Miracle-dz/UniFAR-FTUs huggingface dataset:
II. Geometrical framework for thinking about globular proteins: the power of poking

Recently, we presented a framework for understanding protein structure based on the idea that simple constructs of holding hands or touching of objects can be used to rationalize the common characteristics of globular proteins. We developed a consistent approach for understanding the formation of the two key common building blocks of helices and sheets as well as the compatible assembly of secondary structures into the tertiary structure through the notion of poking pairwise interactions. Here we benchmark our predictions with a detailed analysis of structural data of over 4000 proteins from the Protein Data Bank. We also present the results of detailed computer simulations of a simplified model demonstrating a pre-sculpted free energy landscape, determined by geometry and symmetry, comprising numerous minima corresponding to putative native state structures. We explore the consequences of our model. Our results suggest that symmetry and geometry are a powerful guide to capture the simplicity underlying protein complexity. Statement for broader audience A poking interaction in a chain causes two parts of a chain to poke towards each other. We use empirical data to establish a two-way link between geometry and chemistry through poking interactions and hydrogen bonds. We show that our simple and tractable geometrical model, superficially unrelated to proteins, can capture the essential features of a rich and incredibly complex protein molecule.
Differentially Private Over-the-Air Federated Learning Over MIMO Fading Channels

Federated learning (FL) enables edge devices to collaboratively train machine learning models, with model communication replacing direct data uploading. While over-the-air model aggregation improves communication efficiency, uploading models to an edge server over wireless networks can pose privacy risks. Differential privacy (DP) is a widely used quantitative technique to measure statistical data privacy in FL. Previous research has focused on over-the-air FL with a single-antenna server, leveraging communication noise to enhance user-level DP. This approach achieves the so-called"free DP"by controlling transmit power rather than introducing additional DP-preserving mechanisms at devices, such as adding artificial noise. In this paper, we study differentially private over-the-air FL over a multiple-input multiple-output (MIMO) fading channel. We show that FL model communication with a multiple-antenna server amplifies privacy leakage as the multiple-antenna server employs separate receive combining for model aggregation and information inference. Consequently, relying solely on communication noise, as done in the multiple-input single-output system, cannot meet high privacy requirements, and a device-side privacy-preserving mechanism is necessary for optimal DP design. We analyze the learning convergence and privacy loss of the studied FL system and propose a transceiver design algorithm based on alternating optimization. Numerical results demonstrate that the proposed method achieves a better privacy-learning trade-off compared to prior work.
The Magnetic Fields of Starburst Galaxies. I. Identification and Characterization of the Thermal Polarization in the Galactic Disk and Outflow

Far-infrared polarized emission by means of magnetically aligned dust grains is an excellent tracer of the magnetic fields (B-fields) in the cold phase of the galactic outflows of starburst galaxies. We present a comprehensive study of the B-fields in three nearby (3.5–17.2 Mpc) starbursts (M82, NGC 253, and NGC 2146) at 5 pc–1.5 kpc resolutions using publicly available 53–890 μm imaging polarimetric observations with Stratospheric Observatory for Infrared Astronomy/HAWC+, James Clerk Maxwell Telescope/POL-2, and ALMA. We find that the polarized spectral energy distributions (SEDs) of the full galaxies are dominated by the polarized SEDs of the outflows with dust temperatures of Td,outflowPI∼45 K and an emissive index of βoutflowPI∼2.3 . The disks are characterized by low Td,diskPI=[24,31] K and βdiskPI∼1 . We show that disk- and outflow-dominated galaxies can be better distinguished by using polarized SEDs instead of total SEDs. We compute the 53–850 μm polarization spectrum of the disk and outflow, and find that dust models of the diffuse ISM can reproduce the fairly constant polarization spectrum of the disk, 〈 P disk〉 = 1.2% ± 0.5%. The dust models of heterogenous clouds and two-temperature components are required to explain the polarization spectrum of the outflow (2%–4% at 53 μm, ∼1% at 850 μm, and a minimum within 89–154 μm). We conclude that the polarized dust grains in the outflow arise from a dust population with higher dust temperature and emissivities than those from the total flux. The B-fields of the outflows have maximum extensions within 89–214 μm reaching heights of ∼4 kpc, and have flatter polarized fluxes than total fluxes. The extension of the B-field permeating the circumgalactic medium increases as the star formation rate increases.
A Practical Max-Min Fair Resource Allocation Algorithm for Rate-Splitting Multiple Access

