bluuebunny/crossref_metadata_2025 huggingface dataset:
Modular Synthesis of Efficient Quantum Uncomputation
Authors: Venev Hristo , Gehr Timon , Dimitrov Dimitar , Vechev Martin
Published: October 2024

A key challenge of quantum programming is uncomputation: the reversible deallocation of qubits. And while there has been much recent progress on automating uncomputation, state-of-the-art methods are insufficient for handling today’s expressive quantum programming languages. A core reason is that they operate on primitive quantum circuits, while quantum programs express computations beyond circuits, for instance, they can capture families of circuits defined recursively in terms of uncomputation and adjoints.

In this paper, we introduce the first modular automatic approach to synthesize correct and efficient uncomputation for expressive quantum programs. Our method is based on two core technical contributions: (i) an intermediate representation (IR) that can capture expressive quantum programs and comes with support for uncomputation, and (ii) modular algorithms over that IR for synthesizing uncomputation and adjoints.

We have built a complete end-to-end implementation of our method, including an implementation of the IR and the synthesis algorithms, as well as a translation from an expressive fragment of the Silq programming language to our IR and circuit generation from the IR. Our experimental evaluation demonstrates that we can handle programs beyond the capabilities of existing uncomputation approaches, while being competitive on the benchmarks they can handle. More broadly, we show that it is possible to benefit from the greater expressivity and safety offered by high-level quantum languages without sacrificing efficiency.
Control-Flow Deobfuscation using Trace-Informed Compositional Program Synthesis
Authors: Mariano Benjamin , Wang Ziteng , Pailoor Shankara , Collberg Christian , Dillig Işil
Published: October 2024

Code deobfuscation, which attempts to simplify code that has been intentionally obfuscated to prevent understanding, is a critical technique for downstream security analysis tasks like malware detection. While there has been significant prior work on code deobfuscation, most techniques either do not handle control flow obfuscations that modify control flow or they target specific classes of control flow obfuscations, making them unsuitable for handling new types of obfuscations or combinations of existing ones. In this paper, we study a new deobfuscation technique that is based on program synthesis and that can handle a broad class of control flow obfuscations. Given an obfuscated program P, our approach aims to synthesize a smallest program that is a control-flow reduction of P and that is semantically equivalent. Since our method does not assume knowledge about the types of obfuscations that have been applied to the original program, the underlying synthesis problem ends up being very challenging. To address this challenge, we propose a novel trace-informed compositional synthesis algorithm that leverages hints present in dynamic traces of the obfuscated program to decompose the synthesis problem into a set of simpler subproblems. In particular, we show how dynamic traces can be useful for inferring a suitable control-flow skeleton of the deobfuscated program and performing independent synthesis of each basic block. We have implemented this approach in a tool called Chisel and evaluate it on 546 benchmarks that have been obfuscated using combinations of six different obfuscation techniques. Our evaluation shows that our approach is effective and that it produces code that is almost identical (modulo variable renaming) to the original (non-obfuscated) program in 86% of cases. Our evaluation also shows that Chisel significantly outperforms existing techniques.
Compositionality and Observational Refinement for Linearizability with Crashes
Authors: Oliveira Vale Arthur , Wang Zhongye , Chen Yixuan , You Peixin , Shao Zhong
Published: October 2024

Crash-safety is an important property of real systems, as the main functionality of some systems is resilience to crashes. Toward a compositional verification approach for crash-safety under full-system crashes, one observes that crashes propagate instantaneously to all components across all levels of abstraction, even to unspecified components, hindering compositionality. Furthermore, in the presence of concurrency, a correctness criterion that addresses both crashesandconcurrency proves necessary. For this, several adaptations of linearizability have been suggested, each featuring different trade-offs between complexity and expressiveness. The recently proposed compositional linearizability framework shows that to achieve compositionality with linearizability, both a locality and observational refinement property are necessary. Despite that, no linearizability criterion with crashes has been proven to support an observational refinement property. In this paper, we define a compositional model of concurrent computation with full-system crashes. We use this model to develop a compositional theory of linearizability with crashes, which reveals a criterion,crash-aware linearizability, as its inherent notion of linearizability and supports both locality and observational refinement. We then show that strict linearizability and durable linearizability factor through crash-aware linearizability as two different ways of translating between concurrent computation with and without crashes, enabling simple proofs of locality and observational refinement for a generalization of these two criteria. Then, we show how the theory can be connected with a program logic for durable and crash-aware linearizability, which gives the first program logic that verifies a form of linearizability with crashes. We showcase the advantages of compositionality by verifying a library facilitating programming persistent data structures and a fragment of a transactional interface for a file system.
Making Sense of Multi-threaded Application Performance at Scale with NonSequitur
Authors: Wong Augustine , Bucci Paul , Beschastnikh Ivan , Fedorova Alexandra
Published: October 2024

Modern multi-threaded systems are highly complex. This makes their behavior difficult to understand. Developers frequently capture behavior in the form of program traces and then manually inspect these traces. Existing tools, however, fail to scale to traces larger than a million events.

