Miracle-dz/UniFAR-FTUs huggingface dataset:
Geometric Ramsey Interferometry with a Tripod Scheme

Ramsey interferometry is a key technique for precision spectroscopy and to probe the coherence of quantum systems. Typically, an interferometer is constructed using two quantum states and involves a time-dependent interaction with two short resonant electromagnetic pulses. Here, we explore a different type of Ramsey interferometer where we perform quantum state manipulations by geometrical means, eliminating the temporal dependence of the interaction. We use a resonant tripod scheme in ultracold strontium atoms where the interferometric operation is restricted to a two-dimensional dark-state subspace in the dressed-state picture. The observed interferometric phase accumulation is due to an effective geometric scalar term in the dark-state subspace, which remarkably does not vanish during the free evolution time when the light-matter interaction is turned off. This study opens the door for more robust interferometers operating on multiple input-output ports.
Isometric actions on locally compact finite rank median spaces

We prove that a connected locally compact median space of finite rank which admits a transitive action is isometric to $\mathbb{R}^n$ endowed with the $\ell^1$-metric. In the other side, replacing the transitivity assumption on the group of isometries by a certain regularity of the action on the compactification of the space, we show that all orbits are discrete. In our way to prove these results, we give a characterization of the compactness in complete median spaces of finite rank by the combinatorics of their halfspaces.
A Reduced Order Model formulation for left atrium flow: an Atrial Fibrillation case

A data-driven Reduced Order Model (ROM) based on a Proper Orthogonal Decomposition - Radial Basis Function (POD-RBF) approach is adopted in this paper for the analysis of blood flow dynamics in a patient-specific case of Atrial Fibrillation (AF). The Full Order Model (FOM) is represented by incompressible Navier-Stokes equations, discretized with a Finite Volume (FV) approach. Both the Newtonian and the Casson's constitutive laws are employed. The aim is to build a computational tool able to efficiently and accurately reconstruct the patterns of relevant hemodynamics indices related to the stasis of the blood in a physical parametrization framework including the cardiac output in the Newtonian case and also the plasma viscosity and the hematocrit in the non-Newtonian one. Many FOM-ROM comparisons are shown to analyze the performance of our approach as regards errors and computational speed-up.
PolicyGPT: Automated Analysis of Privacy Policies with Large Language Models

Privacy policies serve as the primary conduit through which online service providers inform users about their data collection and usage procedures. However, in a bid to be comprehensive and mitigate legal risks, these policy documents are often quite verbose. In practical use, users tend to click the Agree button directly rather than reading them carefully. This practice exposes users to risks of privacy leakage and legal issues. Recently, the advent of Large Language Models (LLM) such as ChatGPT and GPT-4 has opened new possibilities for text analysis, especially for lengthy documents like privacy policies. In this study, we investigate a privacy policy text analysis framework PolicyGPT based on the LLM. This framework was tested using two datasets. The first dataset comprises of privacy policies from 115 websites, which were meticulously annotated by legal experts, categorizing each segment into one of 10 classes. The second dataset consists of privacy policies from 304 popular mobile applications, with each sentence manually annotated and classified into one of another 10 categories. Under zero-shot learning conditions, PolicyGPT demonstrated robust performance. For the first dataset, it achieved an accuracy rate of 97%, while for the second dataset, it attained an 87% accuracy rate, surpassing that of the baseline machine learning and neural network models.
Graph-enabled Reinforcement Learning for Time Series Forecasting with Adaptive Intelligence

