bluuebunny/crossref_metadata_2025 huggingface dataset:
Algorithmic Motion Planning Meets Minimially-Invasive Robotic Surgery
Authors: Salzman Oren
Published: August 2023

Robots for minimally-invasive surgery such as steerable needles and concentric-tube robots have the potential to dramatically alter the way common medical procedures are performed. They can decrease patient-recovery time, speed healing and reduce scarring. However, manually controlling such devices is highly un-intuitive and automatic planning methods are in need. For the automation of such medical procedures to be clinically accepted, it is critical from a patient care, safety, and regulatory perspective to certify the correctness and effectiveness of the motion-planning algorithms involved in procedure automation. In this paper, I survey recent and ongoing work where we develop efficient and effective planning capabilities for medical robots that provide provable guarantees on various planner attributes as well as introduce new and exciting research opportunities in the field.
The Economics of Machine Learning
Authors: Xu Haifeng
Published: August 2023

This survey overviews a new research agenda on the economics of machine learning, pursued at the Strategic IntelliGence for Machine Agent (SIGMA) Lab at UChicago. This overall research agenda has two themes: machine learning for economics and, conversely, economics for machine learning (ML). The first theme focuses on designing and analyzing ML algorithms for economic problems, ranging from foundational economic models to influential real-world applications such as recommender systems and national security. The second theme employs economic principles to study machine learning itself, such as the valuation and pricing of data, information and ML models, and designing incentive mechanisms to improve large-scale ML research peer reviews. While our research focuses primarily on developing methodologies, in each theme we also highlight some real-world impacts of these works, including ongoing large-scale live experiments and potential deployments for various applications.
Using Liquid Democracy For Attention-Aware Social Choice
Authors: Alouf-Heffetz Shiri
Published: August 2023

My PhD research deals with the use of Liquid Democracy (LD) for social choice scenarios, while considering the scarcity of attention as a driving factor. My aim is to better understand LD: theoretically as well as practically; in particular, by establishing containment in certain computational classes for corresponding combinatorial problems, suggesting methods to improve the use of LD, and understand its adaptation to specific scenarios. Concretely, I consider the use of LD as a solution for the problem of low voter attention in light of the high cognitive effort needed by voters to actively participate in voting processes.
Reconstruction-Aware Prior Distillation for Semi-supervised Point Cloud Completion
Authors: Fan Zhaoxin , He Yulin , Wang Zhicheng , Wu Kejian , Liu Hongyan , He Jun
Published: August 2023

Real-world sensors often produce incomplete, irregular, and noisy point clouds, making point cloud completion increasingly important. However, most existing completion methods rely on large paired datasets for training, which is labor-intensive. This paper proposes RaPD, a novel semi-supervised point cloud completion method that reduces the need for paired datasets. RaPD utilizes a two-stage training scheme, where a deep semantic prior is learned in stage 1 from unpaired complete and incomplete point clouds, and a semi-supervised prior distillation process is introduced in stage 2 to train a completion network using only a small number of paired samples. Additionally, a self-supervised completion module is introduced to improve performance using unpaired incomplete point clouds. Experiments on multiple datasets show that RaPD outperforms previous methods in both homologous and heterologous scenarios.
Cost-effective Artificial Neural Networks
Authors: Atashgahi Zahra
Published: August 2023

Deep neural networks (DNNs) have gained huge attention over the last several years due to their promising results in various tasks. However, due to their large model size and over-parameterization, they are recognized as being computationally demanding. Therefore, deep learning models are not well-suited to applications with limited computational resources and battery life. Current solutions to reduce computation costs mainly focus on inference efficiency while being resource-intensive during training. This Ph.D. research aims to address these challenges by developing cost-effective neural networks that can achieve decent performance on various complex tasks using minimum computational resources during training and inference of the network.
Predictive Modelling of Human Reasoning Using AGM Belief Revision
Authors: Baker Clayton
Published: August 2023

While many forms of belief change exist, the relationship between belief revision and human reasoning is of primary interest in this work. The theory of belief revision extends classical two-valued logic with an approach to resolve the conflict between a set of beliefs and newly learned information. The goal of this project is to test how humans revise conflicting beliefs. Experiments are proposed in which human subjects are required to resolve conflicting beliefs via relevance and confidence. In our analysis, the human responses will be evaluated against the predictions of two perspectives of propositional belief revision: formal and psychological.
AI Techniques for Urban Traffic Control and Mobility
Authors: Bhatnagar Saumya
Published: August 2023

The growing population has accelerated the process of urbanisation, and is putting under stress the urban transport infrastructure. This results in increased traffic congestion, with significant health, economy, and social issues.

