Abstract: Visualization of human brain activity is crucial for understanding normal and aberrant brain function. Currently available neural activity recording methods are highly invasive, have low sensitivity, and cannot be conducted outside of an operating room. Functional ultrasound imaging (fUSI) is an emerging technique that offers sensitive, large-scale, high-resolution neural imaging; however, fUSI cannot be performed through the adult human skull. Here, we used a polymeric skull replacement material to create an acoustic window compatible with fUSI to monitor adult human brain activity in a single individual. Using an in vitro cerebrovascular phantom to mimic brain vasculature and an in vivo rodent cranial defect model, first, we evaluated the fUSI signal intensity and signal-to-noise ratio through polymethyl methacrylate (PMMA) cranial implants of different thicknesses or a titanium mesh implant. We found that rat brain neural activity could be recorded with high sensitivity through a PMMA implant using a dedicated fUSI pulse sequence. We then designed a custom ultrasound-transparent cranial window implant for an adult patient undergoing reconstructive skull surgery after traumatic brain injury. We showed that fUSI could record brain activity in an awake human outside of the operating room. In a video game "connect the dots" task, we demonstrated mapping and decoding of task-modulated cortical activity in this individual. In a guitar-strumming task, we mapped additional task-specific cortical responses. Our proof-of-principle study shows that fUSI can be used as a high-resolution (200 μm) functional imaging modality for measuring adult human brain activity through an acoustically transparent cranial window.

Abstract: Electrochromic energy storage technology that can store energy electrochemically while controlling the optical transmittance, could be mainly used in the development of next-generation smart window systems for net-zero energy buildings. The resultant apparatus can first implement indoor optical and thermal modulation, then their involved residual electric energy would be reused to power small electronic products. However, there has been confusion as to how much of the electric energy can be used for secondary purposes. Herein, a hyperbranched polyamide with dual redox centra of triphenylamine and pentaaniline was synthesized with good processability and multi-band absorption. Coupled with a zinc frame electrode, an electrochromic energy storage window (EESW) was manufactured, which simultaneously exhibited approving electrochromic performance (high optical contrast of >48 % and good thermal insulation), as well as ideal zinc ions energy storage properties (wide voltage window of 2.4 V and large capacitance of 71.06 mF cm−2). Furthermore, the spectrochronoamperometry was first coupled with rate performance to investigate its energy reused properties. As implementing electrochromism upon 2.4 V for 50 s, the engaged EESW possessed an energy density of 20.21 μWh cm−2, due to the nonoptimal constant-voltage charging mode. After 1-hour transmittance maintenance without any applied voltage, an 80 % energy density of 16.54 μWh cm−2 was determined, which should be considered available electric energy that can be reused.

Abstract: Abstract Electrochromic smart windows (ESWs) are an effective energy‐saving technology for near‐zero energy buildings. They consume electric energy unidirectionally during a round‐trip coloring‐bleaching process, with the energy involved in the bleaching process being wasted. It is highly desirable to reuse this wasted electric energy directly and/or transfer it into other energy storage equipment, further enhancing the overall efficiency of electric energy usage. Herein, a zinc anode‐based ESW (ESW‐PZ) is reported that not only has fascinating visible–near‐infrared (VIS‐NIR) dual‐band electrochromic performance (a high optical contrast of 63%) but also showcases good energy storage characteristics (a wide voltage window of 2.6 V and a high energy density of 127.5 µWh cm −2 ). The buildings utilizing ESW‐PZ to modulate indoor environments demonstrated an average annual energy saving of 366 MJ m −2 based on energy simulations, which is about 16% of the total energy consumption. Impressively, a high utilization efficiency of 90% (855 mWh m −2 ) of the wasted electric energy is realized through an ingenious circuit‐switching strategy, which can be reused to power small household appliances.

Abstract: The energy injection through Hawking evaporation has been used to put strong constraints on primordial black holes as a dark matter candidate at masses below $10^{17}\,\rm{g}$. However, Hawking's semiclassical approximation breaks down at latest after half-decay. Beyond this point, the evaporation could be significantly suppressed as was shown in recent work. In this study, we review existing cosmological and astrophysical bounds on primordial black holes taking this effect into account. We show that the constraints disappear completely for a reasonable range of parameters, which opens a new window below $10^{10}\,\rm{g}$ for light primordial black holes as a dark matter candidate.

