Miracle-dz/UniFAR-FTUs huggingface dataset:
The reproductive status determines tolerance and resistance to Mycobacterium marinum in Drosophila melanogaster

Abstract Sex and reproductive status of the host have a major impact on the immune response against infection. Our aim was to understand their impact on host tolerance or resistance in the systemic Mycobacterium marinum infection of Drosophila melanogaster. We measured host survival and bacillary load at time of death, as well as expression by quantitative real-time polymerase chain reaction of immune genes (diptericin and drosomycin). We also assessed the impact of metabolic and hormonal regulation in the protection against infection by measuring expression of upd3, impl2 and ecR. Our data showed increased resistance in actively mating flies and in mated females, while reducing their tolerance to infection. Data suggests that Toll and immune deficiency (Imd) pathways determine tolerance and resistance, respectively, while higher basal levels of ecR favours the stimulation of the Imd pathway. A dual role has been found for upd3 expression, linked to increased/decreased mycobacterial load at the beginning and later in infection, respectively. Finally, impl2 expression has been related to increased resistance in non-actively mating males. These results allow further assessment on the differences between sexes and highlights the role of the reproductive status in D. melanogaster to face infections, demonstrating their importance to determine resistance and tolerance against M. marinum infection.
TYPES AND CAUSES OF BEACH EROSION ANOMALY AREAS IN THE U.S. EAST COAST BARRIER ISLAND SYSTEM: STABILIZED TIDAL INLETS

Contemporary research suggests that most U.S. beaches are eroding, and this has become an important political and economic issue because beachfront development is increasingly at risk. In fact, the Heinz Center and Federal Emergency Management Agency (FEMA) estimate that over the next 60 years, beach erosion may claim one out of four homes within 150 meters of the U.S. shoreline. This is significant because approximately 350,000 structures are located with 150 meters of the shoreline in the lower 48 states, not including densely populated coastal cities such as New York and Miami. Along the East Coast, most beaches are eroding at 1 to 1.5 meters per year. However, beach erosion is especially problematic along so-called beach erosion anomaly areas (EAAs) where erosion rates can be as much as five times the rate along adjacent beaches. Identifying and understanding these places is important because property values along U.S. East Coast barrier islands are estimated to be in the trillions of dollars, and the resultant beach erosion may also destroy irreplaceable habitat. There are a number of factors⎯natural and anthropogenic⎯that cause beaches to erode at anomalous rates. This paper will focus on the barrier island system of the U.S. East Coast, classify beach EAAs, and examine beach erosion down drift from stabilized tidal inlets because they are perhaps the most spatially extensive and destructive EAAs.
Lobbying by omission: what is known and unknown about harmful industry lobbyists in Australia

Abstract What is unknown about commercial lobbying is far greater than what is known. These omissions distort our understanding of the extent and nature of business influence on politics. Especially when businesses engage in practices that harm health, it is crucial for public health advocates to understand corporate lobbying to counter its influence. Our study proceeded in three phases. First, based on an international audit, we developed a list of the categories of information about lobbying that could be disclosed under four groups (lobby firms, lobbyists, organizations and activities) and benchmarked Australian lobbyist registers against this list. Second, we manually extracted data from lobbyist registers in eight jurisdictions, cleaned the data and created a relational model for analysis. Finally, we classified a sample of organizations as public health organizations or harmful industries to compare their activities. We identified 61 possible categories of information about lobbying in international lobbyist registers. When applied to Australian lobbyist registers, Queensland covered the widest range of categories (13, 21%), though many lacked detail and completeness. Australian lobbyist registers provided data on 462 third-party lobby firms across Australia, currently employing 1036 lobbyists and representing 4101 organizations. Several of these represented harmful industries, with gambling interests hiring the most third-party lobby firms. Ultimately, Australian lobbyist registers do not provide enough information to understand the full extent of lobbying activities taking place. Political transparency is important for public health actors to be able to monitor corporate political activity and to protect policy-making from vested interests.
Nonreciprocal Coulomb drag between quantum wires in the quasi-one-dimensional regime