This letter introduces a novel resource allocation algorithm for achieving max-min fairness (MMF) in a rate-splitting multiple access (RSMA) empowered multi-antenna broadcast channel. Specifically, we derive the closed-form solution for the optimal allocation of the common rate among users and the power between the common and private streams for a given practical low-complexity beamforming direction design. Numerical results show that the proposed algorithm achieves 90% of the MMF rate on average obtained by the conventional iterative optimization algorithm while only takes an average of 0.1 millisecond computational time, which is three orders of magnitude lower than the conventional algorithm. It is therefore a practical resource allocation algorithm for RSMA.
Séminaire Laurent Schwartz EDP et applications

We present recent works on the construction of high-frequency solutions to the Einstein vacuum equations in general relativity, based on [Tou22b, Tou23]. In these articles, the author shows the existence of a family of vacuum spacetimes ( g λ ) λ ∈ (0 , 1] in generalised wave gauge oscillating at frequency λ − 1 and defined by a geometric optics ansatz. In this survey, we first review the Cauchy theory for the Einstein vacuum equations in wave gauge, geometric optics and Burnett’s conjecture in general relativity. This will motivate our main result, for which we provide a sketch of proof high-lighting both quasi-linear and semi-linear challenges.
Phase transition kinetics revealed by in situ X-ray diffraction in laser-heated dynamic diamond anvil cells

We report on a novel approach to dynamic compression of materials that bridges the gap between previous static- and dynamic- compression techniques, allowing to explore a wide range of pathways in the pressure-temperature space. By combining a dynamic-diamond anvil cell setup with double-sided laser-heating and in situ X-ray diffraction, we are able to perform dynamic compression at high temperature and characterize structural transitions with unprecedented time resolution. Using this method, we investigate the $\gamma-\epsilon$ phase transition of iron under dynamic compression for the first time, reaching compression rates of hundreds of GPa/s and temperatures of 2000 K. Our results demonstrate a distinct response of the $\gamma-\epsilon$ and $\alpha-\epsilon$ transitions to the high compression rates achieved. These findings open up new avenues to study tailored dynamic compression pathways in the pressure-temperature space and highlight the potential of this platform to capture kinetic effects in a diamond anvil cell.
3D VR Sketch Guided 3D Shape Prototyping and Exploration

3D shape modeling is labor-intensive, time-consuming, and requires years of expertise. To facilitate 3D shape modeling, we propose a 3D shape generation network that takes a 3D VR sketch as a condition. We assume that sketches are created by novices without art training and aim to reconstruct geometrically realistic 3D shapes of a given category. To handle potential sketch ambiguity, our method creates multiple 3D shapes that align with the original sketch’s structure. We carefully design our method, training the model step-by-step and leveraging multi-modal 3D shape representation to support training with limited training data. To guarantee the realism of generated 3D shapes we leverage the normalizing flow that models the distribution of the latent space of 3D shapes. To encourage the fidelity of the generated 3D shapes to an input sketch, we propose a dedicated loss that we deploy at different stages of the training process. The code is available at https://github.com/Rowl1ng/3Dsketch2shape.
Frequency effects in linear discriminative learning

