Reviewerly/exHarmony huggingface dataset:
Pretraining Data Mixtures Enable Narrow Model Selection Capabilities in Transformer Models

Transformer models, notably large language models (LLMs), have the remarkable ability to perform in-context learning (ICL) -- to perform new tasks when prompted with unseen input-output examples without any explicit model training. In this work, we study how effectively transformers can bridge between their pretraining data mixture, comprised of multiple distinct task families, to identify and learn new tasks in-context which are both inside and outside the pretraining distribution. Building on previous work, we investigate this question in a controlled setting, where we study transformer models trained on sequences of $(x, f(x))$ pairs rather than natural language. Our empirical results show transformers demonstrate near-optimal unsupervised model selection capabilities, in their ability to first in-context identify different task families and in-context learn within them when the task families are well-represented in their pretraining data. However when presented with tasks or functions which are out-of-domain of their pretraining data, we demonstrate various failure modes of transformers and degradation of their generalization for even simple extrapolation tasks. Together our results highlight that the impressive ICL abilities of high-capacity sequence models may be more closely tied to the coverage of their pretraining data mixtures than inductive biases that create fundamental generalization capabilities.
Public Health and Safety on Close Contact Proximity Detection for COVID-19 and Alert via IoT

The social distancing among people is vital in minimizing spread of COVID-19 among community and can be effective in flattening the outbreak. This research work on developing a close contact proximity detection system among smartphone users and particularly of COVID-19 patient using Bluetooth signal to identify and analyze close contact proximity and social distancing from other anonymous smartphone user in his/her surrounding and to alert user if the social distancing is breached. The methodology used the Radio Signal Strength Indicator (RSSI) signal to analyze and estimate the proximity distance and duration of the individual’s exposure to other peoples in surrounding area. The overlap zone of 1-meter is used to indicate detection of closed contact proximity between users. Furthermore, the collected data can be used to do contact tracing that enable health official to identify the closed contact of infected patient systematically, faster and can ensure coverage of people that anonymously and not directly known to the COVID-19 patient. An encouraging result is obtained on the closed contact proximity detection which shown within the mobile apps. Furthermore, the performance of system for close contact proximity detection shown that indoor location has a good signal distribution compared to outdoor location.
Privacy Implications of Central Bank Digital Currencies

One hundred thirty-one countries, representing over 98 percent of the global gross domestic product (GDP), are currently exploring central bank digital currencies (CBDCs), a new form of digital money that is different from privately issued cryptocurrencies and stablecoins.As central banks worldwide grapple with CBDC design options, privacy has become a critical feature and concern.Many central banks, government agencies, nongovernmental organizations (NGOs), think tanks, and even the general public have underscored the importance of privacy in CBDC systems.Moreover, a diverse group of economists, computer scientists, engineers, and legal scholars have embarked on crafting privacy-preserving CBDC designs.But two fundamental questions appear to be overshadowed: (1) How is privacy defined in the context of CBDCs? and ( 2) What specific privacy challenges emerge from CBDCs? Prior to proposing solutions, a clear understanding of these concerns is crucial and necessary.This Article first adopts Daniel Solove's pragmatic approach and Helen Nissenbaum's theory of contextual integrity to conceptualize privacy within the CBDC context.Next, it examines the data flow inherent to four core CBDC designs.It concludes that the most significant privacy concern arises from central banks collecting
Self-Supervised Classification of SAR Images With Optical Image Assistance

Supervised Deep Neural Networks (DNNs) have proven to be powerful tools for SAR image interpretation tasks. However, they present a formidable challenge in acquiring a substantial amount of labeled data. In this paper, we investigate the promising technique of contrastive self-supervised learning for SAR image classification. This approach allows us to take advantage of a large number of available unlabeled images to pre-train a SAR image classification model. Our novel contrastive learning framework conducts both instance-level and cluster-level pretext tasks, which not only enforce consistency between the images and their augmented "views" at the instance level but also their representation within clusters. Besides generating different views through random, low-level image transformations, we proposed two new strategies to construct positive sample pairs to improve contrastive SAR image feature learning: middle-level optical assistance and high-level graph searching. The middle-level optical assistance strategy is inspired by the observation that domain experts typically interpret SAR images with the aid of optical images. This insight spurs us to generate intermediate SAR images as positive samples from geographically matched optical data using CycleGAN. Furthermore, we augment the positive samples of the image with their KNN (K-Nearest Neighbor) counterparts, following the idea that the KNN samples should belong to the same cluster. Extensive experimental results conducted on the SEN12MS land cover classification benchmark dataset demonstrate that our method is competitive with state-of-the-art self-supervised methods for SAR image classification. Even with only a small amount of labeled data for fine-tuning the model, our method rapidly improves classification performance, surpassing models pre-trained on natural image datasets.
Zeno Regime of Collective Emission: Non-Markovianity beyond Retardation