Reinforcement learning is well known for its ability to model sequential tasks and learn latent data patterns adaptively. Deep learning models have been widely explored and adopted in regression and classification tasks. However, deep learning has its limitations such as the assumption of equally spaced and ordered data, and the lack of ability to incorporate graph structure in terms of time-series prediction. Graphical neural network (GNN) has the ability to overcome these challenges and capture the temporal dependencies in time-series data. In this study, we propose a novel approach for predicting time-series data using GNN and monitoring with Reinforcement Learning (RL). GNNs are able to explicitly incorporate the graph structure of the data into the model, allowing them to capture temporal dependencies in a more natural way. This approach allows for more accurate predictions in complex temporal structures, such as those found in healthcare, traffic and weather forecasting. We also fine-tune our GraphRL model using a Bayesian optimisation technique to further improve performance. The proposed framework outperforms the baseline models in time-series forecasting and monitoring. The contributions of this study include the introduction of a novel GraphRL framework for time-series prediction and the demonstration of the effectiveness of GNNs in comparison to traditional deep learning models such as RNNs and LSTMs. Overall, this study demonstrates the potential of GraphRL in providing accurate and efficient predictions in dynamic RL environments.
Structural pathways for ultrafast melting of optically excited thin polycrystalline Palladium films

Due to its extremely short timescale, the non-equilibrium melting of metals is exceptionally difficult to probe experimentally. The knowledge of melting mechanisms is thus based mainly on the results of theoretical predictions. This work reports on the investigation of ultrafast melting of thin polycrystalline Pd films studied by optical laser pump - X-ray free-electron laser probe experiments and molecular-dynamics simulations. By acquiring X-ray diffraction snapshots with sub-picosecond resolution, we capture the sample's atomic structure during its transition from the crystalline to the liquid state. Bridging the timescales of experiments and simulations allows us to formulate a realistic microscopic picture of melting. We demonstrate that the existing models of strongly non-equilibrium melting, developed for systems with relatively weak electron-phonon coupling, remain valid even for ultrafast heating rates achieved in femtosecond laser-excited Pd. Furthermore, we highlight the role of pre-existing and transiently generated crystal defects in the transition to the liquid state.
Gukov-Pei-Putrov-Vafa conjecture for $SU(N)/\mathbb{Z}_m$

In our earlier work, we studied the $\hat{Z}$-invariant(or homological blocks) for $SO(3)$ gauge group and we found it to be same as $\hat{Z}^{SU(2)}$. This motivated us to study the $\hat{Z}$-invariant for quotient groups $SU(N)/\mathbb{Z}_m$, where $m$ is some divisor of $N$. Interestingly, we find that $\hat{Z}$-invariant is independent of $m$.
Comprehensive insights into the genetic background of Chinese populations using Y chromosome markers

China is located in East Asia. With a high genetic and cultural diversity, human migration in China has always been a hot topic of genetics research. To explore the origins and migration routes of Chinese males, 3333 Chinese individuals (Han, Hui, Mongolia, Yi and Kyrgyz) with 27 Y-STRs and 143 Y-SNPs from published literature were analysed. Our data showed that there are five dominant haplogroups (O2-M122, O1-F265, C-M130, N-M231, R-M207) in China. Combining analysis of haplogroup frequencies, geographical positions and time with the most recent common ancestor (TMRCA), we found that haplogroups C-M130, N-M231 and R1-M173 and O1a-M175 probably migrated into China via the northern route. Interestingly, we found that haplogroup C*-M130 in China may originate in South Asia, whereas the major subbranches C2a-L1373 and C2b-F1067 migrated from northern China. The results of BATWING showed that the common ancestry of Y haplogroup in China can be traced back to 17 000 years ago, which was concurrent with global temperature increases after the Last Glacial Maximum.
Guest-induced phase transition leads to polarization enhancement in MHyPbCl3

We present a detailed first-principles investigation of the structural and polar properties of 3D hybrid perovskite, methylhydrazinium lead chloride, MHyPbCl3, as it transitions from a highly polar Phase-I (high temperature, HT) to less polar Phase-II (low temperature, LT) from the perspective of host/guest interactions. Structurally, the two phases vary in the orientation of the guest and the two differently distorted host layers. The transition character (TC) across the path of phase transition and a relatively high guest-reorientation barrier tells that the transition is primarily governed by guest-reorientation. This overall guest/host transition is interesting because it leads to enhancement in polarization with temperature which is quite unusual. Maximally localized Wannier functions (MLWFs) have been used to probe into the atomistic origin of this enhancement which establishes the leading role of the host atoms, especially those lying in the more distorted octahedral layer, with only a negligible contribution from the guest, despite the fact that it is the primary effect leading to transition. Furthermore, we also find a significant feedback polarization of ~9% that the host distortion induces on the guest. This has a direct effect on the density of states occupied by the guest, which shifts away from the band edge with the increase in the host distortion (for Phase-II guest orientation). Thus the organic cations in 3D perovskites can also have a non-trivial contribution to the optoelectronic properties and exciton binding energies.
An Optically Discovered Outburst from XTE J1859+226

