Miracle-dz/UniFAR-FTUs huggingface dataset:
Inverse design of high-NA metalens for maskless lithography

Abstract We demonstrate an axisymmetric inverse-designed metalens to improve the performance of zone-plate-array lithography (ZPAL), one of the maskless lithography approaches, that offer a new paradigm for nanoscale research and industry. First, we derive a computational upper bound for a unit-cell-based axisymmetric metalens. Then, we demonstrate a fabrication-compatible inverse-designed metalens with 85.50% transmission normalized focusing efficiency at 0.6 numerical aperture at 405 nm wavelength; a higher efficiency than a theoretical gradient index lens design (79.98%). We also demonstrate experimental validation for our axisymmetric inverse-designed metalens via electron beam lithography. Metalens-based maskless lithography may open a new way of achieving low-cost, large-area nanofabrication.
OAMixer: Object-aware Mixing Layer for Vision Transformers

Patch-based models, e.g., Vision Transformers (ViTs) and Mixers, have shown impressive results on various visual recognition tasks, alternating classic convolutional networks. While the initial patch-based models (ViTs) treated all patches equally, recent studies reveal that incorporating inductive bias like spatiality benefits the representations. However, most prior works solely focused on the location of patches, overlooking the scene structure of images. Thus, we aim to further guide the interaction of patches using the object information. Specifically, we propose OAMixer (object-aware mixing layer), which calibrates the patch mixing layers of patch-based models based on the object labels. Here, we obtain the object labels in unsupervised or weakly-supervised manners, i.e., no additional human-annotating cost is necessary. Using the object labels, OAMixer computes a reweighting mask with a learnable scale parameter that intensifies the interaction of patches containing similar objects and applies the mask to the patch mixing layers. By learning an object-centric representation, we demonstrate that OAMixer improves the classification accuracy and background robustness of various patch-based models, including ViTs, MLP-Mixers, and ConvMixers. Moreover, we show that OAMixer enhances various downstream tasks, including large-scale classification, self-supervised learning, and multi-object recognition, verifying the generic applicability of OAMixer
Electron injection assisted phase transition in a nano-Au-VO2 junction

The semiconductor-metal transition of vanadium dioxide (VO2) thin films epitaxially grown on C-plane sapphire is studied by depositing Au nanoparticles onto the thermochromic films forming a metal-semiconductor contact, namely, a nano-Au-VO2 junction. It reveals that Au nanoparticles have a marked effect on the reduction in the phase transition temperature of VO2. A process of electron injection in which electrons flow from Au to VO2 due to the lower work function of the metal is believed to be the mechanism. The result may support the Mott–Hubbard phase transition model for VO2.
THE EMISSION EFFECTS BY THE USE OF BIODIESEL FUEL

The smoke emission of diesel engine is being recognized as the main cause for the serious air pollution related problem affecting our environment. In this study, we investigated the possibility of biodiesel fuel to reduce smoke emission as an alternative fuel for diesel engine. Additionally, gas chromatography was used to analyze not only total amount of HC(hydrocarbon) but also the amount of HC components from C1 to C6 in the exhaust gas to determine the exact source responsible for the remarkable reduction in the amount of smoke emission. Because biodiesel fuel has about 10 vol-% oxygen content, the combustion process of the diesel engine is improved and exhausted smoke emission density especially decreased.
Tracer Particles for Core-collapse Supernova Nucleosynthesis: The Advantages of Moving Backward

After decades, the theoretical study of core-collapse supernova explosions is moving from parameterized, spherically symmetric models to increasingly realistic multidimensional simulations. However, obtaining nucleosynthesis yields based on such multidimensional core-collapse supernova simulations is not straightforward. Frequently, tracer particles are employed. Tracer particles may be tracked in situ during the simulation, but often they are reconstructed in a post-processing step based on the information saved during the hydrodynamic simulation. Reconstruction can be done in a number of ways, and here we compare the approaches of backward and forward integration of the equations of motion to the results based on inline particle trajectories. We find that both methods agree reasonably well with the inline results for isotopes for which a large number of particles contribute. However, for rarer isotopes that are produced only by a small number of particle trajectories, deviations can be large. For our setup, we find that backward integration leads to better agreement with the inline particles by more accurately reproducing the conditions following freeze-out from nuclear statistical equilibrium, because the establishment of nuclear statistical equilibrium erases the need for detailed trajectories at earlier times. Based on our results, if inline tracers are unavailable, we recommend backward reconstruction to the point when nuclear statistical equilibrium was last applied, with an interval between simulation snapshots of at most 1 ms for nucleosynthesis post-processing.
AP: Selective Activation for De-sparsifying Pruned Neural Networks

