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Recent neural networks (NNs) with self-attention exhibit competitiveness across different AI domains, but the essential attention mechanism brings massive computation and memory demands. To this end, various sparsity patterns are introduced to reduce the quadratic computation complexity, among which the structured butterfly sparsity has been proven efficient in computation reduction while maintaining model accuracy. However, its complicated data accessing pattern brings utilization degradation and makes parallelism hard to exploit in general block-oriented architecture like GPU. Since the reconfigurable dataflow architecture is known to have better data reusability and architectural flexibility in general NN-based acceleration, we want to apply it to the butterfly sparsity for acquiring better computational efficiency for attention workloads. We first propose a hybrid butterfly-sparsity network to obtain better trade-offs between attention accuracy and performance. Next, we propose a scalable multilayer dataflow method supported by coarse-grained streaming parallelism designs, to orchestrate the butterfly sparsity computation on the dataflow array. The experiments show that compared with Jetson Xavier NX, our design has a speedup of up to $14.34\times$ ($9.29\times$ on average) as well as $11.14\times$ energy efficiency advancement in attention workloads. In comparison with SOTA attention accelerators of the same peak performance, our dataflow architecture acquires $2.38\times$-$4.7\times$ efficiency improvement as well as $6.60\times$-$15.37\times$ energy reduction with butterfly sparsity optimization., Comment: 9 pages, 17 figures, ICCAD 2024, 2024/07/05, Butterfly Sparsity Optimization Using Dataflow

Decision Transformers have recently emerged as a new and compelling paradigm for offline Reinforcement Learning (RL), completing a trajectory in an autoregressive way. While improvements have been made to overcome initial shortcomings, online finetuning of decision transformers has been surprisingly under-explored. The widely adopted state-of-the-art Online Decision Transformer (ODT) still struggles when pretrained with low-reward offline data. In this paper, we theoretically analyze the online-finetuning of the decision transformer, showing that the commonly used Return-To-Go (RTG) that's far from the expected return hampers the online fine-tuning process. This problem, however, is well-addressed by the value function and advantage of standard RL algorithms. As suggested by our analysis, in our experiments, we hence find that simply adding TD3 gradients to the finetuning process of ODT effectively improves the online finetuning performance of ODT, especially if ODT is pretrained with low-reward offline data. These findings provide new directions to further improve decision transformers., Comment: Accepted as NeurIPS 2024 spotlight. 33 pages, 26 figures

The reference field method, known as the difference formulation, is a key variance reduction technique for Monte Carlo simulations of thermal radiation transport problems. When the material temperature is relatively high and the spatial temperature gradient is moderate, this method demonstrates significant advantages in reducing variance compared to classical Monte Carlo methods. However, in problems with larger temperature gradients, this method has not only been found ineffective at reducing statistical noise, but in some cases, it even increases noise compared to classical Monte Carlo methods. The global optimal reference field method, a recently proposed variance reduction technique, effectively reduces the average energy weight of Monte Carlo particles, thereby decreasing variance. Its effectiveness has been validated both theoretically and numerically, demonstrating a significant reduction in statistical errors for problems with large temperature gradients. In our previous work, instead of computing the exact global optimal reference field, we developed an approximate, physically motivated method to find a relatively better reference field using a selection scheme. In this work, we reformulate the problem of determining the global optimal reference field as a linear programming problem and solve it exactly. To further enhance computational efficiency, we use the MindOpt solver, which leverages graph neural network methods. Numerical experiments demonstrate that the MindOpt solver not only solves linear programming problems accurately but also significantly outperforms the Simplex and interior-point methods in terms of computational efficiency. The global optimal reference field method combined with the MindOpt solver not only improves computational efficiency but also substantially reduces statistical errors.

The residual stress greatly affects the mechanical behavior of a material. In this work, the effect of residual stress on the isothermal tensile behavior of a NiTi shape memory alloy is studied. The focused ion beam and digital image correlation are combined to measure the two-dimensional residual stress in nanocrystalline NiTi plates processed with prestrain laser shock peening. A four-point bending experiment verified the accuracy of this measurement method. The FIB-DIC method is an attractive tool for measuring the two-dimensional residual stress in phase transition nanocrystalline materials. The internal residual stress significantly decreases the phase transition stress, and the mechanism is studied via finite element and theoretical analyses. This work implies that the mechanical behavior of NiTi shape memory alloys can be tailored via residual stress engineering., Comment: 43 pages, 20 figures

