Miracle-dz/UniFAR-FTUs huggingface dataset:
Starbursts driven by central gas compaction

Starburst (SB) galaxies are a rare population of galaxies with star formation rates (SFRs) greatly exceeding those of the majority of star-forming galaxies with similar stellar mass. It is unclear whether these bursts are the result of either especially large gas reservoirs or enhanced efficiencies in converting gas into stars. Tidal torques resulting from gas-rich galaxy mergers are known to enhance the SFR by funneling gas towards the centre. However, recent theoretical works show that mergers do not always trigger a SB and not all SB galaxies are interacting systems, raising the question of what drives a SB. We analyse a large sample of SB galaxies and a mass- and redshift-matched sample of control galaxies, drawn from the FIREbox cosmological volume at z=0-1. We find that SB galaxies have both larger molecular gas fractions and shorter molecular depletion times than control galaxies, but similar total gas masses. Control galaxies evolve towards the SB regime by gas compaction in their central regions, over timescales of about 70 Myr, accompanied by an increase in the fraction of ultra-dense and molecular gas. The driving mechanism behind the SB varies depending on the mass of the galaxy. Massive (Mstar>1e10 Msun) galaxies undergoing intense, long-lasting SBs are mostly driven by galaxy interactions. Conversely, SBs in non-interacting galaxies are often triggered by a global gravitational instability, that can result in a breathing mode in low-mass galaxies.
Proof-of-Prospect-Theory: A Novel Game-based Consensus Mechanism for Blockchain

Blockchain technology is a breakthrough in changing the ways of business and organization operations, in which the consensus problem is challenging with practical constraints, such as computational power and consensus standard. In this paper, a novel consensus mechanism named Proof-of-Prospect-Theory (PoPT) is designed from the view of game theory, where the game prospect value is considered as an important election criterion of the block-recorder. PoPT portrays the popularity of a node in the network as an attribute, which is constituted by the subjective sensibilities of nodes. Furthermore, the performances of the PoPT and the willingness of ordinary nodes to participate in the consensus are analyzed, exploring fairness, decentralization, credibility, and the motivating ability of the consensus mechanism. Finally, numerical simulations with optimization of the PoPT consensus mechanism are demonstrated in the scenario of a smart grid system to illustrate the effectiveness of the PoPT.
Recovering from Privacy-Preserving Masking with Large Language Models

Model adaptation is crucial to handle the discrepancy between proxy training data and actual users data received. To effectively perform adaptation, textual data of users is typically stored on servers or their local devices, where downstream natural language processing (NLP) models can be directly trained using such in-domain data. However, this might raise privacy and security concerns due to the extra risks of exposing user information to adversaries. Replacing identifying information in textual data with a generic marker has been recently explored. In this work, we leverage large language models (LLMs) to suggest substitutes of masked tokens and have their effectiveness evaluated on downstream language modeling tasks. Specifically, we propose multiple pre-trained and fine-tuned LLM-based approaches and perform empirical studies on various datasets for the comparison of these methods. Experimental results show that models trained on the obfuscation corpora are able to achieve comparable performance with the ones trained on the original data without privacy-preserving token masking.
Emotion Design for Video Games: A Framework for Affective Interactivity

Emotionally evocative games hold great potential for both industry and our basic understanding about human affect. The question of how best games elicit and respond to player emotion brings together research from psychology, design, and human-computer interaction (HCI). However, there is much confusion regarding the nature of emotions, emotional game-player interaction, and the process of creating emotional games. This article reviews important work from relevant fields to provide an interdisciplinary model that integrates current theories and highlights conceptual gaps for relevant fields. Based on this model, we propose a methodological framework that provides a step-by-step process of developing an emotion-adaptive game. This framework is open to different models of emotion, and its modular nature allows games to adapt to developments in the science of emotion and technological innovations in emotion detection. Together, the theoretical overview and framework can help reduce the confusion that surrounds creating and researching emotions in games.
Blueprints: Systematizing Behavior Change Designs—The Case of Social Comparison Theory

To improve people’s lives, human-computer interaction researchers are increasingly designing technological solutions based on behavior change theory, such as social comparison theory (SCT). However, how researchers operationalize such a theory as a design remains largely unclear. One way to clarify this methodological step is to clearly state which functional elements of a design are aimed at operationalizing a specific behavior change theory construct to evaluate if such aims were successful. In this article, we investigate how the operationalization of functional elements of theories and designs can be more easily conveyed. First, we present a scoping review of the literature to determine the state of operationalizations of SCT as behavior change designs. Second, we introduce a new tool to facilitate the operationalization process. We term the tool blueprints. A blueprint explicates essential functional elements of a behavior change theory by describing it in relation to necessary and sufficient building blocks incorporated in a design. We describe the process of developing a blueprint for SCT. Last, we illustrate how the blueprint can be used during the design refinement and reflection process.
Heuristic Solutions to the Single Depot Electric Vehicle Scheduling Problem with Next Day Operability Constraints

