bluuebunny/crossref_metadata_2025 huggingface dataset:
Fecal microbiota transplantation for the treatment of intestinal diseases after allogeneic hematopoietic stem cell transplantation in children
Authors: Seregin G. Z. , Bespyatykh Yu. A. , Gospodarik A. V. , Kirillov M. Yu. , Ogneva L. V. , Zhilina S. V. , Burtsev E. A. , Burya A. E. , Vlasova I. O. , Zhuravel M. R. , Zhuravel E. A. , Konstantinova V. V. , Kurmanov B. M. , Lifshits A. V. , Natrusova M. V. , Donnikov A. E. , Maschan M. A. , Bronin G. O.
Published: July 2024

Fecal microbiota transplantation (FMT) is a promising therapy for a variety of diseases involving the gastrointestinal (GI) tract. The purpose of our study is to evaluate the effectiveness of FMT for the treatment of different GI complications after allogenic hematopoietic stem cell transplantation (steroid-refractory and steroid-dependent graft versus host disease (SR/ SD GVHD) and infectious enterocolitis) in children from 1.5 to 18 years old. The study was approved by the Local Ethics Committee of the Morozov Children's Clinical Hospital of the Department of Health of Moscow (No. 176 dated 24.05.2022). The patients' parents gave their informed consent to the participation of their children in the study and the publication of the study results. SR/SD GVHD group included 14 patients and GI infections group – 11 patients. In total, 38 FMTs were performed. Eleven children underwent repeated FMTs due to the lack of effect after the first FMT. The resolution of diarrhea followed by the withdrawal of corticosteroids was recorded in 11 out of 14 patients with SR/SD GVHD. Ten out of 11 patients with GI infections also achieved relief of diarrhea. The clinical effect correlated with changes in the microbiome structure: for both groups of patients, the median proportion of normal microbiota before FMT was 6%, after FMT – 92% (p = 0.043). The median number of different taxa for both groups of patients before and after FMT were 3 and 10 taxa, respectively (p = 0.0016).
Knowledge, perception, and practice of health professions students and academics regarding halal pharmaceuticals: A cross-sectional study
Authors: Jaber Deema , Al Tabbah Samaa Akram , Elsalem Lina , Zaid Alkilani Ahlam , Bani Omar Eman , Abu Hammour Khawla , Hasan Hisham E. , Salman Nadeem
Published: July 2024

This cross-sectional study examines the knowledge, perception, and practice of health professions students and academics in Jordan concerning halal pharmaceuticals. Health professions students and academics from various universities in Jordan were surveyed using a structured questionnaire. Data analysis included descriptive statistics and inferential tests to identify factors affecting knowledge, perception, and practice. Participants had a high level of awareness regarding general halal and haram concepts, but there was relatively lower awareness of the term “halal pharmaceuticals” and detailed information about non-halal ingredients. Knowledge scores varied between students and academics, with academics scoring higher. Participants exhibited positive perceptions, acknowledging the importance of knowledge about halal pharmaceuticals and patients’ rights to inquire about medication sources and ingredients. Concerns were raised about the potential controversy surrounding the topic. This research contributes to understanding the role of halal pharmaceuticals in healthcare, particularly in predominantly Muslim countries. The findings highlight the importance of integrating education on halal pharmaceuticals into healthcare curricula, emphasizing patient-centered care, and addressing cultural and religious sensitivity. There is a need for tailored educational approaches and sensitivity training to bridge the gap between knowledge and practice.
Perspectives on project finance and renewable energy infrastructure research
Authors: Gómez-Valencia Santiago , Gonzalez-Ruiz Juan David , Marín-Rodriguez Nini-Johana
Published: July 2024

This study analyzes the studies on project finance (PF) and renewable energy (RE) arena, employing a comprehensive scientometric analysis to illuminate the current research landscape, identify prominent scholars, and uncover emerging trends. Encompassing several analyses, we have charted the evolution of this domain from 1993 to March 2024 and showed the way for further research. We analyzed 80 studies selected from several databases by means scientometric tools. Despite decent citation rates, research in this relatively young field is surprisingly scarce. While geographically diverse, research leadership stems from the UK, USA, Australia, and Germany. Interestingly, a significant portion of the studies originates from broad energy and sustainability areas, highlighting a potential knowledge gap in finance and economics areas. Additionally, the prevalence of case studies points to a strong connection between theory and practice. The research also revealed prominent topics like the interplay between PF and RE, various renewable resources, infrastructure development, financial considerations, risk management, among others. While many themes exist, areas like technological advancements, diverse cost approaches, valuation methodologies, and policy considerations remain underexplored. Other results unveiled an unexpected finding: limited evidence of large-scale collaborations, with individual or small-group research efforts currently dominating the field. However, existing collaborative networks promise future advancements through the emergence of more formalized research groups, which can perform future research endeavors with a wide spectrum of unexplored topics.
Non-Face-to-Face interactions in digital extension for innovating in farming communication: A case from South Sulawesi, Indonesia
Authors: Dahlan Sri Sasmita , Bulkis Sitti , Akhsan Akhsan , Salman Darmawan
Published: July 2024

