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In a letter to Weierstrass Riemann asserted that the number $N_0(T)$ of zeros of $\zeta(s)$ on the critical line to height $T$ is approximately equal to the total number of zeros to this height $N(T)$. Siegel studied some posthumous papers of Riemann trying to find a proof of this. He found a function $\mathop{\mathcal R }(s)$ whose zeros are related to the zeros of the function $\zeta(s)$. Siegel concluded that Riemann's papers contained no ideas for a proof of his assertion, connected the position of the zeros of $\mathop{\mathcal R }(s)$ with the position of the zeros of $\zeta(s)$ and asked about the position of the zeros of $\mathop{\mathcal R }(s)$. This paper is a summary of several papers that we will soon upload to arXiv, in which we try to answer Siegel's question about the position of the zeros of $\mathop{\mathcal R }(s)$. The articles contain also improvements on Siegel's results and also other possible ways to prove Riemann's assertion, but without achieving this goal., Comment: 18 pages, 4 figures. Added links to the papers in arXiv

Emotional, behavioral, and mental health challenges make it difficult for many children and adolescents to engage and succeed at school. Research indicates that at least 20% of all children and adolescents have been diagnosed with one more mental health disorders. Behavioral problems, anxiety, and depression are the most diagnosed mental health issues, and they often co-occur. Moreover, these conditions are being diagnosed at increasingly younger ages. In the past several years there has been a rise in the number of adolescents and young adults with serious mental health issues such as major depression and suicidal ideation, and the COVID-19 pandemic has exacerbated mental health problems for children and adolescents. Schools offer promise for providing intensive clinical support to the most at-risk students, and schools are necessary environment to explore the implementation of multi-modal youth mental health services. This paper provides an analysis of an intensive, in-school mental health services model developed and implemented by Effective School Solutions (ESS), a New Jersey based provider of high acuity school based mental health services for K-12 students. We analyze this multi-modal model for its effectiveness in improving educational outcomes for over 3,000 students identified as requiring intensive clinical mental health support across the 2021-22 school year. This analysis reveals that those students receiving High- versus Low-fidelity programming (i.e., multiple sessions per week for at least half of the school year versus for less than half of the school year) had better educational outcomes. Students receiving High-fidelity programming had greater improvements in grade point average (GPA) and greater reductions in absences across the school year. A higher number of in-school clinical sessions per week significantly predicted a greater increase in GPA and a greater reduction in total disciplinary incidents (including out of school suspensions) across the school year. This report provides initial promising evidence that in-school intensive mental health clinical services yield positive effects on students' educational outcomes. Though future research is needed to validate and extend these findings, schools may consider implementing such services onsite to meet students where they are and to optimize students' mental, behavioral, and educational well-being. [This white paper report was published by the Yale Child Study Center."]

Interest in the field of career and technical education (CTE) has experienced a resurgence over the last decade, as the global economy has grown increasingly competitive while students have continued to leave school underprepared for well-paying twenty-first century jobs. Together and separately, the education and workforce sectors have sought to address these challenges and better prepare students for viable economic futures. The results have been many new, innovative programs at both the secondary and postsecondary education levels that seek to give students technical training for specific careers, general training to prepare them for the workplace, and work-based learning opportunities where they can develop connections to employers and the workforce. While there are still many under-researched areas in CTE, this paper attempts to capture the evidence that has emerged--identifying areas where there is more evidence as well as areas where gaps in evidence still exist. The studies that have been conducted on CTE have demonstrated that it shows promise, but it is imperative to continue building evidence, particularly where there is policy interest and momentum but little data. Doing so will help demonstrate how those programs and models serve students and ensure that the continued scaling up of CTE is supported by a rigorous evidence base. This paper begins with an overview of the issues in the education system and the labor market that have led to the current revival of CTE. It argues that the skills today's employers need are not the ones schools are providing. The paper continues with a description of how various policies have fostered the growth of CTE. In the next section, it provides details on the types of programs and institutions that offer CTE, and the evidence base to support each of them. The paper provides evidence on the effectiveness of CTE at different educational levels, and for specific subgroups, including students with disabilities, and by gender. Further, the paper provides an overview of the available evidence to support different kinds of programs offered at both secondary and postsecondary education levels, touching on the amount of evidence available in each area and the level of rigor used in the studies that generated that evidence. The paper concludes by suggesting that while CTE instruction at the secondary and postsecondary levels could bolster students' economic mobility by helping them gain postsecondary credentials and obtain higher-paying jobs, there are challenges involved in turning that promise into reality. Investments in evidence-based practices can give CTE programs a better chance at success.

