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Generative modeling of crystal data distribution is an important yet challenging task due to the unique periodic physical symmetry of crystals. Diffusion-based methods have shown early promise in modeling crystal distribution. More recently, Bayesian Flow Networks were introduced to aggregate noisy latent variables, resulting in a variance-reduced parameter space that has been shown to be advantageous for modeling Euclidean data distributions with structural constraints (Song et al., 2023). Inspired by this, we seek to unlock its potential for modeling variables located in non-Euclidean manifolds e.g. those within crystal structures, by overcoming challenging theoretical issues. We introduce CrysBFN, a novel crystal generation method by proposing a periodic Bayesian flow, which essentially differs from the original Gaussian-based BFN by exhibiting non-monotonic entropy dynamics. To successfully realize the concept of periodic Bayesian flow, CrysBFN integrates a new entropy conditioning mechanism and empirically demonstrates its significance compared to time-conditioning. Extensive experiments over both crystal ab initio generation and crystal structure prediction tasks demonstrate the superiority of CrysBFN, which consistently achieves new state-of-the-art on all benchmarks. Surprisingly, we found that CrysBFN enjoys a significant improvement in sampling efficiency, e.g., ~100x speedup 10 v.s. 2000 steps network forwards) compared with previous diffusion-based methods on MP-20 dataset. Code is available at https://github.com/wu-han-lin/CrysBFN., Comment: Accepted to ICLR25

Listeners adapt language comprehension based on their mental representations of speakers, but how these representations are dynamically updated remains unclear. We investigated whether listeners probabilistically adapt their comprehension based on the likelihood of speakers producing stereotype-incongruent utterances. Our findings reveal two potential mechanisms: a speaker-general mechanism that adjusts overall expectations about speaker-content relationships, and a speaker-specific mechanism that updates individual speaker models. In two EEG experiments, participants heard speakers make stereotype-congruent or incongruent utterances, with incongruency base rate manipulated between blocks. In Experiment 1, speaker incongruency modulated both high-beta (21-30 Hz) and theta (4-6 Hz) oscillations: incongruent utterances decreased oscillatory power in low base rate condition but increased it in high base rate condition. The theta effect varied with listeners' openness trait: less open participants showed theta increases to speaker-incongruencies, suggesting maintenance of speaker-specific information, while more open participants showed theta decreases, indicating flexible model updating. In Experiment 2, we dissociated base rate from the target speaker by manipulating the overall base rate using an alternative non-target speaker. Only the high-beta effect persisted, showing power decrease for speaker-incongruencies in low base rate condition but no effect in high base rate condition. The high-beta oscillations might reflect the speaker-general adjustment, while theta oscillations may index the speaker-specific model updating. These findings provide evidence for how language processing is shaped by social cognition in real time.

The identity of a speaker significantly influences spoken language comprehension by affecting both perception and expectation. This review explores speaker effects, focusing on how speaker information impacts language processing. We propose an integrative model featuring the interplay between bottom-up perception-based processes driven by acoustic details and top-down expectation-based processes driven by a speaker model. The acoustic details influence lower-level perception, while the speaker model modulates both lower-level and higher-level processes such as meaning interpretation and pragmatic inferences. We define speaker-idiosyncrasy and speaker-demographics effects and demonstrate how bottom-up and top-down processes interact at various levels in different scenarios. This framework contributes to psycholinguistic theory by offering a comprehensive account of how speaker information interacts with linguistic content to shape message construction. We suggest that speaker effects can serve as indices of a language learner's proficiency and an individual's characteristics of social cognition. We encourage future research to extend these findings to AI speakers, probing the universality of speaker effects across humans and artificial agents., Comment: 44 pages, 1 figure

Recent advancements in diffusion models have significantly improved performance in super-resolution (SR) tasks. However, previous research often overlooks the fundamental differences between SR and general image generation. General image generation involves creating images from scratch, while SR focuses specifically on enhancing existing low-resolution (LR) images by adding typically missing high-frequency details. This oversight not only increases the training difficulty but also limits their inference efficiency. Furthermore, previous diffusion-based SR methods are typically trained and inferred at fixed integer scale factors, lacking flexibility to meet the needs of up-sampling with non-integer scale factors. To address these issues, this paper proposes an efficient and elastic diffusion-based SR model (E$^2$DiffSR), specially designed for continuous-scale SR in remote sensing imagery. E$^2$DiffSR employs a two-stage latent diffusion paradigm. During the first stage, an autoencoder is trained to capture the differential priors between high-resolution (HR) and LR images. The encoder intentionally ignores the existing LR content to alleviate the encoding burden, while the decoder introduces an SR branch equipped with a continuous scale upsampling module to accomplish the reconstruction under the guidance of the differential prior. In the second stage, a conditional diffusion model is learned within the latent space to predict the true differential prior encoding. Experimental results demonstrate that E$^2$DiffSR achieves superior objective metrics and visual quality compared to the state-of-the-art SR methods. Additionally, it reduces the inference time of diffusion-based SR methods to a level comparable to that of non-diffusion methods.

