Miracle-dz/UniFAR-FTUs huggingface dataset:
RFold: RNA Secondary Structure Prediction with Decoupled Optimization

The secondary structure of ribonucleic acid (RNA) is more stable and accessible in the cell than its tertiary structure, making it essential for functional prediction. Although deep learning has shown promising results in this field, current methods suffer from poor generalization and high complexity. In this work, we present RFold, a simple yet effective RNA secondary structure prediction in an end-to-end manner. RFold introduces a decoupled optimization process that decomposes the vanilla constraint satisfaction problem into row-wise and column-wise optimization, simplifying the solving process while guaranteeing the validity of the output. Moreover, RFold adopts attention maps as informative representations instead of designing hand-crafted features. Extensive experiments demonstrate that RFold achieves competitive performance and about eight times faster inference efficiency than the state-of-the-art method. The code and Colab demo are available in \href{http://github.com/A4Bio/RFold}{http://github.com/A4Bio/RFold}.
Comparison of meta-learners for estimating multi-valued treatment heterogeneous effects

Conditional Average Treatment Effects (CATE) estimation is one of the main challenges in causal inference with observational data. In addition to Machine Learning based-models, nonparametric estimators called meta-learners have been developed to estimate the CATE with the main advantage of not restraining the estimation to a specific supervised learning method. This task becomes, however, more complicated when the treatment is not binary as some limitations of the naive extensions emerge. This paper looks into meta-learners for estimating the heterogeneous effects of multi-valued treatments. We consider different meta-learners, and we carry out a theoretical analysis of their error upper bounds as functions of important parameters such as the number of treatment levels, showing that the naive extensions do not always provide satisfactory results. We introduce and discuss meta-learners that perform well as the number of treatments increases. We empirically confirm the strengths and weaknesses of those methods with synthetic and semi-synthetic datasets.
UIO at SemEval-2023 Task 12: Multilingual fine-tuning for sentiment classification in low-resource Languages

Our contribution to the 2023 AfriSenti-SemEval shared task 12: Sentiment Analysis for African Languages, provides insight into how a multilingual large language model can be a resource for sentiment analysis in languages not seen during pretraining. The shared task provides datasets of a variety of African languages from different language families. The languages are to various degrees related to languages used during pretraining, and the language data contain various degrees of code-switching. We experiment with both monolingual and multilingual datasets for the final fine-tuning, and find that with the provided datasets that contain samples in the thousands, monolingual fine-tuning yields the best results.
Distributed Multi-Horizon Model Predictive Control for Network of Energy Hubs

The increasing penetration of renewable energy resources has transformed the energy system from traditional hierarchical energy delivery paradigm to a distributed structure. Such development is accompanied with continuous liberalization in the energy sector, giving rise to possible energy trading among networked local energy hub. Joint operation of such hubs can improve energy efficiency and support the integration of renewable energy resource. Acknowledging peer-to-peer trading between hubs, their optimal operation within the network can maximize consumption of locally produced energy. However, for such complex systems involving multiple stakeholders, both computational tractability and privacy concerns need to be accounted for. We investigate both decentralized and centralized model predictive control (MPC) approaches for a network of energy hubs. While the centralized control strategy offers superior performance to the decentralized method, its implementation is computationally prohibitive and raises privacy concerns, as the information of each hub has to be shared extensively. On the other hand, a classical decentralized control approach can ease the implementation at the expense of sub-optimal performance of the overall system. In this work, a distributed scheme based on a consensus alternating direction method of multipliers (ADMM) algorithm is proposed. It combines the performance of the centralized approach with the privacy preservation of decentralized approach. A novel multi-horizon MPC framework is also introduced to increase the prediction horizon without compromising the time discretization or making the problem computationally intractable. A benchmark three-hub network is used to compare the performance of the mentioned methods. The results show superior performance in terms of total cost, computational time, robustness to demand and prices variations.
A Versatile Low-Complexity Feedback Scheme for FDD Systems via Generative Modeling

