Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-Label Topic Model for Financial Textual Data

This paper presents a multi-label topic model for financial texts like ad-hoc announcements, 8-K filings, finance related news or annual reports. I train the model on a new financial multi-label database consisting of 3,044 German ad-hoc announcements that are labeled manually using 20 predefined, economically motivated topics. The best model achieves a macro F1 score of more than 85%. Translating the data results in an English version of the model with similar performance. As application of the model, I investigate differences in stock market reactions across topics. I find evidence for strong positive or negative market reactions for some topics, like announcements of new Large Scale Projects or Bankruptcy Filings, while I do not observe significant price effects for some other topics. Furthermore, in contrast to previous studies, the multi-label structure of the model allows to analyze the effects of co-occurring topics on stock market reactions. For many cases, the reaction to a specific topic depends heavily on the co-occurrence with other topics. For example, if allocated capital from a Seasoned Equity Offering (SEO) is used for restructuring a company in the course of a Bankruptcy Proceeding, the market reacts positively on average. However, if that capital is used for covering unexpected, additional costs from the development of new drugs, the SEO implies negative reactions on average.
SynthEnsemble: A Fusion of CNN, Vision Transformer, and Hybrid Models for Multi-Label Chest X-Ray Classification

Chest X-rays are widely used to diagnose thoracic diseases, but the lack of detailed information about these abnormalities makes it challenging to develop accurate automated diagnosis systems, which is crucial for early detection and effective treatment. To address this challenge, we employed deep learning techniques to identify patterns in chest X-rays that correspond to different diseases. We conducted experiments on the "ChestX-ray14" dataset using various pre-trained CNNs, transformers, hybrid(CNN+Transformer) models, and classical models. The best individual model was the CoAtNet, which achieved an area under the receiver operating characteristic curve (AUROC) of 84.2%. By combining the predictions of all trained models using a weighted average ensemble where the weight of each model was determined using differential evolution, we further improved the AUROC to 85.4%, outperforming other state-of-the-art methods in this field. Our findings demonstrate the potential of deep learning techniques, particularly ensemble deep learning, for improving the accuracy of automatic diagnosis of thoracic diseases from chest X-rays.
Ultra-large polymer-free suspended graphene films

Due to its extraordinary properties, suspended graphene is a critical element in a wide range of applications. Preparation methods that preserve the unique properties of graphene are therefore in high demand. To date, all protocols for the production of large graphene films have relied on the application of a polymer film to stabilize graphene during the transfer process. However, this inevitably introduces contaminations that have proven to be extremely difficult, if not impossible, to remove entirely. Here we report the polymer-free fabrication of suspended films consisting of three graphene layers spanning circular holes of 150 $\mu$m diameter. We find a high fabrication yield, very uniform properties of the freestanding graphene across all holes as well across individual holes. A detailed analysis by confocal Raman and THz spectroscopy reveals that the triple-layer samples exhibit structural and electronic properties similar to those of monolayer graphene. We demonstrate their usability as ion-electron converters in time-of-flight mass spectrometry and related applications. They are two orders of magnitude thinner than previous carbon foils typically used in these types of experiments, while still being robust and exhibiting a sufficiently high electron yield. These results are an important step towards replacing free-standing ultra-thin carbon films or graphene from polymer-based transfers with much better defined and clean graphene.
CV32RT: Enabling Fast Interrupt and Context Switching for RISC-V Microcontrollers

Processors using the open RISC-V ISA are finding increasing adoption in the embedded world. Many embedded use cases have real-time constraints and require flexible, predictable, and fast reactive handling of incoming events. However, RISC- V processors are still lagging in this area compared to more mature proprietary architectures, such as ARM Cortex-M and TriCore, which have been tuned for years. The default interrupt controller standardized by RISC-V, the Core Local Interruptor (CLINT), lacks configurability in prioritization and preemption of interrupts. The RISC-V Core Local Interrupt Controller (CLIC) specification addresses this concern by enabling pre-emptible, low-latency vectored interrupts while also envisioning optional extensions to improve interrupt latency. In this work, we implement a CLIC for the CV32E40P, an industrially supported open-source 32-bit MCU-class RISC-V core, and enhance it with fastirq: a custom extension that provides interrupt latency as low as 6 cycles. We call CV32RT our enhanced core. To the best of our knowledge, CV32RT is the first fully open-source RV32 core with competitive interrupt-handling features compared to the Arm Cortex-M series and TriCore. The proposed extensions are also demonstrated to improve task context switching in real-time operating systems.
Eval-GCSC: A New Metric for Evaluating ChatGPT's Performance in Chinese Spelling Correction