Exploring high-efficiency and low-cost electrocatalysts is valuable for water-splitting technologies. Recently, Si-group compounds have attracted increasing attention in electrocatalysis, considering the abundant Si-group elements on Earth. However, Si-group compounds for HER electrocatalysis have not been systematically studied. In this study, we unveil the activity trends of non-noble metal catalyst A15-type V3M (i.e., V3Si, V3Ge, and V3Sn) superconductors and show that V3Si is the most efficient HER catalyst because of the high electronic conductivity and suitable d-band center. Among them, the V3Si only requires 33.4 mV to reach 10 mA cm-2, and only 57.6 mV and 114.6 mV are required to attain a high current density of 100 mA cm-2 and 500 mA cm-2, respectively. These low overpotentials are close to the 34.3 mV at 10 mA cm-2 of state-of-art Pt/C (20 %) but superior to 168.5 mV of Pt/C (20 %) at 100 mA cm-2. Furthermore, the V3Si illustrates exceptional durability with no obvious decay in the 120 h at the different current densities (i.e., 10 - 250 mA cm-2). The excellent HER activity of V3Si alloy can be ascribed to the synergies of superior electronic conductivity and suitable d-band center. Moreover, DFT calculations reveal that the absolute hydrogen adsorption Gibbs free energy is decreased after introducing the V to Si. Beyond offering a stable and high-performance electrocatalyst in an acidic medium, this work inspires the rational design of desirable silicide electrocatalysts., Comment: 25 pages, 5 figures

Topological semimetals with protected surface states mark a new paradigm of research beyond the early landmarks of band-structure engineering, allowing fabrication of efficient catalyst to harness the rich metallic surface states to activate specific chemical processes. Herein, we demonstrate a facile solid-phase method for in-situ doping of Ir at the Os sites in the Os3Sn7, an alloy with topological states, which significantly improves the photocatalytic performance for the reduction of CO2 to CO and CH4. Experimental evidence combined with theoretical calculations reveal that the nontrivial topological surface states greatly accelerate charge-separation/electron-enrichment and adsorption/activation of CO2 molecules, rendering highly efficient reaction channels to stimulate the formation of *COOH and *CO, as well CHO*. This work shows the promise of achieving high photocatalytic performances with synthesizing topological catalysts and provides hints on the design of novel topological catalysts with superior photoactivity towards the CO2 reduction reaction., Comment: 33 Pages, 6 Figures, 1 Table

Despite ongoing research, the rational design of nontrivial topological semimetal surface states for the selective photocatalytic CO$_2$ conversion into valuable products remains full of challenges. Herein, we present the synthesis of 1T-OsCoTe$_2$ for the photoreduction upgrading of CO$_2$ to tricarbon alkane C$_3$H$_8$,by the integration of experimental work and theory calculation. Experimental studies suggested a high electron based selectivity of 71.2% for C$_3$H$_8$ and an internal quantum efficiency of 54.6% at 380 nm. In-situ X-ray photoelectron spectroscopy and X-ray absorption fine structure spectroscopy demonstrated that Co and Os atoms coordinated with Te atoms enable an efficient Os-Te-Co electron transfer to activate the generation of *CH$_3$,*CHOCO and *CH$_2$OCOCO. Density functional theory calculations further confirmed Os-Te-Co electron bridging on the improved CO$_2$ conversion kinetics. To our knowledge, this is the first report suggesting the role of Os atoms in accelerating the photocatalytic CO$_2$ conversion activity of the topological semimetal 1T-OsCoTe$_2$., Comment: 31 pages, 6 Figures

Vector-based algorithms are novel algorithms in optimal any-angle path planning that are motivated by bug algorithms, bypassing free space by directly conducting line-of-sight checks between two queried points, and searching along obstacle contours if a check collides with an obstacle. The algorithms outperform conventional free-space planners such as A* especially when the queried points are far apart. The thesis presents novel search methods to speed up vector-based algorithms in non-convex obstacles by delaying line-of-sight checks. The "best hull" is a notable method that allows for monotonically increasing path cost estimates even without verifying line-of-sight, utilizing "phantom points" placed on non-convex corners to mimic future turning points. Building upon the methods, the algorithms R2 and R2+ are formulated, which outperform other vector-based algorithms when the optimal path solution is expected to have few turning points. Other novel methods include a novel and versatile multi-dimensional ray tracer for occupancy grids, and a description of the three-dimensional angular sector for future works., Comment: PhD Thesis

Frequency tunable qubit plays a significant role for scalable superconducting quantum processors. The state-of-the-art room-temperature electronics for tuning qubit frequency suffers from unscalable limit, such as heating problem, linear growth of control cables, etc. Here we propose a scalable scheme to tune the qubit frequency by using in situ superconducting circuit, which is based on radio frequency superconducting quantum interference device (rf-SQUID). We demonstrate both theoretically and experimentally that the qubit frequency could be modulated by inputting several single pulses into rf-SQUID. Compared with the traditional scheme, our scheme not only solves the heating problem, but also provides the potential to exponentially reduce the number of cables inside the dilute refrigerator and the room-temperature electronics resource for tuning qubit frequency, which is achieved by a time-division-multiplex (TDM) scheme combining rf-SQUID with switch arrays. With such TDM scheme, the number of cables could be reduced from the usual $\sim 3n$ to $\sim \log_2{(3n)} + 1$ for two-dimensional quantum processors comprising $n$ qubits and $\sim 2n$ couplers. Our work paves the way for large-scale control of superconducting quantum processor., Comment: 9 pages, 6 figures