The purpose of this technical assistance paper is to assist education leaders and administrators in the consistent implementation of the Florida Career and Professional Education (CAPE) Act in Section 1003.491, Florida Statutes (F.S.). This technical assistance paper addresses questions on recent legislation, funding, and data reporting. [For the previous report, see ED616038.]

The United States has been cultivating STEM talent for decades with great success, but that robust talent pipeline is threatened by a growing STEM teacher shortage. Left unaddressed, that shortage could curtail employment in STEM professions and the many related careers that increasingly require some degree of scientific and technological literacy. While the STEM teacher shortage is a national problem, the effects are likely to be felt more acutely in a state such as Massachusetts with a high concentration of STEM jobs. This paper provides an overview of the causes of the STEM teacher shortage and offers three recommendations to address it.

A large share of Wisconsin's 400+ school districts are rural, and nearly half of all students enrolled in public schools statewide attend rural schools. Persistent challenges faced by rural districts in attracting and retaining educators, however, are contributing to an increasingly dire situation for the state's rural communities. This report summarizes findings from a yearlong research project investigating strategies being used by rural districts to address educator shortages, including a diverse set of "grow your own" (GYO) initiatives which involve school districts working with educator preparation programs (EPPs) and other local partners to identify, recruit, and prepare local candidates to become educators. The mixed-methods research design draws upon two statewide surveys (of rural superintendents and local site coordinators of the Educators Rising GYO program), along with site visits to five rural districts, to accomplish two main goals: (1) to identify strategies being used by rural districts to attract, recruit, and hire teachers; and (2) to describe the landscape of GYO initiatives being utilized by rural Wisconsin districts and local partners to address educator shortages. Broadly speaking, the work suggests that rural school districts are not waiting on state or federal policymakers to solve longstanding staffing challenges for them, but instead are actively engaged in implementing a mix of national GYO programs such as Educators Rising and "homegrown" GYO initiatives as one solution to chronic educator shortages. Not surprisingly, a distinct set of both successes and challenges have emerged as rural districts attempt to implement, launch, sustain, and scale up GYO initiatives that meet their needs. [Paper's original version August 2023; revised version September 2023. This work was made possible by a research grant from UW-Madison's Tommy G. Thompson Center for Public Leadership.]

Teaching Computer Science (CS) by having students write programs by hand on paper has key pedagogical advantages: It allows focused learning and requires careful thinking compared to the use of Integrated Development Environments (IDEs) with intelligent support tools or "just trying things out". The familiar environment of pens and paper also lessens the cognitive load of students with no prior experience with computers, for whom the mere basic usage of computers can be intimidating. Finally, this teaching approach opens learning opportunities to students with limited access to computers. However, a key obstacle is the current lack of teaching methods and support software for working with and running handwritten programs. Optical character recognition (OCR) of handwritten code is challenging: Minor OCR errors, perhaps due to varied handwriting styles, easily make code not run, and recognizing indentation is crucial for languages like Python but is difficult to do due to inconsistent horizontal spacing in handwriting. Our approach integrates two innovative methods. The first combines OCR with an indentation recognition module and a language model designed for post-OCR error correction without introducing hallucinations. This method, to our knowledge, surpasses all existing systems in handwritten code recognition. It reduces error from 30\% in the state of the art to 5\% with minimal hallucination of logical fixes to student programs. The second method leverages a multimodal language model to recognize handwritten programs in an end-to-end fashion. We hope this contribution can stimulate further pedagogical research and contribute to the goal of making CS education universally accessible. We release a dataset of handwritten programs and code to support future research at https://github.com/mdoumbouya/codeocr