In this paper we present an approach to compress multi-threaded traces in order to allow developers to visually explore these traces at scale. Our approach is able to compress traces that contain millions of events down to a few hundred events. We use this approach to design and implement a tool called NonSequitur.

We present three case studies which demonstrate how we used NonSequitur to analyze real-world performance issues with Meta's storage engine RocksDB and MongoDB's storage engine WiredTiger, two complex database backends. We also evaluate NonSequitur with 42 participants on traces from RocksDB and WiredTiger. We demonstrate that, in some cases, participants on average scored 11 times higher when performing performance analysis tasks on large execution traces. Additionally, for some performance analysis tasks, the participants spent on average three times longer with other tools than with NonSequitur.
AttackDefense Framework (ADF): Enhancing IoT Devices and Lifecycles Threat Modeling
Authors: Sacchetti Tommaso , Bognar Marton , De Meulemeester Jesse , Gierlichs Benedikt , Piessens Frank , Bezsmertnyi Volodymyr , Molteni Maria Chiara , Cristalli Stefano , Gringiani Arianna , Thomas Olivier , Antonioli Daniele
Published: October 2024

Threat modeling (TM) is essential to manage, prevent, and fix security and privacy issues in our society. TM requires a data model to represent threats and tools to exploit such data. Current TM data models and tools have significant limitations preventing their usage in real-world scenarios. For example, it is challenging to TM embedded devices with current data models and tools as they cannot model their hardware, firmware, and low-level software. Moreover, it is impossible to TM a device lifecycle or security-privacy tradeoffs as these data models and tools were developed for other use cases (e.g., software security or user privacy).

We fill this relevant gap by presenting the AttackDefense Framework (ADF), which provides a novel data model and related tools to augment TM. ADF’s building block is the AD object that can be used to represent heterogeneous and complex threats. Moreover, ADF provides automations to process a collection of AD objects, including ways to create sets, maps, chains, trees, and wordclouds of AD objects. We present
ADF
, a toolkit implementing ADF composed of four modules (Catalog, Parse, Check, and Analyze).

We confirm that the data model and tools provided by ADF are useful by running an extensive set of experiments while threat modeling a crypto wallet and its lifecycle. Our experiments involved seven expert groups from academia and industry, each using the ADF on an orthogonal threat class. The evaluation generated 175 high-quality ADs covering ISA/IEC 62433-4-1 SecDev Lifecycle, side-channels, fault injection, microarchitectural attacks, speculative execution, pre-silicon testing, invasive physical chip modifications, Bluetooth protocol and implementation threats, and FIDO2 authentication.
Content-Aware Selective Encryption for H.265/HEVC Using Deep Hashing Network and Steganography
Authors: Sheng Qingxin , Fu Chong , Lin Zhaonan , Chen Junxin , Wang Xingwei , Sham Chiu-Wing
Published: October 2024

Existing selective encryption schemes for High Efficiency Video Coding (HEVC) only focus on the encoding characteristics of syntax elements in entropy coding and lack an understanding of the video content. Consequently, a large amount of unnecessary encryption operations are utilized to protect insensitive video frames, resulting in low encryption efficiency. In this article, we propose a content-aware selective encryption scheme for H.265/HEVC, which encrypts only the groups of pictures (GOPs) containing sensitive content and thus offers high efficiency. In our scheme, a deep hashing network is first adopted to retrieve video frames to determine the content-sensitive GOPs. Then, multiple prediction and residual syntax elements in sensitive GOPs are encrypted using a keystream sequence generated by the hyper-chaotic Lorenz system. In addition, the direct current coefficient of each

\(4\times 4\)

transform block is exchanged with a pseudo-randomly selected non-zero alternating current coefficient to further offer stronger visual distortion. Finally, the sign bits used for marking each GOP-type are reversibly embedded into the encrypted syntax elements to facilitate the decoder to distinguish the GOPs that need to be decrypted. Experimental results indicate that the proposed content-aware selective encryption scheme can efficiently protect sensitive content and is robust against all common attacks. Furthermore, it outperforms other state-of-the-art HEVC selective encryption algorithms in terms of security performance.
Question-Attentive Review-Level Explanation for Neural Rating Regression
Authors: Le Trung-Hoang , Lauw Hady W.
Published: October 2024

Recommendation explanations help to improve their acceptance by end users. Explanations come in many different forms. One that is of interest here is presenting an existing review of the recommended item as the explanation. The challenge is in selecting a suitable review, which is customarily addressed by assessing the relative importance or “attention” of each review to the recommendation objective. Our focus is improving review-level explanation by leveraging additional information in the form of questions and answers (QA). The proposed framework employs QA in an attention mechanism that aligns reviews to various QAs of an item and assesses their contribution jointly to the recommendation objective. The benefits are two-fold. For one, QA aids in selecting more useful reviews. For another, QA itself could accompany a well-aligned review in an expanded form of explanation. Experiments on datasets of 10 product categories showcase the efficacies of our method as compared to comparable baselines in identifying useful reviews and QAs, while maintaining parity in recommendation performance.
Survey on Knowledge Distillation for Large Language Models: Methods, Evaluation, and Application
Authors: Yang Chuanpeng , Zhu Yao , Lu Wang , Wang Yidong , Chen Qian , Gao Chenlong , Yan Bingjie , Chen Yiqiang
Published: October 2024