This study focuses on the single depot electric vehicle scheduling problem (SDEVSP) within the broader context of the vehicle scheduling problem (VSP). By developing an effective scheduling model using mixed-integer linear programming, we generate bus blocks that accommodate electric vehicles (EVs), ensuring successful completion of each block while considering recharging requirements between blocks and during off-hours. Next day operability constraints are also incorporated, allowing for seamless repetition of blocks on subsequent days. The SDEVSP is known to be computationally complex, deriving optimal solutions unattainable for large-scale problems within reasonable timeframes. To address this, we propose a two-step solution approach: first solving the single depot VSP, and then addressing the block chaining problem (BCP) using the blocks generated in the first step. The BCP focuses on optimizing block combinations to facilitate recharging between consecutive blocks, considering operational constraints. A case study conducted reveals that nearly 100% electrification for Chicago, IL and Austin, TX transit buses is viable yet requires 1.6 EVs at 150-mile range per diesel vehicle.
Data Communication in VANETs: A Survey, Challenges and Applications

VANETs have emerged as an exciting research and application area. Increasingly vehicles are being equipped with embedded sensors, processing and wireless communication capa- bilities opening a myriad of possibilities for powerful and potential life changing applications on safety, efficiency, comfort, public collaboration and participation while they are on the road. Al- though, considered a special case of a Mobile Ad Hoc Networks, VANETS hold a vital feature - the possibility to affect people's life or death decisions. Due to the particular characteristics, from highly dynamic topology to intermittent connectivity, VANETs have great challenges lie ahead, to mention a few: different application QoS requirements, and conflicting privacy and safety issues. In this paper, a view of VANETs is presented from the communication and application challenges perspective.
Transcription Factor Sox9 Exacerbates Kidney Injury through Inhibition of MicroRNA-96-5p and Activation of the Trib3/IL-6 Axis

Introduction: Our study investigated the possible mechanisms of the role of the transcription factor Sox9 in the development and progression of kidney injury through regulation of the miR-96-5p/Trib3/IL-6 axis. Methods: Bioinformatics analysis was performed to identify differentially expressed genes in kidney injury and normal tissues. An in vivo animal model of kidney injury and an in vitro cellular model of kidney injury were constructed using LPS induction in 8-week-old female C57BL/6 mice and human normal renal tubular epithelial cells HK-2 for studying the possible roles of Sox9, miR-96-5p, Trib3, and IL-6 in kidney injury. Results: Sox9 was highly expressed in both mouse and cellular models of kidney injury. Sox9 was significantly enriched in the promoter region of miR-96-5p and repressed miR-96-5p expression. Trib3 was highly expressed in both mouse and cellular models of kidney injury and promoted inflammatory responses and kidney injury. In addition, Trib3 promoted IL-6 expression, which was highly expressed in kidney injury, and promoted the inflammatory response and extent of injury in kidney tissue. In vivo and in vitro experiments confirmed that the knockdown of Sox9 improved the inflammatory response and fibrosis of mouse kidney tissues and HK-2 cells, while the ameliorative effect of silencing Sox9 was inhibited by overexpression of IL-6. Conclusion: Collectively, Sox9 up-regulates miR-96-5p-mediated Trib3 and activates the IL-6 signaling pathway to exacerbate the inflammatory response, ultimately promoting the development and progression of kidney injury.
Universal Metric Learning with Parameter-Efficient Transfer Learning

A common practice in metric learning is to train and test an embedding model for each dataset. This dataset-specific approach fails to simulate real-world scenarios that involve multiple heterogeneous distributions of data. In this regard, we introduce a novel metric learning paradigm, called Universal Metric Learning (UML), which learns a unified distance metric capable of capturing relations across multiple data distributions. UML presents new challenges, such as imbalanced data distribution and bias towards dominant distributions. To address these challenges, we propose Parameter-efficient Universal Metric leArning (PUMA), which consists of a pre-trained frozen model and two additional modules, stochastic adapter and prompt pool. These modules enable to capture dataset-specific knowledge while avoiding bias towards dominant distributions. Additionally, we compile a new universal metric learning benchmark with a total of 8 different datasets. PUMA outperformed the state-of-the-art dataset-specific models while using about 69 times fewer trainable parameters.
Proceedings of the Demonstrations Session

Frolog is a text-adventure game whose goal is to serve as a laboratory for testing pragmatic theories of accommodation. To this end, rather than implementing ad-hoc mechanisms for each task that is necessary in such a conversational agent, Frolog integrates recently developed tools from computational linguistics, theorem proving and artificial intelligence planning.
Music Generation based on Generative Adversarial Networks with Transformer

