Reviewerly/exHarmony huggingface dataset:
Galaxy clustering multi-scale emulation

Simulation based inference has seen increasing interest in the past few years as a promising approach to model the non linear scales of galaxy clustering. The common approach using Gaussian process is to train an emulator over the cosmological and galaxy-halo connection parameters independently for every scales. We present a new Gaussian process model allowing to extend the input parameter space dimensions and to use non-diagonal noise covariance matrix. We use our new framework to emulate simultaneously every scales of the non-linear clustering of galaxies in redshift space from the AbacusSummit N-body simulations at redshift $z=0.2$. The model includes nine cosmological parameters, five halo occupation distribution (HOD) parameters and one scale dimension. Accounting for the limited resolution of the simulations, we train our emulator on scales from $0.3~h^{-1}\mathrm{Mpc}$ to $60~h^{-1}\mathrm{Mpc}$ and compare its performance with the standard approach of building one independent emulator for each scales. The new model yields more accurate and precise constraints on cosmological parameters compared to the standard approach. As the new model is able to interpolate over the scales space, we are also able to account for the Alcock-Paczynski distortion effect leading more accurate constraints on the cosmological parameters.
Large values of quadratic Dirichlet $L$-functions over monic irreducible polynomial in $\mathbb{F}_q[t]$

We prove an $\Omega$-result for the quadratic Dirichlet $L$-function $|L(1/2, \chi_P)|$ over irreducible polynomials $P$ associated with the hyperelliptic curve of genus $g$ over a fixed finite field $\mathbb{F}_q$ in the large genus limit. In particular, we showed that for any $\epsilon\in (0, 1/2)$, \[ \max_{\substack{P\in \mathcal{P}_{2g+1}}}|L(1/2, \chi_P)|\gg \exp\left(\left(\sqrt{\left(1/2-\epsilon\right)\ln q}+o(1)\right)\sqrt{\frac{g \ln_2 g}{\ln g}}\right), \] where $\mathcal{P}_{2g+1}$ is the set of all monic irreducible polynomial of degree $2g+1$. This matches with the order of magnitude of the Bondarenko--Seip bound.
Bias A-head? Analyzing Bias in Transformer-Based Language Model Attention Heads

Transformer-based pretrained large language models (PLM) such as BERT and GPT have achieved remarkable success in NLP tasks. However, PLMs are prone to encoding stereotypical biases. Although a burgeoning literature has emerged on stereotypical bias mitigation in PLMs, such as work on debiasing gender and racial stereotyping, how such biases manifest and behave internally within PLMs remains largely unknown. Understanding the internal stereotyping mechanisms may allow better assessment of model fairness and guide the development of effective mitigation strategies. In this work, we focus on attention heads, a major component of the Transformer architecture, and propose a bias analysis framework to explore and identify a small set of biased heads that are found to contribute to a PLM's stereotypical bias. We conduct extensive experiments to validate the existence of these biased heads and to better understand how they behave. We investigate gender and racial bias in the English language in two types of Transformer-based PLMs: the encoder-based BERT model and the decoder-based autoregressive GPT model. Overall, the results shed light on understanding the bias behavior in pretrained language models.
Simultaneous Synthesis and Verification of Neural Control Barrier Functions through Branch-and-Bound Verification-in-the-loop Training

Control Barrier Functions (CBFs) that provide formal safety guarantees have been widely used for safety-critical systems. However, it is non-trivial to design a CBF. Utilizing neural networks as CBFs has shown great success, but it necessitates their certification as CBFs. In this work, we leverage bound propagation techniques and the Branch-and-Bound scheme to efficiently verify that a neural network satisfies the conditions to be a CBF over the continuous state space. To accelerate training, we further present a framework that embeds the verification scheme into the training loop to synthesize and verify a neural CBF simultaneously. In particular, we employ the verification scheme to identify partitions of the state space that are not guaranteed to satisfy the CBF conditions and expand the training dataset by incorporating additional data from these partitions. The neural network is then optimized using the augmented dataset to meet the CBF conditions. We show that for a non-linear control-affine system, our framework can efficiently certify a neural network as a CBF and render a larger safe set than state-of-the-art neural CBF works. We further employ our learned neural CBF to derive a safe controller to illustrate the practical use of our framework.
The Set of Pure Gaps at Several Rational Places in Function Fields

