arXiv subjects

Ning Dai

Publications and source records attributed to Ning Dai.

At least 19 recordsLinked to original sources

arXiv2026

Sampling-based Continuous Optimization for Messenger RNA Design

Designing messenger RNA (mRNA) sequences for a fixed target protein requires searching an exponentially large synonymous space while optimizing properties that affect stability and downstream performance. This is challenging because practical mRNA design involves multiple coupled objectives beyond classical folding criteria, and different applications prefer different trade-offs. We propose a general sampling-based continuous optimization framework, inspired by SamplingDesign, that iteratively samples candidate synonymous sequences, evaluates them with black-box metrics, and updates a parameterized sampling distribution. Across a diverse UniProt protein set and the SARS-CoV-2 spike protein, our method consistently improves the chosen objective, with particularly strong gains on average unpaired probability and accessible uridine percentage compared to LinearDesign and EnsembleDesign. Moreover, our multi-objective COMBO formulation enables weight-controlled exploration of the design space and naturally extends to incorporate additional computable metrics.

q-bio.BM↗

arXiv2026

Designing RNAs with Language Models

RNA design, the task of finding a sequence that folds into a target secondary structure, has broad biological and biomedical impact but remains computationally challenging due to the exponentially large sequence space and exponentially many competing folds. Traditional approaches treat it as an optimization problem, relying on per-instance heuristics or constraint-based search. We instead reframe RNA design as conditional sequence generation and introduce a reusable neural approximator, instantiated as an autoregressive language model (LM), that maps target structures directly to sequences. We first train our model in a supervised setting on random-induced structure-sequence pairs, and then use reinforcement learning (RL) to optimize end-to-end metrics. We also propose methods to select a small subset for RL that greatly improves RL efficiency and quality. Across four datasets, our approach outperforms state-of-the-art systems on key metrics such as Boltzmann probability while being 1.7x faster, establishing conditional LM generation as a scalable, task-agnostic alternative to per-instance optimization for RNA design. Our code and data are available at https://github.com/KuNyaa/RNA-Design-LM.

cs.LG↗

arXiv2025

Residual Test for the Third Gravitational-Wave Transient Catalog

The residual test is commonly used to check the agreement between the gravitational wave signal and the theoretical waveform template. The basic idea of the residual test is to subtract the best-fit waveform from the data and then check whether the remaining data (i.e., the residuals) are consistent with the instrumental noise or not. We apply the Kolmogorov-Smirnov test, the Anderson-Darling test and the chi-squared test as goodness-of-fit test to examine the residuals of events in the third gravitational-wave transient catalog and find no statistically significant deviation from the noise. Although our method is sensitive only to the loud events, it does not rely on the cross-correlation between detectors. A single-detector event suffices for our residual analysis, and the test is simple and computationally inexpensive.

gr-qc↗

arXiv2025

Seed1.5-Thinking: Advancing Superb Reasoning Models with Reinforcement Learning

We introduce Seed1.5-Thinking, capable of reasoning through thinking before responding, resulting in improved performance on a wide range of benchmarks. Seed1.5-Thinking achieves 86.7 on AIME 2024, 55.0 on Codeforces and 77.3 on GPQA, demonstrating excellent reasoning abilities in STEM and coding. Beyond reasoning tasks, the method demonstrates notable generalization across diverse domains. For instance, it surpasses DeepSeek R1 by 8% in win rate on non-reasoning tasks, indicating its broader applicability. Compared to other state-of-the-art reasoning models, Seed1.5-Thinking is a Mixture-of-Experts (MoE) model with a relatively small size, featuring 20B activated and 200B total parameters. As part of our effort to assess generalized reasoning, we develop two internal benchmarks, BeyondAIME and Codeforces, both of which will be publicly released to support future research. Model trial link: https://www.volcengine.com/experience/ark.

cs.CL↗

arXiv2025

Enhancing Multi-Step Reasoning Abilities of Language Models through Direct Q-Function Optimization