The utilization of digital tools in agricultural extension has facilitated information delivery through non-face-to-face interactions. Therefore, this study aimed to map the variation in digital tools used by agricultural extension workers to access and deliver information and analyse the outcomes of farmers’ adoption. Data were collected through in-depth interviews with agricultural extension workers at 11 Agricultural Extension Centers. The data were processed using the N-Vivo qualitative data analysis software. The results showed that extension workers combined various digital tools as sources of extension materials and channels for delivering information to farmers. Although social interaction between agricultural extension workers and farmers occurred non-face-to-face, messages could be adopted by farmers and yield tangible outcomes. This was reflected in the asynchronous communication, allowing extension workers sufficient time to improve the quality of the delivered messages. Farmers also had sufficient time to review the received information content in this context repeatedly. These results implied that although extension content is delivered through non-face-to-face interaction, it can still drive adoption with significant outcomes.
Cystine and urate cystoliths in dogs are frequently visible on radiographs prior to surgical or nonsurgical removal
Authors: Andrews Camille , Williams Rachel , Arjoonsingh Avin , Vilaplana Grosso Federico R.
Published: July 2024

OBJECTIVE

To investigate the frequency at which cystine and urate cystoliths (stones) are visible on radiographs prior to surgical or nonsurgical retrieval.

METHODS

Records of client-owned dogs (n = 331) were analyzed between January 2019 and December 2023 for cystoliths submitted for stone analysis after surgical removal or nonsurgical retrieval. Records were analyzed for cystolith type; when cystine or urate stones were identified, records were analyzed for signalment, procedure, presence of mineral opaque cystoliths on pre-procedural radiographs, urine pH and crystalluria, history of previous cystoliths, prior prescription diet attempt, recurrence, and genetic, congenital and acquired comorbidities. Descriptive statistics were generated after data collection.

RESULTS

31 of 331 (9%) were cystine stones, 49 of 331 (15%) were urate, and 1 of 331 (0.3%) was a mix of urate and cystine. When radiographs were taken prior to stone removal, 24 of 28 (85%) of urate, 24 of 26 (92%) of cystine, and 1 of 1 (100%) of urate/cystine were visible on radiographs.

CONCLUSIONS

Cystine and urate stones are visible on survey radiography at a high frequency in dogs.

CLINICAL RELEVANCE

While cystine and urate stones have been historically designated as radiolucent, they are frequently radiopaque on radiographs. Radiopacity is commonly used as one of the criterion to determine whether a dissolution or prevention diet is an appropriate management technique, particularly when determination of the stone type has yet to be performed. As a result, these findings may prompt clinicians to investigate other patient-specific factors before a specific dietary recommendation is made.
Ultrasound is an accurate imaging modality for diagnosing hip luxation in dogs presenting with hind limb lameness
Authors: Todd-Donato Amy B. , VanDeventer Gretchen M. , Porter Ian R. , Krotscheck Ursula
Published: July 2024

OBJECTIVE

To determine the accuracy of ultrasound for diagnosing hip luxation in dogs presenting for hind limb lameness.

METHODS

24 client-owned dogs presenting with pelvic limb lameness and concern for hip luxation were enrolled in this prospective diagnostic accuracy study from April 1, 2021, to July 1, 2022. An experienced ultrasonographer and a novice ultrasonographer, both masked to the diagnoses, performed hip ultrasonography using a point-of-care ultrasound machine. The experienced evaluator also performed a more comprehensive ultrasonography with a high-end ultrasound machine to characterize concurrent pelvis and hip disease. Pelvic radiographs served as the criterion standard for the diagnosis of hip luxation.

RESULTS

Ultrasonographic diagnosis of hip luxation demonstrated high accuracy, ranging from 84.2% to 100% for detecting the presence of luxation and 80.6% to 98.1% for diagnosing the direction of luxation for the novice and experienced evaluators, respectively, with substantial interevaluator agreement (κ = 0.722). Compared to radiographs, ultrasound accurately diagnosed the presence of osteoarthrosis with almost perfect agreement (κ = 0.913) and the presence of fractures of the femoral head/neck and the nonaxial portions of the pelvis with substantial intermodality agreement (κ = 0.775). In 36% of patients, ultrasound identified injury to soft tissue structures that contribute to hip stability, including the gluteal muscles, gemelli, and joint capsule.

CONCLUSIONS

Ultrasound can be utilized to accurately diagnose the presence of hip luxation and document concurrent hip and pelvic disease.

CLINICAL RELEVANCE

Applications include the use of ultrasonography as part of a routine point-of-care protocol to catalogue injuries in patients with acute trauma and in mobile practice or remote settings where radiography is not readily available.
Rabies surveillance in the United States during 2022
Authors: Ma Xiaoyue , Boutelle Cassandra , Bonaparte Sarah , Orciari Lillian A. , Condori Rene E. , Kirby Jordona D. , Chipman Richard B. , Fehlner-Gardiner Christine , Thang Cin , Cedillo Veronica Gutiérrez , Aréchiga-Ceballos Nidia , Nakazawa Yoshinori , Wallace Ryan M.
Published: July 2024

OBJECTIVE

To provide comprehensive epidemiological information about the distribution and occurrence of rabies during 2022 in the US, Canada, and Mexico.

METHODS

The US National Rabies Surveillance System collected 2022 animal rabies data from US state and territorial public health departments and USDA Wildlife Services. Temporal and geographic analyses were conducted to evaluate trends in animal rabies cases.

