OpenRaiser/Intern-Atlas huggingface dataset:
Participatory Assessment of Large Language Model Applications in an Academic Medical Center
Venue: arXiv cs.CY 2024
Year: 2024

Although Large Language Models (LLMs) have shown promising performance in healthcare-related applications, their deployment in the medical domain poses unique challenges of ethical, regulatory, and technical nature. In this study, we employ a systematic participatory approach to investigate the needs and expectations regarding clinical applications of LLMs at Lausanne University Hospital, an academic medical center in Switzerland. Having identified potential LLM use-cases in collaboration with thirty stakeholders, including clinical staff across 11 departments as well nursing and patient representatives, we assess the current feasibility of these use-cases taking into account the regulatory frameworks, data protection regulation, bias, hallucinations, and deployment constraints. This study provides a framework for a participatory approach to identifying institutional needs with respect to introducing advanced technologies into healthcare practice, and a realistic analysis of the technology readiness level of LLMs for medical applications, highlighting the issues that would need to be overcome LLMs in healthcare to be ethical, and regulatory compliant.
DK-PRACTICE: An Intelligent Educational Platform for Personalized Learning Content Recommendations Based on Students Knowledge State
Venue: arXiv cs.CY 2024
Year: 2024

This study introduces DK-PRACTICE (Dynamic Knowledge Prediction and Educational Content Recommendation System), an intelligent online platform that leverages machine learning to provide personalized learning recommendations based on student knowledge state. Students participate in a short, adaptive assessment using the question-and-answer method regarding key concepts in a specific knowledge domain. The system dynamically selects the next question for each student based on the correctness and accuracy of their previous answers. After the test is completed, DK-PRACTICE analyzes students' interaction history to recommend learning materials to empower the student's knowledge state in identified knowledge gaps. Both question selection and learning material recommendations are based on machine learning models trained using anonymized data from a real learning environment. To provide self-assessment and monitor learning progress, DK-PRACTICE allows students to take two tests: one pre-teaching and one post-teaching. After each test, a report is generated with detailed results. In addition, the platform offers functions to visualize learning progress based on recorded test statistics. DK-PRACTICE promotes adaptive and personalized learning by empowering students with self-assessment capabilities and providing instructors with valuable information about students' knowledge levels. DK-PRACTICE can be extended to various educational environments and knowledge domains, provided the necessary data is available according to the educational topics. A subsequent paper will present the methodology for the experimental application and evaluation of the platform.
The Three Social Dimensions of Chatbot Technology
Venue: arXiv cs.CY 2024
Year: 2024

The development and deployment of chatbot technology, while spanning decades and employing different techniques, require innovative frameworks to understand and interrogate their functionality and implications. A mere technocentric account of the evolution of chatbot technology does not fully illuminate how conversational systems are embedded in societal dynamics. This study presents a structured examination of chatbots across three societal dimensions, highlighting their roles as objects of scientific research, commercial instruments, and agents of intimate interaction. Through furnishing a dimensional framework for the evolution of conversational systems, from laboratories to marketplaces to private lives, this article contributes to the wider scholarly inquiry of chatbot technology and its impact in lived human experiences and dynamics.
Transparency, Security, and Workplace Training & Awareness in the Age of Generative AI
Venue: arXiv cs.CY 2024
Year: 2024

This paper investigates the impacts of the rapidly evolving landscape of generative Artificial Intelligence (AI) development. Emphasis is given to how organizations grapple with a critical imperative: reevaluating their policies regarding AI usage in the workplace. As AI technologies advance, ethical considerations, transparency, data privacy, and their impact on human labor intersect with the drive for innovation and efficiency. Our research explores publicly accessible large language models (LLMs) that often operate on the periphery, away from mainstream scrutiny. These lesser-known models have received limited scholarly analysis and may lack comprehensive restrictions and safeguards. Specifically, we examine Gab AI, a platform that centers around unrestricted communication and privacy, allowing users to interact freely without censorship. Generative AI chatbots are increasingly prevalent, but cybersecurity risks have also escalated. Organizations must carefully navigate this evolving landscape by implementing transparent AI usage policies. Frequent training and policy updates are essential to adapt to emerging threats. Insider threats, whether malicious or unwitting, continue to pose one of the most significant cybersecurity challenges in the workplace. Our research is on the lesser-known publicly accessible LLMs and their implications for workplace policies. We contribute to the ongoing discourse on AI ethics, transparency, and security by emphasizing the need for well-thought-out guidelines and vigilance in policy maintenance.
Technological Progress and Obsolescence: Analyzing the Environmental Economic Impacts of MacBook Pro I/O Devices
Venue: arXiv cs.CY 2024
Year: 2024