Artificial Intelligence techniques are increasingly demonstrating their capabilities in predicting and supporting urban traffic control, by extending the abilities of traffic authorities in planning and reacting to different traffic conditions. In this context, our main research topic fits in the autonomic traffic control theme, with the aim of supporting the design of autonomous traffic control systems.
On Adaptivity and Safety in Sequential Decision Making
Authors: Chaudhary Sapana
Published: August 2023

Sequential decision making is an important field in machine learning, encompassing techniques such as online optimization, structured bandits, and reinforcement learning, which have numerous applications such as recommendation systems, online advertising, conversational agents, and robot learning. However, two key challenges face real-world sequential decision making: the need for adaptable models and the need for safety during both learning and execution. Adaptability refers to the ability of a model to quickly adapt to new and diverse environments, which is especially challenging in environments where feedback is sparse. To address this challenge, we propose using meta reinforcement learning with sub-optimal demonstration data. Safety is also critical in real-world sequential decision making. A model that adheres to safety requirements can avoid dangerous outcomes and ensure the safety of humans and other agents in the environment. We propose an approach based on online convex optimization that ensures safety at every time step. Addressing these challenges can lead to the development of more robust, safe, and adaptable AI systems that can perform a wide range of tasks and operate in a variety of environments.
Exploring Multilingual Intent Dynamics and Applications
Authors: Mullick Ankan
Published: August 2023

Multilingual Intent Detection and explore its different characteristics are major field of study for last few years. But, detection of intention dynamics from text or voice, especially in the Indian multilingual contexts, is a challenging task. So, my first research question is on intent detection and then I work on the application in Indian Multilingual Healthcare scenario. Speech dialogue systems are designed by a pre-defined set of intents to perform user specified tasks. Newer intentions may surface

over time that call for retraining. However, the newer intents may not be explicitly announced and need to be inferred dynamically.

Hence, here are two crucial jobs: (a) recognizing newly emergent intents; and (b) annotating the data of the new intents in order

to effectively retrain the underlying classifier. The tasks become specially challenging when a large number of new intents emerge

simultaneously and there is a limited budget of manual annotation. We develop MNID (Multiple Novel Intent Detection), a cluster

based framework that can identify multiple novel intents while optimized human annotation cost. Empirical findings on numerous

benchmark datasets (of varying sizes) show that MNID surpasses the baseline approaches in terms of accuracy and F1-score by wisely allocating the budget for annotation. We apply intent detection approach on different domains in Indian multilingual scenarios -

healthcare, finance etc. The creation of advanced NLU healthcare systems is threatened by the lack of data and technology constraints for resource-poor languages in developing nations like India. We evaluate the current state of several cutting-edge language models used in the healthcare with the goal of detecting query intents and corresponding entities. We conduct comprehensive trials on a

number of models different realistic contexts, and we investigate the practical relevance depending on budget and the availability of

data on English.
Argumentation for Interactive Causal Discovery
Authors: Russo Fabrizio
Published: August 2023

Causal reasoning reflects how humans perceive events in the world and establish relationships among them, identifying some as causes and others as effects. Causal discovery is about agreeing on these relationships and drawing them as a causal graph.

Argumentation is the way humans reason systematically about an idea: the medium we use to exchange opinions, to get to know and trust each other and possibly agree on controversial matters.

Developing AI which can argue with humans about causality would allow us to understand and validate the analysis of the AI and would allow the AI to bring evidence for or against humans' prior knowledge.

This is the goal of this project: to develop a novel scientific paradigm of interactive causal discovery and train AI to recognise causes and effects by debating, with humans, the results of different statistical methods
Reliable Neuro-Symbolic Abstractions for Planning and Learning
Authors: Shah Naman
Published: August 2023

Although state-of-the-art hierarchical robot planning algorithms allow robots to efficiently compute long-horizon motion plans for achieving user desired tasks, these methods typically rely upon environment-dependent state and action abstractions that need to be hand-designed by experts. On the other hand, non-hierarchical robot planning approaches fail to compute solutions for complex tasks that require reasoning over a long horizon. My research addresses these problems by proposing an approach for learning abstractions and developing hierarchical planners that efficiently use learned abstractions to boost robot planning performance and provide strong guarantees of reliability.
On Building a Semi-Automated Framework for Generating Causal Bayesian Networks from Raw Text
Authors: Sheikh Solat J.
Published: August 2023

The availability of a large amount of unstructured text has generated interest in utilizing it for future decision-making and developing strategies in various critical domains. Despite some progress, automatically generating accurate reasoning models from the raw text is still an active area of research. Furthermore, most proposed approaches focus on a specific do-main. As such, their suggested transformation methods are usually unreliable when applied to other domains. This research aims to develop a framework, SCANER (Semi-automated CAusal Network Extraction from Raw text), to convert raw text into Causal Bayesian Networks (CBNs). The framework will then be employed in various domains to demonstrate its utilization as a decision-support tool. The preliminary experiments have focused on three domains: political narratives, food insecurity, and medical sciences. The future focus is on developing BNs from political narratives and modifying them through various methods to reduce the level of aggressiveness or extremity in the narratives without causing conflict among the masses or countries.
Automated Content Moderation Using Transparent Solutions and Linguistic Expertise
Authors: Solopova Veronika
Published: August 2023