In this work, using maximal elements in generalized Weierstrass semigroups and its relationship with pure gaps, we extend the results in \cite{CMT2024} and provide a way to completely determine the set of pure gaps at several rational places in an arbitrary function field $F$ over a finite field and its cardinality. As an example, we determine the cardinality and a simple explicit description of the set of pure gaps at several rational places distinct to the infinity place on Kummer extensions, which is a different characterization from that presented by Hu and Yang in \cite{HY2018}. Furthermore, we present some applications in coding theory and AG codes with good parameters.
From Principle to Practice: Vertical Data Minimization for Machine Learning

Aiming to train and deploy predictive models, organizations collect large amounts of detailed client data, risking the exposure of private information in the event of a breach. To mitigate this, policymakers increasingly demand compliance with the data minimization (DM) principle, restricting data collection to only that data which is relevant and necessary for the task. Despite regulatory pressure, the problem of deploying machine learning models that obey DM has so far received little attention. In this work, we address this challenge in a comprehensive manner. We propose a novel vertical DM (vDM) workflow based on data generalization, which by design ensures that no full-resolution client data is collected during training and deployment of models, benefiting client privacy by reducing the attack surface in case of a breach. We formalize and study the corresponding problem of finding generalizations that both maximize data utility and minimize empirical privacy risk, which we quantify by introducing a diverse set of policy-aligned adversarial scenarios. Finally, we propose a range of baseline vDM algorithms, as well as Privacy-aware Tree (PAT), an especially effective vDM algorithm that outperforms all baselines across several settings. We plan to release our code as a publicly available library, helping advance the standardization of DM for machine learning. Overall, we believe our work can help lay the foundation for further exploration and adoption of DM principles in real-world applications.
CNL2ASP: converting controlled natural language sentences into ASP

Answer Set Programming (ASP) is a popular declarative programming language for solving hard combinatorial problems. Although ASP has gained widespread acceptance in academic and industrial contexts, there are certain user groups who may find it more advantageous to employ a higher-level language that closely resembles natural language when specifying ASP programs. In this paper, we propose a novel tool, called CNL2ASP, for translating English sentences expressed in a controlled natural language (CNL) form into ASP. In particular, we first provide a definition of the type of sentences allowed by our CNL and their translation as ASP rules, and then exemplify the usage of the CNL for the specification of both synthetic and real-world combinatorial problems. Finally, we report the results of an experimental analysis conducted on the real-world problems to compare the performance of automatically generated encodings with the ones written by ASP practitioners, showing that our tool can obtain satisfactory performance on these benchmarks. Under consideration in Theory and Practice of Logic Programming (TPLP).
User-Centric Deployment of Automated Program Repair at Bloomberg

Automated program repair (APR) tools have unlocked the potential for the rapid rectification of codebase issues. However, to encourage wider adoption of program repair in practice, it is necessary to address the usability concerns related to generating irrelevant or out-of-context patches. When software engineers are presented with patches they deem uninteresting or unhelpful, they are burdened with more "noise" in their workflows and become less likely to engage with APR tools in future. This paper presents a novel approach to optimally time, target, and present auto-generated patches to software engineers. To achieve this, we designed, developed, and deployed a new tool dubbed B-Assist, which leverages GitHub's Suggested Changes interface to seamlessly integrate automated suggestions into active pull requests (PRs), as opposed to creating new, potentially distracting PRs. This strategy ensures that suggestions are not only timely, but also contextually relevant and delivered to engineers most familiar with the affected code. Evaluation among Bloomberg software engineers demonstrated their preference for this approach. From our user study, B-Assist's efficacy is evident, with the acceptance rate of patch suggestions being as high as 74.56%; engineers also found the suggestions valuable, giving usefulness ratings of at least 4 out of 5 in 78.2% of cases. Further, this paper sheds light on persisting usability challenges in APR and lays the groundwork for enhancing the user experience in future APR tools.
Parsing Millions of URLs per Second

URLs are fundamental elements of web applications. By applying vector algorithms, we built a fast standard-compliant C++ implementation. Our parser uses three times fewer instructions than competing parsers following the WHATWG standard (e.g., Servo's rust-url) and up to eight times fewer instructions than the popular curl parser. The Node.js environment adopted our C++ library. In our tests on realistic data, a recent Node.js version (20.0) with our parser is four to five times faster than the last version with the legacy URL parser.
Perancangan Sistem Absensi Online Bagi Karyawan/ Mahasiswa Praktik Kerja Lapangan Pada Divisi Costumer Care Menggunakan Aplikasi Google Form Tahun Akademik 2022/2023

