Miracle-dz/UniFAR-FTUs huggingface dataset:
RINGER: Rapid Conformer Generation for Macrocycles with Sequence-Conditioned Internal Coordinate Diffusion

Macrocyclic peptides are an emerging therapeutic modality, yet computational approaches for accurately sampling their diverse 3D ensembles remain challenging due to their conformational diversity and geometric constraints. Here, we introduce RINGER, a diffusion-based transformer model for sequence-conditioned generation of macrocycle structures based on internal coordinates. RINGER provides fast backbone sampling while respecting key structural invariances of cyclic peptides. Through extensive benchmarking and analysis against gold-standard conformer ensembles of cyclic peptides generated with metadynamics, we demonstrate how RINGER generates both high-quality and diverse geometries at a fraction of the computational cost. Our work lays the foundation for improved sampling of cyclic geometries and the development of geometric learning methods for peptides.
Transfer reactions of exotic nuclei including core deformations: $^{11}$Be and $^{17}$C

Background: Reactions with halo nuclei from deformed regions exhibit important deviations from the inert core+valence picture. Structure and reaction formalisms have recently been extended or adapted to explore the possibility of exciting the underlying core. Purpose: We will study up to what extent transfer reactions involving halo nuclei $^{11}$Be and $^{17}$C can be reproduced with two different models that have previously shown a good success reproducing the role of the core in light halo nuclei. Methods: We focus on the structure of $^{11}$Be and $^{17}$C with two core+valence models: Nilsson and a semi-microscopic particle-rotor model using Antisymmetrized Molecular Dinamic calculations of the cores. These models are later used to study $^{16}$C(d,p)$^{17}$C and $^{11}$Be(p,d)$^{10}$Be transfer reactions within the Adiabatic Distorted Wave Approximation. Results are compared with three different experimental data sets. Results: A good reproduction of both the structure and transfer reactions of $^{10}$Be and $^{17}$C is found. The Nilsson model provides an overall better agreement for the spectrum and reactions involving $^{17}$C while the semi-microscopic model is more adequate for $^{11}$Be, as expected, since the $^{17}$C core is closer to an ideal rotor. Conclusions: Both models show promising results for the study of transfer reactions with halo nuclei. We expect that including microscopic information in the Nilsson model, following the spirit of the semi-microscopic model, can provide a useful, yet simple framework for studying newly discovered halo nuclei.
Adaptive coding efficiency in recurrent cortical circuits via gain control

Sensory systems across all modalities and species exhibit adaptation to continuously changing input statistics. Individual neurons have been shown to modulate their response gains so as to maximize information transmission in different stimulus contexts. Experimental measurements have revealed additional, nuanced sensory adaptation effects including changes in response maxima and minima, tuning curve repulsion from the adapter stimulus, and stimulus-driven response decorrelation. Existing explanations of these phenomena rely on changes in inter-neuronal synaptic efficacy, which, while more flexible, are unlikely to operate as rapidly or reversibly as single neuron gain modulations. Using published V1 population adaptation data, we show that propagation of single neuron gain changes in a recurrent network is sufficient to capture the entire set of observed adaptation effects. We propose a novel adaptive efficient coding objective with which single neuron gains are modulated, maximizing the fidelity of the stimulus representation while minimizing overall activity in the network. From this objective, we analytically derive a set of gains that optimize the trade-off between preserving information about the stimulus and conserving metabolic resources. Our model generalizes well-established concepts of single neuron adaptive gain control to recurrent populations, and parsimoniously explains experimental adaptation data.
Guiding Computational Stance Detection with Expanded Stance Triangle Framework

