Miracle-dz/UniFAR-FTUs huggingface dataset:
Impact of Pesticides on Amphibians: A Review

Amphibians (cold-blooded vertebrates) show more effect to pesticides than any other vertebrate group from terrestrial as well as aquatic environment. Risk for pesticide exposure increases due to permeable skin for water and ions. Contamination may cause alteration in their behavior. The goal of this review is to assess the risk of pesticides exposure to the juveniles and adult frog. The main emphasis is on dermal exposure of pesticides by directly spray over them or by indirectly spray in aquatic and terrestrial habitat. The reason for the sensitivity to climatic change is that they spend most of their life in aquatic environment and spend minimum life in terrestrial environment. Almost 600-amphibian population is reported in Western Europe and 53% of them is declined in the beginning of 1950s. Chronic effect of low dose on particular pesticides may lethal as compared to the acute exposure. Chemicals which are used to make pesticides are very dangerous for growth and reproduction and cause mutation in developing tadpole. Oral exposure of DDT and Malathion may lead to the immunosuppressive effect. Several pesticides show influence on meta morphological stages and prevent larval development, which reduces the rate of growth in amphibian and decline their population. Further studies require to examine high and low level of pesticide. Further investigation is needed to reduce the population decline of amphibian globally.
Single-cell RNA sequencing unveils unique transcriptomic signatures of endothelial cells and role of ENO1 in response to disturbed flow

Significance Fluid shear stress mediates vascular homeostasis by regulating gene expression in endothelial cells (ECs). Using single-cell RNA sequencing, this study provides a comprehensive perspective of transcriptomic profiles in ECs responding to both disturbed shear (DS) and pulsatile shear (PS) in the same step flow chamber. Analyses of differentially expressed genes, biological pathways, and pseudotime trajectory revealed that DS caused EC transition from atheroprotective to atherogenic phenotypes. Specifically, DS exhibited unique transcriptomic signatures in inflammation, hypoxic responses, transforming growth factor-beta signaling, and metabolic pathways (glycolysis and lipid synthesis) to promote endothelial-to-mesenchymal transition (EndMT). Enolase 1 (ENO1) was identified to mediate DS-induced EndMT to contribute to atherosclerotic phenotype and serves as a potential therapeutic target for the prevention and treatment of vascular diseases.
Scientific Report

The base excision repair (BER) process removes base damage such as oxidation, alkylation or abasic sites. Two BER sub-pathways have been characterized using in vitro methods, and have been classified according to the length of the repair patch as either 'short-patch' BER (one nucleotide) or 'long-patch' BER (LP-BER; more than one nucleotide). To investigate the occurrence of LP-BER in vivo, we developed an assay using a plasmid containing a single modified base in the transcribed strand of the enhanced green fluorescent protein (EGFP) gene and a stop codon, based on a single-nucleotide mismatch, at varying distances on the 3′ side of the lesion. The reversion of the stop codon occurs after DNA repair synthesis and restores EGFP expression after transfection of mismatch-repair-deficient cells. Repair patches longer than one nucleotide were observed for 55–80% or 80–100% of the plasmids with a mean length of 2–6 or 6–12 nucleotides for 8-oxo-7,8-dihydroguanine or a synthetic abasic site, respectively. These data show the existence of LP-BER in vivo, and emphasize the effect of the type of BER substrate lesion on both the yield and the extent of the LP-BER sub-pathway.
Procrustes problem for the inverse eigenvalue problem of normal (skew) $J$-Hamiltonian matrices and normal $J$-symplectic matrices

A square complex matrix $A$ is called (skew) $J$-Hamiltonian if $AJ$ is (skew) hermitian where $J$ is a real normal matrix such that $J^2=-I$, where $I$ is the identity matrix. In this paper, we solve the Procrustes problem to find normal (skew) $J$-Hamiltonian solutions for the inverse eigenvalue problem. In addition, a similar problem is investigated for normal $J$-symplectic matrices.
Boundary and Relation Distillation for Semantic Segmentation