This study investigates how the new release of MacBook Pro I/O devices affects the obsolescence of related accessories. We also explore how these accessories will impact the environment and the economic consequences. As technology progresses, each new MacBook Pro releases outdated prior accessories, making more electronic waste. This phenomenon makes modern people need to change their traditional consumption patterns. We analyze changes in I/O ports and compatibility between MacBook Pro versions to determine which accessories are obsolete and estimate their environmental impact. Our research focuses on the sustainability of current accessories. We explore alternate methods of reusing, recycling, and disposing of these accessories in order to reduce waste and environmental impact. In addition, we will explore the economic consequences of rapid technological advances that make accessories obsolete too quickly. Thereby assessing the impact of such changes on consumers, manufacturers, and the technology industry. This study aims to respond to the rapid advancement of technology while promoting more sustainable approaches to waste management and product design. As the MacBook Pro I/O unit evolves, certain accessories become obsolete with each subsequent version. The purpose of this study is to identify and quantify the environmental and economic impacts of parts end-of-life. We can detect which accessories have become obsolete and assess the environmental impact by comparing I/O port changes and compatibility across MacBook Pro generations. In response to these environmental images, methods are developed to reuse, recycle, and dispose of obsolete accessories to reduce waste and promote sustainable development. Additionally, we evaluate the economic impact of obsolete equipment on consumers and producers.
A multi-dimension and high-granularity equity measurement for transportation services through accessibility and reliability
Venue: arXiv cs.CY 2024
Year: 2024

Transportation equity research has traditionally emphasized service accessibility and destination reachability while often overlooking the critical aspects of service quality, such as infrequent schedules or overcrowded vehicles. This oversight can lead to a skewed understanding of equity, as high accessibility does not guarantee high-quality service. Addressing this gap, we propose a transportation equity index called the multi-dimensional, high-granularity (MDHG) index. Such an index considers service accessibility and quality alongside population demographics. This approach ensures that areas with high accessibility but low service quality are recognized as inequitable. The MDHG Index addresses service performance by incorporating performance data with temporal variations based on actual trip data, thus offering a more nuanced view of transportation equity that reflects the real-world experiences of service users. Furthermore, to effectively identify and address the needs at the user level, we need to use a highly granular population dataset. Due to the low granularity of census and other open-source datasets, we opted to use a highly granular synthetic dataset. To test out the MDHG Index, we coupled a highly granular synthetic population dataset with data from NYC Citi Bike expansion to use as a case study to assess changes in accessibility and service quality before and after the expansion. The MDHG approach effectively identified areas that improved post-expansion and highlighted those requiring further enhancement, thus showing the effectiveness of the index in targeted improvements for transportation equity.
Intent-driven scheduling of backup jobs
Venue: arXiv cs.DC 2024
Year: 2024

Job scheduling under various constraints to achieve global optimization is a well-studied problem. However, in scenarios that involve time-dependent constraints, such as scheduling backup jobs, achieving global optimization may not always be desirable. This paper presents a framework for scheduling new backup jobs in the presence of existing job schedules, focusing on satisfying intent-based constraints without disrupting current schedules. The proposed method accommodates various scheduling intents and constraints, and its effectiveness is validated through extensive testing against a variety of backup scenarios on real-world data from Veritas Netbackup customer policies.
Accelerating PageRank Algorithmic Tasks with a new Programmable Hardware Architecture
Venue: arXiv cs.AR 2024
Year: 2024

Addressing the growing demands of artificial intelligence (AI) and data analytics requires new computing approaches. In this paper, we propose a reconfigurable hardware accelerator designed specifically for AI and data-intensive applications. Our architecture features a messaging-based intelligent computing scheme that allows for dynamic programming at runtime using a minimal instruction set. To assess our hardware's effectiveness, we conducted a case study in TSMC 28nm technology node. The simulation-based study involved analyzing a protein network using the computationally demanding PageRank algorithm. The results demonstrate that our hardware can analyze a 5,000-node protein network in just 213.6 milliseconds over 100 iterations. These outcomes signify the potential of our design to achieve cutting-edge performance in next-generation AI applications.
Investigation on the Shooting Method Ability to Solve Different Mooring Lines Boundary Condition Types
Venue: arXiv cs.CE 2024
Year: 2024

The study of undersea cables and mooring lines statics remains an unavoidable subject of simulation in offshore field for either steady-state analysis or dynamic simulation initialization. Whether the study concerns mooring systems pinned both at seabed and floating platform, cables towed by a moving underwater system or when special links such as stiffeners are needed, the ability to model every combination is a key point. To do so the authors propose to investigate the use of the shooting method to solve the two point boundary value problem (TPBVP) associated with Dirichlet, Robin or mixed boundary conditions representing respectively, displacement, force and force/displacement boundary conditions. 3D nonlinear static string calculations are confronted to a semi-analytic formulation established from the catenary closed form equations. The comparisons are performed on various pairs of boundary conditions developed in five configurations.
Offshore Wind Turbine Tower Design and Optimization: A Review and AI-Driven Future Directions
Venue: arXiv cs.CE 2024
Year: 2024