Research in this community service activity was carried out at the office of a State-Owned Enterprise (BUMN), PT. Telkom Indonesia Makassar City Jl. City Hall, No. 5. The activity aims to make it easier for street vendors to carry out attendance activities when they arrive at the office and also when they are going back to their respective homes and to help when carrying out daily activities and logbook reports. This activity is also useful for street vendor employees to be able to make it easier when going to come and go home and is useful to make it easier when making a final report on street vendor activities. This research uses the research and development method of R&amp;D (Research and Development). This research has research results in the form of a product in the form of an online attendance system on the Google form application which can be used by street vendors when they are going to take attendance, return attendance and also make daily activity reports/logbooks
Intellectual Property Protection of Virtual IP in the Age of Digital Economy and Its Jurisprudence Discussion

This thesis aims to explore the intellectual property protection of virtual IP in the era of the digital economy and its jurisprudential discussion. With the continuous development of science and technology and the rise of the digital economy, virtual IP (virtual image and digital person) presents diverse connotations and expressions as an essential part of the digital economy. However, the intellectual property protection of virtual IP faces many dilemmas and challenges. This paper conducts an in-depth study on the protection of virtual IP through the legal perspective and puts forward some methods and strategies to crack the dilemma of virtual IP legal rights and interests. By improving the legal framework, clarifying the definition standard of virtual IP, and determining the scope of protected rights and interests of virtual IP, the legal protection of virtual IP can be effectively enhanced, and the legitimacy and stability of the property rights and interests of virtual IP can be maintained. This study has important theoretical and practical significance for promoting the development of the digital economy and protecting the intellectual property rights of virtual IP.
Self-trained Panoptic Segmentation

Panoptic segmentation is an important computer vision task which combines semantic and instance segmentation. It plays a crucial role in domains of medical image analysis, self-driving vehicles, and robotics by providing a comprehensive understanding of visual environments. Traditionally, deep learning panoptic segmentation models have relied on dense and accurately annotated training data, which is expensive and time consuming to obtain. Recent advancements in self-supervised learning approaches have shown great potential in leveraging synthetic and unlabelled data to generate pseudo-labels using self-training to improve the performance of instance and semantic segmentation models. The three available methods for self-supervised panoptic segmentation use proposal-based transformer architectures which are computationally expensive, complicated and engineered for specific tasks. The aim of this work is to develop a framework to perform embedding-based self-supervised panoptic segmentation using self-training in a synthetic-to-real domain adaptation problem setting.
Library Management in Improving Library Services in Institute of Technology Palm Indonesia Palm Oil Indonesia

This study was conducted to improve library services at the Indonesian Palm Technology Institute (ITSI) Library by applying the functions of management. This study aims to determine library management in improving library services at the Indonesian Palm Technology Institute (ITSI) Library. This research uses qualitative research methods. The collection method uses observation, interview, and document study methods. The results stated that the success of a college library service is determined by the satisfaction and feasibility of the library users as users and owners of information needs. Optimal service will lead to attraction and visitation as well as the usefulness of the library as an information center. In principle, the quality indicators of library services cannot be separated from the influence of the application of management functions that have been combined in library management, namely planning, organizing, directing, and controlling. Library management is an action in organizing and managing the library both in terms of human resources, systems, information sources, service functions, and the ins and outs of library activities with the target achieved. The Indonesian Sawit Institute of Technology (ITSI) library has currently implemented library management in accordance with the management processes and functions as has been made in a policy and SOP for each service. Although it has been implemented and has been running significantly. However, the application of library management continues to be implemented and continues to be carried out, upgrading both in terms of planning, organizing, directing, and controlling in order to achieve predetermined goals. Keywords: Library Management, Library Services.
Simulating photonic devices with noisy optical elements

Quantum computers are inherently affected by noise. While in the long-term error correction codes will account for noise at the cost of increasing physical qubits, in the near-term the performance of any quantum algorithm should be tested and simulated in the presence of noise. As noise acts on the hardware, the classical simulation of a quantum algorithm should not be agnostic on the platform used for the computation. In this work, we apply the recently proposed noisy gates approach to efficiently simulate noisy optical circuits described in the dual rail framework. The evolution of the state vector is simulated directly, without requiring the mapping to the density matrix framework. Notably, we test the method on both the gate-based and measurement-based quantum computing models, showing that the approach is very versatile. We also evaluate the performance of a photonic variational quantum algorithm to solve the MAX-2-CUT problem. In particular we design and simulate an ansatz which is resilient to photon losses up to $p \sim 10^{-3}$ making it relevant for near term applications.
Scaling TabPFN: Sketching and Feature Selection for Tabular Prior-Data Fitted Networks