Recently, it has been revealed that small semantic segmentation (SS) models exhibit a tendency to make errors in maintaining boundary region completeness and preserving target region connectivity, despite their effective segmentation of the main object regions. To address these errors, we propose a targeted boundary and relation distillation (BRD) strategy using knowledge distillation from large teacher models to small student models. Specifically, the boundary distillation extracts explicit object boundaries from the hierarchical feature maps of the backbone network, subsequently enhancing the student model's mask quality in boundary regions. Concurrently, the relation distillation transfers implicit relations from the teacher model to the student model using pixel-level self-relation as a bridge, ensuring that the student's mask has strong target region connectivity. The proposed BRD is designed concretely for SS and is characterized by simplicity and efficiency. Through experimental evaluations on multiple SS datasets, including Pascal VOC 2012, Cityscapes, ADE20K, and COCO-Stuff 10K, we demonstrated that BRD significantly surpasses the current methods without increasing the inference costs, generating crisp region boundaries and smooth connecting regions that are challenging for small models.
Finite-Precision Arithmetic Transceiver for Massive MIMO Systems

Efficient implementation of massive multiple-input-multiple-output (MIMO) transceivers is essential for the next-generation wireless networks. To reduce the high computational complexity of the massive MIMO transceiver, in this paper, we propose a new massive MIMO architecture using finite-precision arithmetic. First, we conduct the rounding error analysis and derive the lower bound of the achievable rate for single-input-multiple-output (SIMO) using maximal ratio combining (MRC) and multiple-input-single-output (MISO) systems using maximal ratio transmission (MRT) with finite-precision arithmetic. Then, considering the multi-user scenario, the rounding error analysis of zero-forcing (ZF) detection and precoding is derived by using the normal equations (NE) method. The corresponding lower bounds of the achievable sum rate are also derived and asymptotic analyses are presented. Built upon insights from these analyses and lower bounds, we propose a mixed-precision architecture for massive MIMO systems to offset performance gaps due to finite-precision arithmetic. The corresponding analysis of rounding errors and computational costs is obtained. Simulation results validate the derived bounds and underscore the superiority of the proposed mixed-precision architecture to the conventional structure.
Digital Divides in Scene Recognition: Uncovering Socioeconomic Biases in Deep Learning Systems

Computer-based scene understanding has influenced fields ranging from urban planning to autonomous vehicle performance, yet little is known about how well these technologies work across social differences. We investigate the biases of deep convolutional neural networks (dCNNs) in scene classification, using nearly one million images from global and US sources, including user-submitted home photographs and Airbnb listings. We applied statistical models to quantify the impact of socioeconomic indicators such as family income, Human Development Index (HDI), and demographic factors from public data sources (CIA and US Census) on dCNN performance. Our analyses revealed significant socioeconomic bias, where pretrained dCNNs demonstrated lower classification accuracy, lower classification confidence, and a higher tendency to assign labels that could be offensive when applied to homes (e.g.,"ruin","slum"), especially in images from homes with lower socioeconomic status (SES). This trend is consistent across two datasets of international images and within the diverse economic and racial landscapes of the United States. This research contributes to understanding biases in computer vision, emphasizing the need for more inclusive and representative training datasets. By mitigating the bias in the computer vision pipelines, we can ensure fairer and more equitable outcomes for applied computer vision, including home valuation and smart home security systems. There is urgency in addressing these biases, which can significantly impact critical decisions in urban development and resource allocation. Our findings also motivate the development of AI systems that better understand and serve diverse communities, moving towards technology that equitably benefits all sectors of society.
Magnetic activity of red giants: Correlation between the amplitude of solar-like oscillations and chromospheric indicators