Understanding individual ions in solutions is essential for advancing our knowledge of complex chemical systems. However, tracking and detecting ions at the single-ion level in liquid environments remains a challenge. We introduce a strategy for visualization and differentiation of different ions in liquid environment via point defects in hexagonal boron nitride (h-BN) as the ion sensor. Ions interacting with the optically active point defects in h-BN alter emission properties, allowing us to capture these changes and visualize single ions. Using Li+ in organic electrolytes as a model, we observed a spectral shift of over 10 nm upon Li+ addition, and an over 50 nm red shift with applied electric fields due to reactions between Li+ and h-BN point defects. Frequency domain analysis further revealed the rapid dynamics of ion migration and the slow electrochemical reactions. We further spectroscopically differentiated various ions (H+, Li+, Na+, K+, Zn2+, Al3+) in aqueous solution. Each ion, with its distinct electron cloud configuration, interacts distinctively with the electron clouds of h-BN defects, resulting in specific and identifiable spectroscopic signatures. This ion sensing platform enables the direct visualization and differentiation of individual ions in a liquid environment, offering insights into chemical reactions at the single-ion level. This capability presents potential applications in various fields involving ions in liquids, including but not limited to biology, battery technology, and environmental science.

The study of collinear behavior for gauge theories in the spacelike region is of great phenomenological and theoretical importance. We analytically calculate the two-loop spacelike splitting amplitude for the full color N=4 Super-Yang-Mills theory. The result is derived by two complementary methods starting from the known amplitude: one is based on a discontinuity analysis, while the other one is based on analytic continuation. Our result explicitly shows terms that violate naive factorization. However we show that factorization is restored at the level of color-summed unpolarized squared amplitudes at next-to-next-to-next-to leading order. We conjecture that the two-loop tripole terms in the generalized splitting amplitudes in QCD are identical to what we obtain in N=4 super Yang-Mills theory., Comment: second version with minor changes

Finding accurate ground state energy of a many-body system has been a major challenge in quantum chemistry. The integration of classic and quantum computers has shed new light on resolving this outstanding problem. Here we propose QiankunNet-VQE, a transformer based language models enforced with quantum computing to learn and generate quantum states. It has been implemented using up to 12 qubits and attaining an accuracy level competitive with state-of-the-art classical methods. By leveraging both quantum and classical resources, this scheme overcomes the limitations of variational quantum eigensolver(VQE) without the need for cumbersome error mitigation. Moreover, QiankunNet-VQE provides a different route to achieve a practical quantum advantage for solving many-electron Schr\"odinger equation without requiring extremely precise preparation and measurement of the ground-state wavefunction on quantum computer.

A vector-based any-angle path planner, R2, is evolved in to R2+ in this paper. By delaying line-of-sight, R2 and R2+ search times are largely unaffected by the distance between the start and goal points, but are exponential in the worst case with respect to the number of collisions during searches. To improve search times, additional discarding conditions in the overlap rule are introduced in R2+. In addition, R2+ resolves interminable chases in R2 by replacing ad hoc points with limited occupied-sector traces from target nodes, and simplifies R2 by employing new abstract structures and ensuring target progression during a trace. R2+ preserves the speed of R2 when paths are expected to detour around few obstacles, and searches significantly faster than R2 in maps with many disjoint obstacles., Comment: Submitted. The R2 mentioned in the paper is located at https://doi.org/10.1016/j.robot.2023.104606

The hydrodeoxygenation (HDO) of lignin-derived feedstocks into value-added chemicals with high efficiency and selectivity is desirable for the utilization of biomass resource. The complex oxygen-containing groups of lignin-derived substance result in the challenge of the low selectivity toward the required product. In this work, highly dispersed Ru nanoparticles anchored on Ni3Al1 layered double oxides (LDOs) catalyst derived from NiAl layered double hydroxides (LDHs) with flower-shaped morphology was constructed by a simple deposition-reduction method. The introduction of LDHs-derived support can significantly impact the catalytic activity for the HDO of lignin-derived vanillin (VL) into 2-methoxy-4-methylphenol (MMP). The Ru/Ni3Al1-400 catalyst obtained complete conversion of VL and 94.2% yield of MMP at 130 {\deg}C in methanol solvent, much better than the catalysts without LDHs-derived support. The methanol solvent is beneficial for the conversion of reaction intermediate of vanillin alcohol (VA). Detailed characterization reveals that the existence of the enhanced metal-support interaction over Ru/Ni3Al1-400 and the easily accessible acid sites facilitate the production of MMP.

