Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust Semantic Segmentation: Strong Adversarial Attacks and Fast Training of Robust Models

While a large amount of work has focused on designing adversarial attacks against image classifiers, only a few methods exist to attack semantic segmentation models. We show that attacking segmentation models presents task-specific challenges, for which we propose novel solutions. Our final evaluation protocol outperforms existing methods, and shows that those can overestimate the robustness of the models. Additionally, so far adversarial training, the most successful way for obtaining robust image classifiers, could not be successfully applied to semantic segmentation. We argue that this is because the task to be learned is more challenging, and requires significantly higher computational effort than for image classification. As a remedy, we show that by taking advantage of recent advances in robust ImageNet classifiers, one can train adversarially robust segmentation models at limited computational cost by fine-tuning robust backbones.
Multilingual Neural Machine Translation System for Indic to Indic Languages

This paper gives an Indic-to-Indic (IL-IL) MNMT baseline model for 11 ILs implemented on the Samanantar corpus and analyzed on the Flores-200 corpus. All the models are evaluated using the BLEU score. In addition, the languages are classified under three groups namely East Indo- Aryan (EI), Dravidian (DR), and West Indo-Aryan (WI). The effect of language relatedness on MNMT model efficiency is studied. Owing to the presence of large corpora from English (EN) to ILs, MNMT IL-IL models using EN as a pivot are also built and examined. To achieve this, English- Indic (EN-IL) models are also developed, with and without the usage of related languages. Results reveal that using related languages is beneficial for the WI group only, while it is detrimental for the EI group and shows an inconclusive effect on the DR group, but it is useful for EN-IL models. Thus, related language groups are used to develop pivot MNMT models. Furthermore, the IL corpora are transliterated from the corresponding scripts to a modified ITRANS script, and the best MNMT models from the previous approaches are built on the transliterated corpus. It is observed that the usage of pivot models greatly improves MNMT baselines with AS-TA achieving the minimum BLEU score and PA-HI achieving the maximum score. Among languages, AS, ML, and TA achieve the lowest BLEU score, whereas HI, PA, and GU perform the best. Transliteration also helps the models with few exceptions. The best increment of scores is observed in ML, TA, and BN and the worst average increment is observed in KN, HI, and PA, across all languages. The best model obtained is the PA-HI language pair trained on PAWI transliterated corpus which gives 24.29 BLEU.
Matroid Lifts and Representability

A 1965 result of Crapo shows that every elementary lift of a matroid $M$ can be constructed from a linear class of circuits of $M$. In a recent paper, Walsh generalized this construction by defining a rank-$k$ lift of a matroid $M$ given a rank-$k$ matroid $N$ on the set of circuits of $M$, and conjectured that all matroid lifts can be obtained in this way. In this sequel paper we simplify Walsh's construction and show that this conjecture is true for representable matroids but is false in general. This gives a new way to certify that a particular matroid is non-representable, which we use to construct new classes of non-representable matroids. Walsh also applied the new matroid lift construction to gain graphs over the additive group of a non-prime finite field, generalizing a construction of Zaslavsky for these special groups. He conjectured that this construction is possible on three or more vertices only for the additive group of a non-prime finite field. We show that this conjecture holds for four or more vertices, but fails for exactly three.
Varstrometry for Off-nucleus and Dual Subkiloparsec AGN (VODKA). SDSS J1608+2716: A Subarcsecond Quadruply Lensed Quasar at z = 2.575

We report Hubble Space Telescope (HST) Wide Field Camera 3 deep IR (F160W) imaging of SDSS J1608+2716. This system, located at a redshift of z = 2.575, was recently reported as a triple-quasar candidate with subarcsecond separations (∼0.″25) based on selection from Gaia astrometry and follow-up Keck adaptive optics–assisted integral field unit spectroscopy. Our new HST deep IR imaging reveals the presence of a fourth point-like component located ∼0. ″9 away from the triple system. Additionally, we detect an edge-on disk galaxy located in between the four point sources. The entire system exhibits a characteristic cusp structure in the context of strong gravitational lensing, and the observed image configuration can be successfully reproduced using a lens model based on a singular isothermal ellipsoid mass profile. These findings indicate that this system is a quadruply lensed quasar. Our results highlight the challenges associated with identifying dual/multiple quasars on ∼kiloparsec scales at high redshifts and emphasize the crucial role of deep, high-resolution IR imaging in robustly confirming such systems.
MPSTAN: Metapopulation-based Spatio-Temporal Attention Network for Epidemic Forecasting

