Hasil untuk "Public law"

David R. Law, Melanie Clarke

The integral-field unit (IFU) spectrometers on board the James Webb Space Telescope (JWST) undersample the nearly diffraction-limited point spread function provided by the telescope optics. This undersampling produces large oscillating spectral artifacts when the data is resampled into regularly-gridded data cubes, which poses a significant challenge for many scientific analyses. We describe here a generalized technique to use cubic basis spline models to interpolate the observed spectral traces onto a higher-resolution grid prior to data cube rectification, which largely eliminates these artifacts in addition to helping reduce biases in point source spectra from clusters of bad pixels. We demonstrate the utility of this adaptive resampling technique for a variety of JWST NIRSpec and MIRI MRS observations ranging from isolated point sources to embedded AGN, crowded stellar fields with diffuse emission, and protostars with rich molecular bands.

Raphael Morisco

This paper examines how the figure of the hacker is portrayed in German mainstream media and explores the impact of media framing on public discourse. Through a longitudinal content analysis of 301 articles from four of the most widely circulated German newspapers (Die Zeit, Süddeutsche Zeitung, Bild, and Der Spiegel), the study covers reporting between January 2017 and January 2020. The results reveal a strong predominance of negative connotations and dramatizing frames that link hackers to criminality, national security threats, and digital warfare. Drawing on media effects theory, scandalization mechanisms, and constructivist media theory, the article shows how media representations co-construct public perceptions of IT-related risks. The analysis emphasizes the role of agenda setting, framing, and media reality in shaping societal narratives around hackers. The study concludes by reflecting on the broader implications for IT security education and the sociopolitical challenges posed by distorted representations of digital actors.

Amol Harsh, Brainerd Prince, Siddharth Siddharth et al.

This research-to-practice full paper was inspired by the persistent challenge in effective communication among engineering students. Public speaking is a necessary skill for future engineers as they have to communicate technical knowledge with diverse stakeholders. While universities offer courses or workshops, they are unable to offer sustained and personalized training to students. Providing comprehensive feedback on both verbal and non-verbal aspects of public speaking is time-intensive, making consistent and individualized assessment impractical. This study integrates research on verbal and non-verbal cues in public speaking to develop an AI-driven assessment model for engineering students. Our approach combines speech analysis, computer vision, and sentiment detection into a multi-modal AI system that provides assessment and feedback. The model evaluates (1) verbal communication (pitch, loudness, pacing, intonation), (2) non-verbal communication (facial expressions, gestures, posture), and (3) expressive coherence, a novel integration ensuring alignment between speech and body language. Unlike previous systems that assess these aspects separately, our model fuses multiple modalities to deliver personalized, scalable feedback. Preliminary testing demonstrated that our AI-generated feedback was moderately aligned with expert evaluations. Among the state-of-the-art AI models evaluated, all of which were Large Language Models (LLMs), including Gemini and OpenAI models, Gemini Pro emerged as the best-performing, showing the strongest agreement with human annotators. By eliminating reliance on human evaluators, this AI-driven public speaking trainer enables repeated practice, helping students naturally align their speech with body language and emotion, crucial for impactful and professional communication.

Allen Daniel Sunny

Automated eligibility systems increasingly determine access to essential public benefits, but the explanations they generate often fail to reflect the legal rules that authorize those decisions. This thesis develops a legally grounded explainability framework that links system-generated decision justifications to the statutory constraints of CalFresh, California's Supplemental Nutrition Assistance Program. The framework combines a structured ontology of eligibility requirements derived from the state's Manual of Policies and Procedures (MPP), a rule extraction pipeline that expresses statutory logic in a verifiable formal representation, and a solver-based reasoning layer to evaluate whether the explanation aligns with governing law. Case evaluations demonstrate the framework's ability to detect legally inconsistent explanations, highlight violated eligibility rules, and support procedural accountability by making the basis of automated determinations traceable and contestable.

Buyun Zhang, Liang Luo, Yuxin Chen et al.

Scaling laws play an instrumental role in the sustainable improvement in model quality. Unfortunately, recommendation models to date do not exhibit such laws similar to those observed in the domain of large language models, due to the inefficiencies of their upscaling mechanisms. This limitation poses significant challenges in adapting these models to increasingly more complex real-world datasets. In this paper, we propose an effective network architecture based purely on stacked factorization machines, and a synergistic upscaling strategy, collectively dubbed Wukong, to establish a scaling law in the domain of recommendation. Wukong's unique design makes it possible to capture diverse, any-order of interactions simply through taller and wider layers. We conducted extensive evaluations on six public datasets, and our results demonstrate that Wukong consistently outperforms state-of-the-art models quality-wise. Further, we assessed Wukong's scalability on an internal, large-scale dataset. The results show that Wukong retains its superiority in quality over state-of-the-art models, while holding the scaling law across two orders of magnitude in model complexity, extending beyond 100 GFLOP/example, where prior arts fall short.