I analyze the evolution of papers certified by the Graphics Replicability Stamp Initiative (GRSI) to be reproducible, with a specific focus on the subset of publications that address visualization-related topics. With this analysis I show that, while the number of papers is increasing overall and within the visualization field, we still have to improve quite a bit to escape the replication crisis. I base my analysis on the data published by the GRSI as well as publication data for the different venues in visualization and lists of journal papers that have been presented at visualization-focused conferences. I also analyze the differences between the involved journals as well as the percentage of reproducible papers in the different presentation venues. Furthermore, I look at the authors of the publications and, in particular, their affiliation countries to see where most reproducible papers come from. Finally, I discuss potential reasons for the low reproducibility numbers and suggest possible ways to overcome these obstacles. This paper is reproducible itself, with source code and data available from github.com/tobiasisenberg/Visualization-Reproducibility as well as a free paper copy and all supplemental materials at osf.io/mvnbj., Comment: 9 pages plus appendix; 12 figures plus 14 figures in the appendix

Counterfeit products cause severe harm to public safety and health by penetrating untrusted supply chains. Numerous anti-counterfeiting techniques have been proposed, among which the use of inherent, unclonable irregularities of paper surfaces has shown considerable potential as a high-performance economical solution. Prior works do not consider supply chains cohesively, either focusing on creating or improving unclonable identifiers or on securing digital records of products. This work aims to systematically unify these two separate but connected research areas by comprehensively analyzing the needs of supply chains. We construct a generalized paper-based authentication framework and identify important shortcomings and promising ideas in the existing literature. Next, we do a stage-wise security analysis of our consolidated framework by drawing inspiration from works in signal processing, cryptography, and biometric systems. Finally, we examine key representative scenarios that illustrate the range of practical and technical challenges in real-world supply chains, and we outline the best practices to guide future research.

The impact of research papers, typically measured in terms of citation counts, depends on several factors, including the reputation of the authors, journals, and institutions, in addition to the quality of the scientific work. In this paper, we present an approach that combines natural language processing and machine learning to predict the impact of papers in a specific journal. Our focus is on the text, which should correlate with impact and the topics covered in the research. We employed a dataset of over 40,000 articles from ACS Applied Materials and Interfaces spanning from 2012 to 2022. The data was processed using various text embedding techniques and classified with supervised machine learning algorithms. Papers were categorized into the top 20% most cited within the journal, using both yearly and cumulative citation counts as metrics. Our analysis reveals that the method employing generative pre-trained transformers (GPT) was the most efficient for embedding, while the random forest algorithm exhibited the best predictive power among the machine learning algorithms. An optimized accuracy of 80\% in predicting whether a paper was among the top 20% most cited was achieved for the cumulative citation count when abstracts were processed. This accuracy is noteworthy, considering that author, institution, and early citation pattern information were not taken into account. The accuracy increased only slightly when the full texts of the papers were processed. Also significant is the finding that a simpler embedding technique, term frequency-inverse document frequency (TFIDF), yielded performance close to that of GPT. Since TFIDF captures the topics of the paper we infer that, apart from considering author and institution biases, citation counts for the considered journal may be predicted by identifying topics and "reading" the abstract of a paper.