We propose a versatile feedback scheme for both single- and multi-user multiple-input multiple-output (MIMO) frequency division duplex (FDD) systems. Particularly, we propose utilizing a Gaussian mixture model (GMM) with a reduced number of parameters for codebook construction, feedback encoding, and precoder design. The GMM is fitted offline at the base station (BS) to uplink training samples to approximate the channel distribution of all possible mobile terminals (MTs) within the BS cell. Subsequently, a codebook is constructed, with each element based on one GMM component. Extracting directional information from the codebook or exploiting the GMM's sample generation ability facilitates joint precoder design for a multi-user MIMO system using state-of-the-art precoding algorithms. After offloading the GMM to the MTs, they can easily determine their feedback by selecting the index of the GMM component with the highest responsibility for their received pilot signal. This strategy exhibits low complexity and supports parallelization. Simulations demonstrate that the proposed approach outperforms conventional methods, which either estimate the channel and utilize a Lloyd codebook or use a deep neural network to determine the feedback in terms of spectral efficiency or sum-rate. The performance gains can be exploited to deploy systems with fewer pilots or feedback bits.
A One-Dimensional Symmetric Force-Based Blending Method for Atomistic-to-Continuum Coupling

Inspired by the blending method developed by [P. Seleson, S. Beneddine, and S. Prudhome, \emph{A Force-Based Coupling Scheme for Peridynamics and Classical Elasticity}, (2013)] for the nonlocal-to-local coupling, we create a symmetric and consistent blended force-based Atomistic-to-Continuum (a/c) scheme for the atomistic chain in one-dimensional space. The conditions for the well-posedness of the underlying model are established by analyzing an optimal blending size and blending type to ensure the $H^1$ semi-norm stability for the blended force-based operator. We present several numerical experiments to test and confirm the theoretical findings.
A Conjugate System for Twisted Araki-Woods von Neumann Algebras of finite dimensional spaces

We compute the conjugate system of twisted Araki-Woods von Neumann algebras $ \mathcal{L}_T(H) $ for a compatible braided crossing symmetric twist $T$ on a finite dimensional Hilbert space $ \mathcal{H} $ with norm $ \|T\|<1$. This implies that those algebras have finite non-microstates free Fisher information and therefore are always factors of type $\text{III}_\lambda$ ($0<\lambda\leq 1$) or $ \text{II}_1 $. Moreover, using the nontracial free monotone transport, we show that $ \mathcal{L}_T(H) $ is isomorphic to the free Araki-Woods algebra $ \mathcal{L}_0(H) $ when $ \|T\|=q $ is small enough.
MCLFIQ: Mobile Contactless Fingerprint Image Quality

We propose MCLFIQ: Mobile Contactless Fingerprint Image Quality, the first quality assessment algorithm for mobile contactless fingerprint samples. To this end, we re-trained the NIST Fingerprint Image Quality (NFIQ) 2 method, which was originally designed for contact-based fingerprints, with a synthetic contactless fingerprint database. We evaluate the predictive performance of the resulting MCLFIQ model in terms of Error-vs.-Discard Characteristic (EDC) curves on three real-world contactless fingerprint databases using three recognition algorithms. In experiments, the MCLFIQ method is compared against the original NFIQ 2.2 method, a sharpness-based quality assessment algorithm developed for contactless fingerprint images \rev{and the general purpose image quality assessment method BRISQUE. Furthermore, benchmarks on four contact-based fingerprint datasets are also conducted. } Obtained results show that the fine-tuning of NFIQ 2 on synthetic contactless fingerprints is a viable alternative to training on real databases. Moreover, the evaluation shows that our MCLFIQ method works more accurate and robust compared to all baseline methods on contactless fingerprints. We suggest considering the proposed MCLFIQ method as a \rev{starting point for the development of} a new standard algorithm for contactless fingerprint quality assessment.
L\'evy-type operators with low singularity kernels: regularity estimates and martingale problem