Accurate epidemic forecasting plays a vital role for governments to develop effective prevention measures for suppressing epidemics. Most of the present spatio–temporal models cannot provide a general framework for stable and accurate forecasting of epidemics with diverse evolutionary trends. Incorporating epidemiological domain knowledge ranging from single-patch to multi-patch into neural networks is expected to improve forecasting accuracy. However, relying solely on single-patch knowledge neglects inter-patch interactions, while constructing multi-patch knowledge is challenging without population mobility data. To address the aforementioned problems, we propose a novel hybrid model called metapopulation-based spatio–temporal attention network (MPSTAN). This model aims to improve the accuracy of epidemic forecasting by incorporating multi-patch epidemiological knowledge into a spatio–temporal model and adaptively defining inter-patch interactions. Moreover, we incorporate inter-patch epidemiological knowledge into both model construction and the loss function to help the model learn epidemic transmission dynamics. Extensive experiments conducted on two representative datasets with different epidemiological evolution trends demonstrate that our proposed model outperforms the baselines and provides more accurate and stable short- and long-term forecasting. We confirm the effectiveness of domain knowledge in the learning model and investigate the impact of different ways of integrating domain knowledge on forecasting. We observe that using domain knowledge in both model construction and the loss function leads to more efficient forecasting, and selecting appropriate domain knowledge can improve accuracy further.
Quantum Pufferfish Privacy: A Flexible Privacy Framework for Quantum Systems

We propose a versatile privacy framework for quantum systems, termed quantum pufferfish privacy (QPP). Inspired by classical pufferfish privacy, our formulation generalizes and addresses limitations of quantum differential privacy by offering flexibility in specifying private information, feasible measurements, and domain knowledge. We show that QPP can be equivalently formulated in terms of the Datta-Leditzky information spectrum divergence, thus providing the first operational interpretation thereof. We reformulate this divergence as a semi-definite program and derive several properties of it, which are then used to prove convexity, composability, and post-processing of QPP mechanisms. Parameters that guarantee QPP of the depolarization mechanism are also derived. We analyze the privacy-utility tradeoff of general QPP mechanisms and, again, study the depolarization mechanism as an explicit instance. The QPP framework is then applied to privacy auditing for identifying privacy violations via a hypothesis testing pipeline that leverages quantum algorithms. Connections to quantum fairness and other quantum divergences are also explored and several variants of QPP are examined.
Exploring the Role of Audio in Video Captioning

Recent focus in video captioning has been on designing architectures that can consume both video and text modalities, and using large-scale video datasets with text transcripts for pre-training, such as HowTo100M. Though these approaches have achieved significant improvement, the audio modality is often ignored in video captioning. In this work, we present an audio-visual framework, which aims to fully exploit the potential of the audio modality for captioning. Instead of relying on text transcripts extracted via automatic speech recognition (ASR), we argue that learning with raw audio signals can be more beneficial, as audio has additional information including acoustic events, speaker identity, etc. Our contributions are twofold. First, we observed that the model overspecializes to the audio modality when pre-training with both video and audio modality, since the ground truth (i.e., text transcripts) can be solely predicted using audio. We proposed a Modality Balanced Pre-training (MBP) loss to mitigate this issue and significantly improve the performance on downstream tasks. Second, we slice and dice different design choices of the cross-modal module, which may become an information bottleneck and generate inferior results. We proposed new local-global fusion mechanisms to improve information exchange across audio and video. We demonstrate significant improvements by leveraging the audio modality on four datasets, and even outperform the state of the art on some metrics without relying on the text modality as the input.
FLAG: Finding Line Anomalies (in code) with Generative AI

Code contains security and functional bugs. The process of identifying and localizing them is difficult and relies on human labor. In this work, we present a novel approach (FLAG) to assist human debuggers. FLAG is based on the lexical capabilities of generative AI, specifically, Large Language Models (LLMs). Here, we input a code file then extract and regenerate each line within that file for self-comparison. By comparing the original code with an LLM-generated alternative, we can flag notable differences as anomalies for further inspection, with features such as distance from comments and LLM confidence also aiding this classification. This reduces the inspection search space for the designer. Unlike other automated approaches in this area, FLAG is language-agnostic, can work on incomplete (and even non-compiling) code and requires no creation of security properties, functional tests or definition of rules. In this work, we explore the features that help LLMs in this classification and evaluate the performance of FLAG on known bugs. We use 121 benchmarks across C, Python and Verilog; with each benchmark containing a known security or functional weakness. We conduct the experiments using two state of the art LLMs in OpenAI's code-davinci-002 and gpt-3.5-turbo, but our approach may be used by other models. FLAG can identify 101 of the defects and helps reduce the search space to 12-17% of source code.
Towards Regulatable AI Systems: Technical Gaps and Policy Opportunities