Previous studies have found that red giants (RGs) in close binary systems undergoing spin-orbit resonance exhibit an enhanced level of magnetic activity with respect to single RGs rotating at the same rate, from measurements of photometric variability, $S' ph $, and the chromospheric emission S-index $S caii $. Here, we consider a sample of 4465 RGs observed by the NASA Kepler mission for which previous studies have measured $S' ph $ and $S caii in order to measure additional activity indicators that probe different heights in the chromosphere: the near-ultraviolet (NUV) excess from NASA GALEX photometric data, and chromospheric indices based on the depth of Halpha , Mg\ i and infared Ca\ ii absorption lines from LAMOST spectroscopic data. Firstly, as for we observe that RGs belonging to close binaries in a state of spin-orbit resonance display larger chromospheric emission than the cohort of RGs, as is illustrated by an NUV excess and shallower and infrared lines. We report no excess of emission. This result reinforces previous claims that tidal locking leads to enhanced magnetic fields, and allows us to provide criteria to classify active RGs -- single or binary -- based on their rotation periods and magnetic activity indices. Secondly, we strikingly observe that the depths of the and lines are anticorrelated and correlated, respectively, with the amplitude of solar-like oscillations for a given surface gravity, $ g$, regardless of the presence of photometric rotational modulation. Such a correlation opens up future possibilities of estimating the value of magnetic fields at the surface of RG stars, whether quiet or active, by combining spectroscopic and asteroseismic measurements with three-dimensional atmospheric models that include radiative transfer.
Beyond Accuracy-Fairness: Stop evaluating bias mitigation methods solely on between-group metrics

Artificial Intelligence (AI) finds widespread applications across various domains, sparking concerns about fairness in its deployment. While fairness in AI remains a central concern, the prevailing discourse often emphasizes outcome-based metrics without a nuanced consideration of the differential impacts within subgroups. Bias mitigation techniques do not only affect the ranking of pairs of instances across sensitive groups, but often also significantly affect the ranking of instances within these groups. Such changes are hard to explain and raise concerns regarding the validity of the intervention. Unfortunately, these effects largely remain under the radar in the accuracy-fairness evaluation framework that is usually applied. This paper challenges the prevailing metrics for assessing bias mitigation techniques, arguing that they do not take into account the changes within-groups and that the resulting prediction labels fall short of reflecting real-world scenarios. We propose a paradigm shift: initially, we should focus on generating the most precise ranking for each subgroup. Following this, individuals should be chosen from these rankings to meet both fairness standards and practical considerations.
Evaluation of depth perception in crowded volumes

Depth perception in volumetric visualization plays a crucial role in the understanding and interpretation of volumetric data. Numerous visualization techniques, many of which rely on physically based optical effects, promise to improve depth perception but often do so without considering camera movement or the content of the volume. As a result, the findings from previous studies may not be directly applicable to crowded volumes, where a large number of contained structures disrupts spatial perception. Crowded volumes therefore require special analysis and visualization tools with sparsification capabilities. Interactivity is an integral part of visualizing and exploring crowded spaces, but has received little attention in previous studies. To address this gap, we conducted a study to assess the impact of different rendering techniques on depth perception in crowded volumes, with a particular focus on the effects of camera movement. The results show that depth perception considering camera motion depends much more on the content of the volume than on the chosen visualization technique. Furthermore, we found that traditional rendering techniques, which have often performed poorly in previous studies, showed comparable performance to physically based methods in our study.
Can overfitted deep neural networks in adversarial training generalize? - An approximation viewpoint