Nikil Sharan Prabahar Balasubramanian, Sagnik Dakshit

Advancements in deep learning have generated a large-scale interest in the development of foundational deep learning models. The development of Large Language Models (LLM) has evolved as a transformative paradigm in conversational tasks, which has led to its integration and extension even in the critical domain of healthcare. With LLMs becoming widely popular and their public access through open-source models and integration with other applications, there is a need to investigate their potential and limitations. One such crucial task where LLMs are applied but require a deeper understanding is that of self-diagnosis of medical conditions based on bias-validating symptoms in the interest of public health. The widespread integration of Gemini with Google search and GPT-4.0 with Bing search has led to a shift in the trend of self-diagnosis using search engines to conversational LLM models. Owing to the critical nature of the task, it is prudent to investigate and understand the potential and limitations of public LLMs in the task of self-diagnosis. In this study, we prepare a prompt engineered dataset of 10000 samples and test the performance on the general task of self-diagnosis. We compared the performance of both the state-of-the-art GPT-4.0 and the fee Gemini model on the task of self-diagnosis and recorded contrasting accuracies of 63.07% and 6.01%, respectively. We also discuss the challenges, limitations, and potential of both Gemini and GPT-4.0 for the task of self-diagnosis to facilitate future research and towards the broader impact of general public knowledge. Furthermore, we demonstrate the potential and improvement in performance for the task of self-diagnosis using Retrieval Augmented Generation.

Nael Alsaleh, Bilal Farooq

There is an increased interest from transit agencies to replace fixed-route transit services with on-demand public transits (ODT). However, it is still unclear when and where such a service is efficient and sustainable. To this end, we provide a comprehensive framework for assessing the sustainability of ODT systems from the perspective of overall efficiency, environmental footprint, and social equity and inclusion. The proposed framework is illustrated by applying it to the Town of Innisfil, Ontario, where an ODT system has been implemented since 2017. It can be concluded that when there is adequate supply and no surge pricing, crowdsourced ODTs are the most cost-effective transit system when the demand is below 3.37 riders/km2/day. With surge pricing applied to crowdsourced ODTs, hybrid systems become the most cost-effective transit solution when demand ranges between 1.18 and 3.37 riders/km2/day. The use of private vehicles is more environmentally sustainable than providing public transit service at all demand levels below 3.37 riders/km2/day. However, the electrification of the public transit fleet along with optimized charging strategies can reduce total yearly GHG emissions by more than 98%. Furthermore, transit systems have similar equity distributions for waiting and in-vehicle travel times.

Yan Jiang, Ruihong Qiu, Yi Zhang et al.

As social media becomes increasingly popular, more and more public health activities emerge, which is worth noting for pandemic monitoring and government decision-making. Current techniques for public health analysis involve popular models such as BERT and large language models (LLMs). Although recent progress in LLMs has shown a strong ability to comprehend knowledge by being fine-tuned on specific domain datasets, the costs of training an in-domain LLM for every specific public health task are especially expensive. Furthermore, such kinds of in-domain datasets from social media are generally highly imbalanced, which will hinder the efficiency of LLMs tuning. To tackle these challenges, the data imbalance issue can be overcome by sophisticated data augmentation methods for social media datasets. In addition, the ability of the LLMs can be effectively utilised by prompting the model properly. In light of the above discussion, in this paper, a novel ALEX framework is proposed for social media analysis on public health. Specifically, an augmentation pipeline is developed to resolve the data imbalance issue. Furthermore, an LLMs explanation mechanism is proposed by prompting an LLM with the predicted results from BERT models. Extensive experiments conducted on three tasks at the Social Media Mining for Health 2023 (SMM4H) competition with the first ranking in two tasks demonstrate the superior performance of the proposed ALEX method. Our code has been released in https://github.com/YanJiangJerry/ALEX.

David Acuna, Marc T Law, Guojun Zhang et al.