Coulomb drag experiments have been an essential tool to study strongly interacting low-dimensional systems. Historically, this effect has been explained in terms of momentum transfer between electrons in the active and the passive layer. Here, we report Coulomb drag measurements between laterally coupled GaAs/AlGaAs quantum wires in the multiple 1D sub-band regime that break Onsager's reciprocity upon both layer and current direction reversal, in contrast to prior 1D Coulomb drag results. The drag signal shows nonlinear I-V characteristics, which are well characterized by a third-order polynomial fit. These findings are qualitatively consistent with a rectified drag signal induced by charge fluctuations. However, the nonmonotonic temperature dependence of this drag signal suggests that strong electron-electron interactions, expected within the Tomonaga-Luttinger liquid framework, remain important and standard interaction models are insufficient to capture the qualitative nature of rectified 1D Coulomb drag.
Lawrence Lifts, Matroids, and Maximum Likelihood Degrees

We express the maximum likelihood (ML) degrees of a family toric varieties in terms of Mobius invariants of matroids. The family of interest are those parametrized by monomial maps given by Lawrence lifts of totally unimodular matrices with even circuits. Specifying these matrices to be vertex-edge incidence matrices of bipartite graphs gives the ML degrees of some hierarchical models and three dimensional quasi-independence models. Included in this list are the no-three-way interaction models with one binary random variable, for which, we give closed formulae.
Assessing Privacy Risks in Language Models: A Case Study on Summarization Tasks

Large language models have revolutionized the field of NLP by achieving state-of-the-art performance on various tasks. However, there is a concern that these models may disclose information in the training data. In this study, we focus on the summarization task and investigate the membership inference (MI) attack: given a sample and black-box access to a model's API, it is possible to determine if the sample was part of the training data. We exploit text similarity and the model's resistance to document modifications as potential MI signals and evaluate their effectiveness on widely used datasets. Our results demonstrate that summarization models are at risk of exposing data membership, even in cases where the reference summary is not available. Furthermore, we discuss several safeguards for training summarization models to protect against MI attacks and discuss the inherent trade-off between privacy and utility.
Decoding the Silent Majority: Inducing Belief Augmented Social Graph with Large Language Model for Response Forecasting

Automatic response forecasting for news media plays a crucial role in enabling content producers to efficiently predict the impact of news releases and prevent unexpected negative outcomes such as social conflict and moral injury. To effectively forecast responses, it is essential to develop measures that leverage the social dynamics and contextual information surrounding individuals, especially in cases where explicit profiles or historical actions of the users are limited (referred to as lurkers). As shown in a previous study, 97% of all tweets are produced by only the most active 25% of users. However, existing approaches have limited exploration of how to best process and utilize these important features. To address this gap, we propose a novel framework, named SocialSense, that leverages a large language model to induce a belief-centered graph on top of an existent social network, along with graph-based propagation to capture social dynamics. We hypothesize that the induced graph that bridges the gap between distant users who share similar beliefs allows the model to effectively capture the response patterns. Our method surpasses existing state-of-the-art in experimental evaluations for both zero-shot and supervised settings, demonstrating its effectiveness in response forecasting. Moreover, the analysis reveals the framework's capability to effectively handle unseen user and lurker scenarios, further highlighting its robustness and practical applicability.
Beyond power density: unexpected scaling laws in scale up of characterization of reverse-electro-dialysis membranes

Blue energy represents a large reservoir of renewable osmotic energy that can be converted into electricity by reverse electrodialysis (RED). This method is based on ion-exchange membrane. Before large scale production, these membranes are compared on very small samples on the basis of the power they enable to produce per unit area. Through a systematic study of the effect of the membrane size on the power density, we show experimentally that for classical measurement cells, the power density strongly varies with the size of the membrane: the smaller the membrane, the higher the power density. The results are explained by a theoretical modeling which describes the effect of the access resistance at the scale of the membrane. Based on this work, a few recommendations are formulated to perform scalable and meaningful measurements of membrane resistance and power density.
Conversational Financial Information Retrieval Model (ConFIRM)

