Ioannis Stylianou, Jon Francombe, Pablo Martinez-Nuevo
et al.
Conventional audio equalization is a static process that requires manual and cumbersome adjustments to adapt to changing listening contexts (e.g., mood, location, or social setting). In this paper, we introduce a Large Language Model (LLM)-based alternative that maps natural language text prompts to equalization settings. This enables a conversational approach to sound system control. By utilizing data collected from a controlled listening experiment, our models exploit in-context learning and parameter-efficient fine-tuning techniques to reliably align with population-preferred equalization settings. Our evaluation methods, which leverage distributional metrics that capture users' varied preferences, show statistically significant improvements in distributional alignment over random sampling and static preset baselines. These results indicate that LLMs could function as "artificial equalizers," contributing to the development of more accessible, context-aware, and expert-level audio tuning methods.
Adrien Bibal, Steven N. Minton, Deborah Khider
et al.
Open science initiatives seek to make research outputs more transparent, accessible, and reusable, but ensuring that published findings can be independently reproduced remains a persistent challenge. In this paper we describe an AI-driven "Reproducibility Copilot" that analyzes manuscripts, code, and supplementary materials to generate structured Jupyter Notebooks and recommendations aimed at facilitating computational, or "rote", reproducibility. Our initial results suggest that the copilot has the potential to substantially reduce reproduction time (in one case from over 30 hours to about 1 hour) while achieving high coverage of figures, tables, and results suitable for computational reproduction. The system systematically detects barriers to reproducibility, including missing values for hyperparameters, undocumented preprocessing steps, and incomplete or inaccessible datasets. Although preliminary, these findings suggest that AI tools can meaningfully reduce the burden of reproducibility efforts and contribute to more transparent and verifiable scientific communication.
This work explores the relationship between altruism and the genetic system of arrhenotoky through an evolutionary game theory (EGT)-inspired lens, using a dynamic model of beehive populations consisting of three castes: workers, drones, and the queen. Arrhenotoky is a form of asexual reproduction in which unfertilized eggs become males while fertilized eggs develop into females, leading to unusual patterns of genetic relatedness between family members. This mode of reproduction occurs in insects such as the Hymenoptera, including bees. In the hive environment, bees often display altruistic behavior, or actions taken by an organism that reduce its own fitness to increase the fitness of others. Eusociality, an elaborate form of social organization characterized by complex and altruistic social behaviors, is also observed in the Hymenoptera. To explore the interplay between altruism and the reproductive patterns of arrhenotoky, we employ a population dynamics model to simulate beehive populations over a range of parameters, controlling for altruism in workers and the queen. Our results show that altruistic behaviors are essential for beehive success, with optimal worker altruism corresponding to the division of labor observed in eusocial species. Furthermore, we find that modest altruism from the queen is also vital for hive survival, emphasizing the delicate balance that can exist in these complex social systems. Overall, our findings shed light on the co-evolution of altruism, arrhenotoky, and eusociality in the natural world.
Yang Chen, Toufique Ahmed, Reyhaneh Jabbarvand
et al.
Test suites in real-world projects are often large and achieve high code coverage, yet they remain insufficient for detecting all bugs. The abundance of unresolved issues in open-source project trackers highlights this gap. While regression tests are typically designed to ensure past functionality is preserved in the new version, they can also serve a complementary purpose: debugging the current version. Specifically, regression tests can (1) enhance the generation of reproduction tests for newly reported issues, and (2) validate that patches do not regress existing functionality. We present TestPrune, a fully automated technique that leverages issue tracker reports and strategically reuses regression tests for both bug reproduction and patch validation. A key contribution of TestPrune is its ability to automatically minimize the regression suite to a small, highly relevant subset of tests. Due to the predominance of LLM-based debugging techniques, this minimization is essential as large test suites exceed context limits, introduce noise, and inflate inference costs. TestPrune can be plugged into any agentic bug repair pipeline and orthogonally improve overall performance. As a proof of concept, we show that TestPrune leads to a 6.2%-9.0% relative increase in issue reproduction rate within the Otter framework and a 9.4% - 12.9% relative increase in issue resolution rate within the Agentless framework on SWE-Bench Lite and SWE-Bench Verified benchmarks, capturing fixes that were correctly produced by agents but not submitted as final patches. Compared to the benefits, the cost overhead of using TestPrune is minimal, i.e., \$0.02 and \$0.05 per SWE-Bench instance, using GPT-4o and Claude-3.7-Sonnet models, respectively.