Due to the permanent harm to human health and ecosystem balance, antibiotic pollution in water has become an important direction of current environmental governance. Spherical metal oxides (SMOs) have been frequently utilized as effective heterogeneous photocatalysts for the efficient degradation of antibiotics due to the unique properties (e.g., strong light absorption ability, high separation efficiency of photo-generated electron hole pairs, and good catalytic activity). This review will firstly focus on summarizing the rational design and synthesis of SMOs with various tuned microstructures such as hollow, porous shell, yolk shell, core shell, and nanoflowers. These structures can expose more active sites, achieve a higher utilization rate of light, enhance the mass transfer efficiency and improve the effective diffusion of reactive oxygen species (ROS). Secondly, this review will mainly analyze the intrinsic relationship between the structure of SMOs and its photocatalytic property, the ability to generate ROS, and the degradation pathway for antibiotics. Moreover, the photocatalytic mechanisms and recent progress of different SMOs catalysts for degrading typical antibiotics are compared in detail. Finally, challenges and prospects of future direction in the development of SMOs for antibiotic degradation are reviewed. It is expected to provide a rational design of SMOs catalysts for efficient photocatalytic degradation of environmental pollutants.

Nickel-based layered double hydroxides (LDHs) are promising electrode materials in the fields of energy storage (supercapacitors) and conversion (urea oxidation). The rational construction of atomic and electronic structure is crucial for nickel-based LDHs to realize their satisfactory electrochemical performance. Herein, we report a facile, ecofriendly, one-step synthesis process to construct petal-like oxygen-deficient NiAl-LDH nanosheets for hybrid super-capacitors (HSCs) and urea oxidation reaction (UOR). The asprepared NiAl-LDH nanosheets with rich oxygen vacancies possess a large specific surface area of 216.6 m2 g-1 and a desirable electronic conductivity of 3.45 * 10-4 S cm-1 to deliver an ultra-high specific capacitance of 2801 F g-1 (700 C g-1) at 1 A g-1. Furthermore, high specific energy of 50.0 W h kg-1 at 400 W kg-1 and excellent cycle stability with 91% capacitance retention after 10,000 cycles are achieved by the NiAl-LDHs/CFP (carbon fiber paper) (+)//YP-80F (a commercial activated carbon) (-) HSC. Besides, NiAl-LDH nanosheets also work as an efficient electrocatalyst for UOR, which only requires 1.42 V vs. reversible hydrogen electrode to drive 10 mA cm-2 in 1 mol L-1 KOH with 0.33 mol L-1 urea. This remarkable performance is superior to most reported values of previous candidates owing to the thin structure of NiAl-LDH nanosheets for exposing more active sites and abundant oxygen vacancies. In addition, various reaction parameters are investigated to optimize the electrochemical performance. In general, this work paves a new way for the architecture of multifunctional nanostructured energy materials.

Effective regulation of the morphology of transition metal spinel structures is crucial for creating efficient and stable bifunctional catalysts for electrocatalysis of water. In this work, micro-flower NiCo2O4 (F-NCO) assembled by nanosheets via a chemical template method for the simultaneous promotion of hydrogen evolution reaction (HER) and oxygen evolution reaction (OER). Electronic microscope analysis revealed that the thickness of the F-NCO catalyst was only 2.7% of that of the NiCo2O4 bulk (B-NCO), and this ultrathin lamellar structure was conducive to further exposure of the active site and improved reaction kinetics. The F-NCO catalyst exhibited superior HER and OER performance (10 = 236 and 310 mV) and robust long-term stability over the B-NCO catalyst in 1.0 M KOH, with a 2.68-fold and 4.16-fold increase in active surface area and a 0.42-fold and 0.61-fold decrease in charge transfer resistance values, respectively. This micro-flower-structured electrode has remarkable electrocatalytic property and long-term durability, providing a novel insight for characterizing cost-effective and high-performance bifunctional electrocatalysts.

Nanostructured catalysts have attracted the increased attention for biomass conversion into high-valued chemicals due to the rapid depletion of fossil resources and increasingly severe environmental issues. Supercritical carbon dioxide (scCO2) fluid is an attractive medium for synthesizing nanostructured materials due to its favorable properties. In this review, the properties of scCO2 and the roles of scCO2 in the fabrication of metal nanoparticles were assessed in detailed. A general overview of the synthesis of different types of metal nanoparticles (including metal oxide nanoparticles) using scCO2 and the relationship between the structure of the obtained metal nanoparticles and the preparation conditions such as reaction temperature and pressure, types of metal precursors, and deposition time are system summarized and compared in tables. Besides, compared to the meatal catalysts using the conventional methods, the catalysts obtained using scCO2 exhibited excellent catalytic performance on biomass conversion reactions, mainly focused on oxidation, hydrogenation reactions. Finally, opportunities and challenges of metal nanoparticle preparation using scCO2 for biomass valorization to chemicals and liquid fuels are highlighted. This review could be helpful for the rational design of more efficient metal catalysts for the selective synthesis of fine chemicals and fuels from biomass-derived chemicals., Comment: 66 pages, 21 figures