To build up a collective emission, the atoms in an ensemble must coordinate their behavior by exchanging virtual photons. We study this non-Markovian process in a subwavelength atom chain coupled to a one-dimensional (1D) waveguide and find that retardation is not the only cause of non-Markovianity. The other factor is the memory of the photonic environment, for which a single excited atom needs a finite time, the Zeno regime, to transition from quadratic decay to exponential decay. In the waveguide setup, this crossover has a time scale longer than the retardation, thus impacting the development of collective behavior. By comparing a full quantum treatment with an approach incorporating only the retardation effect, we find that the field memory effect, characterized by the population of atomic excitation, is much more pronounced in collective emissions than that in the decay of a single atom. Our results maybe useful for the dissipation engineering of quantum information processings based on compact atom arrays.
EDMC: Efficient Multi-view Clustering via Cluster and Instance Space Learning

Multi-view subspace clustering aims to cluster the data lying in a union of subspaces with low dimensions. The commonly used spectral clustering performs the final clustering based on an n×n affinity graph, which suffers from relative high time and space complexity. Some existing works have chosen key anchors with uniform sampling strategy or <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> -means for dealing with large-scale datasets. However, few of them pay attention to the physical meaning of cluster representation in the column of the dataset for learning informative anchors, which is independent from the instance representation. In this paper, we propose efficient dual multi-view clustering (EDMC) with relative low complexity. To be specific, EDMC makes full use of cluster representation space in the column of the dataset to help produce informative anchors, which has a clear physical meaning and is independent of instance representation in the row. It simultaneously explores the cluster and instance subspace representations to learn anchors for large-scale datasets. We perform anchor learning and efficient multi-view clustering in a unified framework and then adopt an alternative optimization strategy for solving the formulated problem. Extensive experiments performed on different datasets in terms of several metrics validate the superiority of the proposed method.
OLaLa: Ontology Matching with Large Language Models

Ontology (and more generally: Knowledge Graph) Matching is a challenging task where information in natural language is one of the most important signals to process. With the rise of Large Language Models, it is possible to incorporate this knowledge in a better way into the matching pipeline. A number of decisions still need to be taken, e.g., how to generate a prompt that is useful to the model, how information in the KG can be formulated in prompts, which Large Language Model to choose, how to provide existing correspondences to the model, how to generate candidates, etc. In this paper, we present a prototype that explores these questions by applying zero-shot and few-shot prompting with multiple open Large Language Models to different tasks of the Ontology Alignment Evaluation Initiative (OAEI). We show that with only a handful of examples and a well-designed prompt, it is possible to achieve results that are en par with supervised matching systems which use a much larger portion of the ground truth.
An Improved Approximation Algorithm for Quantum Max-Cut on Triangle-Free Graphs

We give an approximation algorithm for Quantum Max-Cut which works by rounding an SDP relaxation to an entangled quantum state. The SDP is used to choose the parameters of a variational quantum circuit. The entangled state is then represented as the quantum circuit applied to a product state. It achieves an approximation ratio of 0.582 on triangle-free graphs. The previous best algorithms of Anshu, Gosset, Morenz, and Parekh, Thompson achieved approximation ratios of 0.531 and 0.533 respectively. In addition, we study the EPR Hamiltonian, which we argue is a natural intermediate problem which isolates some key quantum features of local Hamiltonian problems. For the EPR Hamiltonian, we give an approximation algorithm with approximation ratio <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mn>1</mml:mn><mml:mrow class="MJX-TeXAtom-ORD"><mml:mo>/</mml:mo></mml:mrow><mml:msqrt><mml:mn>2</mml:mn></mml:msqrt></mml:math> on all graphs.
Identifying Credit Card Fraud in Illegal Transactions Using Random Forest and Decision Tree Algorithms

The use of credit cards is increasing in today's digital era. This increase has resulted in many cases of fraud which have had a negative impact on credit card owners. To overcome this, many financial institutions have developed credit card fraud detection systems that can identify suspicious transactions. This study uses a classification method, namely random forest and decision tree to identify illegal transactions using a credit card, which then compares the results and attempts to create a model that can be useful for detecting fraud using a credit card that is more accurate and effective. The result of this study is that the accuracy provided by the Decision Tree Classifier is 0.98, while the accuracy provided by the Random Forest Classification is also 0.975. The conclusion obtained that the decision tree has a higher level of accuracy compared to the Random Forest Classification Algorithm, which is 98%. On the other hand, the Random Forest classification algorithm has a slightly lower level of accuracy compared to the Decision Tree classification algorithm, with an accuracy rate of 97.5%
Hyper-parallel nonlocal CNOT operation assisted by quantum-dot spin in a double-sided optical microcavity