Offshore wind energy leverages the high intensity and consistency of oceanic winds, playing a key role in the transition to renewable energy. As energy demands grow, larger turbines are required to optimize power generation and reduce the Levelized Cost of Energy (LCoE), which represents the average cost of electricity over a project's lifetime. However, upscaling turbines introduces engineering challenges, particularly in the design of supporting structures, especially towers. These towers must support increased loads while maintaining structural integrity, cost-efficiency, and transportability, making them essential to offshore wind projects' success. This paper presents a comprehensive review of the latest advancements, challenges, and future directions driven by Artificial Intelligence (AI) in the design optimization of Offshore Wind Turbine (OWT) structures, with a focus on towers. It provides an in-depth background on key areas such as design types, load types, analysis methods, design processes, monitoring systems, Digital Twin (DT), software, standards, reference turbines, economic factors, and optimization techniques. Additionally, it includes a state-of-the-art review of optimization studies related to tower design optimization, presenting a detailed examination of turbine, software, loads, optimization method, design variables and constraints, analysis, and findings, motivating future research to refine design approaches for effective turbine upscaling and improved efficiency. Lastly, the paper explores future directions where AI can revolutionize tower design optimization, enabling the development of efficient, scalable, and sustainable structures. By addressing the upscaling challenges and supporting the growth of renewable energy, this work contributes to shaping the future of offshore wind turbine towers and others supporting structures.
Simultaneous Go via quantum collapse
Venue: arXiv cs.GT 2025
Year: 2025

We construct a symmetric, simultaneous, deterministic evolution game $SGo$, which is in a certain mathematical sense a symmetrization of the classical board game Go. $SGo$ is in some ways a simpler game than Go, as Komi, Ko and suicide rules are removed. On the other hand it has similar dynamics and move sensitivity, enabled by certain deterministic ``quantum state'' reduction, so that state evolution is deterministic. Using the argument of Nash, we show that $SGo$ has a mixed equilibrium strategy to draw on average.
Cayley Configuration Spaces of a Common Class of Mechanisms in Two Dimensions
Venue: arXiv cs.CG 2025
Year: 2025

We study Cayley configuration spaces of a class of 1 degree-of-freedom linkages (graphs with specified edge lengths), obtained by dropping an edge from a tree-decomposable graph. The class includes well-known mechanisms based on the four-bar, as well as strandbeest, cardioid, limacon etc. The Cayley configuration space is the set of intervals of attainable lengths for a \emph{base} nonedge (e.g. the dropped edge) over the linkage's 2 dimensional realizations. We require \emph{quadratic radical solvability (QRS)} (an extension of ruler-and-compass-realizability) of the interval endpoints, and tree-decomposability guarantees efficient, ruler-and-compass construction of the linkage realization, given the Cayley configuration. Due to these restrictions of Kempe universality, this class of \emph{low Cayley complexity (LCC)} graphs is common in mechanical computer aided design and kinematics. Our main contributions are the following. (1) We show that the definition of LCC is robust, and depends only on the graph, no matter the choice of base nonedge whose addition ensures tree-decomposability. (2) We give an efficient algorithmic characterization of LCC graphs (3) We show (graph) planarity is equivalent to LCC for a natural subclass of 1-degree-of-freedom tree-decomposable graphs. Counterexamples show impossibility of such finite forbidden minor characterizations when the above subclass is enlarged. (4) We give an easily testable definition of genericity of LCC linkages (i.e. with underlying LCC graphs) based on their edge lengths. (5) For generic LCC linkages, we give an algorithm to find both paths of continuous motion (provided they exist) between two distinct realizations, in time linear in a discrete measure of the length of the path. Nontrivial generalizations of these results to non-LCC, 1-degree-of-freedom tree-decomposable linkages. Several accessible open problems are posed.
Ranking Functions for Linear-Constraint Loops
Venue: arXiv cs.PL 2025
Year: 2025

In this paper we study the complexity of the problems: given a loop, described by linear constraints over a finite set of variables, is there a linear or lexicographical-linear ranking function for this loop? While existence of such functions implies termination, these problems are not equivalent to termination. When the variables range over the rationals (or reals), it is known that both problems are PTIME decidable. However, when they range over the integers, whether for single-path or multipath loops, the complexity has not yet been determined. We show that both problems are coNP-complete. However, we point out some special cases of importance of PTIME complexity. We also present complete algorithms for synthesizing linear and lexicographical-linear ranking functions, both for the general case and the special PTIME cases. Moreover, in the rational setting, our algorithm for synthesizing lexicographical-linear ranking functions extends existing ones, because our class of ranking functions is more general, yet it has polynomial time complexity.
Probability Bracket Notation: Markov Sequence Projector of Visible and Hidden Markov Models in Dynamic Bayesian Networks
Venue: arXiv cs.AI 2025
Year: 2025