Since the dawn of Transformer-based models, the trade-off between transparency and accuracy has been a topical issue in the NLP community. Working towards ethical and transparent automated content moderation (ACM), my goal is to find where it is still relevant to implement linguistic expertise. I show that transparent statistical models based on linguistic knowledge can still be competitive, while linguistic features have many other useful applications.
Object Detection in Real Open Environment
Authors: Zhao Xiaowei
Published: August 2023

Although object detection has achieved impressive progress and has a broad impact, it still encounters significant challenges in accurately detecting objects in open-world scenarios. The data in real-world open scenes often exhibit characteristics of limited annotations, such as very few annotated samples or even unknown classes with no annotations. Our studies mainly focus on applying detection to various open scenes and addressing the challenges of sparse samples and unknown classes. The comprehensive research aims to develop more powerful and efficient methods for object detection in the open world, making them more suitable for real-world applications.
Bias On Demand: Investigating Bias with a Synthetic Data Generator
Authors: Baumann Joachim , Castelnovo Alessandro , Cosentini Andrea , Crupi Riccardo , Inverardi Nicole , Regoli Daniele
Published: August 2023

Machine Learning (ML) systems are increasingly being adopted to make decisions that might have a significant impact on people's lives. Because these decision-making systems rely on data-driven learning, the risk is that they will systematically propagate the bias embedded in the data. To prevent harmful consequences, it is essential to comprehend how and where bias is introduced and possibly how to mitigate it. We demonstrate Bias on Demand, a framework to generate synthetic datasets with different types of bias, which is available as an open-source toolkit and as a pip package. We include a demo of our proposed synthetic data generator, in which we illustrate experiments on different scenarios to showcase the interconnection between biases and their effect on performance and fairness evaluations. We encourage readers to explore the full paper for a more detailed analysis.
Incorporating Unlikely Negative Cues for Distinctive Image Captioning
Authors: Fei Zhengcong , Huang Junshi
Published: August 2023

While recent neural image captioning models have shown great promise in terms of automatic metrics, they still struggle with generating generic sentences, which limits their use to only a handful of simple scenarios. On the other hand, negative training has been suggested as an effective way to prevent models from producing frequent yet meaningless sentences. However, when applied to image captioning, this approach may overlook low-frequency but generic and vague sentences, which can be problematic when dealing with diverse and changeable visual scenes. In this paper, we introduce a approach to improve image captioning by integrating negative knowledge that focuses on preventing the model from producing undesirable generic descriptions while addressing previous limitations. We accomplish this by training a negative teacher model that generates image-wise generic sentences with retrieval entropy-filtered data. Subsequently, the student model is required to maximize the distance with multi-level negative knowledge transferring for optimal guiding. Empirical results evaluated on MS COCO benchmark confirm that our plug-and-play framework incorporating unlikely negative knowledge leads to significant improvements in both accuracy and diversity, surpassing previous state-of-the-art methods for distinctive image captioning.
Automated Planning for Generating and Simulating Traffic Signal Strategies
Authors: Bhatnagar Saumya , Guo Rongge , McCabe Keith , McCluskey Thomas , Percassi Francesco , Vallati Mauro
Published: August 2023

There is a growing interest in the use of AI techniques for urban traffic control, with a particular focus on traffic signal optimisation. Model-based approaches such as planning demonstrated to be capable of dealing in real-time with unexpected or unusual traffic conditions, as well as with the usual traffic patterns. Further, the knowledge models on which such techniques rely to generate traffic signal strategies are in fact simulation models of traffic, hence can be used by traffic authorities to test and compare different approaches.

In this work, we present a framework that relies on automated planning to generate and simulate traffic signal strategies in a urban region. To demonstrate the capabilities of the framework, we consider real-world data collected from sensors deployed in a major corridor of the Kirklees region of the United Kingdom.
NeoMaPy: A Framework for Computing MAP Inference on Temporal Knowledge Graphs
Authors: David Victor , Fournier-S'niehotta Raphael , Travers Nicolas
Published: August 2023

Markov Logic Networks (MLN) are used for reasoning on uncertain and inconsistent temporal data. We proposed the TMLN (Temporal Markov Logic Network) which extends them with sorts/types, weights on rules and facts, and various temporal consistencies. The NeoMaPy framework integrates it as a knowledge graph based on conflict graphs which offers flexibility for reasoning with parametric Maximum A Posteriori (MAP) inferences, efficiency with an optimistic heuristic and interactive graph visualization for results explanation.
Latent Inspector: An Interactive Tool for Probing Neural Network Behaviors Through Arbitrary Latent Activation
Authors: Geißler Daniel , Zhou Bo , Lukowicz Paul
Published: August 2023

