Miracle-dz/UniFAR-FTUs huggingface dataset:
New cosmological constraints on $f(T)$ gravity in light of full Planck-CMB and type Ia supernovae data

We investigate two new observational perspectives in the context of torsional gravitational modification of general relativity, i.e., the $f(T)$ gravity: i) We use Pantheon data of type Ia supernovae motivated by a time variation of the Newton's constant on the supernovae distance modulus relation, and find that a joint analysis with Baryon Acoustic Oscillations (BAO) and Big Bang Nucleosynthesis (BBN), i.e., Pantheon+BAO+BBN, provides constraints on the effective free parameter of the theory to be well compatible with the $\Lambda$CDM prediction; ii) We present the framework of $f(T)$ gravity at the level of linear perturbations with the phenomenological functions, namely the effective gravitational coupling $\mu$ and the light deflection parameter $\Sigma$, which are commonly used to parameterize possible modifications of the Poisson equation relating the matter density contrast to the lensing and the Newtonian potentials, respectively. We use the available Cosmic Microwave Background (CMB) data sets from the Planck 2018 release to constrain the free parameters of the $f(T)$ power-law gravity and $\Lambda$CDM models. We find that CMB data, and its joint analyses with Pantheon and BAO data constrain the $f(T)$ power-law gravity scenario to be practically indistinguishable from the $\Lambda$CDM model. We obtain the strongest limits ever reported on $f(T)$ power-law gravity scenario at the cosmological level.
Upper bound on √x J ν(x) and its applications

New sharp upper bound on the magnitude of √x J ν(x) is derived. The comparison with bounds appearing in earlier works is made. Bounds on |J ν(x)−√(2/π x)cos (x−πν/2−π/4)| are obtained and discussed. An application to conditionally convergent Weber–Schafheitlin type integrals is shown.
Implementing and Experimenting with Diffusion Models for Text-to-Image Generation

Taking advantage of the many recent advances in deep learning, text-to-image generative models currently have the merit of attracting the general public attention. Two of these models, DALL-E 2 and Imagen, have demonstrated that highly photorealistic images could be generated from a simple textual description of an image. Based on a novel approach for image generation called diffusion models, text-to-image models enable the production of many different types of high resolution images, where human imagination is the only limit. However, these models require exceptionally large amounts of computational resources to train, as well as handling huge datasets collected from the internet. In addition, neither the codebase nor the models have been released. It consequently prevents the AI community from experimenting with these cutting-edge models, making the reproduction of their results complicated, if not impossible. In this thesis, we aim to contribute by firstly reviewing the different approaches and techniques used by these models, and then by proposing our own implementation of a text-to-image model. Highly based on DALL-E 2, we introduce several slight modifications to tackle the high computational cost induced. We thus have the opportunity to experiment in order to understand what these models are capable of, especially in a low resource regime. In particular, we provide additional and analyses deeper than the ones performed by the authors of DALL-E 2, including ablation studies. Besides, diffusion models use so-called guidance methods to help the generating process. We introduce a new guidance method which can be used in conjunction with other guidance methods to improve image quality. Finally, the images generated by our model are of reasonably good quality, without having to sustain the significant training costs of state-of-the-art text-toimage models.
Loop-current charge density wave driven by long-range Coulomb repulsion on the kagomé lattice

Recent experiments on vanadium-based nonmagnetic kagom´e metals A V 3 Sb 5 ( A = K, Rb, Cs) revealed evidence for possible spontaneous time-reversal symmetry (TRS) breaking in the charge density wave (CDW) ordered state. The long-sought-after quantum order of loop currents has been suggested as a candidate for the TRS breaking state. However, a microscopic model for the emergence of the loop-current CDW due to electronic correlations is still lacking. Here, we calculate the susceptibility of the real and imaginary bond orders on the kagom´e lattice near van Hove ﬁlling, and reveal the importance of next-nearest-neighbor Coulomb repulsion V 2 in triggering the instability toward imaginary bond ordered CDW. The concrete e ﬀ ective single-orbital t - V 1 - V 2 model on the kagom´e lattice is then studied, where t and V 1 are the hopping and Coulomb repulsion on the nearest-neighbor bonds. We obtain the mean-ﬁeld ground states, analyze their properties, and determine the phase diagram in the plane spanned by V 1 and V 2 at van Hove ﬁlling. The region dominated by V 1 is occupied by a 2 a 0 × 2 a 0 real CDW insulator with the inverse of Star-of-David (ISD) bond conﬁguration. Increasing V 2 indeed drives a ﬁrst-order transition from the ISD to stabilized loop-current insulators that exhibit four possible current patterns of di ﬀ erent topological properties, leading to orbital Chern insulators. We then extend these results away from van Hove ﬁlling and show that the loop-current states survive in the lightly doped orbital Chern insulators, and give rise to emergent Chern Fermi pockets carrying large Berry curvature and orbit magnetic moment. Our ﬁndings provide a concrete model realization of the loop-current Chern metal at the mean-ﬁeld level for the TRS breaking normal state of the kagom´e superconductors.
Anyon condensation, topological quantum information scrambling, and Andreev-like reflection of non-Abelian anyons in quantum Hall interfaces