Stance detection determines whether the author of a piece of text is in favor of, against, or neutral towards a specified target, and can be used to gain valuable insights into social media. The ubiquitous indirect referral of targets makes this task challenging, as it requires computational solutions to model semantic features and infer the corresponding implications from a literal statement. Moreover, the limited amount of available training data leads to subpar performance in out-of-domain and cross-target scenarios, as data-driven approaches are prone to rely on superficial and domain-specific features.In this work, we decompose the stance detection task from a linguistic perspective, and investigate key components and inference paths in this task. The stance triangle is a generic linguistic framework previously proposed to describe the fundamental ways people express their stance. We further expand it by characterizing the relationship between explicit and implicit objects. We then use the framework to extend one single training corpus with additional annotation. Experimental results show that strategically-enriched data can significantly improve the performance on out-of-domain and cross-target evaluation.
The list-Ramsey threshold for families of graphs

Given a family of graphs $\mathcal{F}$ and an integer $r$, we say that a graph is $r$-Ramsey for $\mathcal{F}$ if any $r$-colouring of its edges admits a monochromatic copy of a graph from $\mathcal{F}$. The threshold for the classic Ramsey property, where $\mathcal{F}$ consists of one graph, was located in the celebrated work of R\"odl and Ruci\'nski. In this paper, we offer a twofold generalisation to the R\"odl--Ruci\'nski theorem. First, we show that the list-colouring version of the property has the same threshold. Second, we extend this result to finite families $\mathcal{F}$, where the threshold statements might also diverge. This also confirms further special cases of the Kohayakawa--Kreuter conjecture. Along the way, we supply a short(-ish), self-contained proof of the $0$-statement of the R\"odl--Ruci\'nski theorem.
Structure-Aware Language Model Pretraining Improves Dense Retrieval on Structured Data

This paper presents Structure Aware Dense Retrieval (SANTA) model, which encodes user queries and structured data in one universal embedding space for retrieving structured data. SANTA proposes two pretraining methods to make language models structure-aware and learn effective representations for structured data: 1) Structured Data Alignment, which utilizes the natural alignment relations between structured data and unstructured data for structure-aware pretraining. It contrastively trains language models to represent multi-modal text data and teaches models to distinguish matched structured data for unstructured texts. 2) Masked Entity Prediction, which designs an entity-oriented mask strategy and asks language models to fill in the masked entities. Our experiments show that SANTA achieves state-of-the-art on code search and product search and conducts convincing results in the zero-shot setting. SANTA learns tailored representations for multi-modal text data by aligning structured and unstructured data pairs and capturing structural semantics by masking and predicting entities in the structured data. All codes are available at https://github.com/OpenMatch/OpenMatch.
Centralised Design and Production of the Ultra-High Vacuum and Laser-Stabilisation Systems for the AION Ultra-Cold Strontium Laboratories

This paper outlines the centralised design and production of the Ultra-High-Vacuum sidearm and Laser-Stabilisation systems for the AION Ultra-Cold Strontium Laboratories. Commissioning data on the residual gas and steady-state pressures in the sidearm chambers, on magnetic field quality, on laser stabilisation, and on the loading rate for the 3D Magneto-Optical Trap are presented. Streamlining the design and production of the sidearm and laser stabilisation systems enabled the AION Collaboration to build and equip in parallel five state-of-the-art Ultra-Cold Strontium Laboratories within 24 months by leveraging key expertise in the collaboration. This approach could serve as a model for the development and construction of other cold atom experiments, such as atomic clock experiments and neutral atom quantum computing systems, by establishing dedicated design and production units at national laboratories.
Atomically smooth films of CsSb: A chemically robust visible light photocathode

Alkali antimonide semiconductor photocathodes provide a promising platform for the generation of high-brightness electron beams, which are necessary for the development of cutting-edge probes, including x-ray free electron lasers and ultrafast electron diffraction. Nonetheless, to harness the intrinsic brightness limits in these compounds, extrinsic degrading factors, including surface roughness and contamination, must be overcome. By exploring the growth of CsxSb thin films monitored by in situ electron diffraction, the conditions to reproducibly synthesize atomically smooth films of CsSb on 3C–SiC (100) and graphene-coated TiO2 (110) substrates are identified, and detailed structural, morphological, and electronic characterization is presented. These films combine high quantum efficiency in the visible (up to 1.2% at 400 nm), an easily accessible photoemission threshold of 566 nm, low surface roughness (down to 600 pm on a 1 μm scale), and a robustness against oxidation up to 15 times greater than Cs3Sb. These properties lead us to suggest that CsSb has the potential to operate as an alternative to Cs3Sb in electron source applications where the demands of the vacuum environment might otherwise preclude the use of traditional alkali antimonides.
Evolutionary Solution Adaption for Multi-Objective Metal Cutting Process Optimization