This work presents an active software instrument allowing deep learning architects to interactively inspect neural network models' output behavior from user-manipulated values in any latent layer. Latent Inspector offers multiple dimension reduction techniques to visualize the model's high dimensional latent layer output in human-perceptible, two-dimensional plots. The system is implemented with Node.js front end for interactive user input and Python back end for interacting with the model. By utilizing a general and modular architecture, our proposed solution dynamically adapts to a versatile range of models and data structures. Compared to already existing tools, our asynchronous approach of separating the training process from the inspection offers additional possibilities, such as interactive data generation, by actively working with the model instead of visualizing training logs. Overall, Latent Inspector demonstrates the possibilities as well as the appearing limits for providing a generalized, tool-based concept for enhancing model insight in terms of explainable and transparent AI.
Understanding the Night-Sky? Developing AI-Enabled System for Exploring Night-Light Usage Patterns
Authors: Hederich Jakob , Ghosh Shreya , He Zeyu , Mitra Prasenjit
Published: August 2023

We present a demonstration of nighttime light pattern (NTL) analysis system. Our tool named NightVIEW is powered by an efficient system architecture to easily export and analyse a huge volume of spatial data (NTL), image segmentation and clustering algorithms to find unusual NTL patterns and identify hotspots of excess night light usage as well as finding semantics of cities.
Optimized Crystallographic Graph Generation for Material Science
Authors: Klipfel Astrid , Frégier Yaël , Sayede Adlane , Bouraoui Zied
Published: August 2023

Graph neural networks are widely used in machine learning applied to chemistry, and in particular for material science discovery. For crystalline materials, however, generating graph-based representation from geometrical information for neural networks is not a trivial task. The periodicity of crystalline needs efficient implementations to be processed in real-time under a massively parallel environment. With the aim of training graph-based generative models of new material discovery, we propose an efficient tool to generate cutoff graphs and k-nearest-neighbours graphs of periodic structures within GPU optimization. We provide pyMatGraph a Pytorch-compatible framework to generate graphs in real-time during the training of neural network architecture. Our tool can update a graph of a structure, making generative models able to update the geometry and process the updated graph during the forward propagation on the GPU side. Our code is publicly available at https://github.com/aklipf/mat-graph.
SupervisorBot: NLP-Annotated Real-Time Recommendations of Psychotherapy Treatment Strategies with Deep Reinforcement Learning
Authors: Lin Baihan , Cecchi Guillermo , Bouneffouf Djallel
Published: August 2023

We present a novel recommendation system designed to provide real-time treatment strategies to therapists during psychotherapy sessions. Our system utilizes a turn-level rating mechanism that forecasts the therapeutic outcome by calculating a similarity score between the profound representation of a scoring inventory and the patient's current spoken sentence. By transcribing and segmenting the continuous audio stream into patient and therapist turns, our system conducts immediate evaluation of their therapeutic working alliance. The resulting dialogue pairs, along with their computed working alliance ratings, are then utilized in a deep reinforcement learning recommendation system. In this system, the sessions are treated as users, while the topics are treated as items. To showcase the system's effectiveness, we not only evaluate its performance using an existing dataset of psychotherapy sessions but also demonstrate its practicality through a web app. Through this demo, we aim to provide a tangible and engaging experience of our recommendation system in action.
Modeling the Impact of Policy Interventions for Sustainable Development
Authors: Rachuri Sowmith Nandan , Malavalli Arpitha , Parasa Niharika Sri , Bassin Pooja , Srinivasa Srinath
Published: August 2023

There is an increasing demand to design policy interventions to achieve various targets specified by the UN Sustainable Development Goals by 2030. Designing interventions is a complex task given that the system may often respond in unexpected ways to a given intervention. This could be due to interventions towards a given target, affecting other unrelated variables, and/or interventions leading to acute disparities in nearby geographic areas. In order to address such issues, we propose a novel concept called Stress Modeling that analyzes the holistic impact of a policy intervention by taking into account the interactions within a system, after the intervention. The simulation is based on the postulate that complex systems of interacting entities tend to settle down into "low energy'' configurations by minimizing differentials in capabilities of neighbouring entities. The simulation shows how policy impact percolates through geospatial boundaries over time and can be applied at any granularity. The theory and the corresponding package have been explained along with a case study analyzing a fertilizer policy in the Agro-climatic Zones of the state of Karnataka, India.
AutoML for Outlier Detection with Optimal Transport Distances
Authors: Singh Prabhant , Vanschoren Joaquin
Published: August 2023

Automated machine learning (AutoML) has been widely researched and adopted for supervised problems, but progress in unsupervised settings has been limited. We propose `"LOTUS", a novel framework to automate outlier detection based on meta-learning. Our premise is that the selection of the optimal outlier detection technique depends on the inherent properties of the data distribution. We leverage optimal transport to find the dataset with the most similar underlying distribution, and then apply the outlier detection techniques that proved to work best for that data distribution. We evaluate the robustness of our framework and find that it outperforms all state-of-the-art automated outlier detection tools. This approach can also be easily generalized to automate other unsupervised settings.
LISSNAS: Locality-based Iterative Search Space Shrinkage for Neural Architecture Search
Authors: Gopal Bhavna , Sridhar Arjun , Zhang Tunhou , Chen Yiran
Published: August 2023