Implementation of controlled-NOT (CNOT) operation between different nodes in a quantum communication network nonlocally plays an important role in distributed quantum computation. We present a protocol for implementation of hyper-parallel nonlocal CNOT operation via hyperentangled photons simultaneously entangled in spatial-mode and polarization degrees of freedom (DOFs) assisted by quantum-dot spin in a double-sided optical microcavity. The agent Alice lets photons traverse the double-sided optical microcavity sequentially and applies single-qubit measurements on the electron and the hyperentangled photon. The agent Bob first performs corresponding unitary operations according to Alice’s measurement results on his hyperentangled photon, and then lets photons traverse the double-sided optical microcavity sequentially and performs the single-qubit measurements on the electron and the hyperentangled photon. The hyper-parallel nonlocal CNOT operation can be implemented simultaneously in spatial-mode and polarization DOFs if Alice performs single-qubit operations in accordance with Bob’s measurement results. The protocol has the advantage of having high channel capacity for long-distance quantum communication by using a hyperentangled state as the quantum channel.
Monogamy relations for bipartite and tripartite entanglement via intracavity spontaneous parametric down-conversion

Quantum entanglement is an important resource for quantum information processing, quantum communication, quantum computation and quantum sensing. Hence, several proposals have focused on the generation and characterization of entanglement. Some of those proposals are based on intracavity nonlinear processes. Here, we investigate bipartite and tripartite continuous variable entanglement generated by three intracavity spontaneous parametric down-conversion processes. In the model, three beams interact with a nonlinear medium inside a resonant cavity, as well as through the nonlinear process, creating three output fields. We use the positive-P phase-space method to analyze this system and to obtain the steady-states solutions. We also calculate the intracavity fluctuation spectra and utilize different continuous-variable entanglement criteria to certify bipartite/tripartite entanglement. Our results show the frequency regime where bipartite and genuine tripartite entanglement are present in the system. For this system, we also analyze the regimes where the monogamy relations hold.
High-resolution biostratigraphic analysis of the Cretaceous-Paleogene Boundary in the Kurdistan Region (NW Iraq)

A stratigraphical challenge has long been associated with the debatable K/Pg boundary-transition in Iraq, particularly in the high folded zone of the western Zagros fold-thrust belt (Kurdistan Region). However, dissensions regarding the boundary have remained controversial. Because of differing interpretations most of the previous studies considered it as a hiatus and/or erosional surface. Here, we present a high-resolution investigation of the planktonic foraminiferal biostratigraphy across the K/Pg boundary from three sections (Per Fat, Xezava, and Behere) within the Dohuk and Zakho areas of the Kurdistan Region of Iraq. The contact zone shows a lithostratigraphic resemblance and is represented by the upper part of the Shiranish Formation (late Maastrichtian) with the overlying Aaliji Formation (Danian), positioned in a continuous pelagic marlstone that exhibits a change in color from pale blue of the Cretaceous (Shiranish Formation) to light brown at the base of the Paleogene (Aaliji Formation). The biostratigraphic investigations across the studied interval reveal three Upper Cretaceous zones: Pseudoguembelina hariaensis p.p. (CF3) Interval Zone, Pseudoguembelina palpebra (CF2) Interval Zone, and Plummerita hantkeninoides (CF1) Total Range Zone. Four main zones and three subzones of the Danian are recorded: Guembelitria cretacea (P0) Partial Range Zone, Parvularugoglobigerina eugubina (Pa) Total Range Zone, Parasubbotina pseudobulloides (P1) Partial Range Zone (Globoanomalina archaeocompressa (P1a) Partial Range Subzone, Subbotina triloculinoides (P1b) Interval Subzone, Globanomalina compressa - Praemurica inconstans (P1c) Interval Subzone, and Praemurica uncinata p.p. (P2) Interval Zone. High biostratigraphic resolution indicates a complete K/Pg transition with no hiatus at the three studied localities in the Dohuk and Zakho areas.
Prediction of comprehensive dynamic performance for probability screen based on AR model-box dimension

In order to evaluate the comprehensive dynamic performance of probability screen and select the appropriate working conditions, a dynamic model of probability screen vibration system is established. Then, the calculation method of the dynamic characteristic parameters, based on the time series Auto Regression (AR) model of vibration test, is used. The relationship among the comprehensive dynamic characteristics, the screening efficiency and the box dimension of probability screen vibration system is analyzed, and Least Square Support Vector Machine (LSSVM), Generalized Regression Neural Network (GRNN) and Back Propagation Neural Network (BPNN) are used to predict the screening efficiency with box dimension. The analysis result shows that the screening efficiency, the stability, the response rapidity and the comprehensive dynamic characteristic of the system are all related to the box dimension of time series. As for the complexity of probability screen vibration system, it affects the comprehensive dynamic performance, and ultimately touches the screening efficiency of the probability screen; The best working conditions for the system are selected by the curve between box dimension and the working condition parameter; Taking box dimension as the only input variable, the prediction accuracy of the screening efficiency is high by using LSSVM,GRNN and BPNN methods, the prediction results are stable and reliable, and the box dimension can be used as a single input variable to predict the screening efficiency, it has the advantages of fewer input parameters, high prediction efficiency, and high prediction accuracy, which has great potential for expanding application space and further research value.
Optimized faster R-CNN for oil wells detection from high-resolution remote sensing images