Spoken language is often, if not always, understood in a context formed by the identity of the speaker. For example, we can easily make sense of an utterance such as "I'm going to have a manicure this weekend" or "The first time I got pregnant I had a hard time" when spoken by a woman, but it would be harder to understand when it is spoken by a man. Previous event-related potential (ERP) studies have shown mixed results regarding the neurophysiological responses to such speaker-content mismatches, with some reporting an N400 effect and others a P600 effect. In an electroencephalography (EEG) experiment involving 64 participants, we used social and biological mismatches as test cases to demonstrate how these distinct ERP patterns reflect different aspects of rational inference. We showed that when the mismatch involves social stereotypes (e.g., men getting a manicure), listeners can arrive at a "literal" interpretation by integrating the content with their social knowledge, though this integration requires additional effort due to stereotype violations-resulting in an N400 effect. In contrast, when the mismatch involves biological knowledge (e.g., men getting pregnant), a "literal" interpretation becomes impossible, leading listeners to treat the input as potentially containing errors and engage in correction processes-resulting in a P600 effect. Supporting this rational inference framework, we found that the social N400 effect decreased as a function of the listener's personality trait of openness (as more open-minded individuals maintain more flexible social expectations), while the biological P600 effect remained robust (as biological constraints are recognized regardless of individual personalities). Our findings help to reconcile the empirical inconsistencies and show how rational inference shapes speaker-contextualized language comprehension., Comment: The manuscript is under review

Remote sensing (RS) change analysis is vital for monitoring Earth's dynamic processes by detecting alterations in images over time. Traditional change detection excels at identifying pixel-level changes but lacks the ability to contextualize these alterations. While recent advancements in change captioning offer natural language descriptions of changes, they do not support interactive, user-specific queries. To address these limitations, we introduce ChangeChat, the first bitemporal vision-language model (VLM) designed specifically for RS change analysis. ChangeChat utilizes multimodal instruction tuning, allowing it to handle complex queries such as change captioning, category-specific quantification, and change localization. To enhance the model's performance, we developed the ChangeChat-87k dataset, which was generated using a combination of rule-based methods and GPT-assisted techniques. Experiments show that ChangeChat offers a comprehensive, interactive solution for RS change analysis, achieving performance comparable to or even better than state-of-the-art (SOTA) methods on specific tasks, and significantly surpassing the latest general-domain model, GPT-4. Code and pre-trained weights are available at https://github.com/hanlinwu/ChangeChat., Comment: 5 pages, 2 figures

Growth parameters play a significant role in the crystal quality and physical properties of layered materials. Here we present a case study on a van der Waals magnetic NbFeTe2 material. Two different types of polymorphic NbFeTe2 phases, synthesized at different temperatures, display significantly different behaviors in crystal symmetry, electronic structure, electrical transport, and magnetism. While the phase synthesized at low temperature showing behavior consistent with previous reports, the new phase synthesized at high temperature, has completely different physical properties, such as metallic resistivity, long-range ferromagnetic order, anomalous Hall effect, negative magnetoresistance, and distinct electronic structures. Neutron diffraction reveals out-of-plane ferromagnetism below 70K, consistent with the electrical transport and magnetic susceptibility studies. Our work suggests that simply tuning synthetic parameters in a controlled manner could be an effective route to alter the physical properties of existing materials potentially unlocking new states of matter, or even discovering new materials., Comment: 22 Pages, 6 figures

In this work, we report multiple intercalation stages and universal Tc enhancement of superconductivity in 1T-SnSe2 through Li and organic molecules cointercalation. We observe significantly increased lattice parameters up to 40 {\AA} and dramatically enlarged interlayer distance up to ~11{\AA} in Li and N,N-dimethylformamide (DMF) cointercalated SnSe2. Well-separated cointercalation stages with different stacking patterns have been discovered by carefully controlled reaction time and concentration of solutions. These cointercalation stages are superconductors showing different superconducting signals. In addition, Li and various organic species such as Acetone, Dimethyl sulfoxide (DMSO) and Tetrahydrofuran (THF) have been cointercalated into SnSe2 crystals, all of which show enhanced superconducting Tc compared to solely Li intercalated SnSe2. Our findings may provide more insight to effectively tune electronic structure of the lamellar structure through organic molecules co-regulation, and open a new strategy to engineer the physical properties of these layered materials by controlling their different intercalation stages., Comment: 21 pages,5 figures