Abstract: Abstract Path planning is an important task for mobile service robots. Most of the
available path-planning algorithms are applicable only in static environments. Achieving
path planning becomes a difficult task in an unknown dynamic environment. To solve the
problem of path planning in an unknown dynamic environment, this paper proposes a BRRT*-
DWA algorithm with Adaptive Monte Carlo Localization. Bidirectional Rapidly-exploring
Random Tree Star(BRRT*) is used to generate an optimal global path plan, Dynamic Window
Approach(DWA) is a local planner and Adaptive Monte Carlo Localization(AMCL) is used
as a localization technique. By using the map file of the unknown environment created by
SLAM and LiDAR sensor, the robot can navigate while avoiding dynamic as well as static
obstacles. In addition, the object identification algorithm YOLO was adopted, trained, and
used for the robot to recognize objects and people. Results obtained from both simulation
and experiment show the proposed method can achieve better performance in a dynamic
environment compared with other state-of-the-art algorithms.

Abstract: Through careful design of the area ratio (AR) of the back-end-of-line (BEOL)-compatible metal–ferroelectric–metal–insulator–semiconductor (MFMIS) ferroelectric field-effect transistor (FeFET), we are able to modulate the charge injection in the gate-stack and successfully extend the memory window (MW) to ~8 V, far beyond the theoretical limit of double coercive voltage (Vc) for the Fe layer within the gate-stack. Moreover, we have developed and demonstrated, for the first time, the BEOL-compatible antiferroelectric (AFe) FET with the same MFMIS structure by substituting the Fe HfxZr1-xO2 (HZO) to Zr-rich AFe HZO. By adjusting the AR and channel thickness, either volatile or nonvolatile memory can be realized with the same structure. Meanwhile, the MW of anti-FeFETs (AFeFETs) up to ~3 V further confirms the influence of the charging effect. Furthermore, we have validated the reliability of both Fe and AFeFETs, demonstrating decent endurance and retention time. As the operation voltage for AFeFETs can be smaller than their Fe counterparts, they hold great promise to achieve ultrahigh-endurance operation. We have even confirmed an endurance over $10^{{9}}$ cycles without apparent MW degradation and dielectric breakdown for the AFeFETs when operating with unipolar voltage.

Abstract: As artificial intelligence and big data become increasingly prevalent, resistive random-access memory (RRAM) has become one of the most promising alternatives for storing massive amounts of data. In this study, we employed high-quality crystalline TiN/Al2O3/BaTiO3/Pt RRAM with an optimized thin Al2O3 interlayer around 12 nm thick prepared using atomic layer deposition since the thickness of the interlayer affects the memory window size. After insertion of the Al2O3 interlayer, the novel RRAM exhibited outstanding uniform resistive switching voltage and the ON/OFF memory window drastically increased from 10 to 103 without any discernible decline in performance. Moreover, the low-resistance state and high-resistance state operating current values decreased by almost one order and three orders of magnitude, respectively, thereby decreasing the power consumption for the RESET and SET processes by more than three and almost one order of magnitude, respectively. The device also exhibits multilevel resistive switching behavior when varying the applied voltage. Finally, we also developed a 6 × 6 crossbar array which demonstrated consistent and reliable resistive switching behavior with minimal variation. Hence, our approach holds great promise for producing state-of-the-art non-volatile resistive switching devices.

Abstract: Transformer's recent integration into style transfer leverages its proficiency in establishing long-range dependencies, albeit at the expense of attenuated local modeling. This paper introduces Strips Window Attention Transformer (S2WAT), a novel hierarchical vision transformer designed for style transfer. S2WAT employs attention computation in diverse window shapes to capture both short- and long-range dependencies. The merged dependencies utilize the "Attn Merge" strategy, which adaptively determines spatial weights based on their relevance to the target. Extensive experiments on representative datasets show the proposed method's effectiveness compared to state-of-the-art (SOTA) transformer-based and other approaches. The code and pre-trained models are available at https://github.com/AlienZhang1996/S2WAT.