There is increasing attention being given to how to regulate AI systems. As governing bodies grapple with what values to encapsulate into regulation, we consider the technical half of the question: To what extent can AI experts vet an AI system for adherence to regulatory requirements? We investigate this question through the lens of two public sector procurement checklists, identifying what we can do now, what should be possible with technical innovation, and what requirements need a more interdisciplinary approach.
Adaptive Bernstein Change Detector for High-Dimensional Data Streams

Change detection is of fundamental importance when analyzing data streams. Detecting changes both quickly and accurately enables monitoring and prediction systems to react, e.g., by issuing an alarm or by updating a learning algorithm. However, detecting changes is challenging when observations are high-dimensional. In high-dimensional data, change detectors should not only be able to identify when changes happen, but also in which subspace they occur. Ideally, one should also quantify how severe they are. Our approach, ABCD, has these properties. ABCD learns an encoder-decoder model and monitors its accuracy over a window of adaptive size. ABCD derives a change score based on Bernstein’s inequality to detect deviations in terms of accuracy, which indicate changes. Our experiments demonstrate that ABCD outperforms its best competitor by up to 20% in F1-score on average. It can also accurately estimate changes’ subspace, together with a severity measure that correlates with the ground truth.
Auto-BI: Automatically Build BI-Models Leveraging Local Join Prediction and Global Schema Graph

Business Intelligence (BI) is crucial in modern enterprises and billion-dollar business. Traditionally, technical experts like database administrators would manually prepare BI-models (e.g., in star or snowflake schemas) that join tables in data warehouses, before less-technical business users can run analytics using end-user dashboarding tools. However, the popularity of self-service BI (e.g., Tableau and Power-BI) in recent years creates a strong demand for less technical end-users to build BI-models themselves. We develop an Auto-BI system that can accurately predict BI models given a set of input tables, using a principled graph-based optimization problem we propose called k-Min-Cost-Arborescence (k-MCA), which holistically considers both local join prediction and global schema-graph structures, leveraging a graph-theoretical structure called arborescence. While we prove k-MCA is intractable and inapproximate in general, we develop novel algorithms that can solve k-MCA optimally, which is shown to be efficient in practice with sub-second latency and can scale to the largest BI-models we encounter (with close to 100 tables). Auto-BI is rigorously evaluated on a unique dataset with over 100K real BI models we harvested, as well as on 4 popular TPC benchmarks. It is shown to be both efficient and accurate, achieving over 0.9 F1-score on both real and synthetic benchmarks.
Strain-switchable field-induced superconductivity

Field-induced superconductivity is a rare phenomenon where an applied magnetic field enhances or induces superconductivity. Here, we use applied stress as a control switch between a field-tunable superconducting state and a robust non–field-tunable state. This marks the first demonstration of a strain-tunable superconducting spin valve with infinite magnetoresistance. We combine tunable uniaxial stress and applied magnetic field on the ferromagnetic superconductor Eu(Fe0.88Co0.12)2As2 to shift the field-induced zero-resistance temperature between 4 K and a record-high value of 10 K. We use x-ray diffraction and spectroscopy measurements under stress and field to reveal that strain tuning of the nematic order and field tuning of the ferromagnetism act as independent control parameters of the superconductivity. Combining comprehensive measurements with DFT calculations, we propose that field-induced superconductivity arises from a novel mechanism, namely, the uniquely dominant effect of the Eu dipolar field when the exchange field splitting is nearly zero.
Morphological Inflection with Phonological Features

Recent years have brought great advances into solving morphological tasks, mostly due to powerful neural models applied to various tasks as (re)inflection and analysis. Yet, such morphological tasks cannot be considered solved, especially when little training data is available or when generalizing to previously unseen lemmas. This work explores effects on performance obtained through various ways in which morphological models get access to sub-character phonological features that are often the targets of morphological processes. We design two methods to achieve this goal: one that leaves models as is but manipulates the data to include features instead of characters, and another that manipulates models to take phonological features into account when building representations for phonemes. We elicit phonemic data from standard graphemic data using language-specific grammars for languages with shallow grapheme-to-phoneme mapping, and we experiment with two reinflection models over eight languages. Our results show that our methods yield comparable results to the grapheme-based baseline overall, with minor improvements in some of the languages. All in all, we conclude that patterns in character distributions are likely to allow models to infer the underlying phonological characteristics, even when phonemes are not explicitly represented.
PLS FOR PREDICTION OF THERMAL STABILITY OF PROTEIN MUTANTS