Word frequency is a strong predictor in most lexical processing tasks. Thus, any model of word recognition needs to account for how word frequency effects arise. The Discriminative Lexicon Model (DLM) models lexical processing with mappings between words' forms and their meanings. Comprehension and production are modeled via linear mappings between the two domains. So far, the mappings within the model can either be obtained incrementally via error-driven learning, a computationally expensive process able to capture frequency effects, or in an efficient, but frequency-agnostic solution modeling the theoretical endstate of learning (EL) where all words are learned optimally. In the present study we show how an efficient, yet frequency-informed mapping between form and meaning can be obtained (Frequency-informed learning; FIL). We find that FIL well approximates an incremental solution while being computationally much cheaper. FIL shows a relatively low type- and high token-accuracy, demonstrating that the model is able to process most word tokens encountered by speakers in daily life correctly. We use FIL to model reaction times in the Dutch Lexicon Project by means of a Gaussian Location Scale Model and find that FIL predicts well the S-shaped relationship between frequency and the mean of reaction times but underestimates the variance of reaction times for low frequency words. FIL is also better able to account for priming effects in an auditory lexical decision task in Mandarin Chinese, compared to EL. Finally, we used ordered data from CHILDES to compare mappings obtained with FIL and incremental learning. We show that the mappings are highly correlated, but that with FIL some nuances based on word ordering effects are lost. Our results show how frequency effects in a learning model can be simulated efficiently, and raise questions about how to best account for low-frequency words in cognitive models.
Regularized Robust MDPs and Risk-Sensitive MDPs: Equivalence, Policy Gradient, and Sample Complexity

Robust Markov Decision Processes (MDPs) and risk-sensitive MDPs are both powerful tools for making decisions in the presence of uncertainties. Previous efforts have aimed to establish their connections, revealing equivalences in specific formulations. This paper introduces a new formulation for risk-sensitive MDPs, which assesses risk in a slightly different manner compared to the classical Markov risk measure (Ruszczy\'nski 2010), and establishes its equivalence with a class of regularized robust MDP (RMDP) problems, including the standard RMDP as a special case. Leveraging this equivalence, we further derive the policy gradient theorem for both problems, proving gradient domination and global convergence of the exact policy gradient method under the tabular setting with direct parameterization. This forms a sharp contrast to the Markov risk measure, known to be potentially non-gradient-dominant (Huang et al. 2021). We also propose a sample-based offline learning algorithm, namely the robust fitted-Z iteration (RFZI), for a specific regularized RMDP problem with a KL-divergence regularization term (or equivalently the risk-sensitive MDP with an entropy risk measure). We showcase its streamlined design and less stringent assumptions due to the equivalence and analyze its sample complexity.
DsMtGCN: A Direction-sensitive Multi-task framework for Knowledge Graph Completion

To solve the inherent incompleteness of knowledge graphs (KGs), numbers of knowledge graph completion (KGC) models have been proposed to predict missing links from known triples. Among those, several works have achieved more advanced results via exploiting the structure information on KGs with Graph Convolutional Networks (GCN). However, we observe that entity embeddings aggregated from neighbors in different directions are just simply averaged to complete single-tasks by existing GCN based models, ignoring the specific requirements of forward and backward sub-tasks. In this paper, we propose a Direction-sensitive Multi-task GCN (DsMtGCN) to make full use of the direction information, the multi-head self-attention is applied to specifically combine embeddings in different directions based on various entities and sub-tasks, the geometric constraints are imposed to adjust the distribution of embeddings, and the traditional binary cross-entropy loss is modified to reflect the triple uncertainty. Moreover, the competitive experiments results on several benchmark datasets verify the effectiveness of our model.
Partial Hypernetworks for Continual Learning