The dominant line of work in domain adaptation has focused on learning invariant representations using domain-adversarial training. In this paper, we interpret this approach from a game theoretical perspective. Defining optimal solutions in domain-adversarial training as a local Nash equilibrium, we show that gradient descent in domain-adversarial training can violate the asymptotic convergence guarantees of the optimizer, oftentimes hindering the transfer performance. Our analysis leads us to replace gradient descent with high-order ODE solvers (i.e., Runge-Kutta), for which we derive asymptotic convergence guarantees. This family of optimizers is significantly more stable and allows more aggressive learning rates, leading to high performance gains when used as a drop-in replacement over standard optimizers. Our experiments show that in conjunction with state-of-the-art domain-adversarial methods, we achieve up to 3.5% improvement with less than of half training iterations. Our optimizers are easy to implement, free of additional parameters, and can be plugged into any domain-adversarial framework.

Armstrong Aboah, Lydia Johnson, Setul Shah

The rise in urbanization throughout the United States (US) in recent years has required urban planners and transportation engineers to have greater consideration for the transportation services available to residents of a metropolitan region. This compels transportation authorities to provide better and more reliable modes of public transit through improved technologies and increased service quality. These improvements can be achieved by identifying and understanding the factors that influence urban public transit demand. Common factors that can influence urban public transit demand can be internal and/or external factors. Internal factors include policy measures such as transit fares, service headways, and travel times. External factors can include geographic, socioeconomic, and highway facility characteristics. There is inherent simultaneity between transit supply and demand, thus a two-stage least squares (2SLS) regression modeling procedure should be conducted to forecast urban transit supply and demand. As such, two multiple linear regression models should be developed: one to predict transit supply and a second to predict transit demand. It was found that service area density, total average cost per trip, and the average number of vehicles operated in maximum service can be used to forecast transit supply, expressed as vehicle revenue hours. Furthermore, estimated vehicle revenue hours and total average fares per trip can be used to forecast transit demand, expressed as unlinked passenger trips. Additional data such as socioeconomic information of the surrounding areas for each transit agency and travel time information of the various transit systems would be useful to improve upon the models developed.

Javad Kazemitabar

Benford's law is widely used for fraud-detection nowadays. The underlying assumption for using the law is that a "regular" dataset follows the significant digit phenomenon. In this paper, we address the scenario where a shrewd fraudster manipulates a list of numbers in such a way that still complies with Benford's law. We develop a general family of distributions that provides several degrees of freedom to such a fraudster such as minimum, maximum, mean and size of the manipulated dataset. The conclusion further corroborates the idea that Benford's law should be used with utmost discretion as a means for fraud detection.

Jonathan Labbe

This research focuses on the study of relationships between public and private equity investors in France. In this regard, we need to apprehend the formal or informal nature of interactions that can sometimes take place within traditional innovation networks (Djellal \& Gallouj, 2018). For this, our article mobilizes a public-private partnerships approach (PPPs) and the resource-based view theory. These perspectives emphasize the complementary role of disciplinary and incentive mechanisms as well as the exchange of specific resources as levers for value creation. Moreover, these orientations crossed with the perspective of a hybrid form of co-investment allow us to build a coherent and explanatory framework of the mixed syndication phenomenon. Our methodology is based on a qualitative approach with an interpretative aim, which includes twenty-seven semi-structured interviews. These data were subjected to a thematic content analysis using Nvivo software. The results suggest that the relationships between public and private Venture capitalists (VCs) of a formal or informal nature, more specifically in a syndication context, at a national or regional level, are representative of an ''economico-cognitive'' (Farrugia, 2014, page 6) approach to networking and innovation. Moreover, the phenomenon of mixed syndication reveals a context of hybridization of public and private actors that would allow the private VCs to benefit from the distribution of wealth when the company develops its innovation. We can also identify a process related to a quest for legitimacy on the part of the public actor characterized by its controlling role within the public-private partnership (Beuve and Saussier, 2019). Finally, our study has some limitations. One example is the measurement of the effects of relationships on ''visible'' or ''invisible'' innovation (Djellal \& Gallouj, 2018, page 90).

Peter Mell, John Kelsey, James Shook

Most modern electronic devices can produce a random number. However, it is difficult to see how a group of mutually distrusting entities can have confidence in any such hardware-produced stream of random numbers, since the producer could control the output to their gain. In this work, we use public and immutable cryptocurrency smart contracts, along with a set of potentially malicious randomness providers, to produce a trustworthy stream of timestamped public random numbers. Our contract eliminates the ability of a producer to predict or control the generated random numbers, including the stored history of random numbers. We consider and mitigate the threat of collusion between the randomness providers and miners in a second, more complex contract.

