Reviewerly/exHarmony huggingface dataset:
Accreditation of Analogue Quantum Simulators

We present an accreditation protocol for analogue, i.e., continuous-time, quantum simulators. For a given simulation task, it provides an upper bound on the variation distance between the probability distributions at the output of an erroneous and error-free analogue quantum simulator. As its overheads are independent of the size and nature of the simulation, the protocol is ready for immediate usage and practical for the long term. It builds on the recent theoretical advances of strongly universal Hamiltonians and quantum accreditation as well as experimental progress towards the realisation of programmable hybrid analogue-digital quantum simulators.
Directed Spatial Permutations on Asymmetric Tori

We investigate a model of random spatial permutations on two-dimensional tori, and establish that the joint distribution of large cycles is asymptotically given by the Poisson--Dirichlet distribution with parameter one. The asymmetry of the tori we consider leads to a spatial bias in the permutations, and this allows for a simple argument to deduce the existence of mesoscopic cycles. The main challenge is to leverage this mesoscopic structure to establish the existence and distribution of macroscopic cycles. We achieve this by a dynamical resampling argument in conjunction with a method developed by Schramm for the study of random transpositions on the complete graph. Our dynamical analysis implements generic heuristics for the occurrence of the Poisson--Dirichlet distribution in random spatial permutations, and hence may be of more general interest.
SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression

Recent advances in large language model (LLM) pretraining have led to high-quality LLMs with impressive abilities. By compressing such LLMs via quantization to 3-4 bits per parameter, they can fit into memory-limited devices such as laptops and mobile phones, enabling personalized use. However, quantization down to 3-4 bits per parameter usually leads to moderate-to-high accuracy losses, especially for smaller models in the 1-10B parameter range, which are well-suited for edge deployments. To address this accuracy issue, we introduce the Sparse-Quantized Representation (SpQR), a new compressed format and quantization technique which enables for the first time near-lossless compression of LLMs across model scales, while reaching similar compression levels to previous methods. SpQR works by identifying and isolating outlier weights, which cause particularly-large quantization errors, and storing them in higher precision, while compressing all other weights to 3-4 bits, and achieves relative accuracy losses of less than 1% in perplexity for highly-accurate LLaMA and Falcon LLMs. This makes it possible to run 33B parameter LLM on a single 24 GB consumer GPU without any performance degradation at 15% speedup thus making powerful LLMs available to consumer without any downsides. SpQR comes with efficient algorithms for both encoding weights into its format, as well as decoding them efficiently at runtime. Specifically, we provide an efficient GPU inference algorithm for SpQR which yields faster inference than 16-bit baselines at similar accuracy, while enabling memory compression gains of more than 4x.
A General Perspective on Objectives of Reinforcement Learning

In this lecture, we present a general perspective on reinforcement learning (RL) objectives, where we show three versions of objectives. The first version is the standard definition of objective in RL literature. Then we extend the standard definition to the $\lambda$-return version, which unifies the standard definition of objective. Finally, we propose a general objective that unifies the previous two versions. The last version provides a high level to understand of RL's objective, where it shows a fundamental formulation that connects some widely used RL techniques (e.g., TD$(\lambda)$ and GAE), and this objective can be potentially applied to extensive RL algorithms.
Visualize what you learn: a well-explainable joint-learning framework based on multi-view mammograms and associated reports

Abstract Radiological image and report joint pre-training learning lays the foundation for label-limited medical image analysis, especially when training of effective neural networks is hindered by insufficient manually labeled samples. It can extract abundant medical-related knowledge from paired images and reports to learn the well-transferable features, thus alleviating the need for labor-intensive and time-consuming labeling in downstream applications. However, existing methods prioritize exploring transferable representations rather than explaining how these representations are learned. In this study, we propose a well-explained radiograph-report joint pre-training framework, applied to label-efficient abnormality recognition in mammograms. We address the challenges of learning and visualizing the alignment between the exam-level report and the corresponding mammographic examination consisting of bilateral views in several directions. Qualitatively, our model provides researchers with a better understanding of representation learning for further improvement of pre-training and downstream tasks. We evaluate the label-efficiency ability of our method by comparing it to several state-of-the-art uni-modal and multi-modal methods. The results on four datasets show that our approach brings significant improvements under limited supervision for various downstream tasks. It, therefore, may serve as a novel practical framework exploiting co-learning between multi-view radiological images and radiologists' reports. Furthermore, it has the potential to offer valuable insights into interpretable and comparable tools in the domain of pre-training.
Analysis of Education Needs of Adult Literary Education Officials