Reinforcement Learning (RL) plays a crucial role in aligning large language models (LLMs) with human preferences and improving their ability to perform complex tasks. However, current approaches either require significant computational resources due to the use of multiple models and extensive online sampling for training (e.g., PPO) or are framed as bandit problems (e.g., DPO, DRO), which often struggle with multi-step reasoning tasks, such as math problem solving and complex reasoning that involve long chains of thought. To overcome these limitations, we introduce Direct Q-function Optimization (DQO), which formulates the response generation process as a Markov Decision Process (MDP) and utilizes the soft actor-critic (SAC) framework to optimize a Q-function directly parameterized by the language model. The MDP formulation of DQO offers structural advantages over bandit-based methods, enabling more effective process supervision. Experimental results on two math problem-solving datasets, GSM8K and MATH, demonstrate that DQO outperforms previous methods, establishing it as a promising offline reinforcement learning approach for aligning language models.

cs.LG↗

arXiv2025

Process Supervision-Guided Policy Optimization for Code Generation

Reinforcement learning (RL) with unit test feedback has enhanced large language models' (LLMs) code generation, but relies on sparse rewards provided only after complete code evaluation, limiting learning efficiency and incremental improvements. When generated code fails all unit tests, no learning signal is received, hindering progress on complex tasks. To address this, we propose a Process Reward Model (PRM) that delivers dense, line-level feedback on code correctness during generation, mimicking human code refinement and providing immediate guidance. We explore various strategies for training PRMs and integrating them into the RL framework, finding that using PRMs both as dense rewards and for value function initialization significantly boosts performance. Our experimental results also highlight the effectiveness of PRMs in enhancing RL-driven code generation, especially for long-horizon scenarios.

cs.AI↗

arXiv2024

Current density in the anomalous Hall effect regime under weak scattering

A finite equilibrium current density arises in the anomalous Hall effect (AHE) as a result of time-reversal symmetry breaking, affecting both the differential current density and total current. This study illustrates the equilibrium current density pattern in a ribbon-shaped system within the AHE regime, consisting of two sets of counterpropagating channels arranged in a zebra crossing pattern. While the middle channels are susceptible to scattering, the edge channels remain relatively robust. Despite this difference, all channels exhibit the same differential current density when subjected to a differential voltage across the two ends of the ribbon. When a differential voltage is applied to both sides of the ribbon, it results in a snaking pattern of differential current density forming across it. Furthermore, in a four-terminal device comprising an AHE ribbon and two normal leads, it is found that Hall conductance is independent of ribbon width within certain scattering strengths due to differences in robustness between middle and edge channels. These findings underscore the significant role played by current density in AHE transport.

cond-mat.mes-hall↗

arXiv2024

Sampling-based Continuous Optimization with Coupled Variables for RNA Design

The task of RNA design given a target structure aims to find a sequence that can fold into that structure. It is a computationally hard problem where some version(s) have been proven to be NP-hard. As a result, heuristic methods such as local search have been popular for this task, but by only exploring a fixed number of candidates. They can not keep up with the exponential growth of the design space, and often perform poorly on longer and harder-to-design structures. We instead formulate these discrete problems as continuous optimization, which starts with a distribution over all possible candidate sequences, and uses gradient descent to improve the expectation of an objective function. We define novel distributions based on coupled variables to rule out invalid sequences given the target structure and to model the correlation between nucleotides. To make it universally applicable to any objective function, we use sampling to approximate the expected objective function, to estimate the gradient, and to select the final candidate. Compared to the state-of-the-art methods, our work consistently outperforms them in key metrics such as Boltzmann probability, ensemble defect, and energy gap, especially on long and hard-to-design puzzles in the Eterna100 benchmark. Our code is available at: http://github.com/weiyutang1010/ncrna_design.

q-bio.BM↗

arXiv2024

Probing the equation of state of neutron stars with captured primordial black holes