Large Language Models (LLMs) have showcased exceptional capabilities in various domains, attracting significant interest from both academia and industry. Despite their impressive performance, the substantial size and computational demands of LLMs pose considerable challenges for practical deployment, particularly in environments with limited resources. The endeavor to compress language models while maintaining their accuracy has become a focal point of research. Among the various methods, knowledge distillation has emerged as an effective technique to enhance inference speed without greatly compromising performance. This paper presents a thorough survey from three aspects: method, evaluation, and application, exploring knowledge distillation techniques tailored specifically for LLMs. Specifically, we divide the methods into white-box KD and black-box KD to better illustrate their differences. Furthermore, we also explored the evaluation tasks and distillation effects between different distillation methods, and proposed directions for future research. Through in-depth understanding of the latest advancements and practical applications, this survey provides valuable resources for researchers, paving the way for sustained progress in this field.
Deepfake Detection: A Comprehensive Survey from the Reliability Perspective
Authors: Wang Tianyi , Liao Xin , Chow Kam Pui , Lin Xiaodong , Wang Yinglong
Published: October 2024

The mushroomed Deepfake synthetic materials circulated on the internet have raised a profound social impact on politicians, celebrities, and individuals worldwide. In this survey, we provide a thorough review of the existing Deepfake detection studies from the reliability perspective. We identify three reliability-oriented research challenges in the current Deepfake detection domain: transferability, interpretability, and robustness. Moreover, while solutions have been frequently addressed regarding the three challenges, the general reliability of a detection model has been barely considered, leading to the lack of reliable evidence in real-life usages and even for prosecutions on Deepfake-related cases in court. We, therefore, introduce a model reliability study metric using statistical random sampling knowledge and the publicly available benchmark datasets to review the reliability of the existing detection models on arbitrary Deepfake candidate suspects. Case studies are further executed to justify the real-life Deepfake cases including different groups of victims with the help of the reliably qualified detection models as reviewed in this survey. Reviews and experiments on the existing approaches provide informative discussions and future research directions for Deepfake detection.
Generalizing Random Butterfly Transforms to Arbitrary Matrix Sizes
Authors: Lindquist Neil , Luszczek Piotr , Dongarra Jack
Published: October 2024

Parker and Lê introduced random butterfly transforms (RBTs) as a preprocessing technique to replace pivoting in dense LU factorization. Unfortunately, their FFT-like recursive structure restricts the dimensions of the matrix. Furthermore, on multinode systems, efficient management of the communication overheads restricts the matrix’s distribution even more. To remove these limitations, we have generalized the RBT to arbitrary matrix sizes by truncating the dimensions of each layer in the transform. We expanded Parker’s theoretical analysis to generalized RBT, specifically that in exact arithmetic, Gaussian elimination with no pivoting will succeed with probability 1 after transforming a matrix with full-depth RBTs. Furthermore, we experimentally show that these generalized transforms improve performance over Parker’s formulation by up to 62% while retaining the ability to replace pivoting. This generalized RBT is available in the SLATE numerical software library.
Moral Learning and Decision-Making Across the Lifespan
Authors: Lockwood Patricia L. , van den Bos Wouter , Dreher Jean-Claude
Published: October 2024

Moral learning and decision-making are crucial throughout our lives, from infancy to old age. Emerging evidence suggests that there are important differences in learning and decision-making in moral situations across the lifespan, and these are underpinned by co-occurring changes in the use of model-based values and theory of mind. Here, we review the decision neuroscience literature on moral choices and moral learning considering four key concepts. We show how in the earliest years, a sense of self/other distinction is foundational. Sensitivity to intention versus outcome is crucial for several moral concepts and is most similar in our earliest and oldest years. Across all ages, basic shifts in the influence of theory of mind and model-free and model-based learning support moral decision-making. Moving forward, a computational approach to key concepts of morality can help provide a mechanistic account and generate new hypotheses to test across the whole lifespan.
Cognitive Control
Authors: Badre David
Published: October 2024

Humans and other primates have a remarkable ability to perform a wide range of tasks and behaviors, even novel ones, in order to achieve their goals. Further, they are able to shift flexibly among these behaviors as the contexts demand. Cognitive control is the function at the base of this remarkable behavioral generativity and flexibility. The present review provides a survey of current research on cognitive control focusing on two of its primary features within a control systems framework: (a) the ability to select new behaviors based on context and (b) the ability to monitor ongoing behavior and adjust accordingly. Throughout, the review places an emphasis on how differences in the content and structure of task representations affect these core features of cognitive control.
Detectability of Hypoattenuating Liver Lesions with Deep Learning CT Reconstruction: A Phantom and Patient Study
Authors: Cao Jinjin , Mroueh Nayla , Mercaldo Nathaniel , Lennartz Simon , Kongboonvijit Sasiprang , Srinivas Rao Shravya , Pisuchpen Nisanard , Baliyan Vinit , Pierce Theodore T. , Anderson Mark A. , Sertic Madeleine , Shenoy-Bhangle Anuradha S. , Kambadakone Avinash R.
Published: October 2024