Most published Information Systems research are of the behavioral science research (BSR) category rather than the design science research (DSR) category. This is due in part to the BSR orientation of many IS doctoral programs, which often do not involve much technical courses. This includes IS doctoral programs that train Information and Communication Technologies for Development (ICT4D) researchers. Without such technical knowledge many doctoral and postdoctoral researchers will not feel confident in engaging in DSR research. Given the importance of designing artifacts that are appropriate for a given context, an important question is how can ICT4D and other IS researchers increase their IS technical content knowledge and intimacy with the DSR process. In this paper we present, a process for reviewing DSR papers that has as its objectives: enhancing technical content knowledge, increasing knowledge and understanding of approaches to designing and evaluating IS/IT artifacts, and facilitating the identification of new DSR opportunities. This process has been applied for more than a decade at a USA research university., Comment: 44 pages, 9 Figures

Identifying significant references within the complex interrelations of a citation knowledge graph is challenging, which encompasses connections through citations, authorship, keywords, and other relational attributes. The Paper Source Tracing (PST) task seeks to automate the identification of pivotal references for given scholarly articles utilizing advanced data mining techniques. In the KDD CUP 2024, we design a recommendation-based framework tailored for the PST task. This framework employs the Neural Collaborative Filtering (NCF) model to generate final predictions. To process the textual attributes of the papers and extract input features for the model, we utilize SciBERT, a pre-trained language model. According to the experimental results, our method achieved a score of 0.37814 on the Mean Average Precision (MAP) metric, outperforming baseline models and ranking 11th among all participating teams. The source code is publicly available at https://github.com/MyLove-XAB/KDDCupFinal., Comment: KDD CUP 2024 OAG-Challenges, Paper Source Tracing, Technical Report of Team AoboSama @ KDD CUP 2024. August 25--29, 2024. Barcelona, Spain

Seeking answers to questions within long scientific research articles is a crucial area of study that aids readers in quickly addressing their inquiries. However, existing question-answering (QA) datasets based on scientific papers are limited in scale and focus solely on textual content. To address this limitation, we introduce SPIQA (Scientific Paper Image Question Answering), the first large-scale QA dataset specifically designed to interpret complex figures and tables within the context of scientific research articles across various domains of computer science. Leveraging the breadth of expertise and ability of multimodal large language models (MLLMs) to understand figures, we employ automatic and manual curation to create the dataset. We craft an information-seeking task involving multiple images that cover a wide variety of plots, charts, tables, schematic diagrams, and result visualizations. SPIQA comprises 270K questions divided into training, validation, and three different evaluation splits. Through extensive experiments with 12 prominent foundational models, we evaluate the ability of current multimodal systems to comprehend the nuanced aspects of research articles. Additionally, we propose a Chain-of-Thought (CoT) evaluation strategy with in-context retrieval that allows fine-grained, step-by-step assessment and improves model performance. We further explore the upper bounds of performance enhancement with additional textual information, highlighting its promising potential for future research and the dataset's impact on revolutionizing how we interact with scientific literature., Comment: preprint

Occlusal contacts are the locations at which the occluding surfaces of the maxilla and the mandible posterior teeth meet. Occlusal contact detection is a vital tool for restoring the loss of masticatory function and is a mandatory assessment in the field of dentistry, with particular importance in prosthodontics and restorative dentistry. The most common method for occlusal contact detection is articulating paper. However, this method can indicate significant medically false positive and medically false negative contact areas, leaving the identification of true occlusal indications to clinicians. To address this, we propose a multiclass Vision Transformer and Fully Convolutional Network ensemble semantic segmentation model with a combination hierarchical loss function, which we name as Hierarchical Fully Convolutional Branch Transformer (H-FCBFormer). We also propose a method of generating medically true positive semantic segmentation masks derived from expert annotated articulating paper masks and gold standard masks. The proposed model outperforms other machine learning methods evaluated at detecting medically true positive contacts and performs better than dentists in terms of accurately identifying object-wise occlusal contact areas while taking significantly less time to identify them. Code is available at https://github.com/Banksylel/H-FCBFormer., Comment: 15 pages, 5 figures, 2 tables, 5 equations, peer reviewed and accepted to Medical Imaging Understanding and Analysis (MIUA 2024)