Abstract: Processing defects remain the primary cause for fatigue failure of laser beam powder bed fusion (PBF-LB) produced components. Accordingly, process mapping methodologies have been extensively developed to identify optimal processing parameters to avoid defects. For structure-critical applications, it is necessary to validate the defect-based process maps through fatigue testing. We quantify the defect structure (porosity) process map for PBF-LB Ti-6Al-4V based on defect populations and fatigue properties. The defect populations were measured in samples fabricated at constant power and small increments in scanning velocity using X-ray micro-computed tomography and 2D metallography and analyzed using a number density approach. Furthermore, 4-point bend fatigue testing was used to establish stress-cycles to failure properties. Our results reveal distinct defect populations in both keyhole and lack-of-fusion defect regimes, with continuous variation in defect density. The number density-based defect size quantity strongly correlates with process parameters, peak stress, and initiating defect size, offering a quantitative approach to establish process-defect-fatigue relationships. We conclude that the process window exists just as clearly for fatigue as it does for defects, although more sensitive to variability in defects. Consequently, within this fatigue-based process window, one can expect to consistently produce dense components with superior fatigue properties.

Abstract: Electrocatalytic reduction of CO2 powered by renewable electricity provides an elegant route for converting CO2 into valuable chemicals and feedstocks, but normally suffers from a high overpotential and low selectivity. Herein, Ag and Sn heteroatoms were simultaneously introduced into nanoporous Cu (np‐Ag/Sn‐Cu) mainly in the form of an asymmetric local electric field for CO2 electroreduction to CO in an aqueous solution. The designed np‐Ag/Sn‐Cu catalyst realizes a recorded 90% energy efficiency and a 100% CO Faradaic efficiency over ultrawide potential window (ΔE = 1.4 V), outperforming state‐of‐the‐art Au and Ag‐based catalysts. Density functional theory calculations combined with in situ spectroscopy studies reveal that Ag and Sn heteroatoms incorporated into Cu matrix could generate strong and asymmetric local electric field, which promotes the activation of CO2 molecules, enhances the stabilization of the *COOH intermediate, and suppresses the hydrogen evolution reaction, thus favoring the production of CO during CO2RR.

Abstract: Abstract Smart windows effectively respond to the ever‐changing climatic conditions, offering a smart solution for low‐carbon buildings. However, current smart windows derived from chromic materials often have inferior solar modulation ability, or showcase high haze that obstructs outdoor views. Here, instead of developing new chromic materials, a new bistatic window is proposed for ultra‐high solar modulation and luminous transmission. The new developed window can reduce the indoor surface temperature for ≈11 °C, and reduce the building space cooling and heating energy consumption by 30% to 40%, providing significant energy‐related advances over traditional smart windows. In detail, the bistatic window exhibits excellent solar modulation ability (ΔT sol = 61%), high visible transmittance in both bleached ( T lum,bleached = 91%) and colored ( T lum,colored = 56%) states, low haze (< 1%), rapid switching response (switching time < 1 min), high color rendering index (CRI > 80), and long‐cyclic stability after 1000 cycles. With the advantages of facile fabrication and scalability, it is foreseen the developed bistatic window holds promising prospect for the next‐generation low‐carbon buildings, paving a new way for future advancements in the fields of smart windows.

Abstract: Abstract Measurements of 21 cm intensity mapping (IM) during the dark ages can potentially provide us with an unprecedented window on high redshifts and small scales. One of the main advantages this can bring involves the possibility to probe the nature of dark matter. Tests of dark matter models with the large-scale structure of the Universe are limited by non-linearities and astrophysical effects, which are not present for IM measurements during the dark ages. In this paper we focus on constraining the model in which dark matter is comprised, totally or in part, by ultra-light axion-like particles around the 10 -18 – 10 -22 eV mass scale. For this model, the angular power spectrum of 21 cm brightness temperature fluctuations will exhibit a small-scale suppression. However, this effect is intertwined with the imprint of baryon-dark matter relative velocity at recombination, causing at the same time an enhancement at large-scales, which is affected by the mass and abundance of axion dark matter. In this work we forecast how future radio arrays will be able to constrain ultra-light axion mass through both these effects on the angular power spectrum.