There were no differences in radiologist performance for detecting hypoattenuating liver lesions at abdominal CT between adaptive statistical iterative reconstruction-V at 40% strength and deep learning image reconstruction with different strengths.
Enhancing Radiologist Reading Performance by Ordering Screening Mammograms Based on Characteristics That Promote Visual Adaptation
Authors: Gommers Jessie J. J. , Verboom Sarah D. , Duvivier Katya M. , van Rooden Jan-Kees , van Raamt A. Fleur , Houwers Janneke B. , Naafs Dick B. , Duijm Lucien E. M. , Abbey Craig K. , Webster Michael A. , Broeders Mireille J. M. , Sechopoulos Ioannis
Published: October 2024

Reading screening mammography examinations ordered from low to high volumetric breast density improved radiologists' screening performance while reducing reading and fixation times compared with reading randomly ordered screening examinations.
The gut microbiota as a link between Alzheimer’s disease and obesity
Authors: Alvarez Karla Lucia F. , Davila-Del-Carpio Gonzalo
Published: October 2024

Alzheimer’s disease (AD) is a degenerative disease that causes a progressive decline in memory and thinking skills. Over the past few years, diverse studies have shown that there is no single cause of AD; instead, it has been reported that factors such as genetics, lifestyle, and environment contribute to the pathogenesis of the disease. In this sense, it has been shown that obesity during middle age is one of the most prominent modifiable risk factors for AD. Of the multiple potential mechanisms linking obesity and AD, the gut microbiota (GM) has gained increasing attention in recent years. However, the underlying mechanisms that connect the GM with the process of neurodegeneration remain unclear. Through this narrative review, we present a comprehensive understanding of how alterations in the GM of people with obesity may result in systemic inflammation and affect pathways related to the pathogenesis of AD. We conclude with an analysis of the relationship between GM and insulin resistance, a risk factor for AD that is highly prevalent in people with obesity. Understanding the crosstalk between obesity, GM, and the pathogenesis of AD will help to design new strategies aimed at preventing neurodegeneration.
The bronchoalveolar lavage dilution conundrum: an updated view on a long-standing problem
Authors: Haeger Sarah , Moore Camille M. , McManus Shannon A. , Moore Peter K. , Janssen William J. , Mould Kara J.
Published: October 2024

In this report, mathematical prediction models and real-world measurements are used to compare the performance of molecular markers of dilution in bronchoalveolar lavage fluid samples. Effects of acute inflammation within individual subjects are highlighted. These findings inform recommendations for normalizing measurements across bronchoalveolar lavage samples and highlight the need for additional markers to improve the rigor of translational studies utilizing bronchoalveolar lavage measurements.
Analysis of Nuclear Explosion Detection Capability of IMS Hydroacoustic Network
Authors: Wang Yan , Tang Wei , Liu Zhehan , Li Jian , Wang Xiaoming , Zou Shiya
Published: October 2024

The Comprehensive Nuclear Test Ban Treaty (CTBT) organization has established a hydroacoustic network to ensure the effective detection of nuclear explosions in the vast ocean and in the atmosphere over it in order to effectively guarantee the implementation of the treaty. To assess its effectiveness, according to the reciprocity of underwater acoustic field, this study employs the hydrophone position as the central sound source and utilizes the parabolic equation, along with seabed topography and sound velocity profile data, to estimate the transmission loss. Background noise levels of station evaluation are conducted by using historical data so as to estimate the underwater nuclear explosion sound source level via a passive sonar equation. Utilizing the full ship shock trial (FSST) results of the “Ford” aircraft carrier on June 18, 2021, as a case study, the detection capability of the HA10 station for nuclear explosions is investigated. Subsequently, the network’s overall detection capability was evaluated using the source level at 20 Hz corresponding to a 1 kiloton nuclear explosion yield as a reference. Findings indicate the network’s proficiency in identifying long‐distance propagation signals, particularly those with high explosion source levels within the sound fixing and ranging (SOFAR) channel. The sensitivity analysis demonstrates the network’s ability to detect explosions smaller than 1 kg equivalent in close proximity to a station, and larger explosions exceeding 1 kg or 1 ton at greater distances. This evaluation underscores the network’s significance in detecting and monitoring underwater nuclear explosions across extensive oceanic regions.
AZD8055 Is More Effective Than Rapamycin in Inhibiting Proliferation and Promoting Mitochondrial Clearance in Erythroid Differentiation
Authors: Liu Qian , Hao Tao , Lin Ze , Fang Yipeng , Li Lei , Huang Daqi , Wu Jianbo , Zhao Yanchao , Zhang Xin
Published: October 2024

Background: As an important downstream effector of various signaling pathways, mTOR plays critical roles in regulating many physiological processes including erythropoiesis. It is composed of two distinct complexes, mTORC1 and mTORC2, which differ in their components and downstream signaling effects. Our previous study revealed that the inhibition of mTORC1 by rapamycin significantly repressed the erythroid progenitor expansion in the early stage but promoted enucleation and mitochondria clearance in the late stage of erythroid differentiation. However, the particular roles and differences of mTORC1 and mTORC2 in the regulation of erythropoiesis still remain largely unknown. In the present study, we investigated the comparative effects of dual mTORC1/mTORC2 mTOR kinase inhibitor AZD8055 and mTORC1 inhibitor rapamycin on erythroid differentiation in K562 cells induced by hemin and erythropoiesis in β‐thalassemia mouse model.