RESULTS

During 2022, 54 US jurisdictions reported 3,579 animal rabies cases, reflecting a 2.3% decline from 3,663 cases reported in 2021. Six states collectively reported > 50% of animal rabies cases: Texas (395 [11.0%]), Virginia (337 [9.4%]), Pennsylvania (329 [9.2%]), New York (267 [7.5%]), North Carolina (264 [7.4%]), and California (241 [6.7%]). Out of the total reported rabies animal cases, 3,234 (90.4%) were attributed to wildlife, with bats (1,218 [34.0%]), raccoons (1,014 [28.3%]), skunks (660 [18.4%]), and foxes (269 [7.5%]) representing the primary hosts confirmed with rabies. Rabid cats (222 [6.2%]), cattle (42 [1.2%]), and dogs (50 [1.4%]) constituted > 90% of reported domestic animal rabies cases.

CONCLUSIONS

In 2022, there was an increase in the number of animal samples submitted for rabies testing in the US and Canada. A notable geographic expansion of gray fox rabies virus variant was detected in the US. Three human rabies deaths due to vampire bat rabies infection occurred in Mexico; none were reported from the US and Canada.

CLINICAL RELEVANCE

Laboratory diagnosis of rabies in animals is critical to ensure judicious use of human rabies postexposure prophylaxis.
A patient with Beta-Propeller Protein-Associated Neurodegeneration: a new missense mutation of the WDR45 gene
Authors: Ivanova Anastasia , Kulikova Sviatlana , Sivitskaya Larysa , Danilenko Nina , Davydenko Oleg
Published: July 2024

Introduction

Beta-propeller protein-associated neurodegeneration (BPAN) is a neurodegenerative disorder; its estimated prevalence is 2 to 3 per million individuals. All published cases of BPAN have been sporadic, with a clear female predominance and mutations in various exons of the WDR45 gene.

Case presentation

The study aimed to confirm the diagnosis of BPAN in a 9-year-old girl with a developmental delay since early childhood complicated with intellectual disability, lack of speech and febrile and non-febrile tonic-clonic seizures. The patient also had autistic symptoms as well as some Rett-like symptoms: stereotypical movements of the hands–twisting objects, putting hands in the mouth.

Discussion

Clinical exome analysis and Sanger sequencing of the proband have been performed to confirm the diagnosis. The novel heterozygous missense mutation c.755T>C of the WDR45 «autophagy» gene was revealed. Sanger sequencing of the trio (proband and parents) proved the de novo nature of mutation; its clinical significance has been defined as probably pathogenic. Thus, we report a new missense variant of the WDR45 gene in a girl with a clinical picture of BPAN. The use of NGS made it possible to get a correct diagnosis during rather a short period before the second debilitating phase of the disease started so that the physicians and the family would have time to prepare and hopefully choose the way to resist.
EEG Patterns Predicting Clobazam Response in Refractory Status Epilepticus in Adults
Authors: Manmatharayan Arichena , Andrade-Machado Rene , Marawar Rohit , Lee Paul , Lagnf Abdalhamid , Souidan Hassan , Ataya Taha , Abdelhak Farah , Thomas Rohan , Fakhouri Rami , Motii Younes , Rehman Sarah , Durban Margerie , Parker Dennis , Mohamed Wazim , Elsayed Mona
Published: July 2024

Background

Seizure resolution is often difficult to attain promptly in status epilepticus (SE). Clobazam (CLB) is structurally different from typical benzodiazepines with an affinity to the α2 subunit of GABA-A receptors, rendering unique efficacy in seizure termination.

Aim

To assess the response of clobazam in refractory status epilepticus (RSE) and to correlate this response with EEG patterns.

Materials and Methods

This was a single-center retrospective study conducted at a neurocritical care unit between January 2018 and June 2023. The study included fifty consecutive patients with RSE, out of which sixteen adult patients had continuous EEG monitoring both before and during treatment with clobazam until the resolution of RSE. Patients for whom clobazam was the last anti-seizure medication (ASM) used were included in the study. Thirty-four patients were excluded for various reasons, like lack of continuous EEG monitoring (cEEG), status post cardiac arrest, exclusion of clobazam due to medical issues or adverse events, or admission to the hospice floor.

Results

Out of the sixteen patients included in the study, twelve (75%) had focal nonconvulsive RSE on cEEG and four had focal convulsive RSE when clobazam was administered. Thirteen out of sixteen patients (81.25%) responded successfully to clobazam, with an average time of 23.5 hours since starting the medication. On average, three other ASMs were tried before clobazam, initiated at a mean time of 27 hours since SE onset. The study identified EEG patterns correlated with the use of clobazam :1) lateralised periodic discharges that evolved from 1–1.5 Hz to 2–3 Hz, then evolved to rhythmic theta/delta activity with embedded LPDs; 2) repetitive spikes that evolved to theta and delta frequencies (good responders to CLB), and iii) low voltage faster frequencies and evolved to theta delta frequencies (less responders to CLB).