In recent years, the rapid increase in scientific papers has overwhelmed traditional review mechanisms, resulting in varying quality of publications. Although existing methods have explored the capabilities of Large Language Models (LLMs) for automated scientific reviewing, their generated contents are often generic or partial. To address the issues above, we introduce an automated paper reviewing framework SEA. It comprises of three modules: Standardization, Evaluation, and Analysis, which are represented by models SEA-S, SEA-E, and SEA-A, respectively. Initially, SEA-S distills data standardization capabilities of GPT-4 for integrating multiple reviews for a paper. Then, SEA-E utilizes standardized data for fine-tuning, enabling it to generate constructive reviews. Finally, SEA-A introduces a new evaluation metric called mismatch score to assess the consistency between paper contents and reviews. Moreover, we design a self-correction strategy to enhance the consistency. Extensive experimental results on datasets collected from eight venues show that SEA can generate valuable insights for authors to improve their papers.

The paper referred to in the title is Allard/Almgren 1981 [AA81]. Several months ago Francesco Maggi emailed me saying that the inequalities 5.3(4),(5) of \cite{AA81} were wrong. In fact, as he pointed out, their incorrectness is immediately apparent if one takes $Z=0$ there. Maggi and his coauthor wanted to use these inequalities in their paper \cite{MN}. They were able to obtain a version of these inequalities which suffice for the carrying out the work in \cite{MN}. I started writing this paper in order to provide a version of these inequalities as needed in [AA81]. In thinking about this material I began to realize there were other problems with the paper. As a result I ended up {\em completely rewriting 5.1-5.4 on pages 243-248 of \cite{AA81}}; this rewrite is the contents of this paper. In addition to many annoying misprints many of the needed definitions and proofs in 5.1-5.4 are incomplete or absent. For example, it is not said where $z$ in 5.1(2) comes from; this omission completely surprised me since I remember doing a lot of work to come up with $z$ when \cite{AA81} was being written. Also, much of the necessary material about the reach of a submanifold as in \cite{FE2} is not provided in \cite{AA81}. The table of contents can serve as an index. In particular one sees there where the constants $\epsilon_1$ through $\epsilon_6$ are introduced. This material is extremely technical. One way to navigate this paper would be to start looking at Proposition 9.5 and work backwards.

Testing in production-like test environments is an essential part of quality assurance processes in many industries. Provisioning of such test environments, for information-intensive services, involves setting up databases that are rich-enough to enable simulating a wide variety of user scenarios. While production data is perhaps the gold-standard here, many organizations, particularly within the public sectors, are not allowed to use production data for testing purposes due to privacy concerns. The alternatives are to use anonymized data, or synthetically generated data. In this paper, we elaborate on these alternatives and compare them in an industrial context. Further we focus on synthetic data generation and investigate the use of recurrent neural networks for this purpose. In our preliminary experiments, we were able to generate representative and highly accurate data using a recurrent neural network. These results open new research questions that we discuss here, and plan to investigate in our future research., Comment: This paper was published in the proceedings of RAISE@ICSE in 2019

Human-computer interaction scholars are increasingly touching on topics related to politics or democracy. As these concepts are ambiguous, an examination of concepts' invoked meanings aids in the self-reflection of our research efforts. We conduct a thematic analysis of all papers with the word `politics' in abstract, title or keywords ($n$=378) and likewise 152 papers with the word `democracy.' We observe that these words are increasingly being used in human-computer interaction, both in absolute and relative terms. At the same time, we show that researchers invoke these words with diverse levels of analysis in mind: the early research focused on mezzo-level (i.e., small groups), but more recently the work has begun to include macro-level analysis (i.e., society and politics as played in the public sphere). After the increasing focus on the macro-level, we see a transition towards more normative and activist research, in some areas it replaces observational and empirical research. These differences indicate semantic differences, which -- in the worst case -- may limit scientific progress. We bring these differences visible to help in further exchanges of ideas and human-computer interaction community to explore how it orients itself to politics and democracy.

A draft of a paper by Mandelbaum, "The dynamic complementarity problem", was circulated in 1987, but has never been published. We give an exposition of two important results from that paper which are not readily accessible in the literature. The first is an example of a Skorokhod problem in two dimensions in the quadrant for which there is not uniqueness. The second is a proof of uniqueness for the Skorokhod problem in two dimensions in the quadrant in a critical case.