Materials and Methods: In vitro erythroid differentiation model of hemin‐induced K562 cells and β‐thalassemia mouse model were treated with AZD8055 and rapamycin. Cell Counting Kit‐8 was used to detect cell viability. The cell proliferation, cell cycle, erythroid surface marker expression, mitochondrial content, and membrane potential were determined and analyzed by flow cytometry and laser scanning confocal microscopy. Globin gene expression during erythroid differentiation was measured by RT‐qPCR. The mTORC2/mTORC1 and autophagy pathway was evaluated using western blotting.

Results: Both AZD8055 and rapamycin treatments increased the expression levels of the erythroid differentiation‐specific markers, CD235a, α‐globin, γ‐globin, and ε‐globin. Notably, AZD8055 suppressed the cell proliferation and promoted the mitochondrial clearance of hemin‐induced K562 cells more effectively than rapamycin. In a mouse model of β‐thalassemia, both rapamycin and AZD8055 remarkably improve erythroid cell maturation and anemia. Moreover, AZD8055 and rapamycin treatment inhibited the mTORC1 pathway and enhanced autophagy, whereas AZD8055 enhanced autophagy more effectively than rapamycin. Indeed, AZD8055 treatment inhibited both mTORC2 and mTORC1 pathway in hemin‐induced K562 cells.

Conclusion: AZD8055 is more effective than rapamycin in inhibiting proliferation and promoting mitochondrial clearance in erythroid differentiation, which might provide us one more therapeutic option other than rapamycin for ineffective erythropoiesis treatment in the future. These findings also provide some preliminary information indicating the roles of mTORC1 and mTORC2 in erythropoiesis, and further studies are necessary to dissect the underlying mechanisms.
Dietary Pyridoxine Requirements of Coho Salmon (Oncorhynchus kisutch) Post‐Smolts
Authors: Yu Hairui , Zhang Xinyue , Yuan Ziyi , Yu Leyong , Zhao Youzhi , Li Lingyao
Published: October 2024

A 10‐week feeding trial was conducted to investigate the dietary pyridoxine requirements of coho salmon (Oncorhynchus kisutch) post‐smolts with an initial mean body weight of 180.22 ± 0.41 g. Seven diets were prepared with gradient pyridoxine levels of 0.32, 1.25, 2.56, 4.08, 8.24, 16.02, and 32.32 mg/kg, respectively, and each diet was assigned to three replication groups of 10 fish. The results revealed that coho salmon fed the diet with pyridoxine supplementation gained more final body weight (FBW), specific growth rate (SGR), and better feed conversion ratio (FCR). FBW and SGR of the fish fed the diet with 8.24 mg/kg pyridoxine were significantly higher than those of the other groups (P < 0.05). An inverse trend was observed for FCR, which was the lowest in fish fed the diet with 8.24 mg/kg pyridoxine. The gradient pyridoxine levels did not yield any statistically (P > 0.05) significant impact on the whole‐body composition including moisture, ash, crude lipid, and crude protein. The hepatic pyridoxine concentration, aspartate aminotransferase (AST), and alanine aminotransferase (ALT) activities increased significantly with dietary pyridoxine levels increasing from 0.32 to 4.08 mg/kg (P < 0.05) and plateaued after that (P > 0.05). The coho salmon fed the diet with 8.24 mg/kg pyridoxine achieved the maximum superoxide dismutase and catalase, as well as the minimum total cholesterol, triglyceride, and malondialdehyde. Broken line analysis of SGR, FCR, AST, and ALT activities reflected the optimal dietary pyridoxine requirements for coho salmon post‐smolts from 3.92 to 7.08 mg/kg diet.
Simulation and Optimization of Bus Interior Noise Based on Hybrid Method for Engine Vibration Load Identification
Authors: Cui Xintao , Zhai Naibin , Cao Yufan
Published: October 2024