Gravitational waves (GWs) from primordial black holes (PBHs) inspiraling within neutron stars (NSs) -- should they exist -- are detectable by ground-based detectors and offer a unique insight into the internal structure of NSs. To provide accurate templates for GW searches, we solve Einstein's equations within NSs and calculate the orbital motion of the captured PBH by considering dynamical friction, accretion, and gravitational radiation. Equipped with precise GW waveforms for PBHs inspiraling inside NSs, we find that the Einstein Telescope can differentiate between various equations of state for NSs. As PBHs inspiral deeper into NSs, the GW frequency rises near the surface, then decreases to a constant value deeper within NSs. The distinctive characteristics of GW frequency serve as the smoking gun for GW signals emitted by PBHs inspiraling inside NSs and can be used to probe the nuclear matter in the crust and core of NSs.

gr-qc↗

arXiv2024

Extreme mass ratio inspirals in galaxies with dark matter halos

Using an analytic, static, and spherically symmetric metric for a Schwarzschild black hole immersed in a dark matter (DM) halo with the Hernquist-type profile, we derive analytic expressions for the orbital period and precession of eccentric extreme mass ratio inspirals (EMRIs) surrounded by DM halos, and we show how the precession rates decrease and even undergo a prograde-to-retrograde precession transition if the density of DM halo is large enough. The presence of local DM halos also retards the decrease of the semi-latus rectum and the eccentricity. The orbital evolution of EMRIs immersed in DM halos is then calculated numerically by considering the combined effects of gravitational radiation reaction, dynamical friction, and accretion. Comparing the number of orbital cycles accumulated over a one-year evolution for EMRIs with and without DM halos, we find that DM halos with compactness as small as $10^{-5}$ can be detected. From the mismatch between gravitational waveforms of EMRIs with and without DM halos, we show that EMRIs in galaxies can be used to probe the existence of DM halos and detect the compactness of DM halos as small as $10^{-5}$. Employing the Fisher information matrix method, we find that larger compactness and density values of DM halos help to reduce the estimation error of parameters and further break the degeneracy between the parameters.

gr-qc↗

arXiv2024

Distinguishing dark matter halos with Extreme mass ratio inspirals

Using the static, spherically symmetric metric for a black hole (BH) immersed in dark matter (DM) halo characterized by Hernquist, Burkert, and Navarro-Frenk-White (NFW) density distributions, we calculate the orbital periods and precessions, along with the evolution of the semi-latus rectum and eccentricity for extreme mass ratio inspirals (EMRIs) surrounded by DM halos. For the Hernquist model, we find that the gravitational force exerted by the central BH is decreased by DM halos, while DM halos put additional gravitational force on the SCO. The presence of both Burkert-type and NFW-type DM halos enhances the gravitational force acting on the SCO, resulting in a decrease in the period $P$, with the decrease depending on $M/a_0^2$; additionally, we find that the reduction in orbital precession due to DM halos is influenced by $M/a_0^2$. The presence of DM halos leads to a slower evolution of EMRIs within Hernquist-type halos, while it accelerates evolution for EMRIs in Burkert-type and NFW-type halos; furthermore, it slows the decrease of eccentricity across all three types of DM halos. By calculating the number of orbital cycles and the gravitational waveform mismatches among these three types of DM halos, as well as between scenarios with and without DM halos, we find that DM halos can be detected when $M/a_0>10^{-5}$, $M/a_0>10^{-3}$, and $M/a_0>10^{-3}$ for Hernquist-type, NFW-type, and Burkert-type DM halos, respectively. Additionally, we can distinguish between NFW-type and Burkert-type DM halos when $M/a_0> 10^{-3}$; NFW-type and Hernquist-type DM halos, as well as Burkert-type and Hernquist-type DM halos, can be distinguished when $M/a_0> 10^{-5}$.