Conclusion

Rapid pharmacoresistance to first-line benzodiazepines and ASM occurs as seizures persist during SE. Our study provides EEG correlates to the successful use of CLB in RSE based on its unique targeting of the GABA-A subtype receptors involved in postsynaptic phasic inhibition.
An exploration of the child’s perspective in the illustrated book Nicholas by Goscinny and Sempé
Authors: Sousa Constança Freire de
Published: July 2024

Since the beginning of time, storytelling for children has always had a strong pedagogical element, passing along messages that can shape the child-reader. While there are many in-depth studies of fairy tales and their evolution, as well as studies on children’s literature from an English-speaking background, the research is lacking in certain niches of literature. Breaching this gap and analysing a set of short stories such as Nicholas allows for an understanding of how these narratives might have shaped their target audiences, as well as who is represented and how. A combined approach of critical literacy with educational theory, in a qualitative study, structures this article, working to understand how semiotics and the concept of Othering are explored in these stories and how they might contribute to a better understanding of the self, others, and interpersonal and intercultural relationships for the child-reader who consumes them.
CUSTOS VULNERABILIS E ACESSO À JUSTIÇA
Authors: Maciel Pinheiro Pereira Carlos André , Rocha Andrade Filipe
Published: July 2024

O presente artigo tem por objetivo discutir a função custos vulnerabilis atribuída à Defensoria Pública, relacionando-a à democratização da garantia fundamental de acesso à justiça, com vistas à correlação entre a atuação daquele órgão enquanto custos vulnerabilis e os impactos na busca pela concretização de uma ordem jurídica justa. Para tanto, emprega o método dedutivo, com utilização de referencial bibliográfico nas áreas de direito constitucional, processo civil e processo penal combinadas com análise jurisprudencial. Assim, pretende formar pesquisa qualitativa, explicativa e exploratória. Aborda que o conceito mais adequado de acesso à justiça advém da teoria clássica, desenvolvida por Cappelletti e Garth, somada às ideias de acesso ao direito, de origem portuguesa. Entende que o fortalecimento da instituição é preponderante para a derrocada das barreiras ao acesso à justiça – custas processuais, possibilidade das partes e falta de legitimado para propositura de ações coletivas – e que a atuação defensorial não poderá se limitar a defesa de direitos de pessoas economicamente hipossuficientes. Compreende que a atuação da Defensoria Pública, na qualidade de custos vulnerabilis confere características de legitimidade ativa à instituição, tendo em vista que essa atuará em nome próprio, podendo manejar quaisquer recursos que se mostrem adequados à marcha processual, exercida em favor de pessoas ou grupos sociais vulneráveis, com o intuito de equilibrar o jogo processual. Ao término, conclui-se como positivo o reconhecimento da citada missão defensorial, ao passo que compatibiliza a atuação da Defensoria Pública ao conceito de acesso à justiça, definido ao longo do trabalho.
From 2D to 3D: AISG-SLA Visual Localization Challenge
Authors: Gao Jialin , Ong Bill , Lwi Darld , Ng Zhen Hao , Yee Xun Wei , Mak Mun-Thye , Ng Wee Siong , Ng See-Kiong , Teo Hui Ying , Khoo Victor , Bökman Georg , Edstedt Johan , Brodt Kirill , Boittiaux Clémentin , Ferrera Maxime , Konev Stepan
Published: July 2024

Research in 3D mapping is crucial for smart city applications, yet the cost of acquiring 3D data often hinders progress. Visual localization, particularly monocular camera position estimation, offers a solution by determining the camera's pose solely through visual cues. However, this task is challenging due to limited data from a single camera. To tackle these challenges, we organized the AISG–SLA Visual Localization Challenge (VLC) at IJCAI 2023 to explore how AI can accurately extract camera pose data from 2D images in 3D space. The challenge attracted over 300 participants worldwide, forming 50+ teams. Winning teams achieved high accuracy in pose estimation using images from a car-mounted camera with low frame rates. The VLC dataset is available for research purposes upon request via vlc-dataset@aisingapore.org.
Plug-and-Play Unsupervised Fault Detection and Diagnosis for Complex Industrial Monitoring
Authors: Golyadkin Maksim , Shtark Maria , Ivanov Petr , Kozhevnikov Alexander , Zhukov Leonid , Makarov Ilya
Published: July 2024

Today industrial facilities are equipped with lots of sensors throughout all the production line for monitoring means. Gathered data can be used to detect and predict failures; however, manual labeling of large amounts of data for supervised learning is complicated. This paper introduces an innovative approach to unsupervised fault detection and diagnosis tailored for monitoring industrial chemical processes. We showcase the efficacy of our model using two publicly accessible datasets from the Tennessee Eastman Process, each containing various faults. Furthermore, we illustrate that by fine-tuning the model on a limited amount of labeled data, it achieves performance close to that of a state-of-the-art model trained on the entire dataset.
Revealing the Two Sides of Data Augmentation: An Asymmetric Distillation-based Win-Win Solution for Open-Set Recognition
Authors: Jia Yunbing , Kong Xiaoyu , Tang Fan , Gao Yixing , Dong Weiming , Yang Yi
Published: July 2024