Quantum information scrambling is the spread of local information into correlation throughout the entire quantum many-body system. This concept has become a central topic in different contexts. In this work, we restate the connection between anyon condensation and topological quantum information scrambling in quantum Hall interfaces. We consider the interface between the Abelian Halperin-330 state and the non-Abelian Read-Rezayi state. We verify explicitly that the interface can be fully gapped. This allows the transmutation of local pseudospin information carried by an Abelian anyon into topological information stored entirely by the anyons in the non-Abelian quantum Hall liquid, with no scrambled information stored at the interface. In combination with our previous work [K. K. W. Ma and K. Yang, Phys. Rev. B 105, 045306 (2022)], our results demonstrate the dependence of the scrambling mechanism on the gapfulness of the interface. Possible Andreev-like reflection of non-Abelian anyons in the fully gapped interface is also discussed.
Spin-wave study of magnetic perpendicular surface anisotropy in single crystalline MgO/Fe/MgO films

Broadband ferromagnetic resonance is measured in single crystalline Fe films of varying thickness sandwiched between MgO layers. An exhaustive magnetic characterization of the films (exchange constant, cubic, uniaxial and surface anisotropies) is enabled by the study of the uniform and the first perpendicular standing spin wave modes as a function of applied magnetic field and film thickness. Additional measurements of non-reciprocal spin wave propagation allow us to separate each of the two interface contributions to the total surface anisotropy. The results are consistent with the model of a quasi-bulk film interior and two magnetically different top and bottom interfaces, a difference ascribed to different oxidation states.
SERF: Interpretable Sleep Staging using Embeddings, Rules, and Features

The accuracy of recent deep learning based clinical decision support systems is promising. However, lack of model interpretability remains an obstacle to widespread adoption of artificial intelligence in healthcare. Using sleep as a case study, we propose a generalizable method to combine clinical interpretability with high accuracy derived from black-box deep learning. Clinician-determined sleep stages from polysomnogram (PSG) remain the gold standard for evaluating sleep quality. However, PSG manual annotation by experts is expensive and time-prohibitive. We propose SERF, interpretable Sleep staging using Embeddings, Rules, and Features to read PSG. SERF provides interpretation of classified sleep stages through meaningful features derived from the AASM Manual for the Scoring of Sleep and Associated Events. In SERF, the embeddings obtained from a hybrid of convolutional and recurrent neural networks are transposed to the interpretable feature space. These representative interpretable features are used to train simple models like a shallow decision tree for classification. Model results are validated on two publicly available datasets. SERF surpasses the current state-of-the-art for interpretable sleep staging by 2%. Using Gradient Boosted Trees as the classifier, SERF obtains 0.766 κ and 0.870 AUC-ROC, within 2% of the current state-of-the-art black-box models.
The Interaction Gap: A Step Toward Understanding Trust in Autonomous Vehicles Between Encounters

Shared autonomous vehicles (SAVs) will be introduced in greater numbers over the coming decade. Due to rapid advances in shared mobility and the slower development of fully autonomous vehicles (AVs), SAVs will likely be deployed before privately-owned AVs. Moreover, existing shared mobility services are transitioning their vehicle fleets toward those with increasingly higher levels of driving automation. Consequently, people who use shared vehicles on an “as needed” basis will have infrequent interactions with automated driving, thereby experiencing interaction gaps. Using human trust data of 25 participants, we show that interaction gaps can affect human trust in automated driving. Participants engaged in a simulator study consisting of two interactions separated by a one-week interaction gap. A moderate, inverse correlation was found between the change in trust during the initial interaction and the interaction gap, suggesting people “forget” some of their gained trust or distrust in automation during an interaction gap.
An Information Minimization Based Contrastive Learning Model for Unsupervised Sentence Embeddings Learning

Unsupervised sentence embeddings learning has been recently dominated by contrastive learning methods (e.g., SimCSE), which keep positive pairs similar and push negative pairs apart. The contrast operation aims to keep as much information as possible by maximizing the mutual information between positive instances, which leads to redundant information in sentence embedding. To address this problem, we present an information minimization based contrastive learning InforMin-CL model to retain the useful information and discard the redundant information by maximizing the mutual information and minimizing the information entropy between positive instances meanwhile for unsupervised sentence representation learning. Specifically, we find that information minimization can be achieved by simple contrast and reconstruction objectives. The reconstruction operation reconstitutes the positive instance via the other positive instance to minimize the information entropy between positive instances. We evaluate our model on fourteen downstream tasks, including both supervised and unsupervised (semantic textual similarity) tasks. Extensive experimental results show that our InforMin-CL obtains a state-of-the-art performance.
The effect of ambiguity in strategic environments: an experiment

