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Starting this year, tens of thousands of Russian freshmen found themselves attending a new mandatory course -- "Foundations of Russian Statehood." Swiftly designed under the auspices of Putin's administration, this ideologically charged course aims to position Russia as a unique civilization-state, bolstering Putin's political narrative and providing justification for the full-scale invasion of Ukraine. Consider, for example, this excerpt from the course's instructional video: "The 'Russian world' extends beyond current Russian borders, transcending ethnicities, territories, religions, political systems, and ideological preferences." As this curriculum becomes standard in Russian universities, it contributes to the emerging trend of weaponizing Russian universities and turning them into instruments in Russia's war of attrition with Ukraine and its broader stand-off with the West. This report discusses this weaponization process and the impact it is having on Russian universities, faculty, students, and the academic communities they belong to. It is regrettably a story of back to the future, reminiscent of the Soviet era of repression and attempts at control and manipulation of academics.

China's higher-education system has been shaped in recent years by a trio of factors: the COVID-19 pandemic, the ambitions of Chinese leader Xi Jinping to make his country into an innovation superpower that is loyal to the Communist Party, and western alarm about those ambitions. But a fourth development, the slowing of China's formerly super-charged economy, could play a more prominent role going forward. This article examines these four factors.

Since 2020, the federal government has distributed almost $2 billion to American school districts to mitigate the effects of COVID-19 pandemic school closures. Released in three separate tranches through the Elementary and Secondary School Emergency Relief (ESSER) fund, the Trump and Biden administrations outlined parameters for spending this unprecedented amount of money and ensured that most of it went directly to local school districts, with a comparative sliver going to state education agencies. This report gives an overview of the most common ESSER expenditures to date and assesses the factors that might influence whether district ESSER investments will make a difference for students. It also provides recommendations for how stakeholders should evaluate the ESSER experiment once it officially ends.

Waiting times between successive events play an important role in population dynamics and can vary widely, notably due to environmental conditions. In this work, we focus on the zero-sum rock-paper-scissors (zRPS) model, broadly used to describe cyclic dominance in ecology and biology, with non-Markovian dynamics and study how its survival and fixation properties are affected by interevent times. When the dynamics are Markovian, with exponentially-distributed waiting times, the fate of zRPS games in large well-mixed populations obeys the simple, yet counterintuitive, ``law of the weakest'': the species with the lowest predation-reproduction rate is the most likely to prevail, with a fixation/survival probability asymptotically approaching one. Here, we systematically study how the fixation/survival behavior is influenced by power-law and gamma waiting time distributions, and analyze the deviations from the law of the weakest. Importantly, we determine the phases of the parameter space in which each species dominates, and how these crucially depend on memory and history-dependent interactions. Analytical calculations, which yield nontrivial memory kernels, support our numerical results, shedding light on the evolutionary influence of long waiting times., Comment: 14 pages, 8 figures

As the body of academic literature continues to grow, researchers face increasing difficulties in effectively searching for relevant resources. Existing databases and search engines often fall short of providing a comprehensive and contextually relevant collection of academic literature. To address this issue, we propose a novel framework that leverages Natural Language Processing (NLP) techniques. This framework automates the retrieval, summarization, and clustering of academic literature within a specific research domain. To demonstrate the effectiveness of our approach, we introduce CyLit, an NLP-powered repository specifically designed for the cyber risk literature. CyLit empowers researchers by providing access to context-specific resources and enabling the tracking of trends in the dynamic and rapidly evolving field of cyber risk. Through the automatic processing of large volumes of data, our NLP-powered solution significantly enhances the efficiency and specificity of academic literature searches. We compare the literature categorization results of CyLit to those presented in survey papers or generated by ChatGPT, highlighting the distinctive insights this tool provides into cyber risk research literature. Using NLP techniques, we aim to revolutionize the way researchers discover, analyze, and utilize academic resources, ultimately fostering advancements in various domains of knowledge.

Ensuring system correctness, such as memory safety, can eliminate security vulnerabilities that attackers could exploit in the first place. However, high and unpredictable performance degradation remains a primary challenge. Recognizing that it is extremely difficult to achieve complete system correctness for production deployment, researchers make trade-offs between performance, detection coverage, interoperability, precision, and detection timing. This research strikes a balance between comprehensive system protection and the costs required to obtain it, identifies the desirable roles of software and hardware, and presents a tagged pointer-based capability system as a stand-alone software solution and a prototype for future hardware design. This paper presents follow-up plans for the FRAMER/Miu generic framework to achieve these goals., Comment: 8 pages, 4 figures