Using the Zwicky Transient Facility, in 2021 February we identified the first known outburst of the black hole X-ray transient XTE J1859+226 since its discovery in 1999. The outburst was visible at X-ray, UV, and optical wavelengths for less than 20 days, substantially shorter than its full outburst of 320 days in 1999, and the observed peak luminosity was 2 orders of magnitude lower. Its peak bolometric luminosity was only 2 × 1035 erg s−1, implying an Eddington fraction of about 3 × 10−4. The source remained in the hard spectral state throughout the outburst. From optical spectroscopy measurements we estimate an outer disk radius of 1011 cm. The low observed X-ray luminosity is not sufficient to irradiate the entire disk, but we observe a surprising exponential decline in the X-ray light curve. These observations highlight the potential of optical and infrared synoptic surveys to discover low-luminosity activity from X-ray transients.
CaSAR: Contact-aware Skeletal Action Recognition

Skeletal Action recognition from an egocentric view is important for applications such as interfaces in AR/VR glasses and human-robot interaction, where the device has limited resources. Most of the existing skeletal action recognition approaches use 3D coordinates of hand joints and 8-corner rectangular bounding boxes of objects as inputs, but they do not capture how the hands and objects interact with each other within the spatial context. In this paper, we present a new framework called Contact-aware Skeletal Action Recognition (CaSAR). It uses novel representations of hand-object interaction that encompass spatial information: 1) contact points where the hand joints meet the objects, 2) distant points where the hand joints are far away from the object and nearly not involved in the current action. Our framework is able to learn how the hands touch or stay away from the objects for each frame of the action sequence, and use this information to predict the action class. We demonstrate that our approach achieves the state-of-the-art accuracy of 91.3% and 98.4% on two public datasets, H2O and FPHA, respectively.
Single-Image based unsupervised joint segmentation and denoising

In this work, we develop an unsupervised method for the joint segmentation and denoising of a single image. To this end, we combine the advantages of a variational segmentation method with the power of a self-supervised, single-image based deep learning approach. One major strength of our method lies in the fact, that in contrast to data-driven methods, where huge amounts of labeled samples are necessary, our model can segment an image into multiple meaningful regions without any training database. Further, we introduce a novel energy functional in which denoising and segmentation are coupled in a way that both tasks benefit from each other. The limitations of existing single-image based variational segmentation methods, which are not capable of dealing with high noise or generic texture, are tackled by this specific combination with self-supervised image denoising. We propose a unified optimisation strategy and show that, especially for very noisy images available in microscopy, our proposed joint approach outperforms its sequential counterpart as well as alternative methods focused purely on denoising or segmentation. Another comparison is conducted with a supervised deep learning approach designed for the same application, highlighting the good performance of our approach.
Custodial Symmetry Breaking and Higgs Signatures at the LHC

We discuss the simplest model that predicts a tree level modification of the $\rho$ parameter from a shift in the $W$-mass without changing the prediction for the $Z$ mass. This model predicts a new neutral Higgs and two charged Higgses, with very similar masses and suppressed couplings to the Standard Model fermions. We discuss the decay properties of these new scalar bosons, and the main signatures at the Large Hadron Collider. We show that while masses around 200~GeV are excluded for some scenarios, over a wide range of model parameter space the new bosons can have a mass close to the electroweak scale without violating existing limits from experimental searches or destroying the agreement with measurements. We investigate the scenario where the new neutral Higgs is fermiophobic and has a large branching ratio into $W$ gauge bosons and/or photons, and show that this could lead to a signal in the diphoton mass spectrum at low Higgs masses. We discuss the different signatures that can motivate new measurements and searches at the Large Hadron Collider.
Lesquerella: A Winter Oilseed Crop for the Southwest