Adversarial training is a widely used method to improve the robustness of deep neural networks (DNNs) over adversarial perturbations. However, it is empirically observed that adversarial training on over-parameterized networks often suffers from the \textit{robust overfitting}: it can achieve almost zero adversarial training error while the robust generalization performance is not promising. In this paper, we provide a theoretical understanding of the question of whether overfitted DNNs in adversarial training can generalize from an approximation viewpoint. Specifically, our main results are summarized into three folds: i) For classification, we prove by construction the existence of infinitely many adversarial training classifiers on over-parameterized DNNs that obtain arbitrarily small adversarial training error (overfitting), whereas achieving good robust generalization error under certain conditions concerning the data quality, well separated, and perturbation level. ii) Linear over-parameterization (meaning that the number of parameters is only slightly larger than the sample size) is enough to ensure such existence if the target function is smooth enough. iii) For regression, our results demonstrate that there also exist infinitely many overfitted DNNs with linear over-parameterization in adversarial training that can achieve almost optimal rates of convergence for the standard generalization error. Overall, our analysis points out that robust overfitting can be avoided but the required model capacity will depend on the smoothness of the target function, while a robust generalization gap is inevitable. We hope our analysis will give a better understanding of the mathematical foundations of robustness in DNNs from an approximation view.
How to Forget Clients in Federated Online Learning to Rank?

Data protection legislation like the European Union's General Data Protection Regulation (GDPR) establishes the \textit{right to be forgotten}: a user (client) can request contributions made using their data to be removed from learned models. In this paper, we study how to remove the contributions made by a client participating in a Federated Online Learning to Rank (FOLTR) system. In a FOLTR system, a ranker is learned by aggregating local updates to the global ranking model. Local updates are learned in an online manner at a client-level using queries and implicit interactions that have occurred within that specific client. By doing so, each client's local data is not shared with other clients or with a centralised search service, while at the same time clients can benefit from an effective global ranking model learned from contributions of each client in the federation. In this paper, we study an effective and efficient unlearning method that can remove a client's contribution without compromising the overall ranker effectiveness and without needing to retrain the global ranker from scratch. A key challenge is how to measure whether the model has unlearned the contributions from the client $c^*$ that has requested removal. For this, we instruct $c^*$ to perform a poisoning attack (add noise to this client updates) and then we measure whether the impact of the attack is lessened when the unlearning process has taken place. Through experiments on four datasets, we demonstrate the effectiveness and efficiency of the unlearning strategy under different combinations of parameter settings.
Faster Combinatorial k-Clique Algorithms

Detecting if a graph contains a $k$-Clique is one of the most fundamental problems in computer science. The asymptotically fastest algorithm runs in time $O(n^{\omega k/3})$, where $\omega$ is the exponent of Boolean matrix multiplication. To date, this is the only technique capable of beating the trivial $O(n^k)$ bound by a polynomial factor. Due to this technique's various limitations, much effort has gone into designing"combinatorial"algorithms that improve over exhaustive search via other techniques. The first contribution of this work is a faster combinatorial algorithm for $k$-Clique, improving Vassilevska's bound of $O(n^{k}/\log^{k-1}{n})$ by two log factors. Technically, our main result is a new reduction from $k$-Clique to Triangle detection that exploits the same divide-and-conquer at the core of recent combinatorial algorithms by Chan (SODA'15) and Yu (ICALP'15). Our second contribution is exploiting combinatorial techniques to improve the state-of-the-art (even of non-combinatorial algorithms) for generalizations of the $k$-Clique problem. In particular, we give the first $o(n^k)$ algorithm for $k$-clique in hypergraphs and an $O(n^3/\log^{2.25}{n} + t)$ algorithm for listing $t$ triangles in a graph.
Automating Unrealizability Logic: Hoare-style Proof Synthesis for Infinite Sets of Programs

Unrealizability logic (UL) was proposed by Kim et al. as the first Hoare-style proof system for proving properties that hold for an infinite set of programs (defined by a regular tree grammar). The goal of our work is to automate reasoning and proof generation for UL. A key ingredient in UL is the notion of nonterminal summaries-inductive facts that characterize recursive nonterminals in the grammar that defines the set of programs. They are analogous to procedure summaries in Hoare logic. The goal of automating UL led us to reformulate the inference rules-in particular, introducing a unified rule for nonterminal summaries, called the rule of adaptation, which draws inspiration from how procedure summaries are handled in Hoare logic. In the same way that verification conditions can be used to synthesize loop invariants for Hoare logic proofs, our reformulation of UL reduces the problem of synthesizing a nonterminal summary to a Syntax-Guided Synthesis problem. We implement Wuldo, the first checker and synthesizer for UL. Wuldo can express proofs beyond the reach of existing tools, including proofs that establish how infinitely many programs behave on infinitely many inputs, and in some cases Wuldo can even synthesize the needed nonterminal summaries.
Can GPT-3.5 Generate and Code Discharge Summaries?