Developing a green and cost-effective catalytic system for the selective oxidation of biomass-derived alcohols is vital for the sustainable synthesis of fine chemicals. Herein, highly dispersed subnanometric amorphous CoOx clusters in anatase TiO2 nanosheets (Co-TiO2) fabricated by green solvent CO2 assisted approach could directly activate peroxymonosulfate (PMS) for the highly selective oxidation of various biomass-derived alcohols. Advanced characterizations (e.g., EXAFS, EPR, AC HAADF-STEM) reveal that a strong interaction of CoOx clusters and the anatase TiO2 support exist in Co-TiO2 and Co atom in Co-TiO2 is mainly consisted of Co2+ and Co3+. The Co-TiO2 catalyst offers superior catalytic performance in the conversion of six types of alcohols (e.g., benzyl alcohol (BAL), 5-hydroxymethylfurfural (HMF)) with high selectivity to produce corresponding aldehydes. Highly dispersed CoOx clusters and the interaction between CoOx clusters and TiO2 support contribute to the superior performance. Mechanism studies show that SO4 radicals play the dominant role in the selective oxidation of model reactant BAL and 1O2 participates in the non-radical pathway. DFT calculations are well matched with experiment and decipher that the strong interaction between CoOx clusters and TiO2 support promotes the formation of SO4 and SO5., Comment: 29 pages, 9 figures

Rational design and green synthesis of low-cost and robust catalysts efficient for the selective oxidation of various alcohols are full of challenges. Herein, we report a fast and solvent-free arc-melting (AM) method to controllably synthesize semimetal CoSi alloy (abbreviated as AM-CoSi) that is efficient for the base- and solvent-free oxidation of six types of aromatic alcohols. X-ray absorption fine structure (XAFS), electron paramagnetic resonance (EPR), and aberration corrected high angle annular dark field scanning transmission electron microscope (AC HAADF-STEM) confirmed the successful synthesis of AM-CoSi with rich Si vacancy (Siv). The as-prepared CoSi alloy catalysts exhibit an order of magnitude activity enhancement in the oxidation of model reactant benzyl alcohol (BAL) to benzyl benzoate (BBE) compared with its mono counterparts, whereas 70 % yield of BBE which is the highest yield to date. Experimental results and DFT calculations well verify that the CoSi alloy structure improves the BAL conversion and Si vacancy mainly contributes to the generation of BBE. After that, CoSi alloy maintains high stability and a potential pathway is rationally proposed. Besides, CoSi alloy also efficiently works for the selective oxidation of various alcohols with different groups. This work demonstrates for the first time that semimetal CoSi alloy is robust for the green oxidation of various alcohols and provides a vast opportunity for reasonable design and application of other semimetal alloy catalysts., Comment: 22 pages, 6 figures

Rational design and cost-effective fabrication of layered double hydroxides (LDHs) nanosheets with extraordinary electrochemical performance is a key challenge for hybrid supercapacitors (HSCs). Herein, we report a facile in situ growth methodology to eco-friendly synthesize hydrophilic NiCo-LDHs nanosheets on biomass waste-derived porous carbon (BC) for robust high-performance HSC cathode. The in situ growth process under ultrasonication realizes the rational arrangement of NiCo-LDHs nanosheets on the surface of BC, which effectively increases the specific surface area, promotes the electronic conductivity and enhances the wettability of NiCo-LDHs nanosheets without affecting their thickness values. With the beneficial effects of ultrathin thickness of LDHs nanosheets (6.20 nm), large specific surface area (2324.1 m2 g-1), low charge transfer resistance (1.65 ohm), and high wettability with electrolyte (34-35 degree), the obtained Ni2Co1-LDHs/BC50 electrode possesses an ultra-high specific capacitance of 2390 F g-1 (956 C g-1) at 1 A g-1, which is superior to most reported values. Furthermore, an assembled Ni2Co1-LDHs/BC50//YP-80F HSC delivers a maximum specific energy of 52.47 Wh kg-1 at 375 W kg-1, and maintains a high capacitance retention of 75.9% even after 4000 cycles. This work provides a facile approach to fabricate LDHs nanosheets based cathode materials for high-performance HSCs.

Rational synthesis of robust layered double hydroxides (LDHs) nanosheets for high-energy supercapacitors is full of challenges. Herein, we reported an ultrasonication-assisted strategy to eco-friendly fabricate NiFe-LDHs nanosheets for the enhanced capacitive behavior. The experimental results combined with different advanced characterization tools document that the utilization of ultrasonication has a profound effect on the morphology and thickness of the as-obtained NiFe-LDHs, alternatively affecting the capacitive behavior. It shows that NiFe-LDHs nanosheets prepared with 2-h ultrasonic treatments display the exceptional capacitive performance because of the synergetic effect of ultrathin thickness, large specific surface area, and high mesoporous volume. The maximum specific capacitance of Ni3Fe1-LDHs nanosheets with the thickness of 7.39 nm and the specific surface area of 77.16 m2 g-1 reached 1923 F g-1, which is competitive with most previously reported values. In addition, the maximum specific energy of the assembled NiFe-LDHs//AC asymmetric supercapacitor achieved 49.13 Wh kg-1 at 400 W kg-1. This work provides a green technology to fabricate LDHs nanosheets, and offers deep insights for understanding the relationship between the morphology/structure and capacitive behavior of LDHs nanosheets, which is helpful for achieving high-performance LDHs-based electrode materials.