Search spaces hallmark the advancement of Neural Architecture Search (NAS). Large and complex search spaces with versatile building operators and structures provide more opportunities to brew promising architectures, yet pose severe challenges on efficient exploration and exploitation. Subsequently, several search space shrinkage methods optimize by selecting a single sub-region that contains some well-performing networks. Small performance and efficiency gains are observed with these methods but such techniques leave room for significantly improved search performance and are ineffective at retaining architectural diversity. We propose LISSNAS, an automated algorithm that shrinks a large space into a diverse, small search space with SOTA search performance. Our approach leverages locality, the relationship between structural and performance similarity, to efficiently extract many pockets of well-performing networks. We showcase our method on an array of search spaces spanning various sizes and datasets. We accentuate the effectiveness of our shrunk spaces when used in one-shot search by achieving the best Top-1 accuracy in two different search spaces. Our method achieves a SOTA Top-1 accuracy of 77.6% in ImageNet under mobile constraints, best-in-class Kendal-Tau, architectural diversity, and search space size.
Independent Feature Decomposition and Instance Alignment for Unsupervised Domain Adaptation
Authors: He Qichen , Xiao Siying , Ye Mao , Zhu Xiatian , Neri Ferrante , Hou Dongde
Published: August 2023

Existing Unsupervised Domain Adaptation (UDA) methods typically attempt to perform knowledge transfer in a domain-invariant space explicitly or implicitly. In practice, however, the obtained features is often mixed with domain-specific information which causes performance degradation. To overcome this fundamental limitation, this article presents a novel independent feature decomposition and instance alignment method (IndUDA in short). Specifically, based on an invertible flow, we project the base features into a decomposed latent space with domain-invariant and domain-specific dimensions. To drive semantic decomposition independently, we then swap the domain-invariant part across source and target domain samples with the same category and require their inverted features are consistent in class-level with the original features. By treating domain-specific information as noise, we replace it by Gaussian noise and further regularize source model training by instance alignment, i.e., requiring the base features close to the corresponding reconstructed features, respectively. Extensive experiment results demonstrate that our method achieves state-of-the-art performance on popular UDA benchmarks. The appendix and code are available at https://github.com/ayombeach/IndUDA.
MILD: Modeling the Instance Learning Dynamics for Learning with Noisy Labels
Authors: Hu Chuanyang , Yan Shipeng , Gao Zhitong , He Xuming
Published: August 2023

Despite deep learning has achieved great success, it often relies on a large amount of training data with accurate labels, which are expensive and time-consuming to collect. A prominent direction to reduce the cost is to learn with noisy labels, which are ubiquitous in the real-world applications. A critical challenge for such a learning task is to reduce the effect of network memorization on the falsely-labeled data. In this work, we propose an iterative selection approach based on the Weibull mixture model, which identifies clean data by considering the overall learning dynamics of each data instance. In contrast to the previous small-loss heuristics, we leverage the observation that deep network is easy to memorize and hard to forget clean data. In particular, we measure the difficulty of memorization and forgetting for each instance via the transition times between being misclassified and being memorized in training, and integrate them into a novel metric for selection. Based on the proposed metric, we retain a subset of identified clean data and repeat the selection procedure to iteratively refine the clean subset, which is finally used for model training. To validate our method, we perform extensive experiments on synthetic noisy datasets and real-world web data, and our strategy outperforms existing noisy-label learning methods.
Diagram Visual Grounding: Learning to See with Gestalt-Perceptual Attention
Authors: Hu Xin , Zhang Lingling , Liu Jun , Zhang Xinyu , Wu Wenjun , Wang Qianying
Published: August 2023

Diagram visual grounding aims to capture the correlation between language expression and local objects in the diagram, and plays an important role in the applications like textbook question answering and cross-modal retrieval. Most diagrams consist of several colors and simple geometries. This results in sparse low-level visual features, which further aggravates the gap between low-level visual and high-level semantic features of diagrams. The phenomenon brings challenges to the diagram visual grounding. To solve the above issues, we propose a gestalt-perceptual attention model to align the diagram objects and language expressions. For low-level visual features, inspired by the gestalt that simulates human visual system, we build a gestalt-perception graph network to make up the features learned by the traditional backbone network. For high-level semantic features, we design a multi-modal context attention mechanism to facilitate the interaction between diagrams and language expressions, so as to enhance the semantics of diagrams. Finally, guided by diagram features and linguistic embedding, the target query is gradually decoded to generate the coordinates of the referred object. By conducting comprehensive experiments on diagrams and natural images, we demonstrate that the proposed model achieves superior performance over the competitors. Our code will be released at https://github.com/AIProCode/GPA.
Orion: Online Backdoor Sample Detection via Evolution Deviance
Authors: Huang Huayang , Wang Qian , Gong Xueluan , Wang Tao
Published: August 2023