gr-qc↗

arXiv2024

LinearAlifold: Linear-Time Consensus Structure Prediction for RNA Alignments

Predicting the consensus structure of a set of aligned RNA homologs is a convenient method to find conserved structures in an RNA genome, which has many applications including viral diagnostics and therapeutics. However, the most commonly used tool for this task, RNAalifold, is prohibitively slow for long sequences, due to a cubic scaling with the sequence length, taking over a day on 400 SARS-CoV-2 and SARS-related genomes (~30,000nt). We present LinearAlifold, a much faster alternative that scales linearly with both the sequence length and the number of sequences, based on our work LinearFold that folds a single RNA in linear time. Our work is orders of magnitude faster than RNAalifold (0.7 hours on the above 400 genomes, or ~36$\times$ speedup) and achieves higher accuracies when compared to a database of known structures. More interestingly, LinearAlifold's prediction on SARS-CoV-2 correlates well with experimentally determined structures, substantially outperforming RNAalifold. Finally, LinearAlifold supports two energy models (Vienna and BL*) and four modes: minimum free energy (MFE), maximum expected accuracy (MEA), ThreshKnot, and stochastic sampling, each of which takes under an hour for hundreds of SARS-CoV variants. Our resource is at: https://github.com/LinearFold/LinearAlifold (code) and http://linearfold.org/linear-alifold (server).

q-bio.BM↗

arXiv2024

Room temperature Si:S barrier infrared detector with broadband response up to 4.4μm

Mid-infrared spectrum is a critical tool for chemical analysis, industrial inspection, environment, and other fields due to its rich chemical bond information. However, the complicated growth or fabrication procedures of existing mid-infrared sensitive materials hinder the large-scale production and utilization of mid-infrared detectors. To address this issue, we developed Si:S barrier detectors employing sulfur doped silicon and a sophisticated band barrier design. Since the transport of dark current and photo current is separated, the barrier design effectively suppresses the dark current while allowing the photo current to leverage gain mechanisms, thereby substantially improving signal-to-noise ratio. As a result, the detector exhibits an infrared response range covering from 1.12 to 4.4μm with a peak at 3.3μm, excluding its intrinsic response in visible range. Its peak quantum efficiency surpasses that of the best mid-infrared silicon-based detector reported to date by an order of magnitude, reaching 2% at room temperature. The peak detectivity at 90K is 1.4E11 Jones @1.4V and decreases to 4.4E9 Jones @1.4V, 210K, comparable to the typical III-V and IV-VI photodetectors at one thousandth fabrication cost. Leveraging the well-established silicon-based manufacturing process, this device holds promise for large-scale production at a reduced price, offering a cost-effective solution for future mid-infrared detection.

physics.app-ph↗

arXiv2024

Messenger RNA Design via Expected Partition Function and Continuous Optimization

The tasks of designing RNAs are discrete optimization problems, and several versions of these problems are NP-hard. As an alternative to commonly used local search methods, we formulate these problems as continuous optimization and develop a general framework for this optimization based on a generalization of classical partition function which we call "expected partition function". The basic idea is to start with a distribution over all possible candidate sequences, and extend the objective function from a sequence to a distribution. We then use gradient descent-based optimization methods to improve the extended objective function, and the distribution will gradually shrink towards a one-hot sequence (i.e., a single sequence). As a case study, we consider the important problem of mRNA design with wide applications in vaccines and therapeutics. While the recent work of LinearDesign can efficiently optimize mRNAs for minimum free energy (MFE), optimizing for ensemble free energy is much harder and likely intractable. Our approach can consistently improve over the LinearDesign solution in terms of ensemble free energy, with bigger improvements on longer sequences.

q-bio.BM↗

arXiv2024

Detecting dark matter with extreme mass-ratio inspirals

Extreme mass ratio inspirals (EMRIs), where a small compact object inspiralls onto a supermassive black hole, are excellent sources for the space-based laser interferometer gravitational wave (GW) detectors. The presence of dark matter surrounding the supermassive black hole will influence the binary orbital evolution and emitted gravitational waveform. By direct observation of GW signals, we assess the detector's capability to detect whether an EMRI is immersed in a dark matter halo and to measure its characteristic spatial scale $a_0$ and mass $M_{\rm halo}$. Apart from the GW emission, the dynamical friction and accretion caused by the dark matter halo can also affect the dynamics of an EMRI, leaving detectable signatures in the emitted gravitational signal. We perform a Fisher-matrix error analysis to estimate the errors of parameters $a_0$ and $M_{\rm halo}$, as well as their correlation. The results show that the highly correlated parameters $a_0$ and $M_{\rm halo}$ deteriorate the detector's ability to measure dark matter even though the dephasing and mismatch between signals with and without dark matter indicate much difference. The effects of the dynamical friction and accretion can break possible degeneracies between parameters $a_0$ and $M_{\rm halo}$, thus greatly decreasing the uncertainty by about one order of magnitude.