Quasi-one-dimensional electron systems display intrinsic instability towards long-range ordered phases at sufficiently low temperatures. The superconducting orders are of particular interest as they can possess either singlet or triplet pairing symmetry and frequently compete with magnetism. Here we report on muon spin rotation and relaxation ($\mathrm{\mu}$SR) study of Rb$_2$Mo$_3$As$_3$ characterised by one of the highest critical temperatures $T_{\rm c}=10.4\ \mathrm{K}$ among quasi-one-dimensional superconductors. The transverse-field $\mathrm{\mu}$SR signal shows enhanced damping below $T_{\rm c}$ due to the formation of vortex lattice. Comparison of vortex lattice broadening against single gap $s-$, $p-$ and $d-$wave models shows the best agreement for the $s-$wave scenario but with the anomalously small superconducting gap, $\Delta_0$, to $T_{\rm c}$ ratio of $2\Delta_0/k_{\rm B}T_{\rm c}=2.74(1)$. The alternative nodal $p-$wave or $d-$wave scenarios with marginally worse goodness of fit would yield more realistic $2\Delta_0/k_{\rm B}T_{\rm c}=3.50(2)$ and $2\Delta_0/k_{\rm B}T_{\rm c}=4.08(1)$, respectively, and thus they cannot be ruled out when accounting for the superconducting state in Rb$_2$Mo$_3$As$_3$., Comment: 6 pages

The paradigm of classifying three-dimensional (3D) topological insulators into strong and weak ones (STI and WTI) opens the door for the discovery of various topological phases of matter protected by different symmetries and defined in different dimensions. However, in contrast to the vast realization of STIs, very few materials have been experimentally identified as being close to WTI. Even amongst those identified, none exists with topological surface states (TSS) exposed in a global bulk band gap that is stable at all temperatures. Here we report the design and observation of an ideal WTI in a quasi-one-dimensional (quasi-1D) bismuth halide, Bi$_{4}$I$_{1.2}$Br$_{2.8}$ (BIB). Via angle-resolved photoemission spectroscopy (ARPES), we identify that BIB hosts TSS on the (100)$\prime$ side surface in the form of two anisotropic $\pi$-offset Dirac cones (DCs) separated in momentum while topologically dark on the (001) top surface. The ARPES data fully determine a unique side-surface Hamiltonian and thereby identify two pairs of non-degenerate helical saddle points and a series of four Lifshitz transitions. The fact that both the surface Dirac and saddle points are in the global bulk band gap of 195 meV, combined with the small Dirac velocities, nontrivial spin texture, and the near-gap chemical potential, qualifies BIB to be not only an ideal WTI but also a fertile ground for topological many-body physics.

Remote sensing images (RSIs) in real scenes may be disturbed by multiple factors such as optical blur, undersampling, and additional noise, resulting in complex and diverse degradation models. At present, the mainstream SR algorithms only consider a single and fixed degradation (such as bicubic interpolation) and cannot flexibly handle complex degradations in real scenes. Therefore, designing a super-resolution (SR) model that can cope with various degradations is gradually attracting the attention of researchers. Some studies first estimate the degradation kernels and then perform degradation-adaptive SR but face the problems of estimation error amplification and insufficient high-frequency details in the results. Although blind SR algorithms based on generative adversarial networks (GAN) have greatly improved visual quality, they still suffer from pseudo-texture, mode collapse, and poor training stability. In this article, we propose a novel blind SR framework based on the stochastic normalizing flow (BlindSRSNF) to address the above problems. BlindSRSNF learns the conditional probability distribution over the high-resolution image space given a low-resolution (LR) image by explicitly optimizing the variational bound on the likelihood. BlindSRSNF is easy to train and can generate photo-realistic SR results that outperform GAN-based models. Besides, we introduce a degradation representation strategy based on contrastive learning to avoid the error amplification problem caused by the explicit degradation estimation. Comprehensive experiments show that the proposed algorithm can obtain SR results with excellent visual perception quality on both simulated LR and real-world RSIs.

