Miracle-dz/UniFAR-FTUs huggingface dataset:
Prompting Fairness: Artificial Intelligence as Game Players

Utilitarian games such as dictator games to measure fairness have been studied in the social sciences for decades. These games have given us insight into not only how humans view fairness but also in what conditions the frequency of fairness, altruism and greed increase or decrease. While these games have traditionally been focused on humans, the rise of AI gives us the ability to study how these models play these games. AI is becoming a constant in human interaction and examining how these models portray fairness in game play can give us some insight into how AI makes decisions. Over 101 rounds of the dictator game, I conclude that AI has a strong sense of fairness that is dependant of it it deems the person it is playing with as trustworthy, framing has a strong effect on how much AI gives a recipient when designated the trustee, and there may be evidence that AI experiences inequality aversion just as humans.
Off-policy Distributional Q(λ): Distributional RL without Importance Sampling

We introduce off-policy distributional Q($\lambda$), a new addition to the family of off-policy distributional evaluation algorithms. Off-policy distributional Q($\lambda$) does not apply importance sampling for off-policy learning, which introduces intriguing interactions with signed measures. Such unique properties distributional Q($\lambda$) from other existing alternatives such as distributional Retrace. We characterize the algorithmic properties of distributional Q($\lambda$) and validate theoretical insights with tabular experiments. We show how distributional Q($\lambda$)-C51, a combination of Q($\lambda$) with the C51 agent, exhibits promising results on deep RL benchmarks.
Tensor Completion via Integer Optimization

The main challenge with the tensor completion problem is a fundamental tension between computation power and the information-theoretic sample complexity rate. Past approaches either achieve the information-theoretic rate but lack practical algorithms to compute the corresponding solution, or have polynomial-time algorithms that require an exponentially-larger number of samples for low estimation error. This paper develops a novel tensor completion algorithm that resolves this tension by achieving both provable convergence (in numerical tolerance) in a linear number of oracle steps and the information-theoretic rate. Our approach formulates tensor completion as a convex optimization problem constrained using a gauge-based tensor norm, which is defined in a way that allows the use of integer linear optimization to solve linear separation problems over the unit-ball in this new norm. Adaptations based on this insight are incorporated into a Frank-Wolfe variant to build our algorithm. We show our algorithm scales-well using numerical experiments on tensors with up to ten million entries.
Dynamic Geometric Connectivity in the Plane with Constant Query Time

We present the first fully dynamic connectivity data structures for geometric intersection graphs achieving constant query time and sublinear amortized update time for most types of geometric objects in 2D. Our data structures can answer connectivity queries between two objects, as well as"global"connectivity queries (e.g., deciding whether the entire graph is connected). Previously, the data structure by Afshani and Chan (ESA'06) achieved such bounds only in the special case of axis-aligned line segments or rectangles but did not work for arbitrary line segments or disks, whereas the data structures by Chan, P\u{a}tra\c{s}cu and Roditty (FOCS'08) worked for more general classes of geometric objects but required $n^{\Omega(1)}$ query time and could not handle global connectivity queries. Specifically, we obtain new data structures with $O(1)$ query time and amortized update time near $n^{4/5}$, $n^{7/8}$, and $n^{20/21}$ for axis-aligned line segments, disks, and arbitrary line segments respectively. Besides greatly reducing the query time, our data structures also improve the previous update times for axis-aligned line segments by Afshani and Chan (from near $n^{10/11}$ to $n^{4/5}$) and for disks by Chan, P\u{a}tra\c{s}cu, and Roditty (from near $n^{20/21}$ to $n^{7/8}$).
Revisiting Schwarzschild black hole singularity through string theory

The resolution of black hole singularities represents an essential problem in the realm of quantum gravity. Due to the Belinskii, Khalatnikov and Lifshitz (BKL) proposal, the structure of the black hole interior in vacuum Einstein's equations can be described by the Kasner universe, which possesses the $O\left(d,d\right)$ symmetry. It motivates us to use the anisotropic Hohm-Zwiebach action, known as the string effective action with all orders $\alpha^{\prime}$ corrections for the $O\left(d,d\right)$ symmetric background, to study the singularity problem of black hole. In this letter, we obtain the singular condition for black holes and demonstrate that it is possible to resolve the Schwarzschild black hole singularity through the non-perturbative $\alpha^{\prime}$ corrections of string theory.
ML-Enabled Systems Model Deployment and Monitoring: Status Quo and Problems