Autoregressive models based on Transformers have become the prevailing approach for generating music compositions that exhibit comprehensive musical structure. These models are typically trained by minimizing the negative log-likelihood (NLL) of the observed sequence in an autoregressive manner. However, when generating long sequences, the quality of samples from these models tends to significantly deteriorate due to exposure bias. To address this issue, we leverage classifiers trained to differentiate between real and sampled sequences to identify these failures. This observation motivates our exploration of adversarial losses as a complement to the NLL objective. We employ a pre-trained Span-BERT model as the discriminator in the Generative Adversarial Network (GAN) framework, which enhances training stability in our experiments. To optimize discrete sequences within the GAN framework, we utilize the Gumbel-Softmax trick to obtain a differentiable approximation of the sampling process. Additionally, we partition the sequences into smaller chunks to ensure that memory constraints are met. Through human evaluations and the introduction of a novel discriminative metric, we demonstrate that our approach outperforms a baseline model trained solely on likelihood maximization.
Management Research and Practice

Drawing on the theories of person–job fit, relative deprivation, work adjustment and reasoned action, the present study aims to investigate the nexus of perceived overqualification, leader–member exchange (LMX), job satisfaction and job search behaviour. Anchored in philosophical assumptions, the present study adopts a survey research strategy with a cross-sectional time horizon. Data were garnered with a self-reported questionnaire from 607 convenience-sampled employees working in state-owned organisations in Sri Lanka. The study found a significant negative relationship between perceived overqualification and job satisfaction. The results further revealed that LMX moderates the negative relationship between perceived overqualification and job satisfaction such that the negative relationship is weaker at a high quality LMX. No less importantly, the results disclosed that job satisfaction is a complementary mediator of the relationship between perceived overqualification and job search behaviour. The study contributes to the frontiers of HRM literature and provides useful practical implications.
Performance Benefit of Aerocapture for the Design Reference Mission Set

Aerocapture is a maneuver which uses aerodynamic drag to slow down a spacecraft in a single pass through the atmosphere. All planetary orbiters to date have used propulsive orbit insertion. Aerocapture is a promising alternative, especially for small satellite missions and missions to the ice giants. The large {\Delta}V requirement makes it practically impossible for small satellites to enter low circular orbits. Aerocapture can enable insertion of low-cost satellites into circular orbits around Mars and Venus. For ice giant missions, aerocapture can enable orbit insertion from fast arrival trajectories which are impractical with propulsive insertion. By utilizing the atmospheric drag to impart the {\Delta}V, aerocapture can offer significant propellant mass and cost savings for a wide range of planetary missions. The present study analyzes the performance benefit offered by aerocapture for a set of design reference missions and their applications to future Solar System exploration from Venus to Neptune. The estimated performance benefit for aerocapture in terms of delivered mass increase are: Venus (92%), Earth (108%), Mars (17%), and Titan (614%), Uranus (35%), and Neptune (43%). At Uranus and Neptune, aerocapture is a mission enabling technology for orbit insertion from fast arrival interplanetary trajectories.
Cosmological Study in $F(R, T)$ Quasi-dilaton Massive Gravity

This study explores the cosmological implications of the $F(R, T)$ quasi-dilaton massive gravity theory, a modification of the de Rham-Gabadadze-Tolley (dRGT) massive gravity theory. Our analysis focuses on the self-accelerating solution of the background equations of motion, which are shown to exist in the theory. Notably, we find that the theory features an effective cosmological constant, which has important implications for our understanding of the universe's accelerated expansion. To test the viability of the $F(R, T)$ quasi-dilaton massive gravity theory, we utilize the Union2 Type Ia Supernovae (SNIa) dataset, comprising 557 observations. Our results demonstrate that the theory is capable of explaining the accelerated expansion of the universe without requiring the presence of dark energy. This finding supports the potential of the $F(R, T)$ quasi-dilaton massive gravity theory as an alternative explanation for the observed cosmic acceleration. Moreover, we investigate the properties of tensor perturbations within the framework of this theory and derive a novel expression for the dispersion relation of gravitational waves. Our analysis reveals interesting features of the modified dispersion relation in the Friedmann-Lema\^itre-Robertson-Walker cosmology, providing new insights into the nature of gravitational waves in the contest of the $F(R, T)$ quasi-dilaton massive gravity theory.
Distributional Estimation of Data Uncertainty for Surveillance Face Anti-spoofing

