Miracle-dz/UniFAR-FTUs huggingface dataset:
A New Way to Write The Newtonian Gravitational Equation Resolves What The Cosmological Constant Truly Is

We demonstrate that there is a way to represent Newtonian gravity in a form that strongly resembles Einstein’s field equation, but it is still a fundamentally di↵erent type of equation. In the non-relativistic regime, it becomes necessary to ad hoc introduce a cosmological constant in order to align it with observations, similar to Einstein’s field equation. Interestingly, in 1917, Einstein also ad hoc inserted a cosmological constant in the Newtonian equation during his discussion on incorporating it into his own field equation. At that time, the cosmological constant was added to maintain consensus, which favored a steady-state universe. However, with the discovery of cosmological redshift and the shift in consensus towards an expanding universe, Einstein abandoned the cosmological constant. Then, around 1999, the cosmological constant was reintroduced to explain recent observations of distant supernovae. Currently, the cosmological constant is once again a topic of great interest and significance. Nevertheless, we will demonstrate that the cosmological constant is likely an ad hoc adjustment resulting from a failure to properly account for relativistic e↵ects in strong gravitational fields. We are able to derive the cosmological constant and show that it is linked to corrections for relativistic e↵ects in strong gravitational fields. In our model, this constant holds true for any strong field but naturally assumes di↵erent values, indicating that it is not truly a constant. Its value is constant only for the mass under consideration; for example, for the Hubble sphere, it always has the same value. Additionally, we will demonstrate how relativistic modified Newtonian theory also seems to resolves the black hole information paradox by simply removing it. This theory also leads to the conservation of spacetime. In general relativity theory, there are several significant challenges. One of them is how spacetime can change over time, transitioning from infinite curvature just at the beginning of the assumed Big Bang to essentially flat spacetime when the universe end up in cold death, while still maintaining conservation of energy all the way from the Big Bang to the assumed cold death of the universe. Can one really get something from nothing?
“Shhh! It’s Decision Time”: Effect of Traffic Noise on Intertemporal Decision-Making by

Many economic decisions, such as choosing between spending and saving, or deciding whether to pursue education or enter the labor force, are driven by time preferences. It has been well-documented in behavioral research that peo-ple’s intertemporal choices are malleable and can be easily influenced by various factors. Despite the growing issue of noise pollution in modern society and fre-quent exposure to noise in daily life, little research has been done to investigate the effect of noise on intertemporal decision-making. To address this research gap, I conduct laboratory experiments using a between-subject design, where participants completed the Convex Time Budget tasks while being exposed to different levels of pre-recorded traffic noise. Structural estimation results indicate that exposure to traffic noise significantly reduces present bias (larger β ), but has no effect on discounting ( δ ). These findings suggest that ambient noise can play an important role in shaping intertemporal decisions. I also discuss the potential explanations for the observed effects, policy implications, and future research directions
Focus Time: Effectiveness of Computer Assisted Protected Time for Wellbeing and Work Engagement of Information Workers

Having little time for focused work is a major challenge of information work. While research has explored computing-assisted user-facing solutions for protecting time for focused work, there is limited empirical evidence about the long-term effectiveness of these features on wellbeing and work engagement. Towards this problem, we study the effects of automatically scheduling time for focused work on people’s work calendars using the Focus Time feature on Outlook calendars. We conduct an experimental study over six weeks with 15 Treatment and 10 Control participants who responded to survey questions on wellbeing and work engagement throughout the study. We find that the Treatment participants showed significantly higher wellbeing, including increased excitement, relaxation, and satisfaction, and decreased anger, frustration, tiredness, and stress. We study the needs, benefits, and challenges of scheduling focus time, and discuss the importance of enabling mechanisms for focused work in organizations and design recommendations for tools supporting focused work.
ClockstaRX: testing molecular clock hypotheses with genomic data

Phylogenetic studies of genomic data can provide valuable opportunities for evaluating evolutionary timescales and drivers of rate variation. These analyses require statistical tools based on molecular clocks. We present ClockstaRX, a flexible platform for exploring and testing evolutionary rate signals in phylogenomic data. It implements methods that use information from gene trees in Euclidean space, allowing data transformation, visualization, and hypothesis testing. ClockstaRX implements formal tests of the dimensionality reducibility of the Euclidean space of rates, and for identifying loci and branches that have a large influence on rate variation. Using simulations to evaluate the performance of the methods implemented, we find that inferences about rates can be strongly influenced by the overall amount of rate variation in the data, the shared patterns of among-lineage rate heterogeneity across groups of loci, and missing data. In an analysis of phylogenomic data from birds, we find a higher rate of evolution in introns compared with exons across all lineages. In addition, passerine taxa are highlighted as having unique patterns of genomic evolutionary rates compared with other avian lineages. Drawing on these results, we recommend careful exploratory analyses and filtering before performing phylogenomic analyses using molecular clocks.
A Search for the Free Inner Core Nutation in VLBI Data