In this paper, we reveal the two sides of data augmentation: enhancements in closed-set recognition correlate with a significant decrease in open-set recognition. Through empirical investigation, we find that multi-sample-based augmentations would contribute to reducing feature discrimination, thereby diminishing the open-set criteria. Although knowledge distillation could impair the feature via imitation, the mixed feature with ambiguous semantics hinders the distillation. To this end, we propose an asymmetric distillation framework by feeding teacher model extra raw data to enlarge the benefit of teacher. Moreover, a joint mutual information loss and a selective relabel strategy are utilized to alleviate the influence of hard mixed samples. Our method successfully mitigates the decline in open-set and outperforms SOTAs by 2%~3% AUROC on the Tiny-ImageNet dataset and experiments on large-scale dataset ImageNet-21K demonstrate the generalization of our method.
Using Large Language Models and Recruiter Expertise for Optimized Multilingual Job Offer – Applicant CV Matching
Authors: Kavas Hamit , Serra-Vidal Marc , Wanner Leo
Published: July 2024

In the context of the increasingly globalised economy and labour market, recruitment agencies face the challenge to deal with a magnitude of job offers and job applications written in a variety of languages, formats, and styles. Quite often, this leads to a suboptimal evaluation of the CVs of job seekers with respect to their relevance to a job offer. To address this challenge, we propose an interactive system that follows the ``human-in-the-loop'' approach, actively involving recruiters in the job offer -- applicant CV matching. The system uses a fine-tuned state-of-the-art classification model that aligns job seeker CVs with labels of the {\it European Skills, Competences, Qualifications and Occupations} taxonomy to propose an initial match between job offers with the CVs of job candidates. This match is refined in sequential LLM driven-interaction with the recruiter, which culminates in CV relevance scores and reports that justify them.
SPARK: Harnessing Human-Centered Workflows with Biomedical Foundation Models for Drug Discovery
Authors: Kwon Bum Chul , Rabinovici-Cohen Simona , Moturi Beldine , Mwaura Ruth , Wahome Kezia , Njeru Oliver , Shinyenyi Miguel , Wanjiru Catherine , Remy Sekou , Ogallo William , Guez Itai , Suryanarayanan Partha , Morrone Joseph , Sethi Shreyans , Kang Seung-Gu , Huynh Tien , Ng Kenney , Mahajan Diwakar , Li Hongyang , Ninio Matan , Ayati Shervin , Hexter Efrat , Cornell Wendy
Published: July 2024

Biomedical foundation models, trained on diverse sources of small molecule data, hold great potential for accelerating drug discovery. However, their complex nature often presents a barrier for researchers seeking scientific insights and drug candidate generation. SPARK addresses this challenge by providing a user-friendly, web-based interface that empowers researchers to leverage these powerful models in their scientific workflows. Through SPARK, users can specify target proteins and desired molecule properties, adjust pre-trained models for tailored inferences, generate lists of potential drug candidates, analyze and compare molecules through interactive visualizations, and filter candidates based on key metrics (e.g., toxicity). By seamlessly integrating human knowledge and biomedical AI models' capabilities through an interactive web-based system, SPARK can improve the efficiency of collaboration between human experts and AI, thereby accelerating drug candidate discovery and ultimately leading to breakthroughs in finding cures for various diseases.
Real-time Multi-modal Object Detection and Tracking on Edge for Regulatory Compliance Monitoring
Authors: Lim Jia Syuen , Wang Ziwei , Liu Jiajun , Khamis Abdelwahed , Arablouei Reza , Barlow Robert , McAllister Ryan
Published: July 2024

Regulatory compliance auditing in agrifood processing facilities is crucial for upholding the highest standards of quality assurance and traceability. However, the current manual and intermittent approaches to auditing present significant challenges and risks, potentially leading to gaps or loopholes in the system. To address these shortcomings, we introduce a real-time, multi-modal sensing system that utilizes 3D time-of-flight and RGB cameras and leverages unsupervised learning techniques on edge AI devices. The proposed system enables continuous object tracking, leading to improved efficiency in record-keeping and reduced manual labor. We demonstrate the effectiveness of the system in a knife sanitization monitoring scenario, showcasing its capability to overcome occlusion and low-light performance limitations commonly encountered with conventional RGB cameras.
Place Anything into Any Video
Authors: Liu Ziling , Yang Jinyu , Gao Mingqi , Zheng Feng
Published: July 2024

Controllable video editing has demonstrated remarkable potential across diverse applications, particularly in scenarios where capturing or re-capturing real-world videos is either impractical or costly. This paper introduces a novel and efficient system named Place-Anything, which facilitates the insertion of any object into any video solely based on a picture or text description of the target object or element. The system comprises three modules: 3D generation, video reconstruction, and 3D target insertion. This integrated approach offers an efficient and effective solution for producing and editing high-quality videos by naturally inserting realistic objects. Through experiment, we demonstrate that our system can effortlessly place any object into any video using just a photograph of the object. Our demo video can be found at https://youtu.be/afXqgLLRnTE. Please also visit our project page https://place-anything.github.io to get more information.
ReportParse: A Unified NLP Tool for Extracting Document Structure and Semantics of Corporate Sustainability Reporting
Authors: Morio Gaku , In Soh Young , Yoon Jungah , Rowlands Harri , Manning Christopher
Published: July 2024

We introduce ReportParse, a Python-based tool designed to parse corporate sustainability reports.

It combines document structure analysis with natural language processing (NLP) models to extract sustainability-related information from the reports.

We also provide easy-to-use web and command interfaces.