Widely-used DNN models are vulnerable to backdoor attacks, where the backdoored model is only triggered by specific inputs but can maintain a high prediction accuracy on benign samples. Existing backdoor input detection strategies rely on the assumption that benign and poisoned samples are separable in the feature representation of the model. However, such an assumption can be broken by advanced feature-hidden backdoor attacks. In this paper, we propose a novel detection framework, dubbed Orion (online backdoor sample detection via evolution deviance). Specifically, we analyze how predictions evolve during a forward pass and find deviations between the shallow and deep outputs of the backdoor inputs. By introducing side nets to track such evolution divergence, Orion eliminates the need for the assumption of latent separability. Additionally, we put forward a scheme to restore the original label of backdoor samples, enabling more robust predictions. Extensive experiments on six attacks, three datasets, and two architectures verify the effectiveness of Orion. It is shown that Orion outperforms state-of-the-art defenses and can identify feature-hidden attacks with an F1-score of 90%, compared to 40% for other detection schemes. Orion can also achieve 80% label recovery accuracy on basic backdoor attacks.
Anticandidal activity of some plant extracts
Authors: Das Aparna
Published: August 2023

Candidiasis, especially by Candida albicans is the most prevalent disease over the years. To control the infection, several synthetic drugs and their formulations have been applied. Although antifungals are quite effective in treating candidiasis, long term use has been reported to have side effects. Nevertheless, it has other drawbacks such as efficiency as well as cost, recurrence of the infection, emergences of resistant strains etc. Thus, plant based natural compounds are being investigated for their antifungal activity. In the present study, five different plant extracts assessed exhibited retardation of growth and protease production (molar concentration) in C. albicans. The mycelia form of the organism showed growth resistance to tested plant extracts than the yeast extract form which conferred the higher pathogenicity of the mycelia form. The minimum inhibitory concentration (MIC) of each plant extract was experimentally evidenced with the oil obtained from the seeds of Pongamia glabrata showed the MIC values at the lowest concentration (20-30 μL/mL), followed by seed oil of Azadirachta indica and Ricinus communis. The order of candidostatic efficacy of the various oils was observed to be: Pongamia > Azadirachta > Ricinus > Eucalyptus > Curcuma. These findings have paved the way for further investigation of plant based antifungal agents and their clinical appropriateness for the treatment of Candidiasis.
Deep-Learning Strategy Based on Convolutional Neural Network for Wall Heat Flux Prediction
Authors: Dai Gang , Zhao Wenwen , Yao Shaobo , Chen Weifang
Published: August 2023

Aerodynamic thermal prediction plays an important role in the design of hypersonic aircraft, especially in the design of the aircraft’s thermal protection system. The main challenges of the aerothermal prediction lie in the slow converging speed and the strict requirements of the computational grid. In this paper, a convolutional-neural-network-based hybrid-features deep-learning strategy is constructed to efficiently predict aerodynamic heating, which is named the convolutional neural network/hybrid-feature method. The hybrid features of this strategy consist of the normal distribution of physical quantities from the wall and the flow parameters at the extreme temperature point. The strategy, which extends through the multilayer perceptron regression layer method, constructs the relationship between the hybrid features and the wall heat flux to obtain a high-precision model trained by the flowfield data without gradient convergence. It is demonstrated that the model has a better inflow generalization ability to predict wall heat flux with different inflow conditions and angles of attack by zero-angle-of-attack training data, which has great potential in aircraft thermal protection system design and shape optimization.
Effect of Plasma Sheaths on Earth-Entry Magnetohydrodynamics
Authors: Parent Bernard , Rajendran Prasanna T. , Macheret Sergey O. , Little Justin , Moses Robert W. , Johnston Christopher O. , Cheatwood F. McNeil
Published: August 2023

The first study of the full coupling between the aerothermodynamics, the magnetohydrodynamics (MHD), and the plasma sheaths within Earth-entry flows is here performed. The problem addressed herein is representative of a force-generating MHD patch located between the stagnation point and the aft of a capsule entering the Earth’s atmosphere at Mach 34. The reactions are obtained from the Park chemical solver and the transport coefficients from the Gupta–Yos model with modifications. The physical model fully couples the drift–diffusion model for the sheaths to the multispecies Navier–Stokes equations for the plasma flow. The Hall and ion slip effects are taken into consideration within the plasma flow and within the sheaths. The effect of the electrode material on the MHD process is studied. Using thoriated tungsten instead of graphite leads to a thirtyfold increase in the Lorentz forces and also leads to significantly reduced heat fluxes on the cathode. This is attributed to the much higher electrical conductivity of the thoriated tungsten sheath reducing by orders of magnitude the plasma electrical resistance near the surfaces.
Autocuidado de pacientes com insuficiência cardíaca em tempos da COVID-19
Authors: Leal Jessica Santos de Souza , Figueiredo Lyvia da Silva , Oliveira Melissa Barreto Oliveira da , Fiore Ana Carolina Marques , Flores Paula Vanessa Peclat , Cavalcanti Ana Carla Dantas
Published: August 2023