Objectives The purpose of this study is to analyze the educational needs of J-do adult literacy education officials according to the size of the region so that adult literacy education policies and continuous and effective projects can be established according to the characteristics of the region. Methods This study analyzed the survey contents of 150 officials in charge of literacy education at J-do Adult Literature Education Institute. Education needs were measured on a Likert 5-point scale for 16 variables using the survey tool for adult literacy education support policies developed by Heo Jun, Lee Ji-hye, and Gil Hye-ji (2020). Data processing used the SPSS 26.0 statistical package to derive the priority of adult literacy education needs by regional size through the corresponding sample t-test, Borich demand, and The Locus for Focus model analysis. Results First, in regions with a population of 100,000 or more, Borich's demand priority analysis and The Locus for Focus Model analysis revealed the actualization of instructor fees, budget for literacy education institutions, learner support (education costs, counseling, etc.), expansion of literacy education facilities, and short-range learning opportunities. Second, in areas with a population of more than 50,000 and less than 100,000, variables were derived to secure facilities dedicated to literacy education and to provide learning opportunities for short-range literacy education. Third, in areas with a population of less than 50,000, the budget for operating literacy education institutions, expanding literacy education contents, supporting learners (education expenses, counseling, etc.), promoting literacy education, and event variables were derived. Conclusions As a result of analyzing the needs of adult literacy education by regional size, J-do found that none of the 16 variables appeared in common. The highest priority was “realization of instructor fees” in areas with a population of 100,000 or more, “secure facilities dedicated to literacy education” in areas with a population of 50,000 or more, and “budget for operating literacy education institutions” in areas with a population of less than 50,000. If the results of educational demand analysis vary depending on the size of the region, the policy promotion must also vary by region, so it is necessary to analyze educational needs according to regional characteristics to promote adult literacy education policies suitable for regional characteristics.
An Efficient Implementation of the Novel Data Encryption Standard (DES) Algorithm with Improved Key Generation Method Compared with Viterbi Decoder

Aim: This study presents an efficient implementation of the Novel Data Encryption Standard (DES) algorithm with an improved key generation method and the result is compared with the Viterbi decoder algorithm. Materials and Methods: The proposed implementation of the novel Data Encryption Standard (DES) and Viterbi algorithm requires a computer with VHDL synthesis tool and Modelsim software installed. The encryption and decryption are implemented in VHDL language, while the key generation process is executed using the Modelsim software. The runtime analysis of the proposed algorithm was conducted using a set of test data and the results were compared with the Viterbi algorithm. The research consisted of 10 samples and applied the DES and Viterbi algorithms with an equal number of samples. The analysis was conducted using G-power (0.8) with alpha and beta set at 0.05 and 0.2, and a 95% confidence interval. Result: In terms of performance, the proposed implementation showed a run time of 3.3 ns, while the traditional Viterbi decoder showed a run time of 6.6 ns. The research's significance level was determined to be p =0.002(p<0.05). Conclusion: The results of the proposed implementation indicate its potential as a valuable addition to the field of data encryption technology and its potential for wider implementation in real-world applications.
A method to determine the accuracy of the automatic page-turning navigation function

Commonly, learners need to find one other page that they read exploring those that correlated with this page in terms of potential interest or knowledge. However, it is hard to do with online learning materials. In that case, automatic page-turning navigation can recommend learning with pages by algorithms designed to calculate the relevance between pages. However, currently, when performing automatic page-turning navigation, it is mainly calculated by the click-through rate of page kin as well, and does not take into account the knowledge content on the page. In this study, a natural language analysis method based on morphological analysis is designed to detect the accuracy of the current automatic page-turning navigation by calculating the relevance of the knowledge contained within a page.
Non-volatile Optical Memory and Switch with Magnetic Material Integration

We developed a waveguide-based magneto-optical (MO) switch and memory utilizing combination of MO effect and nonvolatile magnetic materials. The MO switch is driven by pulsed current and enables latching operation. The MO memory is based on light-triggered magnetization reversal of magnet loaded on Si waveguides.
Area-Aware Colon Polyps Detection by E- Xception Model

Abstract Colon also named large bowel and large intestine is the lowest part of human gastrointestinal tract. It is the third most common cancer in the world, and its rate of reported cases are continuously increasing. Colonoscopy is now the golden standard for detecting and removing the precancerous polyps. Manually endoscopy data analysis needs a lot of concentration and can be inaccurate at times. In this research work, an efficient Xception (E-Xception) model is trained for the detection of endoscopy absurdities in various areas of colon. We freeze the weights and biases of the model to use in our own model. In early detection of CRC, the fine-tune configuration has obtained 99.46% accuracy.
Phonological Similarity Judgments of Word Pairs Reflect Sensitivity to Large-Scale Structure of the Phonological Lexicon