We consider the linear non-local operator $\mathcal{L}$ denoted by \[ \mathcal{L} u (x) = \int_{\mathbb{R}^d} \left(u(x+z)-u(x)\right) a(x,z)J(z)\,d z. \] Here $a(x,z)$ is bounded and $J(z)$ is the jumping kernel of a L\'evy process, which only has a low-order singularity near the origin and does not allow for standard scaling. The aim of this work is twofold. Firstly, we introduce generalized Orlicz-Besov spaces tailored to accommodate the analysis of elliptic equations associated with $\mathcal{L}$, and establish regularity results for the solutions of such equations in these spaces. Secondly, we investigate the martingale problem associated with $\mathcal{L}$. By utilizing analytic results, we prove the well-posedness of the martingale problem under mild conditions. Additionally, we obtain a new Krylov-type estimate for the martingale solution through the use of a Morrey-type inequality for generalized Orlicz-Besov spaces.
Bond Polarizability as a Probe of Local Crystal Fields in Hybrid Lead-Halide Perovskites

A rotating organic cation and a dynamically disordered soft inorganic cage are the hallmark features of organic-inorganic lead-halide perovskites. Understanding the interplay between these two subsystems is a challenging problem, but it is this coupling that is widely conjectured to be responsible for the unique behavior of photocarriers in these materials. In this work, we use the fact that the polarizability of the organic cation strongly depends on the ambient electrostatic environment to put the molecule forward as a sensitive probe of the local crystal fields inside the lattice cell. We measure the average polarizability of the C/N–H bond stretching mode by means of infrared spectroscopy, which allows us to deduce the character of the motion of the cation molecule, find the magnitude of the local crystal field, and place an estimate on the strength of the hydrogen bond between the hydrogen and halide atoms. Our results pave the way for understanding electric fields in lead-halide perovskites using infrared bond spectroscopy.
A barrier for further approximating Sorting By Transpositions

The transposition distance problem is a classical problem in genome rearrangements, which seeks to determine the minimum number of transpositions needed to transform a linear chromosome into another represented by the permutations π and σ, respectively. This article focuses on the equivalent problem of sorting by transpositions (SBT), where σ is the identity permutation ι. Specifically, we investigate palisades, a family of permutations that are "hard" to sort, as they require numerous transpositions above the celebrated lower bound devised by Bafna and Pevzner. By determining the transposition distance of palisades, we were able to provide the exact transposition diameter for 3-permutations (TD3), a special subset of the symmetric group Sn, essential for the study of approximate solutions for SBT using the simplification technique. The exact value for TD3 has remained unknown since Elias and Hartman showed an upper bound for it. Another consequence of determining the transposition distance of palisades is that, using as lower bound the one by Bafna and Pevzner, it is impossible to guarantee approximation ratios lower than 1.375 when approximating SBT. This finding has significant implications for the study of SBT, as this problem has been the subject of intense research efforts for the past 25 years.
Internal Calibration of LAMOST and Gaia DR3 GSP-Spec Stellar Abundances

Stellar chemical abundances are crucial and fundamental in astrophysics. However, they could suffer from substantial systematic errors according to several investigations but still lack calibrations in bulk. By using Gaia wide binaries, we find the temperature-dependent bias between the two binary components for [Fe/H] and [α/Fe] measurements from the LAMOST low-resolution spectra and Gaia RVS spectra. At T eff = 4000 K, the LAMOST [Fe/H] is significantly underestimated by approximately 0.4 dex when compared with its typical uncertainty of 0.1 dex. Its [α/Fe] is overestimated by about 0.2 dex. For Gaia, the underestimation of [M/H] and overestimation of [α/Fe] becomes pronounced near 7000 K with smaller magnitudes. We perform an internal calibration by minimizing the differences between the binary components and provide the correction curves. After corrections, the standard deviations of the residuals compared to the PASTEL catalog decrease from about 0.045/0.1 to 0.02/0.043 for LAMOST and Gaia, respectively. The chemical homogeneity of the open cluster M 44 is also improved by a factor of two. We stress that the underestimation of [Fe/H] could lead to an overestimation of binary fractions when selecting binary stars by the excess of luminosity. The method of this work could be applied to other data sets in the future. Our results will benefit statistic studies that use LAMOST and Gaia samples with a wide temperature range.
Revisiting the proton synchrotron radiation in blazar jets: Possible contributions from x-ray to 
γ
-ray bands