Control Barrier Functions (CBFs) have emerged as a powerful paradigm in control theory, providing a principled approach to enforcing safety-critical constraints in dynamic systems. This survey paper comprehensively explores the foundational principles of CBFs, delves into the complexities of High Order Control Barrier Functions (HOCBFs), and extends the discussion to the adaptive realm with adaptive Control Barrier Functions (aCBFs). Through a systematic examination of theoretical underpinnings, practical applications, and the evolving landscape of research, this survey highlights the versatility of CBFs in addressing safety and stability challenges., Comment: 6 pages, 1 figure

This paper estimates the causal effect of mineral trade on conflicts in Africa using a Shift-Share IV approach based on an exogenous price-commodity shock. The main result is that an increase in mineral trade significantly increases the number of conflicts while it has no clear effect on fatalities. Exploring heterogeneous effects, I find that a specific group of minerals, oil and fuels, drives the results on the number of conflicts. Moreover, the group of rare minerals such as coltan, precious metals or cobalt has no effect on the number of conflicts but appears to have an important impact on the number of fatalities., Comment: 25 pages (with appendix)

We advocate for a large scale imaging survey of nearby young moving groups and star-forming regions to directly detect exoplanets over an unexplored range of masses, ages and orbits. Discovered objects will be identified early enough in JWST's lifetime to leverage its unparalleled capabilities for long-term atmospheric characterisation, and will uniquely complement the known population of exoplanets and brown dwarfs. Furthermore, this survey will constrain the occurrence of the novel wide sub-Jovian exoplanet population, informing multiple theories of planetary formation and evolution. Observations with NIRCam F200W+F444W dual-band coronagraphy will readily provide sub-Jupiter mass sensitivities beyond ~0.4" (F444W) and can also be used to rule out some contaminating background sources (F200W). At this large scale, targets can be sequenced by spectral type to enable robust self-referencing for PSF subtraction. This eliminates the need for dedicated reference observations required by GO programs and dramatically increases the overall science observing efficiency. With an exposure of ~30 minutes per target, the sub-Jupiter regime can be explored across 250 targets for ~400 hours of exposure time including overheads. An additional, pre-allocated, ~100 hours of observing time would enable rapid multi-epoch vetting of the lowest mass detections (which are undetectable in F200W). The total time required for a survey such as this is not fixed, and could be scaled in conjunction with the minimum number of detected exoplanet companions., Comment: 5 pages, 2 figures. This white paper was submitted following a call from the "Working Group on Strategic Exoplanet Initiatives with HST and JWST" (https://sites.google.com/view/exoplanet-strategy-wg, final report in 10.48550/arXiv.2404.02932)

We advocate for further prioritisation of atmospheric characterisation observations of high mass transiting exoplanets and brown dwarfs. This population acts as a unique comparative sample to the directly imaged exoplanet and brown dwarf populations, of which a range of JWST characterisation observations are planned. In contrast, only two observations of transiting exoplanets in this mass regime were performed in Cycle 1, and none are planned for Cycle 2. Such observations will: improve our understanding of how irradiation influences high gravity atmospheres, provide insights towards planetary formation and evolution across this mass regime, and exploit JWST's unique potential to characterise exoplanets across the known population., Comment: 4 pages, 2 figures. This white paper was submitted following a call from the "Working Group on Strategic Exoplanet Initiatives with HST and JWST" (https://sites.google.com/view/exoplanet-strategy-wg, final report in 10.48550/arXiv.2404.02932)

We present a commentary on the famous 1964 paper of John Bell that rules out the entire class of underlying hidden variable theories for quantum mechanics that are local., Comment: Commentary, 4 pages, to appear in the November issue of Resonance

Equitable urban transportation applications require high-fidelity digital representations of the built environment: not just streets and sidewalks, but bike lanes, marked and unmarked crossings, curb ramps and cuts, obstructions, traffic signals, signage, street markings, potholes, and more. Direct inspections and manual annotations are prohibitively expensive at scale. Conventional machine learning methods require substantial annotated training data for adequate performance. In this paper, we consider vision language models as a mechanism for annotating diverse urban features from satellite images, reducing the dependence on human annotation to produce large training sets. While these models have achieved impressive results in describing common objects in images captured from a human perspective, their training sets are less likely to include strong signals for esoteric features in the built environment, and their performance in these settings is therefore unclear. We demonstrate proof-of-concept combining a state-of-the-art vision language model and variants of a prompting strategy that asks the model to consider segmented elements independently of the original image. Experiments on two urban features -- stop lines and raised tables -- show that while direct zero-shot prompting correctly annotates nearly zero images, the pre-segmentation strategies can annotate images with near 40% intersection-over-union accuracy. We describe how these results inform a new research agenda in automatic annotation of the built environment to improve equity, accessibility, and safety at broad scale and in diverse environments.

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.]