[Context] Systems incorporating Machine Learning (ML) models, often called ML-enabled systems, have become commonplace. However, empirical evidence on how ML-enabled systems are engineered in practice is still limited, especially for activities surrounding ML model dissemination. [Goal] We investigate contemporary industrial practices and problems related to ML model dissemination, focusing on the model deployment and the monitoring of ML life cycle phases. [Method] We conducted an international survey to gather practitioner insights on how ML-enabled systems are engineered. We gathered a total of 188 complete responses from 25 countries. We analyze the status quo and problems reported for the model deployment and monitoring phases. We analyzed contemporary practices using bootstrapping with confidence intervals and conducted qualitative analyses on the reported problems applying open and axial coding procedures. [Results] Practitioners perceive the model deployment and monitoring phases as relevant and difficult. With respect to model deployment, models are typically deployed as separate services, with limited adoption of MLOps principles. Reported problems include difficulties in designing the architecture of the infrastructure for production deployment and legacy application integration. Concerning model monitoring, many models in production are not monitored. The main monitored aspects are inputs, outputs, and decisions. Reported problems involve the absence of monitoring practices, the need to create custom monitoring tools, and the selection of suitable metrics. [Conclusion] Our results help provide a better understanding of the adopted practices and problems in practice and support guiding ML deployment and monitoring research in a problem-driven manner.
Offline Risk-sensitive RL with Partial Observability to Enhance Performance in Human-Robot Teaming

The integration of physiological computing into mixed-initiative human-robot interaction systems offers valuable advantages in autonomous task allocation by incorporating real-time features as human state observations into the decision-making system. This approach may alleviate the cognitive load on human operators by intelligently allocating mission tasks between agents. Nevertheless, accommodating a diverse pool of human participants with varying physiological and behavioral measurements presents a substantial challenge. To address this, resorting to a probabilistic framework becomes necessary, given the inherent uncertainty and partial observability on the human's state. Recent research suggests to learn a Partially Observable Markov Decision Process (POMDP) model from a data set of previously collected experiences that can be solved using Offline Reinforcement Learning (ORL) methods. In the present work, we not only highlight the potential of partially observable representations and physiological measurements to improve human operator state estimation and performance, but also enhance the overall mission effectiveness of a human-robot team. Importantly, as the fixed data set may not contain enough information to fully represent complex stochastic processes, we propose a method to incorporate model uncertainty, thus enabling risk-sensitive sequential decision-making. Experiments were conducted with a group of twenty-six human participants within a simulated robot teleoperation environment, yielding empirical evidence of the method's efficacy. The obtained adaptive task allocation policy led to statistically significant higher scores than the one that was used to collect the data set, allowing for generalization across diverse participants also taking into account risk-sensitive metrics.
What's documented in AI? Systematic Analysis of 32K AI Model Cards

The rapid proliferation of AI models has underscored the importance of thorough documentation, as it enables users to understand, trust, and effectively utilize these models in various applications. Although developers are encouraged to produce model cards, it's not clear how much information or what information these cards contain. In this study, we conduct a comprehensive analysis of 32,111 AI model documentations on Hugging Face, a leading platform for distributing and deploying AI models. Our investigation sheds light on the prevailing model card documentation practices. Most of the AI models with substantial downloads provide model cards, though the cards have uneven informativeness. We find that sections addressing environmental impact, limitations, and evaluation exhibit the lowest filled-out rates, while the training section is the most consistently filled-out. We analyze the content of each section to characterize practitioners' priorities. Interestingly, there are substantial discussions of data, sometimes with equal or even greater emphasis than the model itself. To evaluate the impact of model cards, we conducted an intervention study by adding detailed model cards to 42 popular models which had no or sparse model cards previously. We find that adding model cards is moderately correlated with an increase weekly download rates. Our study opens up a new perspective for analyzing community norms and practices for model documentation through large-scale data science and linguistics analysis.
Significant noise improvement in a Kinetic Inductance Phonon-Mediated detector by use of a wideband parametric amplifier

Microwave Kinetic Inductance Detectors (MKIDs) have been demonstrated as capable phonon sensors when coupled to crystalline substrates, and have been proposed as detectors for next-generation rare-event searches such as for the direct detection of dark matter. These Kinetic Inductance Phonon Mediated (KIPM) detector designs, favoring large superconducting absorber volumes and high readout powers, are oftentimes limited in their sensitivity by low temperature amplifier noise introduced in the signal readout chain. We report here an effort to couple a wideband Kinetic Inductance Travelling Wave Parametric Amplifier (KI-TWPA), operated near the Standard Quantum Limit of minimal added amplifier noise, to sensors spanning a 70 MHz bandwidth at 3.5 GHz. This results in a ~5x improvement in the inferred detector energy resolution in the best sensor and highlights the potential of constructing O(100) meV resolving phonon-mediated particle detectors. We detail limitations introduced by lossy passive components, degraded RF responsivity, and microphysical noise sources like two-level systems (TLS), in achieving ultimate quantum-limited system noise levels.
Descanning: From Scanned to the Original Images with a Color Correction Diffusion Model