Face recognition systems have become increasingly vulnerable to security threats in recent years, prompting the use of Face Anti-spoofing (FAS) to protect against various types of attacks, such as phone unlocking, face payment, and self-service security inspection. While FAS has demonstrated its effectiveness in traditional settings, securing it in long-distance surveillance scenarios presents a significant challenge. These scenarios often feature low-quality face images, necessitating the modeling of data uncertainty to improve stability under extreme conditions. To address this issue, this work proposes Distributional Estimation (DisE), a method that converts traditional FAS point estimation to distributional estimation by modeling data uncertainty during training, including feature (mean) and uncertainty (variance). By adjusting the learning strength of clean and noisy samples for stability and accuracy, the learned uncertainty enhances DisE's performance. The method is evaluated on SuHiFiMask [1], a large-scale and challenging FAS dataset in surveillance scenarios. Results demonstrate that DisE achieves comparable performance on both ACER and AUC metrics.
Neural Parametric Surfaces for Shape Modeling

The recent surge of utilizing deep neural networks for geometric processing and shape modeling has opened up exciting avenues. However, there is a conspicuous lack of research efforts on using powerful neural representations to extend the capabilities of parametric surfaces, which are the prevalent surface representations in product design, CAD/CAM, and computer animation. We present Neural Parametric Surfaces, the first piecewise neural surface representation that allows coarse patch layouts of arbitrary $n$-sided surface patches to model complex surface geometries with high precision, offering greater flexibility over traditional parametric surfaces. By construction, this new surface representation guarantees $G^0$ continuity between adjacent patches and empirically achieves $G^1$ continuity, which cannot be attained by existing neural patch-based methods. The key ingredient of our neural parametric surface is a learnable feature complex $\mathcal{C}$ that is embedded in a high-dimensional space $\mathbb{R}^D$ and topologically equivalent to the patch layout of the surface; each face cell of the complex is defined by interpolating feature vectors at its vertices. The learned feature complex is mapped by an MLP-encoded function $f:\mathcal{C} \rightarrow \mathcal{S}$ to produce the neural parametric surface $\mathcal{S}$. We present a surface fitting algorithm that optimizes the feature complex $\mathcal{C}$ and trains the neural mapping $f$ to reconstruct given target shapes with high accuracy. We further show that the proposed representation along with a compact-size neural net can learn a plausible shape space from a shape collection, which can be used for shape interpolation or shape completion from noisy and incomplete input data. Extensive experiments show that neural parametric surfaces offer greater modeling capabilities than traditional parametric surfaces.
Shining Light on the Hosts of the Nano-Hertz Gravitational Wave Sources: A Theoretical Perspective

The formation of supermassive black holes (SMBHs) in the Universe and its role in the properties of the galaxies is one of the open questions in astrophysics and cosmology. Though, traditionally, electromagnetic waves have been instrumental in direct measurements of SMBHs, significantly influencing our comprehension of galaxy formation, gravitational waves (GW) bring an independent avenue to detect numerous binary SMBHs in the observable Universe in the nano-Hertz range using the pulsar timing array observation. This brings a new way to understand the connection between the formation of binary SMBHs and galaxy formation if we can connect theoretical models with multi-messenger observations namely GW data and galaxy surveys. Along these lines, we present here the first paper on this series based on Romulus25 cosmological simulation on the properties of the host galaxies of SMBHs and propose on how this can be used to connect with observations of nano-Hertz GW signal and galaxy surveys. We show that the most dominant contribution to the background will arise from sources with high chirp masses which are likely to reside in low redshift early-type galaxies with high stellar mass, largely old stellar population, and low star formation rate, and that reside at centers of galaxy groups and manifest evidence of recent mergers. The masses of the sources show a correlation with the halo mass and stellar mass of the host galaxies. This theoretical study will help in understanding the host properties of the GW sources and can help in establishing a connection with observations.
Quantum Pseudorandom Scramblers

Quantum pseudorandom state generators (PRSGs) have stimulated exciting developments in recent years. A PRSG, on a fixed initial (e.g., all-zero) state, produces an output state that is computationally indistinguishable from a Haar random state. However, pseudorandomness of the output state is not guaranteed on other initial states. In fact, known PRSG constructions provably fail on some initial state. In this work, we propose and construct quantum Pseudorandom State Scramblers (PRSSs), which can produce a pseudorandom state on an arbitrary initial state. In the information-theoretical setting, we obtain a scrambler which maps an arbitrary initial state to a distribution of quantum states that is close to Haar random in total variation distance. As a result, our PRSS exhibits a dispersing property. Loosely, it can span an $\epsilon$-net of the state space. This significantly strengthens what standard PRSGs can induce, as they may only concentrate on a small region of the state space as long as the average output state approximates a Haar random state in total variation distance. Our PRSS construction develops a parallel extension of the famous Kac's walk, and we show that it mixes exponentially faster than the standard Kac's walk. This constitutes the core of our proof. We also describe a few applications of PRSSs. While our PRSS construction assumes a post-quantum one-way function, PRSSs are potentially a weaker primitive and can be separated from one-way functions in a relativized world similar to standard PRSGs.