Hypernetworks mitigate forgetting in continual learning (CL) by generating task-dependent weights and penalizing weight changes at a meta-model level. Unfortunately, generating all weights is not only computationally expensive for larger architectures, but also, it is not well understood whether generating all model weights is necessary. Inspired by latent replay methods in CL, we propose partial weight generation for the final layers of a model using hypernetworks while freezing the initial layers. With this objective, we first answer the question of how many layers can be frozen without compromising the final performance. Through several experiments, we empirically show that the number of layers that can be frozen is proportional to the distributional similarity in the CL stream. Then, to demonstrate the effectiveness of hypernetworks, we show that noisy streams can significantly impact the performance of latent replay methods, leading to increased forgetting when features from noisy experiences are replayed with old samples. In contrast, partial hypernetworks are more robust to noise by maintaining accuracy on previous experiences. Finally, we conduct experiments on the split CIFAR-100 and TinyImagenet benchmarks and compare different versions of partial hypernetworks to latent replay methods. We conclude that partial weight generation using hypernetworks is a promising solution to the problem of forgetting in neural networks. It can provide an effective balance between computation and final test accuracy in CL streams.
A numerical study of deep convection in tropical cyclones

Idealized numerical model simulations are used to investigate the generation and evolution of vertical vorticity by deep convection in a warm‐cored vortex of near‐tropical‐storm strength. Deep convective updraughts are initiated by thermal perturbations located at different radii from the vortex axis. It is found that, as the location of the thermal perturbation is moved away from the axis of rotation, the updraught that results becomes stronger, the cyclonic vorticity anomaly generated by the updraught becomes weaker, the structure of the vorticity anomaly changes markedly and the depth of the anomaly increases. For an updraught along or near the vortex axis, the vorticity anomaly has the structure of a monopole and little or no anticyclonic vorticity is generated in the core. Vorticity dipoles are generated in updraughts near or beyond the radius of maximum tangential wind speed and this structure reverses in sign with height. In all cases, the vorticity anomalies persist long after the initial updraught has decayed. Implications of the results for understanding the vorticity consolidation during tropical cyclogenesis are discussed.
Completions of Implicative Assemblies

We continue our work on implicative assemblies by investigating under which circumstances a subset $M \subseteq \mathscr{S}$ gives rise to a lex full subcategory $\mathbf{Asm}_M$ of the quasitopos $\mathbf{Asm}_{\mathcal{A}}$ of all assemblies such that $(\mathbf{Asm}_M)_{reg/lex} \simeq \mathbf{Asm}_{\mathcal{A}}$. We establish a characterisation. Furthermore, this latter is relevant to the study of $\mathbf{Asm}_M$'s ex/lex-completion.
Last-Iterate Convergent Policy Gradient Primal-Dual Methods for Constrained MDPs

We study the problem of computing an optimal policy of an infinite-horizon discounted constrained Markov decision process (constrained MDP). Despite the popularity of Lagrangian-based policy search methods used in practice, the oscillation of policy iterates in these methods has not been fully understood, bringing out issues such as violation of constraints and sensitivity to hyper-parameters. To fill this gap, we employ the Lagrangian method to cast a constrained MDP into a constrained saddle-point problem in which max/min players correspond to primal/dual variables, respectively, and develop two single-time-scale policy-based primal-dual algorithms with non-asymptotic convergence of their policy iterates to an optimal constrained policy. Specifically, we first propose a regularized policy gradient primal-dual (RPG-PD) method that updates the policy using an entropy-regularized policy gradient, and the dual variable via a quadratic-regularized gradient ascent, simultaneously. We prove that the policy primal-dual iterates of RPG-PD converge to a regularized saddle point with a sublinear rate, while the policy iterates converge sublinearly to an optimal constrained policy. We further instantiate RPG-PD in large state or action spaces by including function approximation in policy parametrization, and establish similar sublinear last-iterate policy convergence. Second, we propose an optimistic policy gradient primal-dual (OPG-PD) method that employs the optimistic gradient method to update primal/dual variables, simultaneously. We prove that the policy primal-dual iterates of OPG-PD converge to a saddle point that contains an optimal constrained policy, with a linear rate. To the best of our knowledge, this work appears to be the first non-asymptotic policy last-iterate convergence result for single-time-scale algorithms in constrained MDPs.
Thickness Dependent Sensitivity of GAGG:Ce Scintillation detectors for Thermal Neutrons: GEANT4 Simulations and Experimental Measurements