A significant volume of analog information, i.e., documents and images, have been digitized in the form of scanned copies for storing, sharing, and/or analyzing in the digital world. However, the quality of such contents is severely degraded by various distortions caused by printing, storing, and scanning processes in the physical world. Although restoring high-quality content from scanned copies has become an indispensable task for many products, it has not been systematically explored, and to the best of our knowledge, no public datasets are available. In this paper, we define this problem as Descanning and introduce a new high-quality and large-scale dataset named DESCAN-18K. It contains 18K pairs of original and scanned images collected in the wild containing multiple complex degradations. In order to eliminate such complex degradations, we propose a new image restoration model called DescanDiffusion consisting of a color encoder that corrects the global color degradation and a conditional denoising diffusion probabilistic model (DDPM) that removes local degradations. To further improve the generalization ability of DescanDiffusion, we also design a synthetic data generation scheme by reproducing prominent degradations in scanned images. We demonstrate that our DescanDiffusion outperforms other baselines including commercial restoration products, objectively and subjectively, via comprehensive experiments and analyses.
It's Never Too Late: Fusing Acoustic Information into Large Language Models for Automatic Speech Recognition

Recent studies have successfully shown that large language models (LLMs) can be successfully used for generative error correction (GER) on top of the automatic speech recognition (ASR) output. Specifically, an LLM is utilized to carry out a direct mapping from the N-best hypotheses list generated by an ASR system to the predicted output transcription. However, despite its effectiveness, GER introduces extra data uncertainty since the LLM is trained without taking into account acoustic information available in the speech signal. In this work, we aim to overcome such a limitation by infusing acoustic information before generating the predicted transcription through a novel late fusion solution termed Uncertainty-Aware Dynamic Fusion (UADF). UADF is a multimodal fusion approach implemented into an auto-regressive decoding process and works in two stages: (i) It first analyzes and calibrates the token-level LLM decision, and (ii) it then dynamically assimilates the information from the acoustic modality. Experimental evidence collected from various ASR tasks shows that UADF surpasses existing fusion mechanisms in several ways. It yields significant improvements in word error rate (WER) while mitigating data uncertainty issues in LLM and addressing the poor generalization relied with sole modality during fusion. We also demonstrate that UADF seamlessly adapts to audio-visual speech recognition.
Approximate Keys and Functional Dependencies in Incomplete Databases With Limited Domains-Algorithmic Perspective

A possible world of an incomplete database table is obtained by imputing values from the attributes (infinite) domain to the place of \texttt{NULL} s. A table satisfies a possible key or possible functional dependency constraint if there exists a possible world of the table that satisfies the given key or functional dependency constraint. A certain key or functional dependency is satisfied by a table if all of its possible worlds satisfy the constraint. Recently, an intermediate concept was introduced. A strongly possible key or functional dependency is satisfied by a table if there exists a strongly possible world that satisfies the key or functional dependency. A strongly possible world is obtained by imputing values from the active domain of the attributes, that is from the values appearing in the table. In the present paper, we study approximation measures of strongly possible keys and FDs. Measure $g_3$ is the ratio of the minimum number of tuples to be removed in order that the remaining table satisfies the constraint. We introduce a new measure $g_5$, the ratio of the minimum number of tuples to be added to the table so the result satisfies the constraint. $g_5$ is meaningful because the addition of tuples may extend the active domains. We prove that if $g_5$ can be defined for a table and a constraint, then the $g_3$ value is always an upper bound of the $g_5$ value. However, the two measures are independent of each other in the sense that for any rational number $0\le\frac{p}{q}<1$ there are tables of an arbitrarily large number of rows and a constant number of columns that satisfy $g_3-g_5=\frac{p}{q}$. A possible world is obtained usually by adding many new values not occurring in the table before. The measure $g_5$ measures the smallest possible distortion of the active domains. We study complexity of determining these approximate measures.
The neurohormone tyramine stimulates the secretion of an Insulin-Like Peptide from the intestine to modulate the systemic stress response in C. elegans

The DAF-2/insulin/insulin-like growth factor signaling (IIS) pathway plays an evolutionarily conserved role in regulating reproductive development, lifespan, and stress resistance. In C. elegans, DAF-2/IIS signaling is modulated by an extensive array of insulin-like peptides (ILPs) with diverse spatial and temporal expression patterns. However, the release dynamics and specific functions of these ILPs in adapting to different environmental conditions remain poorly understood. Here, we show that the ILP, INS-3, plays a crucial role in modulating the response to different types of stressors in C. elegans. ins-3 mutants display increased resistance to both heat and oxidative stress; however, under favorable conditions, this advantage is countered by slower reproductive development. ins-3 expression in both neurons and the intestine is downregulated in response to environmental stressors. Conversely, the neurohormone tyramine, which is released during the acute flight response, triggers an upregulation in ins-3 expression. Moreover, we found that tyramine negatively impacts environmental stress resistance by stimulating the release of INS-3 from the intestine. The subsequent release of INS-3 systemically activates the DAF-2 pathway, resulting in the inhibition of cytoprotective mechanisms mediated by DAF-16/FOXO and HSF-1. These studies offer mechanistic insights into the brain-gut communication pathway that weighs adaptive strategies to respond to acute and long-term stress scenarios.
The spread of pESI-mediated extended-spectrum cephalosporin resistance in Salmonella serovars—Infantis, Senftenberg, and Alachua isolated from food animal sources in the United States