Buses are crucial for urban public transportation systems as they help reduce traffic congestion and promote environmental sustainability. The noise inside buses significantly affects the comfort and physical health of both drivers and passengers. Although numerous studies have focused on the interior noise of buses, few have explicitly addressed the methods of reducing bus noise through comprehensive bus simulation and testing. Challenges of the full bus simulation include obtaining accurate engine vibration loads and building the finite element model of the full bus. This study presents a comprehensive approach for analyzing and optimization bus interior noise caused by engine vibration excitation. A novel hybrid method is proposed to identify bus engine vibration load, combining experimental testing of engine cylinder pressure with multibody dynamics simulation. Structure–acoustic coupled simulations were conducted to predict bus interior noise based on a detailed finite element model of the full bus. The study demonstrates a strong correlation between simulation and experimental results, validating the accuracy and effectiveness of the proposed method. The primary causes of the interior booming noise were identified by simulation analysis and experimental diagnosis. Optimization analysis led to a notable noise reduction by adjusting the support stiffness of the drive shaft, body structure, and integrating rear leaf spring dampers. These processes contribute to the analysis and improvement of bus interior noise, ultimately enhancing bus ride comfort and promoting sustainable urban public transportation.
FDA Approval Summary: Capecitabine Labeling Update under Project Renewal
Authors: Agrawal Sundeep , Lee Clara , Pierce William F. , Everhart Elizabeth , King-Ducre Adriene , Royce Melanie , Osgood Christy L. , Amiri-Kordestani Laleh , Chiu Haw-Jyh , Ricks Tiffany K. , Pan Lili , Fourie Zirkelbach Jeanne , Charlab Rosane , Pacanowski Michael , Kim Tamy , Pazdur Richard , Kluetz Paul G. , Gao Jennifer J.
Published: October 2024

On December 14, 2022, the FDA approved revisions to the United States Prescribing Information (USPI) for capecitabine that revised existing indications and dosage regimens, added new indications and their recommended dosage regimens, revised safety information, updated the description of the risk of capecitabine in patients with dihydropyrimidine dehydrogenase deficiency, and edited other sections of the USPI to conform with FDA’s current labeling guidance. These supplements were reviewed and approved under Project Renewal, a public health initiative established by the FDA’s Oncology Center of Excellence that aims to update the prescribing information of certain older oncology drugs to ensure information is clinically meaningful and scientifically up to date. This article summarizes the FDA approach that supported revisions to the capecitabine USPI within the context of Project Renewal.
A Preoperative Window-of-Opportunity Study of Oral SERD, Imlunestrant, in Newly Diagnosed ER-Positive, HER2-Negative Early Breast Cancer: Results from the EMBER-2 Study
Authors: Neven Patrick , Stahl Nicole , Vidal Maria , Martín Miguel , Kaufman Peter A. , Harbeck Nadia , Hunt Kelly K. , Carter Stacey , Bidard Francois-Clement , Fasching Peter A. , Aftimos Philippe , Wheatley Duncan , Hamilton Erika , Aft Rebecca , Kulkarni Swati , Schmid Peter , Bhave Manali , Ismail-Khan Roohi , Karacsonyi Claudia , Estrem Shawn T. , Nguyen Bastien , Ozbek Umut , Yuen Eunice , Rodrik-Outmezguine Vanessa , Ciruelos Eva
Published: October 2024

Purpose:

Imlunestrant is an oral selective estrogen receptor degrader with favorable safety and preliminary efficacy in patients with advanced breast cancer. Pharmacodynamic (PD) biomarker data can optimize drug dosing; in this study, we present PD data from the EMBER-2 study.

Patients and Methods:

Postmenopausal women with untreated, operable estrogen receptor (ER)–positive, HER2-negative early breast cancer were randomized to 400 versus 800 mg of imlunestrant daily for ∼2 weeks before surgery. A single arm study tested a daily dose of 200 mg. PD biomarker changes (ER, progesterone receptor, Ki-67 by IHC, and mRNA expression of ER-related genes) were evaluated in paired tumor samples (pre-/posttreatment). Safety and pharmacokinetics were also assessed.

Results:

Among evaluable paired samples (n = 75), PD profiles demonstrated consistent ER targeting between 400- and 800-mg doses, with less toxicity at the 400-mg dose. Although inducing the lowest rate of complete cell-cycle arrest, PD and pharmacokinetic results were similar for the 200-mg dose.

Conclusions:

EMBER-2 combined with existing phase I data has identified 400 mg as the optimal imlunestrant dose.
Application of artificial intelligence in music generation: a systematic review
Authors: Gutiérrez Paitan Frank Manuel , Acuña Meléndez María , Ovalle Christian
Published: October 2024

Our analysis explores the benefits of artificial intelligence (AI) in music generation, showcasing progress in electronic music, automatic music generation, evolution in music, contributions to music-related disciplines, specific studies, contributions to the renewal of western music, and hardware development and educational applications. The identified methods encompass neural networks, automation and simulation, neuroscience techniques, optimization algorithms, data analysis, and Bayesian models, computational algorithms, and music processing and audio analysis. These approaches signify the complexity and versatility of AI in music creation. The interdisciplinary impact is evident, extending into sound engineering, music therapy, and cognitive neuroscience. Robust frameworks for evaluation include Bayesian models, fractal metrics, and the statistical creator-evaluator. The global reach of this research underscores AI's transformative role in contemporary music, opening avenues for future interdisciplinary exploration and algorithmic enhancements.
Impact of federated learning and explainable artificial intelligence for medical image diagnosis
Authors: Muthuramalingam Sivakumar , Thiyagarajan Padmapriya
Published: October 2024