Network analyses of the phonological mental lexicon show that words are clustered into communities and phonologically dissimilar words can be connected to each other through distant paths. Here we investigate whether behavioral traces of the large-scale structure of the phonological lexicon can be obtained. Participants listened to pairs of spoken words and made phonological similarity judgments for word pairs with varying path lengths and community membership. Path length in the phonological network represented the number of steps needed to traverse from one word to another word in the phonological network. Word pairs were either from the same phonological community or from different communities. Results indicated that participants were sensitive to large-scale structure of the phonological lexicon. Word pairs residing in the same community were more likely rated as similar sounding than word pairs from different communities. Word pairs with longer path lengths were less likely rated as similar sounding than word pairs with shorter path lengths. Computational simulations suggested that the behavioral findings could be accounted for via a spreading activation mechanism implemented on the phonological network. Taken together, our results provide converging evidence that people are sensitive to the large-scale structure of the phonological language network and have implications for our understanding of the nature of phonological similarity representations in the mental lexicon.
Computer Vision Diagnostic Tool Using Deep Learning

&lt;p&gt;Computer vision systems have long surpassed human capabilities in manufacturing tasks. However, humans still excel in tasks that require fine motor skills. Namely, the assembly of large products, requiring the integration of numerous components and sub-assemblies. This study explores the feasibility of a stand-alone anomaly-detection model implemented on a mobile device. The application of this study is to develop a diagnostic tool to identify potential product issues throughout the assembly process.&lt;/p&gt;
DIFL:Multi-Source Domain Adaptation for Point Clouds Using Domain-Invariant-Feature-Learning

&lt;p&gt;Point clouds serve as a vital component in computer vision and robotics, enabling the representation and processing of three-dimensional data. However, their utility is often limited by significant variations in data across domains, impeding the transfer of knowledge between different scenarios. Beside, the existing approaches implement domain adaptation on Point clouds only with single source domain setting. To address this issue, we conduct the first investigation of multi-source domain adaptation for point clouds using the Domain-Invariant-Feature-Learning (DIFL) method. Our approach tackles data variability between domains by integrating multi-domain contrastive learning and pseudo-label guided fine-tuning, yielding a domain-invariant feature representation. Multi-domain contrastive learning maximizes agreement among multiple source point clouds in an invariant feature space, promoting invariance between source and target features. Additionally, pseudo-label guided fine-tuning maximizes the discrepancy by further aligning the pseudo-centroids of each class between the source and target domains. Our experiments demonstrate the superior performance of the DIFL method compared to existing techniques, signifying a significant advancement in the field of multi-source domain adaptation for point clouds.&lt;/p&gt;
A Comparative result-based study on Criminal Call Data Record Analysis

All calls that travel through a phone exchange are recorded in detail in call detail records or CDRs. The telephone exchange maintains this CDR, which includes the time of the call, the length of the call, the source and destination numbers, the type of the call, etc. Call data logs are crucial for handling serious criminal cases. Processing of Call Detail Records is now moving towards real-time streaming data. It assists in real-time call detail record analysis, real-time criminal location tracking, and real-time network behaviour analysis. However, the number, diversity, and data rate of these Call Detail Records are enormous, and the current telecom systems were not developed with these challenges in mind. The largest source, which can be viewed as Call Detail Records, can be used (for storage, processing, and analysis). The issues that the telecom sector has with call detail records analysis are the subject of extensive research. In this paper we demonstrate how to use Excel, data mining &amp; graph mining to analyse call detail records of criminal case.
Care Connect- A Next-Gen IoT Health Monitoring System

Doctors and nurses are obligated to regularly check on patients and maintain track of their vital health information in hospitals. It can be tough for medical professionals to continuously monitor each patient during pandemics like COVID-19 since so many individuals are admitted. The proposed approach to this issue can assist with patient monitoring and is based on IOT. The gadget can record data on Thingspeak, a cloud platform, and Blynk, a mobile app, and can monitor vital health markers including blood oxygen levels, temperature, and heart rate. The system is configured to operate automatically and can be accessed from any place with an internet connection. It continuously monitors and stores sensor data. An alert system is also implemented to inform in the event of crises. Key Words: Health, Covid-19, Monitor, ThingSpeak, Blynk
APPLICATION OF THE FIFO MODEL IN THE DRUG INVENTORY INFORMATION SYSTEM AT THE ASYFA KRUENG MANE PHARMACY