ChatGPT has demonstrated impressive performance in various downstream tasks. However, in the Chinese Spelling Correction (CSC) task, we observe a discrepancy: while ChatGPT performs well under human evaluation, it scores poorly according to traditional metrics. We believe this inconsistency arises because the traditional metrics are not well-suited for evaluating generative models. Their overly strict length and phonics constraints may lead to underestimating ChatGPT's correction capabilities. To better evaluate generative models in the CSC task, this paper proposes a new evaluation metric: Eval-GCSC. By incorporating word-level and semantic similarity judgments, it relaxes the stringent length and phonics constraints. Experimental results show that Eval-GCSC closely aligns with human evaluations. Under this metric, ChatGPT's performance is comparable to traditional token-level classification models (TCM), demonstrating its potential as a CSC tool. The source code and scripts can be accessed at https://github.com/ktlKTL/Eval-GCSC.
A Mobile Quad-Arm Robot ARMS: Wheeled-Legged Tripedal Locomotion and Quad-Arm Loco-Manipulation

This article proposes a mobile quad-arm robot: ARMS, which unifies wheeled-legged tripedal locomotion, wheeled locomotion, and quad-arm loco-manipulation. ARMS's four arms have different mechanisms and are partially designed to be general-purpose arms for the hybrid locomotion and loco-manipulation. One three-degree-of-freedom (DOF) arm has an active wheel, which is used for wheeled-legged tripedal walking and wheeled driving with passive wheels attached to the torso. Two three-DOF general-purpose arms are series elastic and used for wheeled-legged tripedal walking, object grasping, and manipulation. The upper two-DOF arm is used for manipulation only; its position and orientation are determined by coordinating all arms. Each motor is controlled by an angle controller and trajectory modification with angle, angular velocity, angular acceleration, and torque constraints. ARMS was verified with seven experiments involving joint control, wheeled-legged locomotion, wheeled locomotion and grasping, slope locomotion, block terrain locomotion, carrying a bag, and outdoor locomotion.
Continuous measurements of size‐resolved particle chemistry during INDOEX‐Intensive Field Phase 99

During the Indian Ocean Experiment Intensive Field Phase 99, aerosol time-of-flight mass spectrometry (ATOFMS) was used for on-line characterization of the size and chemical composition of individual particles. High temporal resolution single-particle measurements were made on the Island of Kaashidhoo and on board the Research Vessel Ronald H. Brown during February and March, respectively. Sea-salt and dust particles are the major contributors to the aerosol between 1.0 and 2.5 μm. For particles with aerodynamic diameters between 0.2 and 1.0 μm, carbon-containing particles dominated. Differences in the chemical composition of the sampled aerosol were observed for different days and locations, and can be correlated with back trajectory information. For locations north of the Intertropical Convergence Zone (ITCZ), anthropogenic influences on the aerosol chemical composition were indicated by increased contributions from sea-salt particles with non-sea-salt (nss) sulfates, dust with sulfates, and carbon-containing particles with potassium and sulfates. In contrast, in regions close to and south of the ITCZ, the aerosol showed characteristics consistent with a more pristine marine environment. The relative contributions from sulfate-associated particles were evaluated from the single-particle data, as well. The observed sulfate aerosols over the size range analyzed in this study (i.e., aerodynamic diameter between 0.2 and 2.5 μm) consist exclusively of sulfates associated with sea salt, dust, elemental carbon, and/or organic matter. Quantitative contributions from the different types of sulfate-associated particles are evaluated at different locations during the IFP: southern latitude, midlatitude, northern latitude at the Arabian Sea, and mid/northern latitude at the Bay of Bengal.
Use of chamber systems to measure trace gas fluxes

Trace gas exchange between soil-plant systems and the atmosphere is a complex phenomenon driven by a different set of physical, chemical, and biological processes for each chemical species and each environment (Fig. 4-1). For example, the exchange rate of a relatively inert gas like N20 represents simply the difference between its integrated rates of production and consumption by soil biochemical processes. More reactive gases are subject to additional emission and deposition processes, such as foliar exchange (NH3) or photochemical oxidation followed by deposition of the reaction products (NO). Further confounding the measurement and understanding of trace gas exchange across the surface-atmosphere boundary, the relative importances of the source and sink processes shown in Fig. 4-1 also vary with the rate of gas transport by diffusive, advective, and plant-mediated processes, and with the time and space scales over which the exchange is considered. Because of this complexity, great care must be exercised in drawing inferences about regional to global and seasonal to annual trace gas exchange rates from fluxes measured over small areas and short times. Denmead and Raupach (1993) introduced the two most common approaches to field measurement of trace gas exchange-chamber methods and micrometeorology. Because tower-based micrometeorological techniques integrate the flux over a larger area (typically 102_103 m2), they offer a potential advantage where the exchange rate is highly variable on the local scale measured by chamber methods (typically < 1 m2). Chambers offer other advantages, including low cost and ease of use, and they represent the measurement technique of choice in process-level studies and other research requiring replicate measurements coincident in space or time. Our purpose is to provide sufficient insight into the use of chamber systems to enable poten-
Incrementally Enriching the Common Ground: A Research Path