In the present work, we report extensive GEANT4 simulations in order to study the dependence of sensitivity of GAGG:Ce scintillation crystal based detector on thickness of the crystal. All the simulations are made considering a thermalised Am-Be neutron source. The simulations are validated, qualitatively and quantitatively, by comparing the simulated energy spectra and sensitivity values with those obtained from experimental measurements carried out using two different thicknesses of the crystal from our own experiment (0.5mm and 3mm) and validated with three other thicknesses (0.01mm, 0.1 mm and 1 mm) from literature. In this study, we define sensitivity of GAGG:Ce as the ratio of area under 77 keV sum peak to 45 keV peak. The present studies clearly confirm that, while it requires about 0.1 mm thickness for the GAGG:Ce crystal to fully absorb thermal neutrons, it requires about 3 mm to fully absorb the thermal neutron induced events. Further, we propose an equation, that can be used to estimate the thickness of the GAGG:Ce crystal directly from the observed sensitivity of the GAGG:Ce crystal. This equation could be very useful for the neutron imaging community for medical and space applications, as well as for manufactures of cameras meant for nuclear security purposes.
Reconstructing design parameters of a rectangular resonator via peak signal-to-noise ratio and global optimization algorithms

Abstract This article proposes an approach for reconstructing physical parameters of a sample in a rectangular resonator during microwave radiation, knowing a priori, its electric field distribution. The inverse problem was solved using two global optimization algorithms and the peak signal-to-noise ratio (PSNR) criterion. First, the Self-regulated Fretwidth Harmony Search algorithm (SFHS) identified suitable resonant frequencies for a given configuration. Next, the unified Particle Swarm Optimization (UPSO) optimized said configuration. Together, they became a maximization strategy of the PSNR through a dual optimization process. Results showed the ability of the approach for estimating the height of each sample block and the resonating frequency of the cavity. This process takes longer to finish as a higher PSNR is demanded (mainly due to the aforementioned dual optimization). Even so, it allows for more similar electric field distributions between both, the direct and inverse problems.
CF-GODE: Continuous-Time Causal Inference for Multi-Agent Dynamical Systems

Multi-agent dynamical systems refer to scenarios where multiple units (aka agents) interact with each other and evolve collectively over time. For instance, people's health conditions are mutually influenced. Receiving vaccinations not only strengthens the long-term health status of one unit but also provides protection for those in their immediate surroundings. To make informed decisions in multi-agent dynamical systems, such as determining the optimal vaccine distribution plan, it is essential for decision-makers to estimate the continuous-time counterfactual outcomes. However, existing studies of causal inference over time rely on the assumption that units are mutually independent, which is not valid for multi-agent dynamical systems. In this paper, we aim to bridge this gap and study how to estimate counterfactual outcomes in multi-agent dynamical systems. Causal inference in a multi-agent dynamical system has unique challenges: 1) Confounders are time-varying and are present in both individual unit covariates and those of other units; 2) Units are affected by not only their own but also others' treatments; 3) The treatments are naturally dynamic, such as receiving vaccines and boosters in a seasonal manner. To this end, we model a multi-agent dynamical system as a graph and propose a novel model called CF-GODE (C ounterFactual Graph Ordinary Differential Equations). CF-GODE is a causal model that estimates continuous-time counterfactual outcomes in the presence of inter-dependencies between units. To facilitate continuous-time estimation, we propose Treatment-Induced GraphODE, a novel ordinary differential equation based on graph neural networks (GNNs), which can incorporate dynamical treatments as additional inputs to predict potential outcomes over time. To remove confounding bias, we propose two domain adversarial learning based objectives that learn balanced continuous representation trajectories, which are not predictive of treatments and interference. We further provide theoretical justification to prove their effectiveness. Experiments on two semi-synthetic datasets confirm that CF-GODE outperforms baselines on counterfactual estimation. We also provide extensive analyses to understand how our model works.