With the exponential growth in large language models (LLMs), leveraging their emergent properties for specialized domains like finance merits exploration. However, regulated fields such as finance pose unique constraints, requiring domain-optimized frameworks. We present ConFIRM, an LLM-based conversational financial information retrieval model tailored for query intent classification and knowledge base labeling. ConFIRM comprises two modules: 1) a method to synthesize finance domain-specific question-answer pairs, and 2) evaluation of parameter efficient fine-tuning approaches for the query classification task. We generate a dataset of over 4000 samples, assessing accuracy on a separate test set. ConFIRM achieved over 90% accuracy, essential for regulatory compliance. ConFIRM provides a data-efficient solution to extract precise query intent for financial dialog systems.
Completely mixed linear games corresponding to Z-transformations over self-dual cones

In the setting of a self-dual cone in a finite dimensional inner product space, we consider (zero-sum) linear games. In our previous work, we showed that a Z-transformation with positive value is completely mixed. In the present paper, we consider the case when the value is zero. Motivated by the result (in the classical setting) that a Z-matrix with value zero is completely mixed if and only if it is irreducible, we formulate our general results based on the concepts of cone-irreducibility and space-irreducibility. In the setting of a symmetric cone (in a Euclidean Jordan algebra), we show that the space-irreducibility condition is necessary for a Z-transformation with value zero to be completely mixed and that it is sufficient when the Z-transformation is the difference of a Lyapunov-like transformation and a positive transformation. Additionally, we show that cone-irreducibility and space-irreducibility are equivalent for a positive transformation on a symmetric cone.
Bridging the Gap between Synthetic and Authentic Images for Multimodal Machine Translation

Multimodal machine translation (MMT) simultaneously takes the source sentence and a relevant image as input for translation. Since there is no paired image available for the input sentence in most cases, recent studies suggest utilizing powerful text-to-image generation models to provide image inputs. Nevertheless, synthetic images generated by these models often follow different distributions compared to authentic images. Consequently, using authentic images for training and synthetic images for inference can introduce a distribution shift, resulting in performance degradation during inference. To tackle this challenge, in this paper, we feed synthetic and authentic images to the MMT model, respectively. Then we minimize the gap between the synthetic and authentic images by drawing close the input image representations of the Transformer Encoder and the output distributions of the Transformer Decoder. Therefore, we mitigate the distribution disparity introduced by the synthetic images during inference, thereby freeing the authentic images from the inference process.Experimental results show that our approach achieves state-of-the-art performance on the Multi30K En-De and En-Fr datasets, while remaining independent of authentic images during inference.
CAPIVARA: Cost-Efficient Approach for Improving Multilingual CLIP Performance on Low-Resource Languages

This work introduces CAPIVARA, a cost-efficient framework designed to enhance the performance of multilingual CLIP models in low-resource languages. While CLIP has excelled in zero-shot vision-language tasks, the resource-intensive nature of model training remains challenging. Many datasets lack linguistic diversity, featuring solely English descriptions for images. CAPIVARA addresses this by augmenting text data using image captioning and machine translation to generate multiple synthetic captions in low-resource languages. We optimize the training pipeline with LiT, LoRA, and gradient checkpointing to alleviate the computational cost. Through extensive experiments, CAPIVARA emerges as state of the art in zero-shot tasks involving images and Portuguese texts. We show the potential for significant improvements in other low-resource languages, achieved by fine-tuning the pre-trained multilingual CLIP using CAPIVARA on a single GPU for 2 hours. Our model and code is available at https://github.com/hiaac-nlp/CAPIVARA.
An Analysis of $D^\alpha$ seeding for $k$-means