I am broadly interested in evaluation of dialogue systems, in all its many facets: The data they are trained on, their ability to perform a task successfully, their skills with respect to various dialogue phenomena, their resemblance to human cognitive processes, and their ethical and societal impact. More specifically, my research topics focus on understanding the possibilities and limits of current multimodal neural network-based models to incrementally encode information for natural language understanding in general and also for building common ground and asking for clarification. Besides, I am interested in dialogue games as a means to elicit and collect dialogue data and to evaluate the abilities of dialogue models.
OdiaGenAI’s Participation at WAT2023

This paper offers an in-depth overview of the team “ODIAGEN’s” translation system submitted to the Workshop on Asian Translation (WAT2023). Our focus lies in the domain of Indic Multimodal tasks, specifically targeting English to Hindi, English to Malayalam, and English to Bengali translations. The system uses a state-of-the-art Transformer-based architecture, specifically the NLLB-200 model, fine-tuned with language-specific Visual Genome Datasets. With this robust system, we were able to manage both text-to-text and multimodal translations, demonstrating versatility in handling different translation modes. Our results showcase strong performance across the board, with particularly promising results in the Hindi and Bengali translation tasks. A noteworthy achievement of our system lies in its stellar performance across all text-to-text translation tasks. In the categories of English to Hindi, English to Bengali, and English to Malayalam translations, our system claimed the top positions for both the evaluation and challenge sets. This system not only advances our understanding of the challenges and nuances of Indic language translation but also opens avenues for future research to enhance translation accuracy and performance.
A Study on the Effectiveness of Large Language Models for Translation with Markup

In this paper we evaluate the utility of large language models (LLMs) for translation of text with markup in which the most important and challenging aspect is to correctly transfer markup tags while ensuring that the content, both, inside and outside tags is correctly translated. While LLMs have been shown to be effective for plain text translation, their effectiveness for structured document translation is not well understood. To this end, we experiment with BLOOM and BLOOMZ, which are open-source multilingual LLMs, using zero, one and few-shot prompting, and compare with a domain-specific in-house NMT system using a detag-and-project approach for markup tags. We observe that LLMs with in-context learning exhibit poorer translation quality compared to the domain-specific NMT system, however, they are effective in transferring markup tags, especially the large BLOOM model (176 billion parameters). This is further confirmed by our human evaluation which also reveals the types of errors of the different tag transfer techniques. While LLM-based approaches come with the risk of losing, hallucinating and corrupting tags, they excel at placing them correctly in the translation.
Controls over spatial and temporal variations in annual actual evapotranspiration in snow-free California watersheds

OF THE THESIS Controls over Spatial and Temporal Variations in Annual Actual Evapotranspiration in Snow-Free California Watersheds by Allison Marie Clark Master of Science in Geography with a Concentration in Geographic Information Science San Diego State University, 2015 Actual evapotranspiration (Eta) is one of the largest components of the hydrologic budget and accounts for a majority of water lost from a watershed. It is primarily controlled by soil water availability, which is largely controlled by rainfall, and atmospheric demand (potential evapotranspiration). Consequently, Eta is sensitive to changes in meteorologic conditions. Understanding the relationship between Eta and controlling meteorologic variables across time and space is important for future predictions of Eta under a changing climate, especially in California where demand for surface and groundwater is high. A regression modeling approach was used to (1) determine the relative control of rainfall, rainfall intensity, and potential evapotranspiration (Etp) over annual and long-term mean annual Eta across watersheds in western California, and (2) quantify the sensitivity of watershed annual Eta to changes in these variables. Annual Eta data for 20 snow-free California watersheds was derived using the water balance method for hydrologic years 1982-2011. Independent variables examined in this study were annual rainfall, rainfall intensity, and potential evapotranspiration. These quantities were obtained or calculated from daily PRISM rainfall and temperature datasets. Results indicated that rainfall was the dominant control over variations in mean annual Eta across the study region (Adj. R 2 0.935) and was the primary control over interannual variations in Eta for 15 out of 17 study watersheds. Rainfall intensity was a significant but weaker predictor of mean annual Eta (adj. R 2 0.833) and was a significant predictor of annual variations in Eta for 12 out of 17 watersheds. A weak relationship between Etp and Eta was observed across the study region (adj. R 2 = 0.660) and the relationship was found to be negative. Etp was a significant, though weak, predictor of annual Eta for 8 out of 17 watersheds. The amount of variance in annual Eta explained by rainfall, rainfall intensity, and Etp decreased with increasing watershed wetness (P/Etp). Slope coefficients in the sensitivity analysis indicated that Eta was sensitive to rainfall in 15 out of 17 watersheds, while 12 and 8 watersheds were sensitive to changes in rainfall intensity and Etp, respectively. Watershed Eta was most sensitive to changes in Etp (avg. α = -5.46), moderately sensitive to rainfall intensity (avg. α = 1.08), and least sensitive to rainfall (avg. α = 0.59). Inland, water-limited watersheds exhibited the greatest sensitivity to changes in meteorologic variables affecting Eta, which has implications for potential changes in future climate.
Leveraging Highly Accurate Word Alignment for Low Resource Translation by Pretrained Multilingual Model