Objective: To investigate GPT-3.5 in generating and coding medical documents with ICD-10 codes for data augmentation on low-resources labels. Materials and Methods: Employing GPT-3.5 we generated and coded 9,606 discharge summaries based on lists of ICD-10 code descriptions of patients with infrequent (generation) codes within the MIMIC-IV dataset. Combined with the baseline training set, this formed an augmented training set. Neural coding models were trained on baseline and augmented data and evaluated on a MIMIC-IV test set. We report micro- and macro-F1 scores on the full codeset, generation codes, and their families. Weak Hierarchical Confusion Matrices were employed to determine within-family and outside-of-family coding errors in the latter codesets. The coding performance of GPT-3.5 was evaluated both on prompt-guided self-generated data and real MIMIC-IV data. Clinical professionals evaluated the clinical acceptability of the generated documents. Results: Augmentation slightly hinders the overall performance of the models but improves performance for the generation candidate codes and their families, including one unseen in the baseline training data. Augmented models display lower out-of-family error rates. GPT-3.5 can identify ICD-10 codes by the prompted descriptions, but performs poorly on real data. Evaluators note the correctness of generated concepts while suffering in variety, supporting information, and narrative. Discussion and Conclusion: GPT-3.5 alone is unsuitable for ICD-10 coding. Augmentation positively affects generation code families but mainly benefits codes with existing examples. Augmentation reduces out-of-family errors. Discharge summaries generated by GPT-3.5 state prompted concepts correctly but lack variety, and authenticity in narratives. They are unsuitable for clinical practice.
Dynamic Epistemic Logic of Resource Bounded Information Mining Agents

Logics for resource-bounded agents have been getting more and more attention in recent years since they provide us with more realistic tools for modelling and reasoning about multi-agent systems. While many existing approaches are based on the idea of agents as imperfect reasoners, who must spend their resources to perform logical inference, this is not the only way to introduce resource constraints into logical settings. In this paper we study agents as perfect reasoners, who may purchase a new piece of information from a trustworthy source. For this purpose we propose dynamic epistemic logic for semi-public queries for resource-bounded agents. In this logic (groups of) agents can perform a query (ask a question) about whether some formula is true and receive a correct answer. These queries are called semi-public, because the very fact of the query is public, while the answer is private. We also assume that every query has a cost and every agent has a budget constraint. Finally, our framework allows us to reason about group queries, in which agents may share resources to obtain a new piece of information together. We demonstrate that our logic is complete, decidable and has an efficient model checking procedure.
DittoGym: Learning to Control Soft Shape-Shifting Robots

Robot co-design, where the morphology of a robot is optimized jointly with a learned policy to solve a specific task, is an emerging area of research. It holds particular promise for soft robots, which are amenable to novel manufacturing techniques that can realize learned morphologies and actuators. Inspired by nature and recent novel robot designs, we propose to go a step further and explore the novel reconfigurable robots, defined as robots that can change their morphology within their lifetime. We formalize control of reconfigurable soft robots as a high-dimensional reinforcement learning (RL) problem. We unify morphology change, locomotion, and environment interaction in the same action space, and introduce an appropriate, coarse-to-fine curriculum that enables us to discover policies that accomplish fine-grained control of the resulting robots. We also introduce DittoGym, a comprehensive RL benchmark for reconfigurable soft robots that require fine-grained morphology changes to accomplish the tasks. Finally, we evaluate our proposed coarse-to-fine algorithm on DittoGym and demonstrate robots that learn to change their morphology several times within a sequence, uniquely enabled by our RL algorithm. More results are available at https://dittogym.github. io.