One of the most popular clustering algorithms is the celebrated $D^\alpha$ seeding algorithm (also know as $k$-means++ when $\alpha=2$) by Arthur and Vassilvitskii (2007), who showed that it guarantees in expectation an $O(2^{2\alpha}\cdot \log k)$-approximate solution to the ($k$,$\alpha$)-means cost (where euclidean distances are raised to the power $\alpha$) for any $\alpha\ge 1$. More recently, Balcan, Dick, and White (2018) observed experimentally that using $D^\alpha$ seeding with $\alpha>2$ can lead to a better solution with respect to the standard $k$-means objective (i.e. the $(k,2)$-means cost). In this paper, we provide a rigorous understanding of this phenomenon. For any $\alpha>2$, we show that $D^\alpha$ seeding guarantees in expectation an approximation factor of $$ O_\alpha \left((g_\alpha)^{2/\alpha}\cdot \left(\frac{\sigma_{\mathrm{max}}}{\sigma_{\mathrm{min}}}\right)^{2-4/\alpha}\cdot (\min\{\ell,\log k\})^{2/\alpha}\right)$$ with respect to the standard $k$-means cost of any underlying clustering; where $g_\alpha$ is a parameter capturing the concentration of the points in each cluster, $\sigma_{\mathrm{max}}$ and $\sigma_{\mathrm{min}}$ are the maximum and minimum standard deviation of the clusters around their means, and $\ell$ is the number of distinct mixing weights in the underlying clustering (after rounding them to the nearest power of $2$). We complement these results by some lower bounds showing that the dependency on $g_\alpha$ and $\sigma_{\mathrm{max}}/\sigma_{\mathrm{min}}$ is tight. Finally, we provide an experimental confirmation of the effects of the aforementioned parameters when using $D^\alpha$ seeding. Further, we corroborate the observation that $\alpha>2$ can indeed improve the $k$-means cost compared to $D^2$ seeding, and that this advantage remains even if we run Lloyd's algorithm after the seeding.
Land Use/Land Cover Dynamics in the Central Rift Valley Region of Ethiopia: Case of Arsi Negele District

The objective of this research paper was to assess the spatial and temporal Land Use/Land Cover Changes (LU/LCC) in Arsi Negele District. Rapid population growth, agricultural expansion, environmental fluctuations, degradation of natural resource and loss of biodiversity are the most visible socio-economic and environmental problem in the study area. Satellite imagery, ground control point data and household level socioeconomic survey were used to produce land cover maps and explaining the historical trends of the study area. ERDAS Imagine and ArcGIS software was used to accomplish the analysis. The analysis result showed that in 1973 most of the study area had been covered by dense acacia woodland and shrub land. Between 1973 to 1986, cultivated grass and bare land increased by 8.98, 33.9, and 36.5 ha respectively. While, shrub and acacia woodland decreased by 6.17 and 73.21 ha, respectively. Between 1986 and 2010, cultivated acacia woodland and land increased by 15.38, 4.63, and 38.52 ha. However, bare and grass land decreased by 19.23 and 39.3 ha, respectively. Furthermore, the trend and magnitude of LU/LCC between 1973 to 2010 acacia woodland and land decreased by 22.72 and 13.58 ha, but shrub land and cultivated land increased by 22.82 and 13.14 ha. Socio-economic survey result revealed that acacia woodland and shrub land decreased, but cultivated land land and grass land increased in the derg regime. However, currently, natural resource conservation activity has got a great emphasis, thus spatial coverage of acacia woodland has increased. Expansion of agricultural land, population growth and the associated demand for land were the major driving forces for the observed LU/LCC changes in the study area. Therefore, loss of biodiversity, soil degradation, and environmental deterioration are largely the results of LU/LCC. Hence, land resources management practices, utilization of alternative energy sources and family planning education are some of the appropriate interventions to reduce this dramatic change.
Better to Ask in English: Cross-Lingual Evaluation of Large Language Models for Healthcare Queries