Recently, there has been a growing interest in pretraining models in the field of natural language processing. As opposed to training models from scratch, pretrained models have been shown to produce superior results in low-resource translation tasks. In this paper, we introduced the use of pretrained seq2seq models for preordering and translation tasks. We utilized manual word alignment data and mBERT-based generated word alignment data for training preordering and compared the effectiveness of various types of mT5 and mBART models for preordering. For the translation task, we chose mBART as our baseline model and evaluated several input manners. Our approach was evaluated on the Asian Language Treebank dataset, consisting of 20,000 parallel data in Japanese, English and Hindi, where Japanese is either on the source or target side. We also used in-house 3,000 parallel data in Chinese and Japanese. The results indicated that mT5-large trained with manual word alignment achieved a preordering performance exceeding 0.9 RIBES score on Ja-En and Ja-Zh pairs. Moreover, our proposed approach significantly outperformed the baseline model in most translation directions of Ja-En, Ja-Zh, and Ja-Hi pairs in at least one of BLEU/COMET scores.
Learning from Past Mistakes: Quality Estimation from Monolingual Corpora and Machine Translation Learning Stages

Quality Estimation (QE) of Machine Translation output suffers from the lack of annotated data to train supervised models across domains and language pairs. In this work, we describe a method to generate synthetic QE data based on Neural Machine Translation (NMT) models at different learning stages. Our approach consists in training QE models on the errors produced by different NMT model checkpoints, obtained during the course of model training, under the assumption that gradual learning will induce errors that more closely resemble those produced by NMT models in adverse conditions. We test this approach on English-German and Romanian-English WMT QE test sets, demonstrating that pairing translations from earlier checkpoints with translations of converged models outperforms the use of reference human translations and can achieve competitive results against human-labelled data. We also show that combining post-edited data with our synthetic data yields to significant improvements across the board. Our approach thus opens new possibilities for an efficient use of monolingual corpora to generate quality synthetic QE data, thereby mitigating the data bottleneck.
eIF3 engages with 3’-UTR termini of highly translated mRNAs in neural progenitor cells

Stem cell differentiation involves a global increase in protein synthesis to meet the demands of specialized cell types. However, the molecular mechanisms underlying this translational burst and the involvement of initiation factors remains largely unknown. Here, we investigate the roles of eukaryotic initiation factor 3 (eIF3) in early differentiation of human pluripotent stem cell (hPSC)-derived neural progenitor cells (NPCs). Using Quick-irCLIP and alternative polyadenylation (APA) Seq, we show eIF3 crosslinks to many neurologically relevant mRNAs in NPCs. Our data reveal eIF3 predominantly interacts with 3’ untranslated region (3’-UTR) termini of multiple mRNA isoforms, adjacent to the poly(A) tail. High eIF3 crosslinking at 3’-UTR termini of mRNAs correlates with high translational activity, as determined by ribosome profiling. We identify the transcriptional regulator inhibitor of DNA binding 2 (ID2) mRNA as a case in which active translation levels and eIF3 crosslinking are dramatically increased upon early NPC differentiation. Furthermore, we find that eIF3 engagement at 3’-UTR ends is dependent on polyadenylation. The results presented here show that eIF3 engages with 3’-UTR termini of highly translated mRNAs, supporting a role of mRNA circularization in the mechanisms governing mRNA translation in NPCs.
Physiological variability in mitochondrial rRNA predisposes to metabolic syndrome

Metabolic syndrome is a growing concern in developed societies, and due to its polygenic nature, the genetic component is only slowly being elucidated. Common mitochondrial DNA sequence variants have been associated with symptoms of metabolic syndrome and may be relevant players in the genetics of metabolic syndrome. We investigate the effect of mitochondrial sequence variation on the metabolic phenotype in conplastic rat strains with identical nuclear but unique mitochondrial genomes, challenged by high-fat diet. We find that the variation in mitochondrial rRNA sequence represents a risk factor in insulin resistance development, which is caused by diacylglycerols accumulation induced by tissue-specific reduction of the oxidative capacity. These metabolic perturbations stem from the 12S rRNA sequence variation affecting mitochondrial ribosome assembly and translation. Our work demonstrates that physiological variation in mitochondrial rRNA might represent a relevant underlying factor in the progression of metabolic syndrome. Competing interests The authors declare that no competing interests exist.