The tool is expected to aid researchers and analysts in evaluating corporate commitment to and management of sustainability efforts.
AVIN-Chat: An Audio-Visual Interactive Chatbot System with Emotional State Tuning
Authors: Park Chanhyuk , Cho Jungbin , Kim Junwan , Lee Seongmin , Kim Jungsu , Lee Sanghoon
Published: July 2024

This work presents an audio-visual interactive chatbot (AVIN-Chat) system that allows users to have face-to-face conversations with 3D avatars in real-time. Compared to the previous chatbot services, which provide text-only or speech-only communications, the proposed AVIN-Chat can offer audio-visual communications providing users with a superior experience quality. In addition, the proposed AVIN-Chat emotionally speaks and expresses according to the user's emotional state. Thus, it enables users to establish a strong bond with the chatbot system, increasing the user's immersion. Through user subjective tests, it is demonstrated that the proposed system provides users with a higher sense of immersion than previous chatbot systems. The demonstration video is available at https://www.youtube.com/watch?v=Z74uIV9k7_k.
SmartTransit.AI: A Dynamic Paratransit and Microtransit Application
Authors: Pavia Sophie , Rogers David , Sivagnanam Amutheezan , Wilbur Michael , Edirimanna Danushka , Kim Youngseo , Mukhopadhyay Ayan , Pugliese Philip , Samaranayake Samitha , Laszka Aron , Dubey Abhishek
Published: July 2024

New rideshare and shared mobility services have transformed urban mobility in recent years. Such services have the potential to improve efficiency and reduce costs by allowing users to share rides in high-capacity vehicles and vans. Most transit agencies already operate various ridepooling services, including microtransit and paratransit. However, the objectives and constraints for implementing these services vary greatly between agencies and can be challenging. First, off-the-shelf ridepooling formulations must be adapted for real-world conditions and constraints. Second, the lack of modular and reusable software makes it hard to implement and evaluate new ridepooling algorithms and approaches in real-world settings. We demonstrate a modular on-demand public transportation scheduling software for microtransit and paratransit services. The software is aimed at transit agencies looking to incorporate state-of-the-art rideshare and ridepooling algorithms in their everyday operations. We provide management software for dispatchers and mobile applications for drivers and users and conclude with results from the demonstration in Chattanooga, TN.
Inside Out: Emotional Multiagent Multimodal Dialogue Systems
Authors: Savchenko Andrey V. , Savchenko Lyudmila V.
Published: July 2024

In this paper, we introduce the novel technological framework for the development of emotional dialogue systems. Inspired by the "Inside Out" film, we propose to use multiple emotional agents based on Large Language Models (LLMs) to prepare answers to a user query. Their answers are aggregated into a single response, taking into account the current emotional state of a user. The latter is estimated by video-based facial expression recognition (FER). We introduce several publicly available lightweight neural networks that show near state-of-the-art results on the AffectNet dataset. Qualitative examples using either GPT-3.5 or LLama2 and Mistral demonstrate that the proposed approach leads to more emotional responses in LLMs.
Rhythm Inference Helping Writing Music Scores
Authors: Schwarzentruber François
Published: July 2024

We present a new problem called rhythm inference. It consists in inferring the duration of each note and each rest from a partial specification. We then formulate rhythm inference as a constraint satisfaction problem and we use mixed linear programming to solve it. The solution is implemented for a language representing music scores, called abcd. Interestingly, the rhythm is inferred in real-time from partial musical indications.
O2ARC 3.0: A Platform for Solving and Creating ARC Tasks
Authors: Shim Suyeon , Ko Dohyun , Lee Hosung , Lee Seokki , Song Doyoon , Hwang Sanha , Kim Sejin , Kim Sundong
Published: July 2024

We introduce O2ARC 3.0 interface for the Abstraction and Reasoning Corpus (ARC). O2ARC 3.0 gamifies the experience, fostering user engagement through competitive features and community-driven problem creation and evaluation. Built with a React frontend and NestJS backend, the platform provides a responsive and intuitive interface for efficient rule inference. This approach not only improves data collection for AI training but also enhances the problem-solving process, offering a scalable solution for advancing cognitive AI research. O2ARC is available at https://o2arc.com.
Towards a Resilient Intelligent Automation System
Authors: Shlomov Segev , Marreed Sami , Yaeli Avi
Published: July 2024

Intelligent Process Automation (IPA) solutions must adapt to changes in user interfaces autonomously, without manual intervention. Addressing this critical challenge and aiming to advance the state-of-the-art, last year we introduced the IPA Challenge competition at IJCAI 2023. This demo paper presents IDA - our novel UI automation solution, developed to tackle complex resiliency issues. Leveraging the capabilities of large language models and employing grounded instructions, our system demonstrates a significant advancement towards resilient IPA. We provide an overview of the IPA Challenge, detail the architecture of our system, and illustrate its effectiveness in overcoming the resiliency challenges. A link to the demo video can be found at: https://youtu.be/G5nI3V9Umjc
ToDo: Token Downsampling for Efficient Generation of High-Resolution Images
Authors: Smith Ethan , Saxena Nayan , Saha Aninda
Published: July 2024