This work is motivated by two key trends. On one hand, large language models (LLMs) have shown remarkable versatility in various generative tasks such as writing, drawing, and question answering, significantly reducing the time required for many routine tasks. On the other hand, researchers, whose work is not only time-consuming but also highly expertise-demanding, face increasing challenges as they have to spend more time reading, writing, and reviewing papers. This raises the question: how can LLMs potentially assist researchers in alleviating their heavy workload? This study focuses on the topic of LLMs assist NLP Researchers, particularly examining the effectiveness of LLM in assisting paper (meta-)reviewing and its recognizability. To address this, we constructed the ReviewCritique dataset, which includes two types of information: (i) NLP papers (initial submissions rather than camera-ready) with both human-written and LLM-generated reviews, and (ii) each review comes with "deficiency" labels and corresponding explanations for individual segments, annotated by experts. Using ReviewCritique, this study explores two threads of research questions: (i) "LLMs as Reviewers", how do reviews generated by LLMs compare with those written by humans in terms of quality and distinguishability? (ii) "LLMs as Metareviewers", how effectively can LLMs identify potential issues, such as Deficient or unprofessional review segments, within individual paper reviews? To our knowledge, this is the first work to provide such a comprehensive analysis.

In recent years, many studies have been focusing on predicting the scientific impact of research papers. Most of these predictions are based on citations count or rely on features obtainable only from already published papers. In this study, we predict the likelihood for a research paper of winning an award only relying on information available at publication time. For each paper, we build the citation subgraph induced from its bibliography. We initially consider some features of this subgraph, such as the density and the global clustering coefficient, to make our prediction. Then, we mix this information with textual features, extracted from the abstract and the title, to obtain a more accurate final prediction. We made our experiments considering the ArnetMiner citation graph, while the ground truth on award-winning papers has been obtained from a collection of best paper awards from 32 computer science conferences. In our experiment, we obtained an encouraging F1 score of 0.694. Remarkably, The high recall and the low false negatives rate, show how the model performs very well at identifying papers that will not win an award. This behavior can help researchers in getting a first evaluation of their work at publication time. Lastly, we made some first experiments on interpretability. Our results highlight some interesting patterns both in topological and textual features.

Research-paper blog posts help scientists disseminate their work to a larger audience, but translating papers into this format requires substantial additional effort. Blog post creation is not simply transforming a long-form article into a short output, as studied in most prior work on human-AI summarization. In contrast, blog posts are typically full-length articles that require a combination of strategic planning grounded in the source document, well-organized drafting, and thoughtful revisions. Can tools powered by large language models (LLMs) assist scientists in writing research-paper blog posts? To investigate this question, we conducted a formative study (N=6) to understand the main challenges of writing such blog posts with an LLM: high interaction costs for 1) reviewing and utilizing the paper content and 2) recurrent sub-tasks of generating and modifying the long-form output. To address these challenges, we developed Papers-to-Posts, an LLM-powered tool that implements a new Plan-Draft-Revise workflow, which 1) leverages an LLM to generate bullet points from the full paper to help users find and select content to include (Plan) and 2) provides default yet customizable LLM instructions for generating and modifying text (Draft, Revise). Through a within-subjects lab study (N=20) and between-subjects deployment study (N=37 blog posts, 26 participants) in which participants wrote blog posts about their papers, we compared Papers-to-Posts to a strong baseline tool that provides an LLM-generated draft and access to free-form LLM prompting. Results show that Papers-to-Posts helped researchers to 1) write significantly more satisfying blog posts and make significantly more changes to their blog posts in a fixed amount of time without a significant change in cognitive load (lab) and 2) make more changes to their blog posts for a fixed number of writing actions (deployment)., Comment: 28 pages, 9 figures in main text (not appendix)