Diego Kozlowski, Thema Monroe-White, Vincent Larivière
et al.
The US higher education system concentrates the production of science and scientists within a few institutions. This has implications for minoritized scholars and the topics with which they are disproportionately associated. This paper examines topical alignment between institutions and authors of varying intersectional identities, and the relationship with prestige and scientific impact. We observe a Howard-Harvard effect, in which the topical profile of minoritized scholars are amplified in mission-driven institutions and decreased in prestigious institutions. Results demonstrate a consistent pattern of inequality in topics and research impact. Specifically, we observe statistically significant differences between minoritized scholars and White men in citations and journal impact. The aggregate research profile of prestigious US universities is highly correlated with the research profile of White men, and highly negatively correlated with the research profile of minoritized women. Furthermore, authors affiliated with more prestigious institutions are associated with increasing inequalities in both citations and journal impact. Academic institutions and funders are called to create policies to mitigate the systemic barriers that prevent the United States from achieving a fully robust scientific ecosystem.
HIV is known for causing the destruction of the immune system by affecting different types of cells, while SARS-CoV-2 is an extremely contagious virus that leads to the development of COVID-19. In this study, we propose a mathematical model to investigate the interaction between HIV and SARS-CoV-2 under highly active antiretroviral therapy (HAART). We determine the conditions for the endemic equilibria of both viruses, showing that transcritical bifurcations occur when the basic reproduction numbers of HIV and SARS-CoV-2 pass through 1. We set the condition for the stability of the disease-free equilibrium point of the model with coinfection as a function of the basic reproduction number $\mathcal{R}_0$. Through numerical simulations, we conclude that HAART, used to control HIV, also reduces the proliferation of SARS-CoV-2-infected cells in coinfected hosts. These findings provide important insights into the epidemiological dynamics of HIV and SARS-CoV-2 coinfection.
Ryan Donald, Brendan Hertel, Stephen Misenti
et al.
Robot skill learning and execution in uncertain and dynamic environments is a challenging task. This paper proposes an adaptive framework that combines Learning from Demonstration (LfD), environment state prediction, and high-level decision making. Proactive adaptation prevents the need for reactive adaptation, which lags behind changes in the environment rather than anticipating them. We propose a novel LfD representation, Elastic-Laplacian Trajectory Editing (ELTE), which continuously adapts the trajectory shape to predictions of future states. Then, a high-level reactive system using an Unscented Kalman Filter (UKF) and Hidden Markov Model (HMM) prevents unsafe execution in the current state of the dynamic environment based on a discrete set of decisions. We first validate our LfD representation in simulation, then experimentally assess the entire framework using a legged mobile manipulator in 36 real-world scenarios. We show the effectiveness of the proposed framework under different dynamic changes in the environment. Our results show that the proposed framework produces robust and stable adaptive behaviors.
Two isolation performance metrics, Inter-Zone Isolation (IZI) and Inter-Program Isolation (IPI), are introduced for evaluating Personal Sound Zone (PSZ) systems. Compared to the commonly-used Acoustic Contrast metric, IZI and IPI are generalized for multichannel audio, and quantify the isolation of sound zones and of audio programs, respectively. The two metrics are shown to be generally non-interchangeable and suitable for different scenarios, such as generating dark zones (IZI) or minimizing audio-on-audio interference (IPI). Furthermore, two examples with free-field simulations are presented and demonstrate the applications of IZI and IPI in evaluating PSZ performance in different rendering modes and PSZ robustness.
Michele Bertone, Alex Mikszewski, Luca Stabile
et al.