Transition-metal-based layered double hydroxides (TM-LDHs) nanosheets are promising electrocatalysts in the renewable electrochemical energy conversion system, which are regarded as alternatives to noble metal-based materials. In this review, recent advances on effective and facile strategies to rationally design TM-LDHs nanosheets as electrocatalysts, such as increasing the number of active sties, improving the utilization of active sites (atomic-scale catalysts), modulating the electron configurations, and controlling the lattice facets, are summarized and compared. Then, the utilization of these fabricated TM-LDHs nanosheets for oxygen evolution reaction, hydrogen evolution reaction, urea oxidation reaction, nitrogen reduction reaction, small molecule oxidations, and biomass derivatives upgrading is articulated through systematically discussing the corresponding fundamental design principles and reaction mechanism. Finally, the existing challenges in increasing the density of catalytically active sites and future prospects of TM-LDHs nanosheets-based electrocatalysts in each application are also commented.

Electrocatalytic hydrogenation of nitrobenzene (Ph-NO2) reaction (EHNR) has been considered as a potential alternative to the traditional thermocatalytic process in the production of high-value aniline (Ph-NH2). However, due to the absence of robust catalyst and low surface H* coverage, the EHNR faces the challenges of undesired performance and indetermined mechanism. Herein, we construct a type of noble-metal free topological FeSi (M-FeSi) materials through a solvent-free microwave strategy for efficient EHNR in neutral medium. Impressively, benefiting from abundant active H* intermediates on the surface of M-FeSi catalyst, the topological M-FeSi catalyst exhibits 99.7% conversion of Ph-NO2 and 93.8% yield of Ph-NH2 after 200 C in neutral medium, which are superior to previous candidates and FeSi catalyst synthesized via the traditional arc-melting method under same conditions. Besides, theoretical calculations validate that high surface H* coverage over M-FeSi catalyst is conducive to switching the rate-determining step from Ph-NO2* Ph-NO* to Ph-NO* Ph-NHOH*, and thus decreasing the total energy barrier of electrocatalytic Ph-NH2 production.

Iron-based molybdenum disulfide (Fe-MoS$_2$) has emerged as a Fenton-like catalyst for the highly efficient degradation of antibiotics, but the structure-activity relationship remains elusive. Herein, garland-like MIL-101(Fe)/MoS$_2$ nanosheets (MMS) with dual metal active sites (Fe and Mo) and rich sulfur vacancies were fabricated to directly activate peroxymonosulfate (PMS) for fast degradation of different organic pollutants (phenols, dyes and drugs), even in real water bodies. The MMS exhibited extremely fast catalytic rate constant of 0.289 min$^{-1}$ in the degradation of sulfamethoxazole (SMX), which was about 36 and 29 times that of single MoS$_2$ (0.008 min$^{-1}$) and MIL-101(Fe) (0.01 min$^{-1}$). Moreover, MMS with good stability and reusability could reach 92% degradation of SMX after 5 cycles. Quenching experiments and electron spin resonance (ESR) tests revealed that hydroxyl radicals (.OH) and singlet oxygen ($^1$O$_2$) were the dominant reactive oxygen species (ROS) for SMX degradation. The integration of experimental works, characterization techniques and density functional theory (DFT) calculations unraveled that the formation of sulfur vacancies in MMS catalyst could expose more Mo sites, improve the charge density and boost the electron transfer, which was conducive to accelerating the Fe$^{3+}$/Fe$^{2+}$ cycle for enhancing the activation of PMS. Finally, the C-N, N-O, S-N, C-O and C-S bonds of SMX were easily attacked by ROS to generate the nontoxic intermediates in the MMS/PMS/SMX system. This study offers a new approach to designing high-performance Fe-MoS$_2$ catalysts for the removal of organic pollutants.

With the rapid industrial development, many dye pollutants have entered the water, along with heavy metals like As, leading to complex pollution that threatens the ecological environment and human health. Therefore, designing an effective strategy for treating complex dye wastewater is urgent. Herein, we have constructed high-purity pyrite FeS$_2$ nanoplates as bifunctional catalysts for the simultaneous removal of dyes and arsenite (As(III)).

The coexistence of tetracycline (TC) and arsenite (As(III)) in livestock wastewater threatens public health, and the heterogeneous Fenton-like system is a practical approach for the simultaneous removal of TC and As(III). In this work, fine CoFe$_2$O$_4$ nanoparticles are facilely anchored on heretically porous carbon (CoFe$_2$O$_4$@PC) via a microwave-assisted calcination method and used for eliminating TC and As(III) via peroxymonosulfate (PMS) activation.