We investigate the time-frequency spectrum of VLBI nutation series, including operational series released by IVS analysis centers. We focus on the interannual prograde band, where the signal as- sociated with the free inner core nutation (FICN) should be searched. We do not conclude on the detection of the FICN. However, we point out some signi cant but unexplained structures within the FICN frequency band. We also investigate whether their origin is geophysical or they are artifacts due to VLBI analysis methods or reference frame effects.
Perceptually Based Frugal Models for Low-Carbon AI

Artificial Intelligence (AI) tools have become ubiquitous in today society. AI allows computers to perform “intelligent” tasks such as decision making, problem solving, perception/identification and even understanding human communication and behavior. At the same time, the impact of AI on the environment has become nonnegligible because of its carbon footprint. Indeed, the carbon footprint of computer systems is that of the part of fossil sources in the production and consumption of electricity. Moreover, fossil sources currently represent a significant part of the energy mix. For example, the rate of production of renewable electricity close to 100% is only reached in Iceland or Norway within Europe. In other countries, the decarbonization of electricity is only expected several decades into the future. When it comes to AI, its carbon footprint and role in climate change has recently come under intense scrutiny [1]. Awareness was triggered by the observation that training a single large language model (NLP) could approach 300 tons of emissions of carbon dioxide [2] – that is five times the lifetime emissions of an average car. Compared to other industrial sectors the AI impact is also non-negligible: for example, the AI environmental impact already globally exceeds that of air transport. Indeed, AI, being computationally intensive, is a major carbon emitter.
Algorithms for the Integrative Modeling of Viral RNA Architectures

Understanding the structure of RNA molecules and their complexes is essential for modern molecular biology; directly impacting fundamental insights and bio-medical applications. Notorious examples of large RNAs include the genomes of RNA viruses (Influenza, HIV, Chikungunya, SARS-CoV2.), whose lengths exceed the current capabilities of predictive computational meth-ods, as well as high-res experimental structural techniques. Within the ANR-funded project INSSANE, we are developing integrated experimental protocols, together with efficient computational methods for the structural modeling of large RNAs. We accurately probe and predict the genomic RNA architectures of human-pathogen viruses. Our bioinformatics methods are applicable even beyond viruses, and could be used to model the structure of other large RNAs (lncRNAS, Introns). This PhD project will aim to overcome algorithmic hurdles to improve the processing of sequencing data produced by RNA structure-targeting experiments, and ultimately contribute computational structural modeling strategies of increased accuracy. All modern RNA probing protocols are based on sequencing technologies, and reveal structural information indirectly, through observable alterations at the RNA sequence level (mutations, stops/cut). Meanwhile, novel crosslinking protocols reveal interacting RNA regions. The selected candidate will design an integrative structure modeling method to interpret probing data, combine the resulting reactivity profiles with crosslinking interactions, along with evolutionary information (compensatory mutations). Complete virus architectures will be predicted as the solution of a Maximum-Independent-Set (MIS) graph problem for an associated conflict graph including both alternative local structure and long-range interactions. It will be implemented as a Fixed Parameter Tractable algorithm based on the treewidth to produce models with maximal experimental support and thermodynamic stability.
FinTech Adoption and Household Risk-Taking: From Digital Payments to Platform Investments

We study household finance in the age of FinTech, where consumption, payments, and investments take place via all-in-one super-apps. We hypothesize that FinTech adoption can improve household risk-taking by breaking down the traditional physical and psychological barriers and enhance financial inclusion. Taking advantage of an individual-level FinTech dataset, we find that higher FinTech adoption, both at the individual-level and the county-level instrumented by distance-from-Hangzhou, results in higher participation and more risk-taking in mutual-fund investments. Moreover, individuals who are otherwise more constrained, those with higher risk tolerance or living in under-banked counties, stand to benefit more from the advent of FinTech. *Hong (yrhong@saif.sjtu.edu.cn) is from Shanghai Advanced Institute of Finance at Shanghai Jiao Tong University. Lu (xiaomenglu@fudan.edu.cn) is from Fanhai International School of Finance at Fudan University. Pan (junpan@saif.sjtu.edu.cn) is from Shanghai Advanced Institute of Finance at Shanghai Jiao Tong University and CAFR. We benefited from extensive discussions with Jun Liu and are grateful to John Campbell (our discussant at ABFER) for valuable comments and suggestions. We also benefited from discussions with Darrell Duffie, Greg Buchak, Jennifer Carpenter, Bing Han, Yang Sun, Baixiao Liu, Wenlan Qian, Huan Tang, Sabine Bernard, Shangjin Wei, and Haikun Zhu. We thank conference participants at the EFA Annual Meeting 2021, FIRS 2021, CICF 2021, NFA 2021, China FinTech Research Conference 2021, China International Risk Forum 2021, ABFER Annual Meeting 2022, SIF 2022, CFRC 2022, and seminar participants at ABFER Capital Market Development, NYU Stern CGEB China Initiative Research Seminar, Luohan Academy Webinar, Asia-Pacific Corporate Finance Workshop, the Bank of Finland Institute for Emerging Economies, Central Banks of the SEACEN Centre, the Bank of Lithuania, Korean University, Monash University, PBC School of Finance at Tsinghua University, FISF at Fudan University, Shanghai University of Finance and Economics, Shanghai Jiao Tong University, SAIF, Renmin University of China, IFS at Southwestern University of Finance and Economics. We thank Yijian Sun for excellent research assistance, and Ant Group for providing the data used in this article.
Goal-driven Autonomy Without GDA