Abstract: Stimulator of interferon genes (STING) is a promising antitumor target via bridging innate and adaptive immunity, yet the transient nature of immune signal transduction renders small-molecule agonists susceptible to short time effectiveness. Here, we report a dual-STING-activating micelle system (D-SAM) to dynamically program STING kinetics. Mechanistically, the natural ligand cGAMP encapsulated in D-SAM initiates STING signaling, while the pH-sensitive polymeric agonist PC7A disassembled from micelle shell buffers lysosomal protons and retards STING degradation. This prolonged STING activity facilitates dendritic cell (DC) antigen presentation and extends cytotoxic T lymphocyte priming. D-SAM improves efficacy over single soluble or delivered agonists against established, metastatic, and recurring murine tumors. Specific depletion of STING in DCs or blockade of CD8

Abstract: Deep networks can usually depend on extracting more structural information to improve denoising results. However, they may ignore correlation between pixels from an image to pursue better denoising performance. Window transformer can use long- and short-distance modeling to interact pixels to address mentioned problem. To make a tradeoff between distance modeling and denoising time, we propose a heterogeneous window transformer (HWformer) for image denoising. HWformer first designs heterogeneous global windows to capture global context information for improving denoising effects. To build a bridge between long and short-distance modeling, global windows are horizontally and vertically shifted to facilitate diversified information without increasing denoising time. To prevent the information loss phenomenon of independent patches, sparse idea is guided a feed-forward network to extract local information of neighboring patches. The proposed HWformer only takes 30% of popular Restormer in terms of denoising time.

Abstract: Quantum protocols commonly require a certain number of quantum resource states to be available simultaneously. An important class of examples is quantum network protocols that require a certain number of entangled pairs. Here, we consider a setting in which a process generates a quantum resource state with some probability $p$ in each time step, and stores it in a quantum memory that is subject to time-dependent noise. To maintain sufficient quality for an application, each resource state is discarded from the memory after $w$ time steps. Let $s$ be the number of desired resource states required by a protocol. We characterise the probability distribution $X_{(w,s)}$ of the ages of the quantum resource states, once $s$ states have been generated in a window $w$ . Combined with a time-dependent noise model, knowledge of this distribution allows for the calculation of fidelity statistics of the $s$ quantum resources. We also give exact solutions for the first and second moments of the waiting time $\tau _{(w,s)}$ until $s$ resources are produced within a window $w$ , which provides information about the rate of the protocol. Since it is difficult to obtain general closed-form expressions for statistical quantities describing the expected waiting time $\mathbb {E}(\tau _{(w,s)})$ and the distribution $X_{(w,s)}$ , we present two novel results that aid their computation in certain parameter regimes. The methods presented in this work can be used to analyse and optimise the execution of quantum protocols. Specifically, with an example of a Blind Quantum Computing (BQC) protocol, we illustrate how they may be used to infer $w$ and $p$ to optimise the rate of successful protocol execution.

Abstract: Perovskite solar cells (PSCs) have shown promising progress in efficiency and stability, but their application needs further development from small-area cell to large-area module. Fabricating solar cell modules in large-area...

Abstract: Multi-scale learning is central to semantic segmentation. We visualize the effective receptive field (ERF) of canonical multi-scale representations and point out two risks in learning them: scale inadequacy and field inactivation. A novel multi-scale learner, varying window attention (VWA), is presented to address these issues. VWA leverages the local window attention (LWA) and disentangles LWA into the query window and context window, allowing the context's scale to vary for the query to learn representations at multiple scales. However, varying the context to large-scale windows (enlarging ratio R) can significantly increase the memory footprint and computation cost (R^2 times larger than LWA). We propose a simple but professional re-scaling strategy to zero the extra induced cost without compromising performance. Consequently, VWA uses the same cost as LWA to overcome the receptive limitation of the local window. Furthermore, depending on VWA and employing various MLPs, we introduce a multi-scale decoder (MSD), VWFormer, to improve multi-scale representations for semantic segmentation. VWFormer achieves efficiency competitive with the most compute-friendly MSDs, like FPN and MLP decoder, but performs much better than any MSDs. For instance, using nearly half of UPerNet's computation, VWFormer outperforms it by 1.0%-2.5% mIoU on ADE20K. With little extra overhead, ~10G FLOPs, Mask2Former armed with VWFormer improves by 1.0%-1.3%.