Planting date and planting method Although lesquerella is classified botanically as a shortlived perennial, it is produced as a winter annual crop in the southwest. The temperature range for germination is 41-95oF, with 68-83oF being optimal (Adam et al., 2007). The crop has adequate tolerance to freezing temperatures across the growing region. The recommended planting dates are October in Arizona and late September in Texas and New Mexico. Late planted lesquerella (January to February) does not require as much time as early (i.e., September and October) plantings to form a closed canopy. However, late plantings reduce plant biomass, the number of flowers, and crop yield compared to early plantings in Arizona and Oregon (Nelson et al., 1996; Dierig et al., 2010; Roseberg, 1993). Earlier planting in Arizona also allows earlier harvest, before the monsoon rains, which avoids rain-induced shattering and further reductions in crop yield. ARIZONA COOPERATIVE E TENSION
Preference estimation with unobserved choice set heterogeneity using sufficient sets

In this paper, we provide an introduction to the problems that arise in estimating discrete choice models when choice sets are heterogeneous and unobserved to the econometrician. We discuss the two most popular approaches to tackling these problems and propose the idea of “sufficient sets” motivated by economic theory, which help to practically implement them. ∗Griffith gratefully acknowledges financial support from the European Research Council (ERC) under ERC-2009AdG-249529 and ERC-20150AdG-694822, and the Economic and Social Research Council (ESRC) under RES-54428-0001 and ES/N011562/1. Iaria gratefully acknowledges financial support from the research grant Labex Ecodec: Investissements d’Avenir (ANR-11-IDEX-0003/Labex Ecodec/ANR-11-LABX-0047). †Dept. of Economics, University of Zürich and CEPR, gregory.crawford@econ.uzh. ch ‡Dept. of Economics, University of Manchester and IFS, rgriffith@ifs.org. uk §Dept. of Economics, University of Bristol, alessandro.iaria@bristol.ac. uk
The coupled-cluster self-energy

Coupled-cluster and Green's function theories are highly successful in treating many-body electron correlation and there has been significant interest in identifying and leveraging connections between them. Here we present a diagrammatic definition of the irreducible coupled-cluster self-energy that directly embeds coupled-cluster theory within the framework of many-body field theory. The EOM-CC treatment emerges naturally from our definition via the Dyson and Bethe-Salpeter equations, providing a unified description of RPA, $GW$-BSE and CC theory for ground state and excitation energies. This clarifies the origin of previously established connections between RPA, $GW$-BSE and coupled-cluster theory, and exposes the relationship between vertex corrections and the coupled-cluster amplitude equations.
Correction to: ‘A model of cue integration as vector summation in the insect brain’ (2023) Mitchell et al.

Replacement for fig. 6 in [1]. Effect of weight and reliability on cue influence in behavioural simulation of cue conflict. The population mean vector (black) falls closer to the theoretical vector sum where magnitudes are determined by cue weight (green) than where magnitudes are determined by reliability (magenta). Dashed rings indicate the threshold for significance using a Rayleigh test (p < 0.05).
Vertical Power Delivery for Emerging Packaging and Integration Platforms - Power Conversion and Distribution