Deep learning-based algorithms have greatly improved the performance of remote sensing image (RSI) super-resolution (SR). However, increasing network depth and parameters cause a huge burden of computing and storage. Directly reducing the depth or width of existing models results in a large performance drop. We observe that the SR difficulty of different regions in an RSI varies greatly, and existing methods use the same deep network to process all regions in an image, resulting in a waste of computing resources. In addition, existing SR methods generally predefine integer scale factors and cannot perform stepless SR, i.e., a single model can deal with any potential scale factor. Retraining the model on each scale factor wastes considerable computing resources and model storage space. To address the above problems, we propose a saliency-aware dynamic routing network (SalDRN) for lightweight and stepless SR of RSIs. First, we introduce visual saliency as an indicator of region-level SR difficulty and integrate a lightweight saliency detector into the SalDRN to capture pixel-level visual characteristics. Then, we devise a saliency-aware dynamic routing strategy that employs path selection switches to adaptively select feature extraction paths of appropriate depth according to the SR difficulty of sub-image patches. Finally, we propose a novel lightweight stepless upsampling module whose core is an implicit feature function for realizing mapping from low-resolution feature space to high-resolution feature space. Comprehensive experiments verify that the SalDRN can achieve a good trade-off between performance and complexity. The code is available at \url{https://github.com/hanlinwu/SalDRN}.

Single-image super-resolution (SR) with fixed and discrete scale factors has achieved great progress due to the development of deep learning technology. However, the continuous-scale SR, which aims to use a single model to process arbitrary (integer or non-integer) scale factors, is still a challenging task. The existing SR models generally adopt static convolution to extract features, and thus unable to effectively perceive the change of scale factor, resulting in limited generalization performance on multi-scale SR tasks. Moreover, the existing continuous-scale upsampling modules do not make full use of multi-scale features and face problems such as checkerboard artifacts in the SR results and high computational complexity. To address the above problems, we propose a scale-aware dynamic network (SADN) for continuous-scale SR. First, we propose a scale-aware dynamic convolutional (SAD-Conv) layer for the feature learning of multiple SR tasks with various scales. The SAD-Conv layer can adaptively adjust the attention weights of multiple convolution kernels based on the scale factor, which enhances the expressive power of the model with a negligible extra computational cost. Second, we devise a continuous-scale upsampling module (CSUM) with the multi-bilinear local implicit function (MBLIF) for any-scale upsampling. The CSUM constructs multiple feature spaces with gradually increasing scales to approximate the continuous feature representation of an image, and then the MBLIF makes full use of multi-scale features to map arbitrary coordinates to RGB values in high-resolution space. We evaluate our SADN using various benchmarks. The experimental results show that the CSUM can replace the previous fixed-scale upsampling layers and obtain a continuous-scale SR network while maintaining performance. Our SADN uses much fewer parameters and outperforms the state-of-the-art SR methods.

We report a layered ternary selenide BaPt4Se6 featuring sesqui-selenide Pt2Se3 layers sandwiched by Ba atoms. The Pt2Se3 layers in this compound can be derived from the Dirac-semimetal PtSe2 phase with Se vacancies that form a honeycomb structure. This structure results in a Pt (VI) and Pt (II) mixed-valence compound with both PtSe6 octahedra and PtSe4 square net coordination configurations. Temperature dependent electrical transport measurements suggest two distinct anomalies: a resistivity crossover, mimic to the metal-insulator (M-I) transition at ~150K, and a resistivity plateau at temperatures below 10K. The resistivity crossover is not associated with any structural, magnetic or charge order modulated phase transitions. Magnetoresistivity, Hall and heat capacity measurements concurrently suggest an existing hidden state below 5K in this system. Angle-resolved photoemission spectroscopy measurements reveal a metallic state and no dramatic reconstruction of the electronic structure up to 200K., Comment: 25 pages, 5 figures

We report a combined experimental and theoretical study of the PdSe2-xTex system. With increasing Te fraction, structural evolutions, first from an orthorhombic phase (space group Pbca) to a monoclinic phase (space group C2/c) and then to a trigonal phase (space group P-3m1), are observed accompanied with clearly distinct electrical transport behavior. The monoclinic phase (C2/c) is a completely new polymorphic phase and is discovered within a narrow range of Te composition (0.3 \leq x \leq 0.8). This phase has a different packing sequence from all known transition metal dichalcogenides to date. Electronic calculations and detailed transport analysis of the new polymorphic PdSe1.3Te0.7 phase are presented. In the trigonal phase region, superconductivity with enhanced critical temperature is also observed within a narrow range of Te content (1.0 \leq x \leq 1.2). The rich phase diagram, new polymorphic structure as well as abnormally enhanced superconductivity could further stimulate more interest to explore new types of polymorphs and investigate their transport and electronic properties in the transition metal dichalcogenides family that are of significant interest., Comment: 16 pages, 7 figures