Objetivo: Analisar o autocuidado de pacientes com insuficiência cardíaca acompanhados por uma clínica especializada durante a pandemia da COVID-19. Métodos: Trata-se de estudo transversal, realizado em uma clínica multiprofissional especializada, durante acompanhamento por consulta telefônica. Utilizou-se o instrumento Versão Brasileira da European Heart Failure Self-care Behavior Scale, os dados foram coletados no período de abril e julho de 2021. Resultados: A amostra apresentou uma média de 25,64 (±6,53). Os maiores percentuais no score 5 (discordo plenamente) foram evidenciados nos itens correspondentes às ações de buscar ajuda no aumento de peso em curto intervalo de tempo, vigilância diária do peso e realizar exercício físico regularmente. Os maiores percentuais no score 1 (concordo totalmente), verificou-se nos itens correspondente às ações seguir tratamento farmacológico, ingerir uma dieta com pouco sal e tomar a vacina contra gripe anualmente. Conclusão: A amostra deste estudo apresentou comportamento de autocuidado satisfatório, demonstrando que os pacientes acompanhados por consulta telefônica durante distanciamento social se mantêm engajados e empoderados para realizar o autocuidado.
Aspectos epidemiológicos e econômicos das hospitalizações por acidente vascular cerebral isquêmico em Sergipe entre 2009 e 2019
Authors: Lima Antônio Vinícius Pimentel , Lima Maria Victória Pimentel , Barbosa Jayanne Larissa Cavalcante , Moreno Rafael Alexandre Meneguz , Oliveira Makson Gleydson Brito de
Published: August 2023

Objetivo: Descrever o perfil epidemiológico das hospitalizações por AVCi em Sergipe durante o período de 2009 a 2019. Métodos: Trata-se de uma pesquisa epidemiológica, do tipo ecológica, de caráter descritivo, usando dados secundários de cunho documental, através das autorizações de internação hospitalar (AIH) compiladas no Sistema de Informações Hospitalares do Sistema Único de Saúde (SIH/SUS), com dados coletados a partir da plataforma TABNET do Departamento de informática SUS (DATASUS). Utilizou-se de uma análise estatística descritiva e comparativa dos dados, através de números absolutos e relativos (proporção na base 100). Resultados: No período de 2009 a 2019, foram registradas 343 hospitalizações por AVCi no estado de Sergipe, com uma média de 31,18 hospitalizações por ano. Foram registados 109 óbitos nesse período, com uma taxa de letalidade total de 31,78% e de letalidade média anual de 34,69%. A incidência total foi de 15,67 hospitalizações por AVCi por 105 habitantes em Sergipe no tempo verificado. Conclusão: Houve um aumento significativo do número anual de hospitalizações por AVCi em Sergipe durante o período avaliado, com incidência na capital Aracaju e nas regiões mais próximas.
The use of pediatric behavior management techniques among Syrian dentists - A cross-sectional study
Authors: Alsibai Ekram , Karkoutly Mawia , Samra Elian Gattas Abu , Almonakel MHD Bashier , Bshara Nada
Published: August 2023

Objectives:

The aim of this study was to evaluate and compare the attitude toward several behavior management techniques among Syrian pediatric dentists (PDs), general dental practitioners (GDPs), and other dental specialists (ODSs). Dental fear and anxiety are highly prevalent among schoolchildren and pre-school children. Behavior management techniques can be either pharmacological or non-pharmacological. Distraction is a non-pharmacological behavior management technique, which is safe, economical, and effective.

Materials and Methods:

A self-designed questionnaire was distributed through social networks. Syrian GDPs, PDs, and ODSs participated in the online questionnaire. The questionnaire consisted of four sections and required responses regarding participants’ demographic characteristics, work setting, the use of several behavior management techniques, and attitude toward tablet distraction use. Pearson’s Chi-square test was performed at 5% for statistical analysis using SPSS version 23.0.

Result:

A total of 642 participants responded. Most of the participants reported using tell-show-do (63.08%) and positive reinforcement (65.58%) techniques. Less than half of the participants (45.79%) used virtual reality (VR) eyeglasses for distraction. PDs used cartoons and storytelling techniques significantly more than GDPs and ODSs, with P = 0.023 and P < 0.001, respectively. However, most of the participants never used touch screen video games (74.45%) or joystick video games (92.21%) for distraction. Half of the questionnaire participants (52.34%) were unsure about using a tablet device for distraction.