With the symbolic framework of Probability Bracket Notation (PBN), the Markov Sequence Projector (MSP) is introduced to expand the evolution formula of Homogeneous Markov Chains (HMCs). The well-known weather example, a Visible Markov Model (VMM), illustrates that the full joint probability of a VMM corresponds to a specifically projected Markov state sequence in the expanded evolution formula. In a Hidden Markov Model (HMM), the probability basis (P-basis) of the hidden Markov state sequence and the P-basis of the observation sequence exist in the sequential event space. The full joint probability of an HMM is the product of the (unknown) projected hidden sequence of Markov states and their transformations into the observation P-bases. The Viterbi algorithm is applied to the famous Weather-Stone HMM example to determine the most likely weather-state sequence given the observed stone-state sequence. Our results are verified using the Elvira software package. Using the PBN, we unify the evolution formulas for Markov models like VMMs, HMMs, and factorial HMMs (with discrete time). We briefly investigated the extended HMM, addressing the feedback issue, and the continuous-time VMM and HMM (with discrete or continuous states). All these models are subclasses of Dynamic Bayesian Networks (DBNs) essential for Machine Learning (ML) and Artificial Intelligence (AI).
The Z1: Architecture and Algorithms of Konrad Zuse's First Computer
Venue: arXiv cs.AR 2025
Year: 2025

This paper provides the first comprehensive description of the Z1, the mechanical computer built by the German inventor Konrad Zuse in Berlin from 1936 to 1938. The paper describes the main structural elements of the machine, the high-level architecture, and the dataflow between components. The computer could execute the four basic arithmetic operations using binary floating-point numbers. Instructions were read from punched tape. A program consisted of a sequence of arithmetical operations, intermixed with memory store and load instructions, interrupted possibly by input and output operations. Numbers were stored in a mechanical memory. The machine did not include conditional branching in the instruction set. While the architecture of the Z1 is similar to the relay computer Zuse finished in 1941 (the Z3) there are some significant differences. The Z1 implements operations as sequences of microinstructions, as in the Z3, but does not use rotary switches as micro-steppers. The Z1 uses a digital counter and a set of conditions which trigger microinstructions for the exponent and mantissa units, as well as for the memory blocks. Microinstructions select one out of 12 layers in a machine with a 3D mechanical structure of binary mechanical elements. The exception circuits for mantissa zero, necessary for normalized floating-point, were lacking; they were first implemented in the Z3. The information for this article was extracted from careful study of the blueprints drawn by Zuse for the reconstruction of the Z1 for the German Technology Museum in Berlin, from some letters, and from sketches in notebooks. Although the machine has been in exhibition since 1989 (non-operational), no detailed high-level description of the machine's architecture had been available. This paper fills that gap.
An $O(n^3)$ time algorithm for the maximum-weight limited-capacity many-to-many matching
Venue: arXiv cs.DS 2025
Year: 2025

Given an undirected bipartite graph $G=(A \cup B, E)$, a many-to-many matching (MM) in $G$ matches each vertex $v$ in $A$ (resp. $B$) to at least one vertex in $B$ (resp. $A$). In this paper, we consider the limited-capacity many-to-many matching (LCMM) in $G$, where each vertex $v\in A\cup B$ is matched to at least one and at most $Cap(v)$ vertices; the function $Cap : A\cup B \rightarrow \mathbb{Z}> 0$ denotes the capacity of $v$ (an upper bound on its degree in the LCMM). We give an $O(n^3)$ time algorithm for finding a maximum (respectively minimum) weight LCMM in $G$ with non-positive real (respectively non-negative real) edge weights, where $\lvert A \rvert+\lvert B \rvert=n$.
The CTU Prague Relational Learning Repository
Venue: arXiv cs.LG 2025
Year: 2025

The aim of the Prague Relational Learning Repository is to support machine learning research with multi-relational data. The repository currently contains 148 SQL databases hosted on a public MySQL server located at https://relational.fel.cvut.cz. The server is provided by the Czech Technical University (CTU). A searchable meta-database provides metadata (e.g., the number of tables in the database, the number of rows and columns in the tables, the number of self-relationships).
A Note on Quantum-Secure PRPs
Venue: arXiv cs.CR 2025
Year: 2025

We show how to construct pseudorandom permutations (PRPs) that remain secure even if the adversary can query the permutation, both in the forward and reverse directions, on a quantum superposition of inputs. Such quantum-secure PRPs have found numerous applications in cryptography and complexity theory. Our construction combines a quantum-secure pseudorandom function together with constructions of classical format preserving encryption. By combining known results, we show how to construct quantum-secure PRP in this model whose security relies only on the existence of one-way functions.