Medical image recognition has enormous potential to benefit from the recent developments in federated learning (FL) and interpretable artificial intelligence (AI). The function of FL and explainable artificial intelligence (XAI) in the diagnosis of brain cancers is discussed in this paper. XAI and FL techniques are vital for ensuring data ethics during medical image processing. This paper highlights the benefits of FL, such as cooperative model training and data privacy preservation, and the significance of XAI approaches in providing logical justifications for model predictions. A number of case studies on the segmentation of medical images employing FL were reviewed to compares and contrasts various methods for assessing the efficacy of FL and XAI based diagnostic models for brain tumors. The relevance of FL and XAI to improve the accuracy and interpretability during medical image diagnosis have been presented. Future research directions are also described indicating as to integrate data from various modes, create standardised evaluation processes, and manage ethical issues. This paper is intended to provide a deeper insight on relevance of FL and XAI in medical image diagnosis.
Artificial intelligence for deepfake detection: systematic review and impact analysis
Authors: Sunkari Venkateswarlu , Sri Nagesh Ayyagari
Published: October 2024

Deep learning and artificial intelligence (AI) have enabled deepfakes, prompting concerns about their social impact. deepfakes have detrimental effects in several businesses, despite their apparent benefits. We explore deepfake detection research and its social implications in this study. We examine capsule networks' ability to detect video deepfakes and their design implications. This strategy reduces parameters and provides excellent accuracy, making it a promising deepfake defense. The social significance of deepfakes is also highlighted, underlining the necessity to understand them. Despite extensive use of face swap services, nothing is known about deepfakes' social impact. The misuse of deepfakes in image-based sexual assault and public figure distortion, especially in politics, highlight the necessity for further research on their social impact. Using state-of-the-art deepfake detection methods like fake face and deepfake detectors and a broad forgery analysis tool reduces the damage deepfakes do. We inquire about to review deepfake detection research and its social impacts in this work. In this paper we analysed various deepfake methods, social impact with misutilization of deepfake technology, and finally giving clear analysis of existing machine learning models. We want to illuminate the potential effects of deepfakes on society and suggest solutions by combining study data.
Implications of artificial intelligence chatbot models in higher education
Authors: Khandakar Hissan , Ali Fazal Syed , Fattah Afnan Kazi , Kamrul Hasan Khandakar
Published: October 2024

Artificial intelligence (AI) is becoming increasingly influential in the academic sector, which is why it is important to explore the ethical dilemmas and concerns surrounding AI chatbots’ design, development, and deployment in educational contexts. Conducted as a thematic literature review, this paper explores existing research on AI in education, AI chatbots, and their integration with higher education to gather evidence and insights that discuss ethical implications and challenges. The study has analyzed several articles on AI chatbots and their integration into academic fields. Significant gaps have been identified, such as the need for more practical implications and the recognition of AI chatbots as a collaborative tool for academic purposes. More AI chatbots should be explicitly trained on data relevant to the learners’ study to examine their usefulness properly. The paper discusses the ethical dilemmas and concerns about the design, development, and deployment of AI chatbots in higher education. It seeks to provide insights and recommendations to ensure the ethical use of AI chatbots in higher education by identifying significant gaps in the existing literature and providing scenarios to expect in the development of AI in education.
Real-time anomaly detection in electric motor operation noise
Authors: Nguyen Van-Khanh , Thai Bao-Toan , Tran Vy-Khang , Pham Hai , Nguyen Chi-Ngon
Published: October 2024

Anomaly detection plays a very important role in many fields to identify abnormalities occurring in the system earlier. This study proposes a new abnormality detection solution for 3-phase electric motors based on their working noise. Normal and abnormal operating noise data sets for an electric motor were acquired in the laboratory. These datasets are converted into the corresponding two-dimensional gray spectrogram image sets. The normal set is used to train the autoencoder (AE) model to find the abnormality evaluation threshold. This threshold is validated again with anomalous data sets. The trained AE is then quantized to be installed on a system consisting of two duo-core microcontroller units (MCUs) for real-time testing. Free real-time operating system (FreeRTOS), a real-time operating system, is used to schedule tasks on the system. Experimental results show that the designed anomaly detector can accurately detect over 99% of abnormal events. The system can communicate with a supervisory control and data acquisition (SCADA) application running on the S7-1200 programmable logic controller (PLC) platform using the Modbus transmission control protocol (TCP) protocol. The SCADA application can continuously record evaluated results from the system and adjust abnormal thresholds for the system directly on the human-machine interface (HMI) screen.
Implementation of global navigation satellite system softwaredefined radio baseband processing algorithms in system on chip
Authors: Devi Kh Chetna , Panduranga Rao Malode Vishwanatha
Published: October 2024