Efficient delivery of current from PCB to point-of-load (POL) is a primary concern in modern high-power high-density integrated systems. Traditionally, a 48 V power signal is converted to the low, POL voltage at the board and/or package level. As interconnect has become the dominant power loss component, minimizing voltage drop across the laterally routed portions of the board-to-die interconnect (referred to as horizontal interconnect) is a promising approach to enhance the efficiency of the power delivery system. Delivering lower current vertically, at a higher voltage should therefore be considered. High-power conversion near POL, however, results in higher switching and inductor losses, exhibiting an undesired power efficiency tradeoff. To address this problem, four vertical power delivery architectures are proposed in this paper, considering state-of-the-art power converter topologies, integration levels, and voltage conversion schemes. Embedding Silicon (Si) and Gallium Nitride (GaN) power devices and inductors on top of and/or within the interposer is investigated. Integrating GaN power devices on a dedicated power die is also discussed. Various multistage 48V-to-1V power conversion schemes are examined and state-of-the-art power conversion circuits are reviewed. Power delivery characteristics with these architectures are determined for a high power (1 kW) high-current density (2 A/mm2) system.
Adjustbot: Bio-Inspired Quadruped Robot with Adjustable Posture and Undulated Body for Challenging Terradynamic Tasks

The ability to modify morphology in response to environmental changes represents a highly advantageous feature in biological organisms, facilitating their adaptation to diverse environmental conditions. While some robots have the capability to modify their morphology by utilizing adaptive body parts, the practical implementation of morphological transformations in robotic systems is still relatively restricted. This limitation can be attributed, in part, to the intricate nature of achieving such transformations, which necessitates the integration of advanced materials, control systems, and design approaches. In nature, a range of morphology adaptation strategies is employed to achieve optimal performance and efficiency, such as those employed by crocodiles and alligators, who adjust their body posture depending on the speed and the surface they traverse on. Drawing inspiration from these biological examples, this paper introduces Adjustbot, a quadruped robot with an undulating body capable of adjusting its body posture. Its adaptive morphology allows it to traverse a wide range of terradynamically challenging surfaces and facilitates avoidance of collisions, navigation through narrow channels, obstacle traversal, and incline negotiation.
MAGIC-TBR: Multiview Attention Fusion for Transformer-based Bodily Behavior Recognition in Group Settings

Bodily behavioral language is an important social cue, and its automated analysis helps in enhancing the understanding of artificial intelligence systems. Furthermore, behavioral language cues are essential for active engagement in social agent-based user interactions. Despite the progress made in computer vision for tasks like head and body pose estimation, there is still a need to explore the detection of finer behaviors such as gesturing, grooming, or fumbling. This paper proposes a multiview attention fusion method named MAGIC-TBR that combines features extracted from videos and their corresponding Discrete Cosine Transform coefficients via a transformer-based approach. The experiments are conducted on the BBSI dataset and the results demonstrate the effectiveness of the proposed feature fusion with multiview attention. The code is available at: https://github.com/surbhimadan92/MAGIC-TBR
Contribution a l'etude de la dynamique des populations d'acridiens dans la vallee du senegal

Inventaire de la population d'acridiens. Etude de l. Adaptation des especes a differents habitats. Evaluation de l'efficacite des pieges lumineux avec prise en compte de l'eclairement lunaire. Observation de migrations. Etude des fluctuations demographiques par la mise en evidence des facteurs influencants les stades de developpement
The supersingular Endomorphism Ring and One Endomorphism problems are equivalent

The supersingular Endomorphism Ring problem is the following: given a supersingular elliptic curve, compute all of its endomorphisms. The presumed hardness of this problem is foundational for isogeny-based cryptography. The One Endomorphism problem only asks to find a single non-scalar endomorphism. We prove that these two problems are equivalent, under probabilistic polynomial time reductions. We prove a number of consequences. First, assuming the hardness of the endomorphism ring problem, the Charles--Goren--Lauter hash function is collision resistant, and the SQIsign identification protocol is sound. Second, the endomorphism ring problem is equivalent to the problem of computing arbitrary isogenies between supersingular elliptic curves, a result previously known only for isogenies of smooth degree. Third, there exists an unconditional probabilistic algorithm to solve the endomorphism ring problem in time O~(sqrt(p)), a result that previously required to assume the generalized Riemann hypothesis. To prove our main result, we introduce a flexible framework for the study of isogeny graphs with additional information. We prove a general and easy-to-use rapid mixing theorem. The proof of this result goes via an augmented Deuring correspondence and the Jacquet-Langlands correspondence.