The proton synchrotron radiation is considered as the origin of high-energy emission of blazars at times. However, extreme physical parameters are often required. In this work, we propose an analytical method to study the parameter space when applying the proton synchrotron radiation to fit the keV, GeV, and very-high-energy emission of blazar jets. We find that proton synchrotron radiation can fit the high-energy hump when it peaks beyond tens GeV without violating basic observations and theories. For the high-energy hump peaked around GeV band, extreme parameters, such as a super-Eddington jet power and a very strong magnetic field, are required. For the high-energy hump peaked around keV band, if an acceptable parameter space can be found depends on the object's keV luminosity.
Multi-Party Chat: Conversational Agents in Group Settings with Humans and Models

Current dialogue research primarily studies pairwise (two-party) conversations, and does not address the everyday setting where more than two speakers converse together. In this work, we both collect and evaluate multi-party conversations to study this more general case. We use the LIGHT environment to construct grounded conversations, where each participant has an assigned character to role-play. We thus evaluate the ability of language models to act as one or more characters in such conversations. Models require two skills that pairwise-trained models appear to lack: (1) being able to decide when to talk; (2) producing coherent utterances grounded on multiple characters. We compare models trained on our new dataset to existing pairwise-trained dialogue models, as well as large language models with few-shot prompting. We find that our new dataset, MultiLIGHT, which we will publicly release, can help bring significant improvements in the group setting.
Bouncing universe for deformed non-minimally coupled inflation model

In this paper, we consider a non-minimally coupled gravity model to study the bouncing universe. The holographic principle has various effects on the bouncing universe. We choose some suitable new variables and achieve the new Hamiltonian and Lagrangian which have harmonic oscillator form. The corresponding Lagrangian is deformed by non-commutative geometry. In order to have a solution for the bouncing universe we specify the potential in the equation state. In that case, we draw the equation of state in terms of time and show that the equation of state crosses $-1$. Such bouncing behavior leads us to apply some conditions on $\theta$ and $\beta$ from non-commutative geometry. Here, we can also check the system's stability due to the deformation of the non-minimally coupled gravity model. In order to examine the stability of the system we obtain the variation of pressure with respect to density energy. Also, we draw the variation of pressure with respect to energy density and show the stability condition.
Lowering the Entry Bar to HPC-Scale Uncertainty Quantification

Treating uncertainties in models is essential in many fields of science and engineering. Uncertainty quantification (UQ) on complex and computationally costly numerical models necessitates a combination of efficient model solvers, advanced UQ methods and HPC-scale resources. The resulting technical complexities as well as lack of separation of concerns between UQ and model experts is holding back many interesting UQ applications. The aim of this paper is to close the gap between advanced UQ methods and advanced models by removing the hurdle of complex software stack integration, which in turn will offer a straightforward way to scale even prototype-grade UQ applications to high-performance resources. We achieve this goal by introducing a parallel software architecture based on UM-Bridge, a universal interface for linking UQ and models. We present three realistic applications from different areas of science and engineering, scaling from single machines to large clusters on the Google Cloud Platform.
Occ3D: A Large-Scale 3D Occupancy Prediction Benchmark for Autonomous Driving