In this study, we apply a novel combination of close proximity and room-scale risk assessment approaches for people sharing public transport environments to predict their contagion risk due to SARS-CoV-2 respiratory infection. In particular, the individual infection risk of susceptible subjects and the transmissibility of SARS-CoV-2 (expressed through the reproduction number) are evaluated for two types of buses, differing in terms of exposure time and crowding index: urban and long-distance buses. Infection risk and reproduction number are calculated for different scenarios as a function of the ventilation rates (both measured and estimated according to standards), crowding indexes, and travel times. The results show that for urban buses, the close proximity contribution significantly affects the maximum occupancy to maintain a reproductive number of < 1. In particular, full occupancy of the bus would be permitted only for an infected subject breathing, whereas for an infected subject speaking, masking would be required. For long-distance buses, full occupancy of the bus can be maintained only if specific mitigation solutions are simultaneously applied. For example, for an infected person speaking for 1 h, appropriate filtration of the recirculated air and simultaneous use of FFP2 masks would permit full occupancy of the bus for a period of almost 8 h. Otherwise, a high percentage of immunized persons (> 80%) would be needed.
In a move described as unprecedented in public health history, starting 24 January 2020, China imposed quarantine and isolation restrictions in Wuhan, a city of more than 10 million people. This raised the question: is mass quarantine and isolation effective as a social tool in addition to its scientific use as a medical tool? In an effort to address this question, using a epidemiological model driven approach augmented by machine learning, we show that the quarantine and isolation measures implemented in Wuhan brought down the effective reproduction number R(t) of the CoVID-19 spread from R(t) > 1 to R(t) <1 within a month after the imposition of quarantine control measures in Wuhan, China. This ultimately resulted in a stagnation phase in the infected case count in Wuhan. Our results indicate that the strict public health policies implemented in Wuhan may have played a crucial role in halting down the spread of infection and such measures should potentially be implemented in other highly affected countries such as South Korea, Italy and Iran to curtail spread of the disease. Finally, our forecasting results predict a stagnation in the quarantine control measures implemented in Wuhan towards the end of March 2020; this would lead to a subsequent stagnation in the effective reproduction number at R(t) <1. We warn that immediate relaxation of the quarantine measures in Wuhan may lead to a relapse in the infection spread and a subsequent increase in the effective reproduction number to R(t) >1. Thus, it may be wise to relax quarantine measures after sufficient time has elapsed, during which maximum of the quarantined/isolated individuals are recovered.
Extensive post reproductive lifespan (PRLS) is observed only in a few species, such as humans or resident killer whales, and its origin is under debate. Hypotheses like mother-care and grandmother-care invoke strategies of investment--provision to one's descendants to enhance one's overall reproductive success--to explain PRLS. The contribution of an investment strategy varies with the age of the caregiver, as the number of care-receiving descendant changes with age. Here we simulated an agent based model, which is sensitive to age-specific selection, to examine how the investment strategies in different hypotheses affect survival and reproduction across different stages of life. We found that extensive PRLS emerges if we combine multiple investment strategies, including grandmother-care but not mother-care, which allow an individual to have an increasing contribution as it ages. We also found that, if mother-care is further introduced to the PRLS-enabling strategies, it will let contribution at mid-life to substitute contribution at late life, which consequently terminates extensive PRLS.
Kevin Moran, Mario Linares-Vasquez, Carlos Bernal-Cardenas
et al.
As the popularity of mobile smart devices continues to climb the complexity of "apps" continues to increase, making the development and maintenance process challenging. Current bug tracking systems lack key features to effectively support construction of reports with actionable information that directly lead to a bug's resolution. In this demo we present the implementation of a novel bug reporting system, called Fusion, that facilitates users including reproduction steps in bug reports for mobile apps. Fusion links user-provided information to program artifacts extracted through static and dynamic analysis performed before testing or release. Results of preliminary studies demonstrate that Fusion both effectively facilitates reporting and allows for more reliable reproduction of bugs from reports compared to traditional issue tracking systems by presenting more detailed contextual app information. Tool website: www.fusion-android. com Video url: https://youtu.be/AND9h0ElxRg