This paper presents Papilusion, an AI-generated scientific text detector developed within the DAGPap24 shared task on detecting automatically generated scientific papers. We propose an ensemble-based approach and conduct ablation studies to analyze the effect of the detector configurations on the performance. Papilusion is ranked 6th on the leaderboard, and we improve our performance after the competition ended, achieving 99.46 (+9.63) of the F1-score on the official test set., Comment: to appear in "The 4th Workshop on Scholarly Document Processing @ ACL 2024" proceedings

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

Previous research on the Adiar BDD package has been successful at designing algorithms capable of handling large Binary Decision Diagrams (BDDs) stored in external memory. To do so, it uses consecutive sweeps through the BDDs to resolve computations. Yet, this approach has kept algorithms for multi-variable quantification, the relational product, and variable reordering out of its scope. In this work, we address this by introducing the nested sweeping framework. Here, multiple concurrent sweeps pass information between eachother to compute the result. We have implemented the framework in Adiar and used it to create a new external memory multi-variable quantification algorithm. Compared to conventional depth-first implementations, Adiar with nested sweeping is able to solve more instances of our benchmarks and/or solve them faster., Comment: 26 pages, 14 figures, 2 tables

We present a detailed analysis of the detached eclipsing binary system HO Telescopii, which contains two A-type stars in a circular orbit of period 1.613 d. We use light curves from the Transiting Exoplanet Survey Satellite (TESS), which observed HO Tel in three sectors, to determine its photometric properties and a precise orbital ephemeris. We augment these results with radial velocity measurements from Surgit et al. to determine the masses and radii of the component stars: M_A = 1.906 +/- 0.031 Msun, M_B = 1.751 +/- 0.034 Msun, R_A = 2.296 +/- 0.027 Rsun and R_B = 2.074 +/- 0.028 Rsun. Combined with temperature measurements from Surgit et al. and optical-infrared apparent magnitudes from the literature, we find a distance to the system of 280.8 +/- 4.6 pc which agrees well with the distance from the Gaia DR3 parallax measurement. Theoretical predictions do not quite match the properties of the system, and there are small discrepancies in measurements of the spectroscopic orbits of the stars. Future observations from Gaia will allow further investigation of these issues., Comment: Accepted for publication in The Observatory. 12 pages, 5 tables, 3 black/white figures

The cross-prompt injection attack (XPIA) is an effective technique that can be used for data exfiltration, and that has seen increasing use. In this attack, the attacker injects a malicious instruction into third party data which an LLM is likely to consume when assisting a user, who is the victim. XPIA is often used as a means for data exfiltration, and the estimated cost of the average data breach for a business is nearly $4.5 million, which includes breaches such as compromised enterprise credentials. With the rise of gradient-based attacks such as the GCG suffix attack, the odds of an XPIA occurring which uses a GCG suffix are worryingly high. As part of my work in Microsoft's AI Red Team, I demonstrated a viable attack model using a GCG suffix paired with an injection in a simulated XPIA scenario. The results indicate that the presence of a GCG suffix can increase the odds of successful data exfiltration by nearly 20%, with some caveats., Comment: 8 pages, 8 figures. Conducted as part of employment at Microsoft Corporation

The iterative character of work in machine learning (ML) and artificial intelligence (AI) and reliance on comparisons against benchmark datasets emphasize the importance of reproducibility in that literature. Yet, resource constraints and inadequate documentation can make running replications particularly challenging. Our work explores the potential of using downstream citation contexts as a signal of reproducibility. We introduce a sentiment analysis framework applied to citation contexts from papers involved in Machine Learning Reproducibility Challenges in order to interpret the positive or negative outcomes of reproduction attempts. Our contributions include training classifiers for reproducibility-related contexts and sentiment analysis, and exploring correlations between citation context sentiment and reproducibility scores. Study data, software, and an artifact appendix are publicly available at https://github.com/lamps-lab/ccair-ai-reproducibility ., Comment: 9 pages, 4 figures

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.]

To comply with the Every Student Succeeds Act of 2015 and related U.S. Department of Education peer review guidance, state education agencies must provide evidence demonstrating clear alignment (technically referred to as correspondence) between their K-12 English language proficiency standards and their academic content standards. This technical paper provides evidence of match, breadth, consistency [balance of representation], and depth of correspondence between WIDA consortium member state academic content standards in English language arts, mathematics, science, and social studies, as of Fall 2022, and the components of the WIDA English Language Development Standards Framework, 2020 Edition--specifically the four WIDA Key Language Uses and their grade-level cluster and content area instantiation in the WIDA Language Expectations. It also illustrates the relationship between the grade-level cluster Language Expectations and Proficiency Level 5 of the Proficiency Level Descriptors. State correspondence evidence helps ensure that K-12 students identified as English learners (referred to as multilingual learners by WIDA) have the opportunity to learn the critical elements of language that facilitate access to and achievement of academic content. [For the summary report, see ED631052.]

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 OAG-Challenge PST track, 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.