Spinel oxides are recognized as promising Fenton-like catalysts for the degradation of antibiotics. However, the catalytic performance is restrained by the poor electron transfer rate (ETR). Herein, hollow NiCo2O4@C nanocages are rationally designed and prepared to accelerate ETR in peroxymonosulfate (PMS) activation for tetracycline (TC) degradation.

The famous Drazin inverse and generalized Drazin inverse were introduced by Drazin in 1958 and Koliha in 1996, respectively. In the present paper, the author introduces the concepts of left and right (generalized) Drazin inverses, which are the one-sided versions of classical (generalized) Drazin inverses, in Banach algebras. Several characterizations of one-sided (generalized) Drazin invertible operators on Banach spaces are given. By utilizing the one-sided Drazin invertible spectra, the characterizations of B-Fredholm spectra for Banach space operators are obtained. These perturbational results can be regarded as generalizations of classical Fredholm theory in Banach spaces., Comment: 28 pages, Comments welcome

We present a compact formula, expressed in terms of classical polylogarithms up to weight three, for the leading order four-point energy correlator in maximally supersymmetric Yang-Mills theory, in the limit where the four detectors are collinear. This formula is derived by combining a simplified, manifestly dual conformal invariant form of the 1 -> 4 splitting function obtained from the square of the tree-level five-particle form factor of stress-tensor multiplet operators, with a novel integration-by-parts algorithm operating directly on Feynman parameter integrals. Our results provide valuable data for exploring the structure of physical observables in perturbation theory, and for calculations of jet substructure observables in quantum chromodynamics., Comment: 9 pages, 4 figures

Recently, some research show that deep neural networks are vulnerable to the adversarial attacks, the well-trainned samples or patches could be used to trick the neural network detector or human visual perception. However, these adversarial patches, with their conspicuous and unusual patterns, lack camouflage and can easily raise suspicion in the real world. To solve this problem, this paper proposed a novel adversarial patch method called the Latent Diffusion Patch (LDP), in which, a pretrained encoder is first designed to compress the natural images into a feature space with key characteristics. Then trains the diffusion model using the above feature space. Finally, explore the latent space of the pretrained diffusion model using the image denoising technology. It polishes the patches and images through the powerful natural abilities of diffusion models, making them more acceptable to the human visual system. Experimental results, both digital and physical worlds, show that LDPs achieve a visual subjectivity score of 87.3%, while still maintaining effective attack capabilities.

In real-world scenarios, arbitrary interactions with the environment can often be costly, and actions of expert demonstrations are not always available. To reduce the need for both, offline Learning from Observations (LfO) is extensively studied: the agent learns to solve a task given only expert states and task-agnostic non-expert state-action pairs. The state-of-the-art DIstribution Correction Estimation (DICE) methods, as exemplified by SMODICE, minimize the state occupancy divergence between the learner's and empirical expert policies. However, such methods are limited to either $f$-divergences (KL and $chi^2$) or Wasserstein distance with Rubinstein duality, the latter of which constrains the underlying distance metric crucial to the performance of Wasserstein-based solutions. To enable more flexible distance metrics, we propose Primal Wasserstein DICE (PW-DICE). It minimizes the primal Wasserstein distance between the learner and expert state occupancies and leverages a contrastively learned distance metric. Theoretically, our framework is a generalization of SMODICE, and is the first work that unifies $f$-divergence and Wasserstein minimization. Empirically, we find that PW-DICE improves upon several state-of-the-art methods. The code is available at https://github.com/KaiYan289/PW-DICE., Comment: 25 pages. Accepted to ICML 2024

Offline imitation from observations aims to solve MDPs where only task-specific expert states and task-agnostic non-expert state-action pairs are available. Offline imitation is useful in real-world scenarios where arbitrary interactions are costly and expert actions are unavailable. The state-of-the-art "DIstribution Correction Estimation" (DICE) methods minimize divergence of state occupancy between expert and learner policies and retrieve a policy with weighted behavior cloning; however, their results are unstable when learning from incomplete trajectories, due to a non-robust optimization in the dual domain. To address the issue, in this paper, we propose Trajectory-Aware Imitation Learning from Observations (TAILO). TAILO uses a discounted sum along the future trajectory as the weight for weighted behavior cloning. The terms for the sum are scaled by the output of a discriminator, which aims to identify expert states. Despite simplicity, TAILO works well if there exist trajectories or segments of expert behavior in the task-agnostic data, a common assumption in prior work. In experiments across multiple testbeds, we find TAILO to be more robust and effective, particularly with incomplete trajectories., Comment: 35 pages; Accepted as a poster for NeurIPS2023

The general structure of infrared divergences in the scattering of massive particles is captured by the soft anomalous dimension matrix. The latter can be computed from a correlation function of multiple Wilson lines. The state-of-the-art two-loop result has a tantalizingly simple structure that is not manifest in the calculations. We argue that the complexity in intermediate steps of the known calculations comes from spurious, regulator-dependent terms. Based on this insight we propose a different infrared regulator that is associated to only one of the Wilson lines. We demonstrate that this streamlines obtaining the two-loop result: computing the required Feynman integrals via the differential equations method, only multiple polylogarithmic functions appear (to all orders in the dimensional regulator), as opposed to elliptic polylogarithms. We show that the new method is promising for higher-loop applications by computing a three-loop diagram of genuine complexity, and provide the answer in terms of multiple polylogarithms. The relatively simple symbol alphabet we obtain may be of interest for bootstrap approaches., Comment: 22 pages, 4 figures