We experimentally study a game in which success requires a sufficient total contribution by members of a group. There are significant uncertainties surrounding the chance and the total effort required for success. A theoretical model with max-min preferences towards ambiguity predicts higher contributions under ambiguity than under risk. However, in a large representative sample of the Spanish population (1,500 participants) we find that the ATE of ambiguity on contributions is zero. The main significant interaction with the personal characteristics of the participants is with risk attitudes, and it increases contributions. This suggests that policymakers concerned with ambiguous problems (like climate change) do not need to worry excessively about ambiguity.
Homophone Reveals the Truth: A Reality Check for Speech2Vec

Generating spoken word embeddings that possess semantic information is a fascinating topic. Compared with text-based embeddings, they cover both phonetic and semantic characteristics, which can provide richer information and are potentially helpful for improving ASR and speech translation systems. In this paper, we review and examine the authenticity of a seminal work in this ﬁeld: Speech2Vec. First, a homophone-based inspection method is proposed to check the speech embeddings released by the author of Speech2Vec. There is no indication that these embeddings are generated by the Speech2Vec model. More-over, through further analysis of the vocabulary composition, we suspect that a text-based model fabricates these embeddings. Finally, we reproduce the Speech2Vec model, referring to the ofﬁcial code and optimal settings in the original paper. Experiments showed that this model failed to learn effective semantic embeddings. In word similarity benchmarks, it gets a correlation score of 0.08 in MEN and 0.15 in WS-353-SIM tests, which is over 0.5 lower than those described in the original paper. Our data and code are available 1 .
Identifiability and generalizability from multiple experts in Inverse Reinforcement Learning

While Reinforcement Learning (RL) aims to train an agent from a reward function in a given environment, Inverse Reinforcement Learning (IRL) seeks to recover the reward function from observing an expert's behavior. It is well known that, in general, various reward functions can lead to the same optimal policy, and hence, IRL is ill-defined. However, (Cao et al., 2021) showed that, if we observe two or more experts with different discount factors or acting in different environments, the reward function can under certain conditions be identified up to a constant. This work starts by showing an equivalent identifiability statement from multiple experts in tabular MDPs based on a rank condition, which is easily verifiable and is shown to be also necessary. We then extend our result to various different scenarios, i.e., we characterize reward identifiability in the case where the reward function can be represented as a linear combination of given features, making it more interpretable, or when we have access to approximate transition matrices. Even when the reward is not identifiable, we provide conditions characterizing when data on multiple experts in a given environment allows to generalize and train an optimal agent in a new environment. Our theoretical results on reward identifiability and generalizability are validated in various numerical experiments.
Accretion physics at high X-ray spectral resolution: New frontiers and game-changing science

Microcalorimeters have demonstrated success in delivering high spectral resolution, and have paved the path to revolutionary new science possibilities in the coming decade of X-ray astronomy. There are several research areas in compact object science that can only be addressed with energy resolution Δ 𝐸 ∼ < 5 eV at photon energies of a few keV, corresponding to velocity resolution of ∼ < a few hundred km s − 1 , to be ushered in by microcalorimeters. Here, we review some of these outstanding questions, focusing on how the research landscape is set to be transformed (i) at the interface between accreting supermassive black holes and their host galaxies, (ii) in unravelling the structures of accretion environments, (iii) in resolving long-standing issues on the origins of energy and matter feedback, and (iv) to test mass-scaled uniﬁcation of accretion and feedback. The need to learn lessons from Hitomi and to make improvements in laboratory atomic data precision as well as plasma modeling are highlighted.
Gaussian Agency Problems With Memory and Linear Contracts

Can a principal still oﬀer optimal dynamic contracts that are linear in end-of-period outcomes when the agent controls a process that exhibits memory? We provide a positive answer by considering a general Gaussian setting where the output dynamics are not necessarily semi-martingales or Markov processes. We introduce a rich class of principal-agent models that encompasses dynamic agency models with memory. From the mathematical point of view, we develop a methodology to deal with the possi-ble non-Markovianity and non-semimartingality of the control problem, which can no longer be directly solved by means of the usual Hamilton-Jacobi-Bellman equation. Our main contribution is to show that, for one-dimensional models, this setting always allows for optimal linear contracts in end-of-period observable outcomes with a deterministic optimal level of eﬀort. In higher dimension, we show that linear contracts are still optimal when the eﬀort cost function is radial and we quantify the gap between linear contracts and optimal contracts for more general quadratic costs of eﬀorts.
Consecutive primes which are widely digitally delicate and Brier numbers