The goal of this study is to investigate the origin, prevalence, and evolution of the pESI megaplasmid in Salmonella isolated from animals, foods, and humans. We queried 510,097 Salmonella genomes under the National Center for Biotechnology Information (NCBI) Pathogen Detection (PD) database for the presence of potential sequences containing the pESI plasmid in animal, food, and environmental sources. The presence of the pESI megaplasmid was confirmed by using seven plasmid-specific markers (rdA, pilL, SogS, TrbA, ipf, ipr2 and IncFIB(pN55391)). The plasmid and chromosome phylogeny of these isolates was inferred from single nucleotide polymorphisms (SNPs). Our search resolved six Salmonella clusters carrying the pESI plasmid. Four were emergent Salmonella Infantis clusters, and one each belonged to serovar Senftenberg and Alachua. The Infantis cluster with a pESI plasmid carrying blaCTX-M-65 gene was the biggest of the four emergent Infantis clusters, with over 10,000 isolates. This cluster was first detected in South America and has since spread widely in United States. Over time the composition of pESI in United States has changed with the average number of resistance genes showing a decrease from 9 in 2014 to 5 in 2022, resulting from changes in gene content in two integrons present in the plasmid. A recent and emerging cluster of Senftenberg, which carries the blaCTX-M-65 gene and is primarily associated with turkey sources, was the second largest in the United States. SNP analysis showed that this cluster likely originated in North Carolina with the recent acquisition of the pESI plasmid. A single Alachua isolate from turkey was also found to carry the pESI plasmid containing blaCTX-M-65 gene. The study of the pESI plasmid, its evolution and mechanism of spread can help us in developing appropriate strategies for the prevention and further spread of this multi-drug resistant plasmid in Salmonella in poultry and humans.
IFNγ induces epithelial reprogramming driving CXCL11-mediated T cell migration

The cytokine interferon-gamma (IFNγ) plays a multifaceted role in intestinal immune responses ranging from anti-to pro-inflammatory depending on the setting. Here, using a 3D co-culture system based on human intestinal epithelial organoids, we explore the capacity of IFNγ-exposure to reprogram intestinal epithelia and thereby directly modulate lymphocyte responses. IFNγ treatment of organoids led to transcriptional reprogramming, marked by a switch to a pro-inflammatory gene expression profile, including transcriptional upregulation of the chemokines CXCL9, CXCL10, and CXCL11. Proteomic analysis of organoid-conditioned medium post-treatment confirmed chemokine secretion. Furthermore, IFNγ-treatment of organoids led to enhanced T cell migration in a CXCL11-dependent manner without affecting T cell activation status. Taken together, our results suggest a specific role for CXCL11 in T cell recruitment that can be targeted to prevent T cell trafficking to the inflamed intestine.
Microtubule polymerization tuned by macromolecular crowdant size and density

Microtubule (MT) polymerization is regulated by biochemical as well as physical factors such as macromolecular crowding. Crowding agents or crowdants affect MT elongation rates differently depending on crowdant size due to opposing ef-fects on polymerization: microviscosity reduces polymer elongation, while volume exclusion increases reaction rates by local concentration. In order to address how crowdant size and concentration collectively affect MT populations, we combine in vitro MT polymerization experiments with kinetic Monte Carlo simulations. Our experiments in bulk with nucleators validate decreasing MT elongation rates with increasing concentrations of small molecular weight crowdants in bulk assays and a corresponding increase for large crowdants. Kinetic Monte Carlo simulations can explain the result with packing fractions dependence of small as compared to large crowdants increasing microviscosity more dramatically. In contrast MT bulk polymerization rates in absence of nucleators increased with crowdant con-centration, irrespective of their size, with a corresponding decrease in the critical concentration. Microscopy of filament growth dynamics demonstrates that small crowdants result in shorter filaments in a concentration dependent manner, consis-tent with their role in reducing elongation rates, but this decrease is compensated by increased number of filaments. Large crowdants increase the filament numbers while elongation is slightly decreased. Our results provide evidence for MT nucle-ation being rate-limited and elongation diffusion limited, resulting in differences in the effect of crowdant sizes on nucleation and elongation. These results are of gen-eral relevance to understand physical effects of crowding on collective cytoskeletal polymerization dynamics.