While language models (LMs) have shown potential across a range of decision-making tasks, their reliance on simple acting processes limits their broad deployment as autonomous agents. In this paper, we introduce Language Agent Tree Search (LATS) -- the first general framework that synergizes the capabilities of LMs in reasoning, acting, and planning. By leveraging the in-context learning ability of LMs, we integrate Monte Carlo Tree Search into LATS to enable LMs as agents, along with LM-powered value functions and self-reflections for proficient exploration and enhanced decision-making. A key feature of our approach is the incorporation of an environment for external feedback, which offers a more deliberate and adaptive problem-solving mechanism that surpasses the constraints of existing techniques. Our experimental evaluation across diverse domains, including programming, interactive question-answering (QA), web navigation, and math, validates the effectiveness and generality of LATS in decision-making while maintaining competitive or improved reasoning performance. Notably, LATS achieves state-of-the-art pass@1 accuracy (92.7%) for programming on HumanEval with GPT-4 and demonstrates gradient-free performance (average score of 75.9) comparable to gradient-based fine-tuning for web navigation on WebShop with GPT-3.5. Code can be found at https://github.com/lapisrocks/LanguageAgentTreeSearch, Comment: Code at https://github.com/lapisrocks/LanguageAgentTreeSearch

Topological quantum materials (TQMs) have symmetry protected band structures with useful electronic properties that have applications in information, sensing, energy, and other technologies. In the past 10 years, the applications of TQMs in the field of energy conversion and storage mainly including water splitting, ethanol electro-oxidation, battery, supercapacitor, and relative energy-efficient devices have attracted increasing attention. The novel quantum states in TQMs provide a stable electron bath with high conductivity and carrier mobility, long lifetime, and determined spin states, making TQMs an ideal platform for understanding the surface reactions and looking for highly efficient materials for energy conversion and storage. In this Perspective, we present an overview of the recent progress regarding topological quantum catalysis. We describe the open problems, and the potential applications of TQMs in water splitting, batteries, supercapacitors, and other prospects in energy conversion and storage., Comment: 51 pages, 4 figures, 2 tables, 1 box

The high-energy diffuse gamma-ray emission and neutrino emission are expected from the Galactic plane, generated by hadronuclear interactions between cosmic rays (CR) and interstellar medium (ISM). Therefore, measurements of these diffuse emissions will provide important clues on the origin and nature of Galactic CRs. Comparing the latest observations of LHAASO and IceCube on the diffuse Galactic gamma-ray and neutrino emissions respectively, we suggest that the diffuse gamma-ray emission at multi-TeV energies contains a considerable contribution of a leptonic component. By modelling the gamma-ray halos powered by middle-aged pulsars in our Galaxy with taking into account the magnetic field configuration and the interstellar radiation field in the Galaxy, we demonstrate that the collective contribution of pulsar halos can account for the excess in the measured diffuse gamma-ray emission with respect to the predicted flux from CR-ISM interactions. The resulting one-dimensional profile along the Galactic longitude is also consistent with the observation., Comment: 28 pages, 13 figures; published in Nature Astronomy

We study the matrix quantum mechanics of two free hermitian $N\times N$ matrices subject to a singlet constraint in the microcanonical ensemble. This is the simplest example of a theory that at large $N$ has a confinement/deconfinement transition. In the microcanonical ensemble, it also exhibits partial confinement with a Hagedorn density of states. We argue that the entropy of these configurations, calculated by a counting of states based on the fact that Young diagrams are dominated by Young diagrams that have the VKLS shape. When the shape gets to the maximal depth allowed for a Young diagram of $SU(N)$, namely $N$, we argue that the system stops exhibiting the Hagedorn behavior. The number of boxes (energy) at the transition is $N^2/4$, independent of the charge of the state., Comment: 20 pages, 8 figures. v2: typos corrected, references added

A 3D digital scene contains many components: lights, materials and geometries, interacting to reach the desired appearance. Staging such a scene is time-consuming and requires both artistic and technical skills. In this work, we propose PSDR-Room, a system allowing to optimize lighting as well as the pose and materials of individual objects to match a target image of a room scene, with minimal user input. To this end, we leverage a recent path-space differentiable rendering approach that provides unbiased gradients of the rendering with respect to geometry, lighting, and procedural materials, allowing us to optimize all of these components using gradient descent to visually match the input photo appearance. We use recent single-image scene understanding methods to initialize the optimization and search for appropriate 3D models and materials. We evaluate our method on real photographs of indoor scenes and demonstrate the editability of the resulting scene components.