Goal-driven autonomy (GDA) is an agent control architecture proposed to enable long-term autonomous operation. At its heart is the idea of goal reasoning, in which a dedicated subsystem decides which goals the system should pursue given the current context. In this position paper, we summarize and extend a line of work that argues that a specialized goal reasoning subsystem is unnecessary and that long-term autonomous operation of complex intelligent systems should simply be directed by a planner (although perhaps not a classical one). We present several examples of how functionality that has been described as goal reasoning can be performed by modern planning techniques.
Complementarity and genetic algorithms for an optimization shell problem

In this paper an optimization thin laminated shallow shell problem is discussed. The existence of a solution for both the linear and nonlinear versions of this problem is firstly studied by exploiting their reductions into variational inequalities. The discretization of these continuous problems by using appropriate finite elements leads into a Mathematical Programming Problem with Equilibrium Constraints (MPEC) in which some of their variables assume integer values and the remaining variables are implicitly defined as the solution of a Mixed Complementarity Problem (MCP). A genetic algorithm incorporating a complementarity path-following technique is proposed for the solution of this MPEC. It is shown that the efficiency of this hybrid problem depends on the problem to be linear or nonlinear. Some computational experience with this algorithm on the solution of special cases of this MPEC has been reported elsewhere and is briefly described to highlight the performance of the proposed methodology.
Diminishing Return of Value Expansion Methods in Model-Based Reinforcement Learning

Model-based reinforcement learning is one approach to increase sample efficiency. However, the accuracy of the dynamics model and the resulting compounding error over modelled trajectories are commonly regarded as key limitations. A natural question to ask is: How much more sample efficiency can be gained by improving the learned dynamics models? Our paper empirically answers this question for the class of model-based value expansion methods in continuous control problems. Value expansion methods should benefit from increased model accuracy by enabling longer rollout horizons and better value function approximations. Our empirical study, which leverages oracle dynamics models to avoid compounding model errors, shows that (1) longer horizons increase sample efficiency, but the gain in improvement decreases with each additional expansion step, and (2) the increased model accuracy only marginally increases the sample efficiency compared to learned models with identical horizons. Therefore, longer horizons and increased model accuracy yield diminishing returns in terms of sample efficiency. These improvements in sample efficiency are particularly disappointing when compared to model-free value expansion methods. Even though they introduce no computational overhead, we find their performance to be on-par with model-based value expansion methods. Therefore, we conclude that the limitation of model-based value expansion methods is not the model accuracy of the learned models. While higher model accuracy is beneficial, our experiments show that even a perfect model will not provide an un-rivalled sample efficiency but that the bottleneck lies elsewhere.
Triangle Search: An Anytime Beam Search

Anytime heuristic search algorithms try to find a (potentially suboptimal) solution as quickly as possible and then work to find better and better solutions until an optimal solution is obtained or time is exhausted. The most widely-known anytime search algorithms are based on best-first search. In this paper, we propose a new algorithm, triangle search, that is based on beam search. Experimental results on a suite of popular search benchmarks suggest that it is competitive with fixed-width beam search and often performs better than the previous best anytime search algorithms.
THE ORBIT, ROTATION, AND GRANULATION OF THE G7 GIANT β Her