Large language models (LLMs) are transforming the ways the general public accesses and consumes information. Their influence is particularly pronounced in pivotal sectors like healthcare, where lay individuals are increasingly appropriating LLMs as conversational agents for everyday queries. While LLMs demonstrate impressive language understanding and generation proficiencies, concerns regarding their safety remain paramount in these high-stake domains. Moreover, the development of LLMs is disproportionately focused on English. It remains unclear how these LLMs perform in the context of non-English languages, a gap that is critical for ensuring equity in the real-world use of these systems.This paper provides a framework to investigate the effectiveness of LLMs as multi-lingual dialogue systems for healthcare queries. Our empirically-derived framework XlingEval focuses on three fundamental criteria for evaluating LLM responses to naturalistic human-authored health-related questions: correctness, consistency, and verifiability. Through extensive experiments on four major global languages, including English, Spanish, Chinese, and Hindi, spanning three expert-annotated large health Q&A datasets, and through an amalgamation of algorithmic and human-evaluation strategies, we found a pronounced disparity in LLM responses across these languages, indicating a need for enhanced cross-lingual capabilities. We further propose XlingHealth, a cross-lingual benchmark for examining the multilingual capabilities of LLMs in the healthcare context. Our findings underscore the pressing need to bolster the cross-lingual capacities of these models, and to provide an equitable information ecosystem accessible to all.
The magnetized Gross-Neveu model at finite chemical potential

We study the (2+1)-dimensional Gross-Neveu model at non-zero chemical potential and subjected to a homogeneous background magnetic field. We do so both analytically, in the limit of an infinite number of fermion flavors in which mean-field approaches become exact, as well as on the lattice for a single flavor. The rich and exotic phase structure observed in the mean-field limit is found to be destroyed when strong quantum fluctuations are present in the system. Instead, in the phase of spontaneously broken chiral symmetry the magnetic field enhances this breaking for all choices of parameters. As a byproduct, we find indications for a first-order phase transition in the chemical potential for vanishing magnetic field but also provide hints that this could rather be a finite-size than a finite-flavor-number effect.
Ask Language Model to Clean Your Noisy Translation Data

Transformer models have demonstrated remarkable performance in neural machine translation (NMT). However, their vulnerability to noisy input poses a significant challenge in practical implementation, where generating clean output from noisy input is crucial. The MTNT dataset is widely used as a benchmark for evaluating the robustness of NMT models against noisy input. Nevertheless, its utility is limited due to the presence of noise in both the source and target sentences. To address this limitation, we focus on cleaning the noise from the target sentences in MTNT, making it more suitable as a benchmark for noise evaluation. Leveraging the capabilities of large language models (LLMs), we observe their impressive abilities in noise removal. For example, they can remove emojis while considering their semantic meaning. Additionally, we show that LLM can effectively rephrase slang, jargon, and profanities. The resulting datasets, called C-MTNT, exhibit significantly less noise in the target sentences while preserving the semantic integrity of the original sentences. Our human and GPT-4 evaluations also lead to a consistent conclusion that LLM performs well on this task. Lastly, experiments on C-MTNT showcased its effectiveness in evaluating the robustness of NMT models, highlighting the potential of advanced language models for data cleaning and emphasizing C-MTNT as a valuable resource.
Steve-Eye: Equipping LLM-based Embodied Agents with Visual Perception in Open Worlds

Recent studies have presented compelling evidence that large language models (LLMs) can equip embodied agents with the self-driven capability to interact with the world, which marks an initial step toward versatile robotics. However, these efforts tend to overlook the visual richness of open worlds, rendering the entire interactive process akin to"a blindfolded text-based game. "Consequently, LLM-based agents frequently encounter challenges in intuitively comprehending their surroundings and producing responses that are easy to understand. In this paper, we propose Steve-Eye, an end-to-end trained large multimodal model designed to address this limitation. Steve-Eye integrates the LLM with a visual encoder which enables it to process visual-text inputs and generate multimodal feedback. In addition, we use a semi-automatic strategy to collect an extensive dataset comprising 850K open-world instruction pairs, empowering our model to encompass three essential functions for an agent: multimodal perception, foundational knowledge base, and skill prediction and planning. Lastly, we develop three open-world evaluation benchmarks, then carry out extensive experiments from a wide range of perspectives to validate our model's capability to strategically act and plan. Codes and datasets will be released.