As the oil and gas industry is crucial to the global energy market, policymakers need accurate information about local oil reserves and the harmful environmental effects of drilling, such as damage to public land and wildlife. Therefore, accurate automatic detection of the location, distribution and quantity of oil wells is essential. Recent advancements in remote sensing and deep learning technologies provide potential solutions for automatic oil wells detection using high-resolution remote sensing images. This study proposes an optimized Faster R-CNN-based model that incorporates three key modifications to improve the accuracy of oil wells detection. The modifications include replacing the VGG16 network with the ResNet50 network to improve the model’s feature extraction capabilities, substituting the ordinary convolution of ResNet with a dilated convolution to improve the model’s receptive field, and constructing a feature pyramid to improve the model’s ability to detect small targets and objects at different scales. Also, an edge detection module is added to further improve the detection accuracy. Furthermore, a new framework based on Faster R-CNN and leveraging Soft-NMS (Non-Maximum Suppression) and the proposed ClusterRPN sub-network is combined to address the problem of clustered oil wells detection. Experimental results demonstrate that the proposed optimized model outperforms existing models.
Making LLMs Worth Every Penny: Resource-Limited Text Classification in Banking

Standard Full-Data classifiers in NLP demand thousands of labeled examples, which is impractical in data-limited domains. Few-shot methods offer an alternative, utilizing contrastive learning techniques that can be effective with as little as 20 examples per class. Similarly, Large Language Models (LLMs) like GPT-4 can perform effectively with just 1-5 examples per class. However, the performance-cost trade-offs of these methods remain underexplored, a critical concern for budget-limited organizations. Our work addresses this gap by studying the aforementioned approaches over the Banking77 financial intent detection dataset, including the evaluation of cutting-edge LLMs by OpenAI, Cohere, and Anthropic in a comprehensive set of few-shot scenarios. We complete the picture with two additional methods: first, a cost-effective querying method for LLMs based on retrieval-augmented generation (RAG), able to reduce operational costs multiple times compared to classic few-shot approaches, and second, a data augmentation method using GPT-4, able to improve performance in data-limited scenarios. Finally, to inspire future research, we provide a human expert's curated subset of Banking77, along with extensive error analysis.
3DGAUnet: 3D generative adversarial networks with a 3D U-Net based generator to achieve the accurate and effective synthesis of clinical tumor image data for pancreatic cancer

Pancreatic ductal adenocarcinoma (PDAC) presents a critical global health challenge, and early detection is crucial for improving the 5-year survival rate. Recent medical imaging and computational algorithm advances offer potential solutions for early diagnosis. Deep learning, particularly in the form of convolutional neural networks (CNNs), has demonstrated success in medical image analysis tasks, including classification and segmentation. However, the limited availability of clinical data for training purposes continues to represent a significant obstacle. Data augmentation, generative adversarial networks (GANs), and cross-validation are potential techniques to address this limitation and improve model performance, but effective solutions are still rare for 3D PDAC, where the contrast is especially poor, owing to the high heterogeneity in both tumor and background tissues. In this study, we developed a new GAN-based model, named 3DGAUnet, for generating realistic 3D CT images of PDAC tumors and pancreatic tissue, which can generate the inter-slice connection data that the existing 2D CT image synthesis models lack. The transition to 3D models allowed the preservation of contextual information from adjacent slices, improving efficiency and accuracy, especially for the poor-contrast challenging case of PDAC. PDAC's challenging characteristics, such as an iso-attenuating or hypodense appearance and lack of well-defined margins, make tumor shape and texture learning challenging. To overcome these challenges and improve the performance of 3D GAN models, our innovation was to develop a 3D U-Net architecture for the generator, to improve shape and texture learning for PDAC tumors and pancreatic tissue. Thorough examination and validation across many datasets were conducted on the developed 3D GAN model, to ascertain the efficacy and applicability of the model in clinical contexts. Our approach offers a promising path for tackling the urgent requirement for creative and synergistic methods to combat PDAC. The development of this GAN-based model has the potential to alleviate data scarcity issues, elevate the quality of synthesized data, and thereby facilitate the progression of deep learning models, to enhance the accuracy and early detection of PDAC tumors, which could profoundly impact patient outcomes. Furthermore, the model has the potential to be adapted to other types of solid tumors, hence making significant contributions to the field of medical imaging in terms of image processing models.