Attention has been a crucial component in the success of image diffusion models, however, their quadratic computational complexity limits the sizes of images we can process within reasonable time and memory constraints. This paper investigates the importance of dense attention in generative image models, which often contain redundant features, making them suitable for sparser attention mechanisms. We propose a novel training-free method ToDo that relies on token downsampling of key and value tokens to accelerate Stable Diffusion inference by up to 2x for common sizes and up to 4.5x or more for high resolutions like 2048 × 2048. We demonstrate that our approach outperforms previous methods in balancing efficient throughput and fidelity.
PiShield: A PyTorch Package for Learning with Requirements
Authors: Stoian Mihaela C. , Tatomir Alex , Lukasiewicz Thomas , Giunchiglia Eleonora
Published: July 2024

Deep learning models have shown their strengths in various application domains, however, they often struggle to meet safety requirements for their outputs. In this paper, we introduce PiShield, the first package ever allowing for the integration of the requirements into the neural networks' topology. PiShield guarantees compliance with these requirements, regardless of input. Additionally, it allows for integrating requirements both at inference and/or training time, depending on the practitioners' needs. Given the widespread application of deep learning, there is a growing need for frameworks allowing for the integration of the requirements across various domains. Here, we explore three application scenarios: functional genomics, autonomous driving, and tabular data generation.
A Framework for Centralized Traffic Routing in Urban Areas
Authors: Švadlenka Matyáš , Chrpa Lukas
Published: July 2024

Dealing with the ever-increasing demand for traffic management is one of the main challenges of the 21st century. The issue is much more apparent in urban areas during rush hours. Traffic congestion causes economic losses due to delays and increased fuel consumption and, on top of that, is a major health risk. Intelligent centralized traffic routing is an important concept aiming at reducing traffic congestion in urban areas by more effectively utilizing road networks.

In this demo, we present a framework that, in a nutshell, integrates techniques for intelligent centralized traffic routing into the well-known SUMO simulator, so these techniques can be evaluated in realistic settings on real/realistic datasets. In particular, the framework automatically identifies ``problematic'' urban regions by analyzing historical traffic data, then simplifies the road networks by precomputing promising routes (for each considered traffic flow), and finally, leverages a planning-based approach to generate routes. Our framework is evaluated on a real dataset from Dublin's metropolitan area.
AI-Olympics: Exploring the Generalization of Agents through Open Competitions
Authors: Wang Chen , Song Yan , Wu Shuai , Wu Sa , Zhang Ruizhi , Lin Shu , Zhang Haifeng
Published: July 2024

Between 2021 and 2023, AI-Olympics---a series of online AI competitions, was hosted by the online evaluation platform Jidi in collaboration with the IJCAI committee. In these competitions, an agent is required to accomplish diverse sports tasks in a two-dimensional continuous world, while competing against an opponent. This paper provides a brief overview of the competition series and highlights notable findings. We aim to contribute insights to the field of multi-agent decision-making and explore the generalization of agents through engineering efforts.
Carbon Market Simulation with Adaptive Mechanism Design
Authors: Wang Han , Li Wenhao , Zha Hongyuan , Wang Baoxiang
Published: July 2024

A carbon market is a market-based tool that incentivizes economic agents to align individual profits with the global utility, i.e., reducing carbon emissions to tackle climate change. Cap and trade stands as a critical principle based on allocating and trading carbon allowances (carbon emission credit), enabling economic agents to follow planned emissions and penalizing excess emissions. A central authority is responsible for introducing and allocating those allowances in cap and trade. However, the complexity of carbon market dynamics makes accurate simulation intractable, which in turn hinders the design of effective allocation strategies. To address this, we propose an adaptive mechanism design framework, simulating the market using hierarchical, model-free multi-agent reinforcement learning (MARL). Government agents allocate carbon credits, while enterprises engage in economic activities and carbon trading. This framework illustrates agents’ behavior comprehensively. Numerical results show MARL enables government agents to balance productivity, equality, and carbon emissions. Our project is available at https:

//anonymous.4open.science/r/Carbon-Simulator.
Benchmarking Stroke Forecasting with Stroke-Level Badminton Dataset
Authors: Wang Wei-Yao , Du Wei-Wei , Peng Wen-Chih , Ik Tsi-Ui
Published: July 2024

In recent years, badminton analytics has drawn attention due to the advancement of artificial intelligence and the efficiency of data collection. While there is a line of effective applications to improve and investigate player performance, there are only a few public badminton datasets that can be used by researchers outside the badminton domain. Existing badminton singles datasets focus on specific matchups; however, they cannot provide comprehensive studies on different players and various matchups. In this paper, we provide a badminton singles dataset, ShuttleSet22, which is collected from high-ranking matches in 2022. ShuttleSet22 consists of 30,172 strokes in 2,888 rallies in the training set, 1,400 strokes in 450 rallies in the validation set, and 2,040 strokes in 654 rallies in the testing set, with detailed stroke-level metadata within a rally. To benchmark existing work with ShuttleSet22, we hold a challenge, Track 2: Forecasting Future Turn-Based Strokes in Badminton Rallies, at CoachAI Badminton Challenge @ IJCAI 2023, to encourage researchers to tackle this real-world problem through innovative approaches and to summarize insights between the state-of-the-art baseline and improved techniques, exchanging inspiring ideas. The baseline codes and the dataset are made available at https://github.com/wywyWang/CoachAI-Projects/tree/main/CoachAI-Challenge-IJCAI2023.
FasterVD: On Acceleration of Video Diffusion Models
Authors: Yu Pinrui , Luo Dan , Rupprecht Timothy , Lu Lei , Kong Zhenglun , Zhao Pu , Li Yanyu , Camps Octavia , Lin Xue , Wang Yanzhi
Published: July 2024