Optimizing manufacturing process parameters is typically a multi-objective problem with often contradictory objectives such as production quality and production time. If production requirements change, process parameters have to be optimized again. Since optimization usually requires costly simulations based on, for example, the Finite Element method, it is of great interest to have means to reduce the number of evaluations needed for optimization. To this end, we consider optimizing for different production requirements from the viewpoint of a framework for system flexibility that allows us to study the ability of an algorithm to transfer solutions from previous optimization tasks, which also relates to dynamic evolutionary optimization. Based on the extended Oxley model for orthogonal metal cutting, we introduce a multi-objective optimization benchmark where different materials define related optimization tasks, and use it to study the flexibility of NSGA-II, which we extend by two variants: 1) varying goals, that optimizes solutions for two tasks simultaneously to obtain in-between source solutions expected to be more adaptable, and 2) active-inactive genotype, that accommodates different possibilities that can be activated or deactivated. Results show that adaption with standard NSGA-II greatly reduces the number of evaluations required for optimization to a target goal, while the proposed variants further improve the adaption costs, although further work is needed towards making the methods advantageous for real applications.
Physics-Informed Ensemble Representation for Light-Field Image Super-Resolution

Recent learning-based approaches have achieved significant progress in light field (LF) image super-resolution (SR) by exploring convolution-based or transformer-based network structures. However, LF imaging has many intrinsic physical priors that have not been fully exploited. In this paper, we analyze the coordinate transformation of the LF imaging process to reveal the geometric relationship in the LF images. Based on such geometric priors, we introduce a new LF subspace of virtual-slit images (VSI) that provide sub-pixel information complementary to sub-aperture images. To leverage the abundant correlation across the four-dimensional data with manageable complexity, we propose learning ensemble representation of all $C_4^2$ LF subspaces for more effective feature extraction. To super-resolve image structures from undersampled LF data, we propose a geometry-aware decoder, named EPIXformer, which constrains the transformer's operational searching regions with a LF physical prior. Experimental results on both spatial and angular SR tasks demonstrate that the proposed method outperforms other state-of-the-art schemes, especially in handling various disparities.
OVNS: Opportunistic Variable Neighborhood Search for Heaviest Subgraph Problem in Social Networks

We propose a hybrid heuristic algorithm for solving the Heaviest k-Subgraph Problem in online social networks -- a combinatorial graph optimization problem central to many important applications in weighted social networks, including detection of coordinated behavior, maximizing diversity of a group of users, and detecting social groups. Our approach builds upon an existing metaheuristic framework known as Variable Neighborhood Search and takes advantage of empirical insights about social network structures to derive an improved optimization heuristic. We conduct benchmarks in both real life social networks as well as synthetic networks and demonstrate that the proposed modifications match and in the majority of cases supersede those of the current state-of-the-art approaches.
Analysing high resolution digital Mars images using machine learning