The G7 IIIa single-lined spectroscopic binary, β Her, is studied with high-resolution, high-signal-to-noise spectra taken over 10 seasons from 23MR2000 to 10MY2009. Absolute radial velocities, corrected for convective blueshifts, are determined and new orbital parameters are derived. Line-depth ratios are used to measure temperature variation ∼2 K. A Fourier analysis is done for the line broadening, yielding a projected rotation velocity of 3.27 ± 0.20 km s−1 and a radial–tangential macroturbulence dispersion of 6.43 ± 0.08 km s−1. The “C” shaped bisector of Fe i λ6253 has its blue-most point at a relative flux level of 0.52, consistent with what is expected from β Her’s absolute magnitude. The third-signature plot indicates granulation velocities 20% larger than the Sun’s. Mapping the λ6253 line bisector onto the third-signature curve results in a flux deficit of 12.6 ± 1.0% that can be interpreted as arising from a temperature difference between granules and inter-granular lanes of 132 K. The flux deficit peaks near 5.5 km s−1 from the line center, suggesting the velocity difference between granules and lanes is ∼20% larger than that found for recently analyzed K giants.
Towards Generalization in Multi-View Pedestrian Detection

Detecting humans in images and videos has emerged as an essential aspect of intelligent video systems that solve pedestrian detection, tracking, crowd counting, etc. It has many real-life applications varying from visual surveillance and sports to autonomous driving. Despite achieving high performance, the single camera-based detection methods are susceptible to occlusions caused by humans, which drastically degrades the performance where crowd density is very high. Therefore multi-camera setup becomes necessary, which incorporates multiple camera views for detections by computing precise 3D locations that can be visualized and transformed to Top View also termed as Bird’s Eye View (BEV) representation and thus permits better occlusion reasoning in crowded scenes. The thesis, therefore, presents a multi-camera approach that globally aggregates the multi-view cues for detection and alleviates the impact of occlusions in a crowded environment. But it was still primarily unknown how satisfactorily the multi-view detectors generalize to unseen data. In different camera setups, this becomes critical because a practical multi-view detector should be usable in scenarios such as i) when the model trained with few camera views is deployed, and one of the cameras fails during testing/inference or when we add more camera views to the existing setup, ii) when we change the camera positions in the same environment and finally iii) when deploying the system on the unseen environment; an ideal multi-camera setup system should be adaptable to such changing conditions. While recent works using deep learning have made significant advances in the field, they have overlooked the generalization aspect, which makes them impractical for real-world deployment. We formalized three critical forms of generalization and outlined the experiments to evaluate them: generalization with i) a varying number of cameras, ii) varying camera positions, and finally, iii) to new scenes. We discover that existing state-of-the-art models show poor generalization by overfitting to a single scene and camera configuration. To address the concerns: (a) we generated a novel Generalized MVD (GMVD) dataset, assimilating diverse scenes with changing daytime, camera configurations, varying number of cameras, and (b) we discuss the properties essential to bring generalization to MVD and developed a barebones model to incorporate them. We performed a series of experiments on the WildTrack, MultiViewX, and the GMVD datasets to motivate the necessity to evaluate the generalization abilities of MVD methods and to demonstrate the efficacy of the developed approach.
Restrictions of holder continuous functions

For 0 < α < 1 let V (α) denote the supremum of the numbers v such that every α-H¨older continuous function is of bounded variation on a set of Hausdorff dimension v. Kahane and Katznelson (2009) proved the estimate 1/2 ≤ V (α) ≤ 1/(2−α) and asked whether the upper bound is sharp. We show that in fact V (α) = max{1/2, α}. Let dimH and dimM denote the Hausdorff and upper Minkowski dimension, respectively. The upper bound on V (α) is a consequence of the following theorem. Let {B(t) : t ∈ [0, 1]} be a fractional Brownian motion of Hurst index α. Then, almost surely, there exists no set A ⊂ [0, 1] such that dimMA > max{1 − α, α} and B : A → R is of bounded variation. Furthermore, almost surely, there exists no set A ⊂ [0, 1] such that dimMA > 1 − α and B : A → R is β-H¨older continuous for some β > α. The zero set and the set of record times of B witness that the above theorems give the optimal dimensions. We also prove similar restriction theorems for deterministic self-affine functions and generic α-H¨older continuous functions. Finally, let {B(t) : t ∈ [0, 1]} be a two-dimensional Brownian motion. We prove that, almost surely, there is a compact set D ⊂ [0, 1] such that dimH D ≥ 1/3 and B: D → R2 is non-decreasing in each coordinate. It remains open whether 1/3 is best possible.
Marginal models for the association structure of hierarchical binary responses

ABSTRACT Clustered binary responses are often found in ecological studies. Data analysis may include modeling the marginal probability response. However, when the association is the main scientific focus, modeling the correlation structure between pairs of responses is the key part of the analysis. Second-order generalized estimating equations (GEE) are established in the literature. Some of them are more efficient in computational terms, especially facing large clusters. Alternating logistic regression (ALR) and orthogonalized residual (ORTH) GEE methods are presented and compared in this paper. Simulation results show a slightly superiority of ALR over ORTH. Marginal probabilities and odds ratios are also estimated and compared in a real ecological study involving a three-level hierarchical clustering. ALR and ORTH models are useful for modeling complex association structure with large cluster sizes.