gr-qc↗

arXiv2023

Importance of including higher signal harmonics in the modeling of extreme mass-ratio inspirals

Extreme mass-ratio inspirals (EMRIs) are the most potential sources detectable by the Laser Interferometer Space Antenna (LISA). To analyze the influence of higher harmonics on parameter estimation for EMRIs efficiently, we use the waveform model that the phase trajectories are relativistic flux-based adiabatic trajectories and the waveforms are constructed by the augmented analytic kludge method. We perform a Fisher-matrix error analysis of the EMRI parameters using signals taking into account the motion of the LISA constellation and higher harmonics of gravitational waves. Our results demonstrate that including higher harmonics greatly reduces the errors on the exterior parameters such as inclination angle $ι$, the luminosity distance $d_L$, the polarization angle $ψ$, and the initial phase $Φ_0$, except for source localization $ΔΩ$ when EMRIs face us. However, the influence of higher harmonics on parameters $(ι,d_L,ψ,Φ_0)$ can be negligible when the inclination angle is above $1.0$. For intrinsic parameters such as the spin of central black and the masses of binaries, the influence of higher harmonics can be negligible for any inclination angle. Our findings are independent of the mass or spin of the EMRI system.

gr-qc↗

arXiv2023

LinearSankoff: Linear-time Simultaneous Folding and Alignment of RNA Homologs

The classical Sankoff algorithm for the simultaneous folding and alignment of homologous RNA sequences is highly influential, but it suffers from two major limitations in efficiency and modeling power. First, it takes $O(n^6)$ for two sequences where n is the average sequence length. Most implementations and variations reduce the runtime to $O(n^3)$ by restricting the alignment search space, but this is still too slow for long sequences such as full-length viral genomes. On the other hand, the Sankoff algorithm and all its existing implementations use a rather simplistic alignment model, which can result in poor alignment accuracy. To address these problems, we propose LinearSankoff, which seamlessly integrates the original Sankoff algorithm with a powerful Hidden Markov Model-based alignment module. This extension substantially improves alignment quality, which in turn benefits secondary structure prediction quality, confirmed over a diverse set of RNA families. LinearSankoff also applies beam search heuristics and the A$^\star$-like algorithm to achieve that runtime scales linearly with sequence length. LinearSankoff is the first linear-time algorithm for simultaneous folding and alignment, and the first such algorithm to scale to coronavirus genomes (n $\approx$ 30,000nt). It only takes 10 minutes for a pair of SARS-CoV-2 and SARS-related genomes, and outperforms previous work at identifying crucial conserved structures between the two genomes.

q-bio.BM↗

arXiv2023

Detecting new fundamental fields with Pulsar Timing Arrays

Strong evidence of the existence of the Stochastic Gravitational-Wave Background (SGWB) has been reported by the NANOGrav, PPTA, EPTA and CPTA collaborations. The Bayesian posteriors of the Gravitational-Wave Background (GWB) amplitude and spectrum are compatible with current astrophysical predictions for the GWB from the population of supermassive black hole binaries (SMBHBs). In this paper, we discuss the corrections arising from the extra scalar or vector radiation to the characteristic dimensionless strain in PTA experiments and explore the possibility to detect charges surrounding massive black holes, which could give rise to SGWB with vector or scalar polarizations. The parametrized frequency-dependent characteristic dimensionless strain is used to take a Bayesian analysis and the Bayes factor is also computed for charged and neutral SMBHBs. The Bayesian posterior of GWB tensor amplitude is $\log_{10} A_T=-14.85^{+0.26}_{-0.38}$ and spectral exponent $α=-0.60^{+0.32}_{-0.36}$. The Bayesian posterior for vector or scalar amplitude $A_{V, S}$ is nearly flat and there is nearly no constraint from the current observation data. The Bayesian factor is $0.71$ far less than 100, so the current observation can not support the existence of the charged SMBHB.

gr-qc↗