The search for ephemeral liquid water on Mars is an ongoing activity. After the recession of the seasonal polar ice cap on Mars, small water ice patches may be left behind in shady places due to the low thermal conductivity of the Martian surface and atmosphere. During late spring and early summer, these patches may be exposed to direct sunlight and warm up rapidly enough for the liquid phase to emerge. To see the spatial and temporal occurrence of such ice patches, optical images should be searched for and checked. Previously a manual image analysis was conducted on 110 images from the southern hemisphere, captured by the High Resolution Imaging Science Experiment (HiRISE) camera onboard the Mars Reconnaissance Orbiter space mission. Out of these, 37 images were identified with smaller ice patches, which were distinguishable by their brightness, colour and strong connection to local topographic shading. In this study, a convolutional neural network (CNN) is applied to find further images with potential water ice patches in the latitude band between -40{\deg} and -60{\deg}, where the seasonal retreat of the polar ice cap happens. Previously analysed HiRISE images were used to train the model, where each image was split into hundreds of pieces (chunks), expanding the training dataset to 6240 images. A test run conducted on 38 new HiRISE images indicates that the program can generally recognise small bright patches, however further training might be needed for more precise identification. This further training has been conducted now, incorporating the results of the previous test run. To retrain the model, 18646 chunks were analysed and 48 additional epochs were ran. In the end the model produced a 94% accuracy in recognising ice, 58% of these images showed small enough ice patches on them. The rest of the images was covered by too much ice or showed CO2 ice sublimation in some places.
A fresh look at nonsmooth Levenberg–Marquardt methods with applications to bilevel optimization

In this paper, we revisit the classical problem of solving over-determined systems of nonsmooth equations numerically. We suggest a nonsmooth Levenberg--Marquardt method for its solution which, in contrast to the existing literature, does not require local Lipschitzness of the data functions. This is possible when using Newton-differentiability instead of semismoothness as the underlying tool of generalized differentiation. Conditions for fast local convergence of the method are given. Afterwards, in the context of over-determined mixed nonlinear complementarity systems, our findings are applied, and globalized solution methods, based on a residual induced by the maximum and the Fischer--Burmeister function, respectively, are constructed. The assumptions for fast local convergence are worked out and compared. Finally, these methods are applied for the numerical solution of bilevel optimization problems. We recall the derivation of a stationarity condition taking the shape of an over-determined mixed nonlinear complementarity system involving a penalty parameter, formulate assumptions for local fast convergence of our solution methods explicitly, and present results of numerical experiments. Particularly, we investigate whether the treatment of the appearing penalty parameter as an additional variable is beneficial or not.
Quantum Speedups for Bayesian Network Structure Learning

The Bayesian network structure learning (BNSL) problem asks for a directed acyclic graph that maximizes a given score function. For networks with $n$ nodes, the fastest known algorithms run in time $O(2^n n^2)$ in the worst case, with no improvement in the asymptotic bound for two decades. Inspired by recent advances in quantum computing, we ask whether BNSL admits a polynomial quantum speedup, that is, whether the problem can be solved by a quantum algorithm in time $O(c^n)$ for some constant $c$ less than $2$. We answer the question in the affirmative by giving two algorithms achieving $c \leq 1.817$ and $c \leq 1.982$ assuming the number of potential parent sets is, respectively, subexponential and $O(1.453^n)$. Both algorithms assume the availability of a quantum random access memory.
Observation Denoising in CYRUS Soccer Simulation 2D Team For RoboCup 2023

The RoboCup competitions hold various leagues, and the Soccer Simulation 2D League is a major one among them. Soccer Simulation 2D (SS2D) match involves two teams, including 11 players and a coach, competing against each other. The players can only communicate with the Soccer Simulation Server during the game. This paper presents the latest research of the CYRUS soccer simulation 2D team, the champion of RoboCup 2021. We will explain our denoising idea powered by long short-term memory networks (LSTM) and deep neural networks (DNN). The CYRUS team uses the CYRUS2D base code that was developed based on the Helios and Gliders bases.
Towards Omni-generalizable Neural Methods for Vehicle Routing Problems

Learning heuristics for vehicle routing problems (VRPs) has gained much attention due to the less reliance on hand-crafted rules. However, existing methods are typically trained and tested on the same task with a fixed size and distribution (of nodes), and hence suffer from limited generalization performance. This paper studies a challenging yet realistic setting, which considers generalization across both size and distribution in VRPs. We propose a generic meta-learning framework, which enables effective training of an initialized model with the capability of fast adaptation to new tasks during inference. We further develop a simple yet efficient approximation method to reduce the training overhead. Extensive experiments on both synthetic and benchmark instances of the traveling salesman problem (TSP) and capacitated vehicle routing problem (CVRP) demonstrate the effectiveness of our method. The code is available at: https://github.com/RoyalSkye/Omni-VRP.
A tree perspective on stick-breaking models in covariate-dependent mixtures