Conclusion:

This study concluded that tell-show-do, positive reinforcement techniques, and VR eyeglasses were used the most. However, the vast majority of the participants never used video games for distraction. A good proportion of the study participants were hesitant about using a tablet device as a means of distraction.
Two new subspecies of Crocus biflorus L. (Iridaceae) from the Iraqi Kurdistan region using morphological and molecular methods
Authors: Majeed Hoshman , Salih Rubar
Published: August 2023

Species belonging to the Crocus genus are considered among the most attractive horticultural species and valuable economic plants in the world. Crocus plants are present at several locations in the Iraqi Kurdistan region. However, a comprehensive study has not been conducted yet in recognition of the species in this area. In the current work we used Internal Transcribed Spacers of nuclear ribosomal DNA (ITSnr) markers and morphological traits to provide information on this genus. The study revealed two new subspecies within species Crocus biflorus; Crocus biflorus subsp. penjwenii and Crocus biflorus subsp. qaradaghii. In addition, the circumscriptions of Crocus cancellatus and Crocus pallasii, as well as their subspecies ranks (haussknechtii and cancellatus) respectively, are confirmed in Iraq for the first time by using molecular means.
Upscaling sample size for microscopical detection of nanoplastics
Authors: Hiltunen Arto , Huopalainen Joona , Häkkilä Sirkku , Damlin Pia , Hänninen Jari
Published: August 2023

The extent of nanoplastic pollution has raised severe environmental and health concerns. While the means for microplastic detection are abundant, improved tools for nanoplastic detection are called-for. State- of-the-art microscopic techniques can detect nanoplastics down to tens of nanometers, however, only from very small sample sizes (typically ∼10 µl). In this work, we describe a method that enables sampling of 1 liter of seawater by the means of correlative Raman- and SEM-techniques. This is achieved by adapting common microplastic sample purification proto- cols (i.e. chemical digestion) to suit the nanoplastic study. In addition, we decorate a membrane filter with SERS-property to amplify the Raman signals. Together, the purification method combined with the use of the SERS-activated-membrane-filter enables identification and imaging of in- dividual nanoplastic particles from significantly larger sample sizes than before. These results aim to provide useful tools for researchers in the fight against plastic pollution.
Unravelling the Highest Magnetic Anisotropy Among All the nd-Shells in [WCp2]0 Metallocene
Authors: Nain Sakshi , Mukhopadhyaya Aritra , Ali Md. Ehesan
Published: August 2023

Single-molecule magnets (SMMs) with large magnetization reversal barrier are predominated by the lanthanide systems due to their strong spin-orbit coupling (SOC). However, the transition metals have also emerged as potential contenders and the largest magnetic anisotropy has been identified for a cobalt system among any d-series based SMMs (Bunting et al. Science 2018, 362, eaat7319). In this work, we have explored the magnetic anisotropy in highly axial ligand field systems of metallocene, having different d-subshell (3d4, 4d4 and 5d4). The wavefunction based multireference methods including static and dynamic electron correlations, have been employed to investigate the zero-field splitting (ZFS) parameters. Here, we report exceptionally large magnetic anisotropy for a 5d complex of [WCp2]0 with highest energy barrier that is nearly twice as high as the previous record value for the Co complex. We have also observed that the axial ZFS parameter (D) is increasing down the group in the order of 3d < 4d < 5d, pertaining to large SOC.
Complexity-Generating Transformations Enabled by Intramolecular Radical Migration: Alkyl–Arylation of Simple Olefins
Authors: Babcock Dylan J. , Wolfram Andrew J. , Barney Jaxon L. , Servagno Santino M. , Sharma Ayush , Nacsa Eric D.
Published: August 2023

A free-radical cascade approach has enabled the develop-ment of a synthetically versatile alkyl–arylation of olefins. This transformation engages an excellent range of olefins, from mono- to tetrasubstituted, without requiring directing or electronically activating groups. Further synthetic advantages, such as the facile generation of quaternary cen-ters and the introduction of heteroaryl groups with Lewis basic nitrogen atoms, also complement transition-metal-catalyzed alkyl–arylation. Vicinal stereoarrays were gener-ated with high levels of diastereoselectivity. The synthetic potential of this transformation was demonstrated by serving as the key step in a concise synthesis of oliceridine, a new painkiller that received FDA approval in 2020.
Predicting solvents with the help of Artificial Intelligence
Authors: Schilter Oliver Tobias , Baldassari Carlo , Laino Teodoro , Schwaller Philippe
Published: August 2023

The right solvent is a crucial factor in achieving environmentally friendly, selective, and highly converted chemical reactions. Artificial intelligence-based tools, often lack the ability to reliably predict reaction conditions such as the appropriate solvent. Here, we present a comprehensive investigation into the efficacy of data-driven machine-learning models for solvent prediction for a broad spectrum of single-solvent organic reactions. Remarkably, our models achieve a Top-3 accuracy of 86.88%, showcasing outstanding performance in predicting solvents from underrepresented classes. An uncertainty analysis revealed that the models' misclassifications could be explained by the fact that the reaction can be run in multiple solvents. In the experimental validation, 8 out of 11 reactions succeeded with the predicted solvent. Our work addresses a key challenge in organic synthesis and demonstrates the practical application of machine learning models in predicting reaction solvents for more efficient and sustainable chemical synthesis.