Asyfa Krueng Mane Pharmacy is a business entity engaged in the sale of drugs. The drug supply data processing system that was carried out at the Asyfa Krueng Mane Pharmacy still uses ordinary stationery to process drug supply data so that it is deemed less effective and efficient. Therefore, there is a need for an information system that can be used to assist and support the efficiency of the drug data processing process at the Asyfa Krueng Mane Pharmacy using the FEFO (First First Out) method, which is a method that dispenses drugs with shorter expiry dates than drugs with a later expiry date. long. With the application of the FEFO method in the Asyfa Krueng Mane Pharmacy information system, it is hoped that there will be no more accumulation of drugs and drugs with shorter expiration than drugs with longer expiration can be issued in order. As for the research results, you can see the Asyfa Pharmacy and type of Obt001 and type of Paracetamol and drug stock and date of entry and exit&#x0D; &#x0D; &#x0D; &#x0D;
Citation Analysis of serials in Postgraduate Research Theses and Dissertations of Library and Information Science of Public universities in Southeast, Nigeria

This study is a citation analysis of serials in postgraduate research reports of library and information science of public universities in Southeast Nigeria from 2013 to 2021. The study was guided by six research questions in line with the objectives of the study. The study employed a descriptive survey design with a sampled population of 296 derived through census method from four public universities offering library and information science. The principle instruments used in collecting data for this study were self-designed checklists. With the checklists, 12455 serials citations were analyzed using descriptive statistics of mode, mean and range and data presented in tables, charts and graphs using frequencies and percentile. The study found that the most cited serials types were journals with 76.5% citations followed by conference proceedings/reports with 20% and the least cited being government publication with only .5 citations . It was further discovered that serials used were either in print or electronic form. . The study also revealed that multi authorship is the most frequently cited author pattern in library graduates theses and dissertations, while annual serials citations range from 1034 to 2205. The study indicated that the average age of serials used was within the bracket of 0 – 20 years. The study recommended among other things that there should be documentation librarian in each university library whose duty should be to track down research reports, ensuring proper documentations and upward delivery to the university library circulation unit. This study could serve as a collection development tool that can be used as a model for the library to identify the primary sources for acquisitions and also as a guide for collection maintenance
Fully Integrated Photonic Dot-product Engine in 45-nm SOI CMOS for Photonic Computing

We demonstrate the building blocks for a photonic dot-product engine in 45nm SOI CMOS technology. To calculate the dot-product, two microrings and a balanced photodetector are employed. In parallel, two DACs drive the rings at resonance while a TIA converts the output photocurrent into voltage.
Path planning of Mobile Robot in Dynamic Environment by Evolutionary Algorithms

Mobile robot route planning is the process of identifying the ideal path for a robot to take in order to accomplish a particular objective. It is a crucial consideration of mobile robotics since it helps robots to navigate their environment and execute tasks in a safe and efficient manner. Path planning for mobile robots presents numerous difficulties, including the need to take into account the robot's physical capabilities and constraints, the presence of static obstacles and other dynamic elements of the environment, and the need to optimize for factors such as energy efficiency and time. Path planning is crucial for the successful deployment of mobile robots in a range of applications, including search and rescue, transportation, inspection, and production. It can also play a crucial role in enabling robots to function in collaborative contexts alongside people. Inspired by natural evolution and genetics, evolutionary algorithms are a kind of optimization technique. They are widely utilized in numerous fields, including mobile robot path planning. Evolutionary algorithms have the potential to find solutions to difficult, nonlinear problems, which is a major advantage for mobile robot path planning. Evolutionary algorithms are able to avoid local minima and produce globally optimal solutions, whereas conventional optimization approaches frequently become trapped in unsatisfactory solutions. This is especially crucial in real-world contexts where the robot may encounter unforeseen impediments or environmental changes. Using the ant colony optimization algorithm as an efficient evolutionary approach, this work seeks to determine the path of a mobile robot in an environment containing a number of static and dynamic obstacles. The presented model demonstrated that ACO is an effective real-time calculating solution for this type of problem.
Research on Question Severity Detection for Automatic Essay Scoring

In the process of essay scoring, the item-specific index is an important factor affecting the final score. In previous studies, the item-specific index was not included in the characteristic system of objective essay scoring. Therefore, it is easy to produce the phenomenon that the deviation from the topic with high language standardization, rich vocabulary and correct grammar is judged high, resulting in the decrease of the accuracy of the score prediction model, which is quite different from the results of manual evaluation. In this paper, the LDA topic model is used to model the article, and the topic probability distribution distance and word vector are used to calculate the topic degree index of the article. The index is added to the composition automatic scoring system to improve the accuracy of the machine scoring.