Equipped with Denoising Diffusion Probabilistic Models, video content generation has gained significant research interest recently. However, diffusion pipelines call for intensive computation and model storage, which poses challenges for their wide and efficient deployment. In this work, we address this issue by integrating LCM-LoRA to reduce the denoising steps and escalating the video generation process by frame skipping and interpolation. Our framework achieves an approximately 10× inference acceleration for high-quality realistic video generation on commonly available GPUs.
M2RL: A Multi-player Multi-agent Reinforcement Learning Framework for Complex Games
Authors: Yu Tongtong , He Chenghua , Yin Qiyue
Published: July 2024

Distributed deep reinforcement learning (DDRL) has gained increasing attention due to the emerging requirements for addressing complex games like Go and StarCraft. However, how to effectively and stably train bots with asynchronous and heterogeneous agents cooperation and competition for multiple players under multiple machines (with multiple CPUs and GPUs) using DDRL is still an open problem. We propose and open M2RL, a Multi-player and Multi-agent Reinforcement Learning framework, to make training bots for complex games an easy-to-use warehouse. Experiments involving training a two-player multi-agent Wargame AI, and a sixteen-player multi-agent community game Neural MMO AI, demonstrate the effectiveness of the proposed framework by winning a silver award and beating high-level AI bots designed by professional players.
TFLOP: Table Structure Recognition Framework with Layout Pointer Mechanism
Authors: Khang Minsoo , Hong Teakgyu
Published: July 2024

Table Structure Recognition (TSR) is a task aimed at converting table images into a machine-readable format (e.g. HTML), to facilitate other applications such as information retrieval.

Recent works tackle this problem by identifying the HTML tags and text regions, where the latter is used for text extraction from the table document. These works however, suffer from misalignment issues when mapping text into the identified text regions. In this paper, we introduce a new TSR framework, called TFLOP (TSR Framework with LayOut Pointer mechanism), which reformulates the conventional text region prediction and matching into a direct text region pointing problem. Specifically, TFLOP utilizes text region information to identify both the table's structure tags and its aligned text regions, simultaneously. Without the need for region prediction and alignment, TFLOP circumvents the additional text region matching stage, which requires finely-calibrated post-processing. TFLOP also employs span-aware contrastive supervision to enhance the pointing mechanism in tables with complex structure. As a result, TFLOP achieves the state-of-the-art performance across multiple benchmarks such as PubTabNet, FinTabNet, and SynthTabNet. In our extensive experiments, TFLOP not only exhibits competitive performance but also shows promising results on industrial document TSR scenarios such as documents with watermarks or in non-English domain. Source code of our work is publicly available at: https://github.com/UpstageAI/TFLOP.
A Coarse-to-Fine Fusion Network for Event-Based Image Deblurring
Authors: Li Huan , Shi Hailong , Gao Xingyu
Published: July 2024

Event-driven image deblurring is an innovative approach involving the input of events obtained from the event camera alongside blurred frames to facilitate the deblurring process. Unlike conventional cameras, event cameras in event-driven imaging exhibit low-latency characteristics and are immune to motion blur, resulting in significant advancements in image deblurring. In this paper, we propose a pioneering event-based coarse-to-fine image deblurring network named CFFNet. In contrast to existing deblurring methods, our approach incorporates event data, generating multiple coarse frames from a single frame before further refining them into a sharp image. We introduce an Event Image Fusion Block (EIFB) for the coarse fusion of events and images, producing coarse frames at different time points. Additionally, we propose a Bidirectional Frame Fusion Block (BFFB) for the fine fusion of coarse frames. CFFNet effectively harnesses the spatiotemporal information of event data through a comprehensive fusion process from coarse to fine. Experimental results on the GoPro and REBlur datasets demonstrate that our method achieves state-of-the-art performance for image deblurring task.
Invertible Residual Rescaling Models
Authors: Li Jinmin , Dai Tao , Zha Yaohua , Luo Yilu , Lu Longfei , Chen Bin , Wang Zhi , Xia Shu-Tao , Zhang Jingyun
Published: July 2024

Invertible Rescaling Networks (IRNs) and their variants have witnessed remarkable achievements in various image processing tasks like image rescaling. However, we observe that IRNs with deeper networks are difficult to train, thus hindering the representational ability of IRNs. To address this issue, we propose Invertible Residual Rescaling Models (IRRM) for image rescaling by learning a bijection between a high-resolution image and its low-resolution counterpart with a specific distribution. Specifically, we propose IRRM to build a deep network, which contains several Residual Downscaling Modules (RDMs) with long skip connections. Each RDM consists of several Invertible Residual Blocks (IRBs) with short connections. In this way, RDM allows rich low-frequency information to be bypassed by skip connections and forces models to focus on extracting high-frequency information from the image. Extensive experiments show that our IRRM performs significantly better than other state-of-the-art methods with much fewer parameters and complexity. Particularly, our IRRM has respectively PSNR gains of at least 0.3 dB over HCFlow and IRN in the x4 rescaling while only using 60% parameters and 50% FLOPs. The code will be available at https://github.com/THU-Kingmin/IRRM.