Two-dimensional transition metal dichalcogenide PdTe$_2$ recently attracts much attention due to its phase coexistence of type-II Dirac semimetal and type-I superconductivity. Here we report a 67 % enhancement of superconducting transition temperature in the 1T-PdSeTe in comparison to that of PdTe2 through partial substitution of Te atoms by Se. The superconductivity has been unambiguously confirmed by the magnetization, resistivity and specific heat measurements. 1T-PdSeTe shows type-II superconductivity with large anisotropy and non-bulk superconductivity nature with volume fraction ~ 20 % estimated from magnetic and heat capacity measurements. 1T-PdSeTe expands the family of superconducting transition metal dichalcogenides and thus provides additional insights for understanding superconductivity and topological physics in the 1T-PdTe$_2$ system, Comment: 7 pages, 6 figures

In this work, we have thoroughly studied the effects of flux composition and temperature on the crystal growth of the BaCu2As2 compound. While Pb and CuAs self-flux produce the well-known {\alpha}-phase ThCr2Si2-type structure (Z=2), a new polymorphic phase of BaCu2As2 (\b{eta} phase) with a much larger c lattice parameter (Z=10), which could be considered an intergrowth of the ThCr2Si2- and CaBe2Ge2-type structures, has been discovered via Sn flux growth. We have characterized this structure through single-crystal X-ray diffraction, transmission electron microscopy (TEM), and scanning transmission electron microscopy (STEM) studies. Furthermore, we compare this new polymorphic intergrowth structure with the {\alpha}-phase BaCu2As2 (ThCr2Si2 type with Z=2) and the \b{eta}-phase BaCu2Sb2 (intergrowth of ThCr2Si2 and CaBe2Ge2 types with Z=6), both with the same space group I4/mmm. Electrical transport studies reveal p-type carriers and magnetoresistivity up to 22% at 5 K and under a magnetic field of 7 T. Our work suggests a new route for the discovery of new polymorphic structures through flux and temperature control during material synthesis., Comment: 15 pages, 4 figures

Boron arsenide (c-BAs) is at the forefront of research on ultrahigh thermal conductivity materials. We present a Raman scattering study of isotopically tailored cubic boron arsenide single crystals for 11 isotopic compositions spanning the range from nearly pure c-$^{10}$BAs to nearly pure c-$^{11}$BAs. Our results provide insights on the effects of strong mass disorder on optical phonons and the appearance of two-mode behavior in the Raman spectra of mixed crystals. Strong isotope disorder also relaxes the one-phonon Raman selection rules, resulting in disorder-activated Raman scattering by acoustic phonons., Comment: 16 pages, 6 figures, "submitted to Physical Review Materials"

Recent reported very high thermal conductivities in the cubic boron arsenide (BAs) and boron phosphide (BP) crystals could potentially provide a revolutionary solution in the thermal management of high power density devices. To fully facilitate such application, compatible coefficient of thermal expansion (CTE) between the heat spreader and device substrate, in order to minimize the thermal stress, need to be considered. Here we report our experimental CTE studies of BAs and BP in the temperature range from 100K to 1150K, through a combination of X-ray single crystal diffraction and neutron powder diffraction. We demonstrated the room temperature CTE, 3.6 $\pm$ 0.15 $\times$ 10E-6 /K for BAs and 3.2 $\pm$ 0.2 $\times$ 10E-6 /K for BP, are more compatible with most of the semiconductors including Si and GaAs, in comparison with diamond, and thus could be better candidates for the future heat spreader materials in power electronic devices., Comment: 13 pages, 3 figures, accepted by applied physics letters

The weak van der Waals interlayer interactions in the transition metal dichalcogenide (TMD) materials have created a rich platform to study their exotic electronic properties through chemical doping or physical gating techniques. We reported bulk superconductivity up to 7.6 K through careful manipulation of the charge carrier density and interlayer spacing d in the chemically intercalated two dimensional 1T-SnSe2 phase. We found, for the first time in the two dimensional SnSe2, that polar organic molecules cointercalated with the alkali metal Li into the basal layers, thus significantly enhancing the superconducting Tc. We observed that the Tc scales with the basal spacing distance, meanwhile being almost independent of x in Lix(THF)ySnSe2 system. Our results offers a new general chemical route to study the rich electron correlations and the interplay of charge density wave and unconventional superconductivity in the two dimensional material., Comment: 6 pgaes, 6 figures