Robotic perception requires the modeling of both 3D geometry and semantics. Existing methods typically focus on estimating 3D bounding boxes, neglecting finer geometric details and struggling to handle general, out-of-vocabulary objects. 3D occupancy prediction, which estimates the detailed occupancy states and semantics of a scene, is an emerging task to overcome these limitations. To support 3D occupancy prediction, we develop a label generation pipeline that produces dense, visibility-aware labels for any given scene. This pipeline comprises three stages: voxel densification, occlusion reasoning, and image-guided voxel refinement. We establish two benchmarks, derived from the Waymo Open Dataset and the nuScenes Dataset, namely Occ3D-Waymo and Occ3D-nuScenes benchmarks. Furthermore, we provide an extensive analysis of the proposed dataset with various baseline models. Lastly, we propose a new model, dubbed Coarse-to-Fine Occupancy (CTF-Occ) network, which demonstrates superior performance on the Occ3D benchmarks. The code, data, and benchmarks are released at https://tsinghua-mars-lab.github. io/Occ3D/.
Controllable Data Augmentation for Context-Dependent Text-to-SQL

The limited scale of annotated data constraints existing context-dependent text-to-SQL models because of the complexity of labeling. The data augmentation method is a commonly used method to solve this problem. However, the data generated by current augmentation methods often lack diversity. In this paper, we introduce ConDA, which generates interactive questions and corresponding SQL results. We designed the SQL dialogue state to enhance the data diversity through the state transition. Meanwhile, we also present a filter method to ensure the data quality by a grounding model. Additionally, we utilize a grounding model to identify and filter low-quality questions that mismatch the state information. Experimental results on the SParC and CoSQL datasets show that ConDA boosts the baseline model to achieve an average improvement of $3.3\%$ on complex questions. Moreover, we analyze the augmented data, which reveals that the data generated by ConDA are of high quality in both SQL template hardness and types, turns, and question consistency.
Network Analysis as a Tool for Shaping Conservation and Development Policy: A Case Study of Timber Market Optimization in India

The incorporation of trees on farms can help to improve livelihoods and build resilience among small-holder farmers in developing countries. On-farm trees can help gen- erate additional income from commercial tree harvest as well as contribute significant environmental benefits and ecosystem services to increase resiliency. Long-term benefits from tree-based livelihoods, however, depend on sustainable timber harvesting. In this paper, we discuss the potential for network analysis as a tool to inform conservation and development decision-making. Specifically, we formulate the commercial tree market between farmers and traders as a transportation problem and optimize the transactions. We create a model of the commercial tree market in the Bilaspur district of Himachal Pradesh, India based on a detailed dataset of market interactions between farmers and timber traders, using the existing road network of this region. Using this model, we perform a maximum-flow-minimum-cost optimization for tree commodity flow. We compare the results of our optimized model with actual flow within the network, and we find a high potential to increase efficiency of market transactions within this region, noting a significant reduction to the minimum- cost flow value for our optimized model compared to the flow cost for actual transactions. We propose that using this network flow optimization model to strategically distribute permits can reduce costs associated with market transactions. Our results suggest that this direct policy action would be beneficial to the region. Finally, we suggest that cost savings could be used to establish tree planting programs to support a long-term sustainable tree market. Shaping policies to address these market inefficiencies in developing regions could help support and elevate tree-based livelihoods for farmers, traders, and industries.
ContraNeRF: 3D-Aware Generative Model via Contrastive Learning with Unsupervised Implicit Pose Embedding

Although 3D-aware GANs based on neural radiance fields have achieved competitive performance, their applicability is still limited to objects or scenes with the ground-truths or prediction models for clearly defined canonical camera poses. To extend the scope of applicable datasets, we propose a novel 3D-aware GAN optimization technique through contrastive learning with implicit pose embeddings. To this end, we first revise the discriminator design and remove dependency on ground-truth camera poses. Then, to capture complex and challenging 3D scene structures more effectively, we make the discriminator estimate a high-dimensional implicit pose embedding from a given image and perform contrastive learning on the pose embedding. The proposed approach can be employed for the dataset, where the canonical camera pose is ill-defined because it does not look up or estimate camera poses. Experimental results show that our algorithm outperforms existing methods by large margins on the datasets with multiple object categories and inconsistent canonical camera poses.