Making use of covering systems and a theorem of D. Shiu, the first and second authors showed that for every positive integer $k$, there exist $k$ consecutive widely digitally delicate primes. They also noted that for every positive integer $k$, there exist $k$ consecutive primes which are Brier numbers. We show that for every positive integer $k$, there exist $k$ consecutive primes that are both widely digitally delicate and Brier numbers.
MUI-TARE: Multi-Agent Cooperative Exploration with Unknown Initial Position

Multi-agent exploration of a bounded 3D environment with unknown initial positions of agents is a challenging problem. It requires quickly exploring the environments as well as robustly merging the sub-maps built by the agents. We take the view that the existing approaches are either aggressive or conservative: Aggressive strategies merge two sub-maps built by different agents together when overlap is detected, which can lead to incorrect merging due to the false-positive detection of the overlap and is thus not robust. Conservative strategies direct one agent to revisit an excessive amount of the historical trajectory of another agent for verification before merging, which can lower the exploration efficiency due to the repeated exploration of the same space. To intelligently balance the robustness of sub-map merging and exploration efficiency, we develop a new approach for lidar-based multi-agent exploration, which can direct one agent to repeat another agent's trajectory in an \emph{adaptive} manner based on the quality indicator of the sub-map merging process. Additionally, our approach extends the recent single-agent hierarchical exploration strategy to multiple agents in a \emph{cooperative} manner by planning for agents with merged sub-maps together to further improve exploration efficiency. Our experiments show that our approach is up to 50\% more efficient than the baselines on average while merging sub-maps robustly.
Azadkia-Chatterjee's correlation coefficient adapts to manifold data

In their seminal work, Azadkia and Chatterjee (2021) initiated graph-based methods for measuring variable dependence strength. By appealing to nearest neighbor graphs, they gave an elegant solution to a problem of R´enyi (R´enyi, 1959). Their idea was later developed in Deb et al. (2020) and the authors there proved that, quite interestingly, Azadkia and Chatterjee’s correlation coeﬃcient can automatically adapt to the manifold structure of the data. This paper furthers their study in terms of calculating the statistic’s limiting variance under independence and showing that it only depends on the manifold dimension.
DIG: Draping Implicit Garment over the Human Body

Existing data-driven methods for draping garments over human bodies, despite being effective, cannot handle garments of arbitrary topology and are typically not end-to-end differentiable. To address these limitations, we propose an end-to-end differentiable pipeline that represents garments using implicit surfaces and learns a skinning field conditioned on shape and pose parameters of an articulated body model. To limit body-garment interpenetrations and artifacts, we propose an interpenetration-aware pre-processing strategy of training data and a novel training loss that penalizes self-intersections while draping garments. We demonstrate that our method yields more accurate results for garment reconstruction and deformation with respect to state of the art methods. Furthermore, we show that our method, thanks to its end-to-end differentiability, allows to recover body and garments parameters jointly from image observations, something that previous work could not do.
Optical properties and carrier localization in the layered phosphide EuCd$_\mathbf{2}$P$_\mathbf{2}$

The temperature dependence of the complex optical properties of the layered phosphide material EuCd$_2$P$_2$ have been measured over a wide frequency range above and below $T_{\rm N} \simeq 11.5$ K for light polarized in the $a-b$ planes. At room temperature, the optical conductivity is well described by a weak free-carrier component with a Drude plasma frequency of $\simeq 1100$ cm$^{-1}$ and a scattering rate of $1/\tau_D\simeq 700$ cm$^{-1}$, with the onset of interband absorptions above $\simeq 2000$ cm$^{-1}$. Two infrared-active $E_u$ modes are observed at $\simeq\,$89 and 239 cm$^{-1}$. As the temperature is reduced the scattering rate decreases and the low-frequency conductivity increases slightly; however, below $\simeq 50$ K the conductivity decreases until at the resistivity maximum at $\simeq 18$ K (just below $2T_{\rm N}$) the spectral weight associated with free carriers is transferred to a localized excitation at $\simeq 500$ cm$^{-1}$. Below $T_{\rm N}$, metallic behavior is recovered. Interestingly, the $E_u$ modes are largely unaffected by these changes, with only the position of the high-frequency mode showing any signs of anomalous behavior. While several scenarios are considered, the prevailing view is that the resistivity maximum and subsequent carrier localization is due to the formation of ferromagnetic domains below $\simeq 2T_{\rm N}$ that result in spin-polarized clusters due to spin-carrier coupling [1].