Prediction of the stability of proteins is a critical step in the goal of protein design. The Partial Least Squares (PLS) regression method was used to predict the thermal stability of protein variants produced by single point mutations. The efficacy of this method was tested by using data on changes in free energy of unfolding (ddG) of mutations of phage T4 lysozyme. These data were obtained from the ProTherm database. PLS regression models were built by using physico-chemical descriptors. Structural information can also be incorporated into these models, if it is available. The models obtained in this manner were able to fit the experimental data on free energy of unfolding with an rms error of 0.42 kcal/mol and a two state classification accuracy of 89.9%. The magnitude of the rms error for these models is comparable to the magnitude of the expected errors in the experimental data. However, it was observed that less complex models, obtained from PLS models of lower rank, are more robust. The predictive rms error for these robust models were 1.2 kcal/mol (without structural information) and 1.1 kcal/mol (with structural information).
Lactiplantibacillus paraplantarum BPF2 and Pediococcus acidilactici ST6, Two Bacteriocinogenic Isolated Strains from Andalusian Spontaneous Fermented Sausages

Traditional spontaneously fermented foods are well known for their sensory and safety properties, which is mainly due to their indigenous microflora. Within this group of food, Mediterranean dry-cured sausages stand out as a significant source of lactic-acid bacterial strains (LAB) with biotechnological properties, such as their antimicrobial activity. The aim of this study was to investigate the biodiversity of antagonistic LAB strains from different Andalusian traditional sausages, such as salchichón and chorizo. First, a screening was carried out focusing on the antimicrobial activity against foodborne pathogens, such as Listeria monocytogenes, Escherichia coli, Clostridium perfringens, and Staphylococcus aureus, selecting two strains due to their higher antibiosis properties, both in agar and liquid media. These bacteria were identified as Lactiplantibacillus paraplantarum BPF2 and Pediococcus acidilactici ST6. In addition, genomic studies confirmed the presence of certain structural genes related to the production of bacteriocins. Finally, the culture supernatants of both strains were purified and analyzed by LC-MS/MS, obtaining the relative molecular mass and the amino acid sequence and identifying the peptides as the bacteriocins Pediocin-PA and Leucocin K. In conclusion, genomes and antimicrobial substances of P. acidilactici ST6, a Pediocin-PA producer, and Lpb. paraplantarum BPF2, a Leucocin K producer, isolated from Andalusian salchichón and chorizo, respectively, are presented in this work. Although further studies are required, these strains could be used alone or in combination as starters or protective cultures for the food industry.
Effects of Trophic Acclimation on Growth and Expression Profiles of Genes Encoding Enzymes of Primary Metabolism and Plastid Transporters of Chlamydomonas reinhardtii

In this paper, the effect of prolonged trophic acclimation on the subsequent growth of Chlamydomonas reinhardtii batch cultures was studied. The mixotrophic (light + acetate) acclimation stimulated subsequent growth at both mixotrophy and autotrophy conditions and altered the expression profile of genes encoding enzymes of primary metabolism and plastid transporters. Besides the trophic effect, the influence of Chlamydomonas culture growth stage on gene expression was determined. Under mixotrophic conditions, this effect was most pronounced in the first half of the exponential growth with partial retention of the previous acclimation period traits. The autotrophy acclimation effect was more complex and its significance was enhanced at the end of the growth and in the stationary phase.
Dynamical Simulation, Sensitivity, and Productivity Analysis of a Light-Photoacclimation Model for Microalgae-Based Carbohydrate Production in Continuous Photobioreactors

The world’s human population is increasing as is the demand for new sustainable sources of energy. Accordingly, microalgae-based carbohydrates for biofuel production are being considered as an alternative source of raw materials for producing biofuels. Microalgae grow in photobioreactors under constantly changing conditions. Models improve our understanding of microalgae growth. In this paper, a photoacclimated model for continuous microalgae cultures in photobioreactors was used to study the time-varying behavior and sensitivity of solutions under optimal productivity conditions. From the perspective of dynamic simulation in this work, light intensity was found to play an influential role in modifying metabolic pathways as a cell stressor. Enhancing carbohydrate productivity by combining nutritional deficiency and light intensity regulation modeling strategies could be helpful to optimize the process for the highest yield in large-scale cultivation systems. Under the proposed simulation conditions, a maximum carbohydrate productivity of 48.11 gCm−3d−1 was achieved using an optimal dilution rate of 0.2625 d−1 and 350 μmolm−2s−1 of light intensity. However, it is important to note that, a particular set of manipulated inputs can generate multiple outputs at a steady state. A numerical solution of the sensitivity functions indicated that the model outputs were especially sensitive to changes in parameters corresponding to a minimum nitrogen quota, maximum nitrogen intake rate, dilution rate, and maximum nitrogen quota compared to to other model parameters.