Tabular classification has traditionally relied on supervised algorithms, which estimate the parameters of a prediction model using its training data. Recently, Prior-Data Fitted Networks (PFNs) such as TabPFN have successfully learned to classify tabular data in-context: the model parameters are designed to classify new samples based on labelled training samples given after the model training. While such models show great promise, their applicability to real-world data remains limited due to the computational scale needed. Here we study the following question: given a pre-trained PFN for tabular data, what is the best way to summarize the labelled training samples before feeding them to the model? We conduct an initial investigation of sketching and feature-selection methods for TabPFN, and note certain key differences between it and conventionally fitted tabular models.
A note on the convergence of the Bayesian entropy estimator for exchangeable partitions

We show that when the proportions of a countable set of species are organized as an exchangeable partition of the unit interval and we take a sample on it, then the Bayesian posterior entropy converges a.s. and in L^1 to the entropy of the species when the sample size diverges to infinity.
ARTIFICIAL INTELLIGENCE SYSTEMS IN SCHOOL EDUCATION: HISTORY AND MODERNITY, PROSPECTS AND CHALLENGES

The possibilities of using artificial intelligence (AI) systems in school education are considered. An overview of the main stages of AI development is presented, from its appearance and first experiments to modern achievements. Particular attention is paid to the role of AI in individualising learning, developing the necessary skills in students, in the new organisation of the educational process and in the professional development of teachers. It is shown that the use of AI in schools makes it possible to optimise students’ learning and provide access to education without spatial and time restrictions. The prospects for using new technologies, such as ChatGPT and virtual reality, in the educational process are considered. The evolution of ChatGPT in the educational field and the potential of this technology in the future are analysed. At the same time, it is shown that the use of AI systems in schools is associated with certain challenges, including ethical, pedagogical and technical aspects. In summary, this article provides information on how the benefits of AI can be maximised in school education, and also examines the future prospects for the development of AI in the context of school applications.
Vision-based Autonomous Landing of UAV on Dynamic Apron

Aiming at the landing problem of Unmanned Aerial Vehicle (UAV) without landmarks and global coordinates, this paper proposes an efficient vision-based UAV autonomous landing solution. Firstly, image information is obtained from the bottom visual sensor of the UAV, and the YOLOV5 object detection method is employed to determine the initial position of the dynamic apron. Subsequently, an improved dynamic apron tracking method based on Spatio-Temporal Transformer is designed. This method updates an initial template through target detection algorithm and utilizes multi-source perceptual data for confidence evaluation and dynamic template updates. It outputs the distribution estimation of the predicted bounding box corner coordinates, and achieves the high reliability continuous positional smoothing estimation and dynamic apron loss re-detection based on UAV vision. Finally, the UAV calculates the horizontal control quantity based on the horizontal relative position between the UAV and the apron obtained. The vertical control quantity is calculated based on the vertical relative position between the UAV and the apron. The vertical relative position is estimated using laser ranging and visual odometry techniques. Based on the results of horizontal tracking control and vertical regulation, velocity commands for the UAV in all three directions are generated, enabling the UAV to achieve autonomous landing on the dynamic apron. This paper achieves precise landing for dynamic apron in real-world environments. The experimental results demonstrate that this method avoids the limitations of labeled data and global information on autonomous landing of UAV. Even under GPS-denied conditions, the UAV can generate control commands based on visual data and achieve autonomous landing for randomly moving apron.
The Students’ Perception Toward the Use of Smartphones in Learning English at MAN Sidenreng Rappang

Technology and education are two things that cannot be separated until now. Especially when the pandemic outbreak hit which required the education world to implement an online learning system, smartphones became one of the choices for learning media. However, researchers found a phenomenon that occurred in MAN Sidenreng Rappang after the covid-19 pandemic, where students could use smartphones in the learning process in the classroom. This study aims to determine how students' perceptions of smartphone use in learning English at MAN Sidenreng Rappang. Participants in the study consisted of seven students from twelfth grade. In addition, this study used observation, interview, and documentation techniques to obtain data. From the findings, researchers found that smartphones can be used as one of the learning media in accordance with the results of research that shows a positive impact related to the use of smartphones in learning English in the classroom, such as making learning more fun, making it easier for students to access learning materials. Smartphones also provide various features and applications that help in the learning process, such as easier assignment collection, helping the translation process, and various applications that can support the learning process. The use of smartphones also improves students' skills, especially in improving vocabulary, speaking, and listening skills. In addition, the use of smartphones also motivates students to learn. But from the findings, researchers also found several obstacles faced by students while using smartphones, such as disruption of learning focus, data packages, and poor internet connection.