Sebastien Blandin, Laura Wynter, Hasan Poonawala et al.

Increasing urban concentration raises operational challenges that can benefit from integrated monitoring and decision support. Such complex systems need to leverage the full stack of analytical methods, from state estimation using multi-sensor fusion for situational awareness, to prediction and computation of optimal responses. The FASTER platform that we describe in this work, deployed at nation scale and handling 1.5 billion public transport trips a year, offers such a full stack of techniques for this large-scale, real-time problem. FASTER provides fine-grained situational awareness and real-time decision support with the objective of improving the public transport commuter experience. The methods employed range from statistical machine learning to agent-based simulation and mixed-integer optimization. In this work we present an overview of the challenges and methods involved, with details of the commuter movement prediction module, as well as a discussion of open problems.

Reza Hooshmand, Masoumeh Koochak Shooshtari, Mohammad Reza Aref

Polar codes are novel and efficient error correcting codes with low encoding and decoding complexities. These codes have a channel dependent generator matrix which is determined by the code dimension, code length and transmission channel parameters. This paper studies a variant of the McEliece public key cryptosystem based on polar codes, called "PKC-PC". Due to the fact that the structure of polar codes' generator matrix depends on the parameters of channel, we used an efficient approach to conceal their generator matrix. Then, by the help of the characteristics of polar codes and also introducing an efficient approach, we reduced the public and private key sizes of the PKC-PC and increased its information rate compared to the McEliece cryptosystem. It was shown that polar codes are able to yield an increased security level against conventional attacks and possible vulnerabilities on the code-based public key cryptosystems. Moreover, it is indicated that the security of the PKC-PC is reduced to solve NP-complete problems. Compared to other post-quantum public key schemes, we believe that the PKC-PC is a promising candidate for NIST post-quantum crypto standardization.

Chandralekha Singh

We discuss the development and evaluation of five research-based tutorials on Coulomb's law, superposition, symmetry and Gauss's Law to help students in the calculus-based introductory physics courses learn these concepts. We discuss the performance of students on the pre-/post-tests given before and after the tutorials in three calculus-based introductory physics courses. We also discuss the performance of students who used the tutorials and those who did not use it on a multiple-choice test which employs concepts covered in the tutorials.

Anatol V. Guglielmi

The known Omori law is presented in the form of differential equation that describes the evolution of the aftershock activity. This equation is derived hypothetically with taking into account deactivation of the faults in epicentral zone of the main shock. A generalization of the Omori law is proposed.

Stephen C. Anco

Symmetry properties of conservation laws of partial differential equations are developed by using the general method of conservation law multipliers. As main results, simple conditions are given for characterizing when a conservation law and its associated conserved quantity are invariant (and, more generally, homogeneous) under the action of a symmetry. These results are used to show that a recent conservation law formula (due to Ibragimov) is equivalent to a standard formula for the action of an infinitesimal symmetry on a conservation law multiplier.

Håkon Hoel, Kody J. H. Law, Raul Tempone

This work embeds a multilevel Monte Carlo sampling strategy into the Monte Carlo step of the ensemble Kalman filter (EnKF) in the setting of finite dimensional signal evolution and noisy discrete-time observations. The signal dynamics is assumed to be governed by a stochastic differential equation (SDE), and a hierarchy of time grids is introduced for multilevel numerical integration of that SDE. The resulting multilevel EnKF is proved to asymptotically outperform EnKF in terms of computational cost versus approximation accuracy. The theoretical results are illustrated numerically.

Attila Szolnoki, Matjaz Perc

The fact that individuals will most likely behave differently in different situations begets the introduction of conditional strategies. Inspired by this, we study the evolution of cooperation in the spatial public goods game, where besides unconditional cooperators and defectors, also different types of conditional cooperators compete for space. Conditional cooperators will contribute to the public good only if other players within the group are likely to cooperate as well, but will withhold their contribution otherwise. Depending on the number of other cooperators that are required to elicit cooperation of a conditional cooperator, the latter can be classified in as many types as there are players within each group. We find that the most cautious cooperators, such that require all other players within a group to be conditional cooperators, are the undisputed victors of the evolutionary process, even at very low synergy factors. We show that the remarkable promotion of cooperation is due primarily to the spontaneous emergence of quarantining of defectors, which become surrounded by conditional cooperators and are forced into isolated convex "bubbles" from where they are unable to exploit the public good. This phenomenon can be observed only in structured populations, thus adding to the relevance of pattern formation for the successful evolution of cooperation.