese deep learning model that directly compares a reference schematic of the desired print and a camera image of the achieved print. The model then solves an image segmentation problem, precisely identifying the locations of defects of different types with respect to the reference schematic. Our model is designed to enable comparison of heterogeneous images from different domains while being robust against perturbations in the imaging setup such as different camera angles and illumination. Crucially, we show that our simple architecture, which is easy to pre-train for enhanced performance on new datasets, outperforms more complex state-of-the-art approaches based on generative adversarial networks and transformers. Using our model, defect localization predictions can be made in less than half a second per layer using a standard MacBook Pro while achieving an F1-score of more than 0.9, demonstrating the efficacy of using our method for in-situ defect detection in 3D printing.
Sensitivity analysis of extreme efficient decision making units in DEA

Abstract Sensitivity analysis of extreme efficient DMUs was studied by Zhu (J. Zhu, EOR, 90 (3) (1996), pp. 451–460) by using the modified CCR (A. Charnes, W. W. Cooper. E. Rhodes, EOR, 2 (1978), pp. 429–444) model. After that a short communication was presented by V. Boljuncic (V. Boljuncic, EOR, 112 (1999), pp. 240–244) and in a few sections it was shown that in certain case the results that were implied by Zhu do not hold. V. Boljuncic preferred some approaches to get rid of these problems. The aim of this paper is to consider a new method based on CCR model and to solve one of the most important of problems which V. Boljuncic has considered.
Influence of Sub-Grid Scale Variability of Clouds on the Solar Radiative Transfer Computations in the ECHAM5 Climate Model

Most numerical climate models use the plane parallel homogeneous (PPH) approximation when computing the interactions of radiation and clouds. The latter actually means that clouds are seen as boxes stretching over the complete vertical extension of a model layer and horizontally cover an area according to their cloud fraction. Ice and liquid water content within the cloud are constant. This is of course in contrast to real clouds revealing structures on scales much smaller than the typical grid size of a climate model. It can be shown that neglecting this sub-grid scale variability leads to a systematic error, such that the reflectivity (transmissivity) of clouds is overestimated (underestimated). This is called PPH-bias. Two different correction approaches are tested in this study: The effective thickness approach (ETA) and the statistical approach of weighted two-stream approximation. In the former, empirical reduction factors χ < 1 are determined such that the effective optical thickness of clouds becomes τeff = χτ , where τ is the mean cloud optical thickness in a single level of a model grid cell. When the sub-grid scale statistics of the distributions of cloud liquid and ice water and therefrom of optical thickness are known, expressed by the probability distribution function p(τ), the refelectivities and transmissivities may be computed by weighting the standard two-stream formulas with p. When this approach is to be applied to multiple layers, one has to account for the correlation of the PDFs in adjacent layers. In order to test the correction approaches, spatially highly resolved data from two large eddy simulations are used: A nocturnal stratocumulus and a shallow trade wind cumulus. By taking the independent column approximation (ICA) as reference and comparing the ICA fluxes to the PPH analogues, the PPH-bias can clearly be identified. It is relatively small for the stratus cloud (∼ 5%), while it is tremendous for the trade wind cumulus (up to 100%). The ETA shows good agreement with the ICA calculation with χ = 0.9 for the stratus cloud and χ = 0.4 for the trade wind cumulus. Clearly, the reduction factor is no unique constant, but rather crucially depends on the cloud type and thus variability. For the statistical approach, Gamma distributions are fitted to the cloud data and the
Motor Skill Retention Impairments in Parkinson's Disease: A Systematic Review with Meta-analysis.

The ability to acquire and retain motor skills is essential for persons with Parkinson's Disease (PD), who usually experience a progressive loss of mobility during the disease. Deficits in the rate of motor skill acquisition have been previously reported in these patients. Whether motor skill retention is also impaired is currently not known. We conducted a review that included 46 studies to determine whether, compared with neurologically intact individuals, motor skill retention is impaired in PD. Meta-analyses revealed that, following a single practice session, persons with PD have deficits in skill retention (SMD = -0.17; 95% CI = -0.32, -0.02; p = 0.0225). However, these deficits are task-specific, affecting sensory motor (SMD = -0.31; 95% CI -0.47, -0.15; p = 0.0002) and visuomotor adaptation (SMD = -1.55; 95% CI = -2.32, -0.79; p = 0.0001) tasks, but not sequential fine motor (SMD = 0.17; 95% CI = -0.05, 0.39; p = 0.1292) and gross motor tasks (SMD = 0.04; 95% CI = -0.25, 0.33; p = 0.7771). Importantly, retention deficits became non-significant when augmented feedback during practice was provided. Similarly, additional sessions of motor practice restored the deficits observed in sensory motor tasks. Meta-regression analyses confirmed that retention deficits were independent of performance during motor skill acquisition, as well as the duration and severity of the disease. These results are in line with prominent neurodegenerative models of PD progression and emphasize the importance of developing targeted interventions to enhance motor memory processes supporting the retention of motor skills in people with PD.
Yoga and mental health: what every psychiatrist needs to know

SUMMARY Yoga was developed primarily as a tool for self-mastery and spiritual progress. However, over the past few decades, the therapeutic applications of yoga in mental healthcare have been explored with promising results. This article aims to inform psychiatrists about the clinical usefulness of yoga for mental disorders. We discuss the rationale and latest evidence base for the use of yoga in psychiatric practice, including the neurobiological mechanisms and indications and contraindications for yoga therapy. We suggest practical yoga techniques that can be used as an add-on for managing common psychiatric conditions. Finally, we discuss the setting up and running of yoga clinical services in a tertiary psychiatric hospital in India and explore what can be learnt to facilitate yoga as a therapeutic approach in the Western world.
Optimal sidelobe suppression filters design with a constraint of maximum loss in process gain

For the problem of loss in process gain due to the mismatched filters, a method for the design of sidelobe suppression filters under a constraint of maximum loss in process gain is proposed. The design of sidelobe suppression filters, considering an available loss in process gain, is converted to a second-order cone programming (SOCP), which can be solved with great efficiency by interior-point methods. The proposed method provides an optimal tradeoff among the sidelobe level, the loss in process gain and the filter length, which has many advantages such as flexible design, high accuracy and quick convergence. The validity of method is demonstrated by the results of the simulate data.