Stick-breaking (SB) processes are often adopted in Bayesian mixture models for generating mixing weights. When covariates influence the sizes of clusters, SB mixtures are particularly convenient as they can leverage their connection to binary regression to ease both the specification of covariate effects and posterior computation. Existing SB models are typically constructed based on continually breaking a single remaining piece of the unit stick. We view this from a dyadic tree perspective in terms of a lopsided bifurcating tree that extends only in one side. We show that several unsavory characteristics of SB models are in fact largely due to this lopsided tree structure. We consider a generalized class of SB models with alternative bifurcating tree structures and examine the influence of the underlying tree topology on the resulting Bayesian analysis in terms of prior assumptions, posterior uncertainty, and computational effectiveness. In particular, we provide evidence that a balanced tree topology, which corresponds to continually breaking all remaining pieces of the unit stick, can resolve or mitigate several undesirable properties of SB models that rely on a lopsided tree.
Nonlocal ergodic control problem in $\mathbb{R}^d$

We study the existence-uniqueness of solution $(u, \lambda)$ to the ergodic Hamilton-Jacobi equation $$(-\Delta)^s u + H(x, \nabla u) = f-\lambda\quad \text{in}\; \mathbb{R}^d,$$ and $u\geq 0$, where $s\in (\frac{1}{2}, 1)$. We show that the critical $\lambda=\lambda^*$, defined as the infimum of all $\lambda$ attaining a non-negative supersolution, attains a nonnegative solution $u$. Under suitable conditions, it is also shown that $\lambda^*$ is the supremum of all $\lambda$ for which a non-positive subsolution is possible. Moreover, uniqueness of the solution $u$, corresponding to $\lambda^*$, is also established. Furthermore, we provide a probabilistic characterization that determines the uniqueness of the pair $(u, \lambda^*)$ in the class of all solution pair $(u, \lambda)$ with $u\geq 0$. Our proof technique involves both analytic and probabilistic methods in combination with a new local Lipschitz estimate obtained in this article.
Perimeter Control Using Deep Reinforcement Learning: A Model-Free Approach Towards Homogeneous Flow Rate Optimization

Perimeter control maintains high traffic efficiency within protected regions by controlling transfer flows among regions to ensure that their traffic densities are below critical values. Existing approaches can be categorized as either model-based or model-free, depending on whether they rely on network transmission models (NTMs) and macroscopic fundamental diagrams (MFDs). Although model-based approaches are more data efficient and have performance guarantees, they are inherently prone to model bias and inaccuracy. For example, NTMs often become imprecise for a large number of protected regions, and MFDs can exhibit scatter and hysteresis that are not captured in existing model-based works. Moreover, no existing studies have employed reinforcement learning for homogeneous flow rate optimization in microscopic simulation, where spatial characteristics, vehicle-level information, and metering realizations - often overlooked in macroscopic simulations - are taken into account. To circumvent issues of model-based approaches and macroscopic simulation, we explore a model-free deep reinforcement learning approach that optimizes the flow rate homogeneously at the perimeter at the microscopic level. Additionally, we investigate different arrangements of the agent's state space to assess the importance of different state variables. Results demonstrate that the model-free reinforcement learning approach without any knowledge of NTMs or MFDs can compete and match the performance of a model-based approach, and exhibits enhanced generalizability and scalability.
The Method of Proof Analysis: Background, Developments, New Directions

Motivations and background Overview Hilbert-style systems Gentzen systems Axioms-as-rules Developments Proof-theoretic semantics for non-classical logics Basic modal logic Intuitionistic and intermediate logics Intermediate logics and modal embeddings Gödel-Löb provability logic Displayable logics First-order modal logic Transitive closure Completeness, correspondence, decidability New directions Social and epistemic logics Knowability logic References