The global navigation satellite system (GNSS) is an international navigation system that determines users' locations globally using a constellation of satellites. Conventional hardware-based receivers often face challenges related to cost-effectiveness and lack of reconfigurability. To address these issues, GNSS software receivers have emerged, executing baseband processing methods on host computers. However, host PC-based GNSS software receivers encounter obstacles during real-time signal acquisition, such as computational complexity and data loss. This research paper introduces a real-time system on chip (SoC)-based GNSS software receiver to mitigate these concerns. The receiver utilizes the USRP N210 radio frequency (RF) front end to acquire GNSS signals in real-time. Baseband processing algorithms are executed using the Zynq 7000 SoC board, with modifications applied to the acquisition module. The effectiveness of the SoC-based GNSS receiver is evaluated under both stationary and dynamic conditions. Experimental outcomes indicate that the receiver provides precise user localization and facilitates prototype development. This methodology not only overcomes the limitations of conventional hardware-based receivers but also leverages the benefits of SoC architecture to process GNSS signals in a flexible and efficient manner.
A systematic assertive wide-band routing using location and potential aware technique
Authors: Mohammed Saifuddin Karur , D. Devangavi Geetha
Published: October 2024

Delays occur when packets must be routed over several paths in a wireless sensor network with multiple origins and destinations. There are several causes, delays may occur everywhere, even in a multi-hop wireless network. Due to the broadcast nature of wireless networks, opportunistic routing was able to circumvent these delays. To avoid unnecessary delays, wide-band routing may be used to calculate the smaller path between two nodes. In this case, we address the shortcomings of the standard approach by taking into account the node's power. Path routing as well as the broadcast nature of wireless signals help mitigate the effects of shoddy wireless connections. The results show that the suggested approach outperformed the baseline in both end-to-end latency and packet delivery ratio.
Framework for abnormal event detection and tracking based on effective sparse factorization strategy
Authors: Divyaprabha Divyaprabha , Seebaiah Guruprasad
Published: October 2024

The idea of tracking video objects has evolved to facilitate the area of surveillance systems. However, most current research efforts lie in speedy abnormal event detection and tracking of objects of interest tracking. However, the primary challenge is dealing with complex video structures' inherent redundancy. The existing research models for video tracking are more inclined towards improving accuracy. In contrast, the consideration of a more significant proportion of mobile object dynamics, e.g. abnormal events, in motion over the crowded video frame sequence is mainly overlooked, which is essential to study a specific movement pattern of the object of interest appearing in the frame sequence concerning the cost of computation factors. The study thereby introduces a unique strategy of speedy abnormal event detection and tracking, which facilitates video tracking to assess a specific pattern of object of interest movement over complex and crowded video scenes, considering a unique learning-based approach. The extensive simulation outcome further shows that the proposed tracking model accomplishes better tracking accuracy yet retains an optimized computation cost compared to the baseline studies. The computation of video tracking also accomplishes higher detection rates even in the challenging constraints of partial/complete occlusion, illumination variation and background clutter.
Harnessing adapted capsule networks for accurate lumpy skin disease diagnosis in cattle
Authors: Mallikarjun Goddeti , Narayana V. A.
Published: October 2024

Lumpy skin disease (LSD) poses a substantial risk to livestock, emphasizing the critical need for reliable diagnostic tools to ensure timely intervention. The considerable economic impact of LSD further accentuates the imperative for efficient diagnosis. In this context, artificial intelligence (AI) emerges as a transformative solution, playing a pivotal role in providing swift detection capabilities. Rapid identification of LSD not only alleviates economic burdens but also impedes the disease’s propagation with in herds. A ground breaking in iterative involves the implementation of an adapted capsule network (CapsNet) expressly designed for diagnosing LSD. This innovative strategy is finely tuned to discern intricate patterns in disease manifestation, achieving an impressive accuracy rateof 97.6%. The model’s effectiveness is evident in it is capacity to differentiate between infected and healthy cases, with precision, recall, and F1 score metrics registering at 9.65%, 97.1%, and 96.3%, respectively. This high level of precision underscores the model’s proficiency in minimizing errors, solidifying its role as a dependable tool for precise LSD diagnosis and intervention, contributing significantly to the overall health and economic well-being of livestock populations.
Development of a prioritized traffic light control system for emergency vehicles
Authors: Oluwatobi Aworinde Halleluyah , Emmanuel Adeniyi Abidemi , Adebayo Segun , Adeniji Faith , Julius Aroba Oluwasegun
Published: October 2024

This research presents a model for an adaptive traffic signal control system aimed at improving urban traffic regulation. It dynamically adjusts signal timing based on vehicle volume at intersections, prioritizing emergency vehicles by allowing them immediate passage. Utilizing Arduino coding, the system controls traffic light intensity according to the traffic flow, enhancing road safety and efficiency. This innovative approach not only facilitates faster clearance for emergency services without human intervention but also reduces congestion and accident rates. This research creates a model for a prioritized traffic signal control system. When the vehicular volume at the intersection varies, the signal time alters autonomously. It identifies the ambulance/emergency vehicles and allows the green light for emergency vehicles like ambulances, and fire engines. This approach may be used to detect traffic accidents and infractions of automobile spiral motions. When erected on the road, the entire system allows for quick traffic clearing for rescue vehicles without requiring a policeman. The system's design eliminates the need for sensors or radio frequency identification (RFID) tags, simplifying traffic management. Simulations validate that emergency vehicle travel time is significantly reduced, proving the system's effectiveness in streamlining urban traffic flows.