Recent advancements in Artificial Intelligence (AI), particularly the widespread adoption of Large Language Models (LLMs), have significantly enhanced text analysis capabilities. This technological evolution offers considerable promise for automating the review of scientific papers, a task traditionally managed through peer review by fellow researchers. Despite its critical role in maintaining research quality, the conventional peer-review process is often slow and subject to biases, potentially impeding the swift propagation of scientific knowledge. In this paper, we propose RelevAI-Reviewer, an automatic system that conceptualizes the task of survey paper review as a classification problem, aimed at assessing the relevance of a paper in relation to a specified prompt, analogous to a "call for papers". To address this, we introduce a novel dataset comprised of 25,164 instances. Each instance contains one prompt and four candidate papers, each varying in relevance to the prompt. The objective is to develop a machine learning (ML) model capable of determining the relevance of each paper and identifying the most pertinent one. We explore various baseline approaches, including traditional ML classifiers like Support Vector Machine (SVM) and advanced language models such as BERT. Preliminary findings indicate that the BERT-based end-to-end classifier surpasses other conventional ML methods in performance. We present this problem as a public challenge to foster engagement and interest in this area of research.

This document addresses the description of the corresponding "Urubots" Team for the 2024 Fira Air League, "Air Emergency Service (Indoor)." We introduce our team and an autonomous Unmanned Aerial Vehicle (UAV) that relies on computer vision for its flight control. This UAV has the capability to perform a wide variety of navigation tasks in indoor environments, without requiring the intervention of an external operator or any form of external processing, resulting in a significant decrease in workload and manual dependence. Additionally, our software has been designed to be compatible with the vehicle's structure and for its application to the competition circuit. In this paper, we detail additional aspects about the mechanical structure, software, and application to the FIRA competition., Comment: Team Description Paper for the FIRA RoboWorld Cup 2024

This document presents the design of an autonomous car developed by the UruBots team for the 2024 FIRA Autonomous Cars Race Challenge. The project involves creating an RC-car sized electric vehicle capable of navigating race tracks with in an autonomous manner. It integrates mechanical and electronic systems alongside artificial intelligence based algorithms for the navigation and real-time decision-making. The core of our project include the utilization of an AI-based algorithm to learn information from a camera and act in the robot to perform the navigation. We show that by creating a dataset with more than five thousand samples and a five-layered CNN we managed to achieve promissing performance we our proposed hardware setup. Overall, this paper aims to demonstrate the autonomous capabilities of our car, highlighting its readiness for the 2024 FIRA challenge, helping to contribute to the field of autonomous vehicle research., Comment: Team Description Paper for the FIRA RoboWorld Cup 2024

This paper proposes a mini autonomous car to be used by the team UruBots for the 2024 FIRA Autonomous Cars Race Challenge. The vehicle is proposed focusing on a low cost and light weight setup. Powered by a Raspberry PI4 and with a total weight of 1.15 Kilograms, we show that our vehicle manages to race a track of approximately 13 meters in 11 seconds at the best evaluation that was carried out, with an average speed of 1.2m/s in average. That performance was achieved after training a convolutional neural network with 1500 samples for a total amount of 60 epochs. Overall, we believe that our vehicle are suited to perform at the FIRA Autonomous Cars Race Challenge 2024, helping the development of the field of study and the category in the competition., Comment: Team Description Paper for the FIRA RoboWorld Cup 2024

An emerging area of research in situated and multimodal interactive conversations (SIMMC) includes interactions in scientific papers. Since scientific papers are primarily composed of text, equations, figures, and tables, SIMMC methods must be developed specifically for each component to support the depth of inquiry and interactions required by research scientists. This work introduces Conversational Papers (cPAPERS), a dataset of conversational question-answer pairs from reviews of academic papers grounded in these paper components and their associated references from scientific documents available on arXiv. We present a data collection strategy to collect these question-answer pairs from OpenReview and associate them with contextual information from LaTeX source files. Additionally, we present a series of baseline approaches utilizing Large Language Models (LLMs) in both zero-shot and fine-tuned configurations to address the cPAPERS dataset., Comment: 14 pages, 1 figure