The rectified linear unit (ReLU) is a highly successful activation function in neural networks as it allows networks to easily obtain sparse representations, which reduces overfitting in overparameterized networks. However, in network pruning, we find that the sparsity introduced by ReLU, which we quantify by a term called dynamic dead neuron rate (DNR), is not beneficial for the pruned network. Interestingly, the more the network is pruned, the smaller the dynamic DNR becomes during optimization. This motivates us to propose a method to explicitly reduce the dynamic DNR for the pruned network, i.e., de-sparsify the network. We refer to our method as Activating-while-Pruning (AP). We note that AP does not function as a stand-alone method, as it does not evaluate the importance of weights. Instead, it works in tandem with existing pruning methods and aims to improve their performance by selective activation of nodes to reduce the dynamic DNR. We conduct extensive experiments using popular networks (e.g., ResNet, VGG) via two classical and three state-of-the-art pruning methods. The experimental results on public datasets (e.g., CIFAR-10/100) suggest that AP works well with existing pruning methods and improves the performance by 3% - 4%. For larger scale datasets (e.g., ImageNet) and state-of-the-art networks (e.g., vision transformer), we observe an improvement of 2% - 3% with AP as opposed to without. Lastly, we conduct an ablation study to examine the effectiveness of the components comprising AP.
A generalized Townsend's theory for Paschen curves in planar, cylindrical, and spherical geometries

In this work, we focus on plasma discharges produced between two electrodes with a high potential difference, resulting in the ionization of the neutral particles supporting a current in the gaseous medium. At low currents and low temperatures, this process can create luminescent emissions: the so-called glow and corona discharges. The parallel plate geometry used in Townsend’s (1900) theory lets us develop a theoretical formalism, with explicit solutions for the critical voltage effectively reproducing experimental Paschen curves. However, most discharge processes occur in non-parallel plate geometries, such as discharges between grains or ice particles in multiphase flows. Here, we propose a generalization of the classic parallel plate configurations to concentric spherical and coaxial cylindrical geometries in Earth, Mars, Titan, and Venus atmospheres. In a spherical case, a small radius effectively represents a sharp tip rod, while larger, centimeter-scale radii represents rounded or blunted tips. Similarly, in a cylindrical case, a small radius would correspond to a thin wire. We solve continuity equations in the gap and estimate a critical
Rapid compression induced solidification of two amorphous phases of poly(ethylene terephthalate)

Melts of poly(ethylene terephthalate) were solidified by rapid compression to 2 GPa within 20 ms and by a series of comparative processes including natural cooling, slow compressing and rapid cooling, respectively. By combining XRD and differential scanning calorimetry data of the recovered samples, it is made clear that rapid compression induces two kinds of amorphous phases. One is relatively stable and can also be formed in the slow compression and the cooling processes. Another is metastable and transforms to crystalline phase at 371 K. This metastable amorphous phase cannot be obtained by slow compression or natural cooling, and its crystallization temperature is remarkably different from that of the metastable amorphous phase formed in the rapid cooling sample.
A Search for Coincident Neutrino Emission from Fast Radio Bursts with Seven Years of IceCube Cascade Events

This paper presents the results of a search for neutrinos that are spatially and temporally coincident with 22 unique, nonrepeating fast radio bursts (FRBs) and one repeating FRB (FRB 121102). FRBs are a rapidly growing class of Galactic and extragalactic astrophysical objects that are considered a potential source of high-energy neutrinos. The IceCube Neutrino Observatory’s previous FRB analyses have solely used track events. This search utilizes seven years of IceCube cascade events which are statistically independent of track events. This event selection allows probing of a longer range of extended timescales due to the low background rate. No statistically significant clustering of neutrinos was observed. Upper limits are set on the time-integrated neutrino flux emitted by FRBs for a range of extended time windows.
Quick and Reliable LoRa Physical-layer Data Aggregation through Multi-Packet Reception

This paper presents a Long Range (LoRa) physical-layer data aggregation system (LoRaPDA) that aggregates data (e.g., sum, average, min, max) directly in the physical layer. In particular, after coordinating a few nodes to transmit their data simultaneously, the gateway leverages a new multi-packet reception (MPR) approach to compute aggregate data from the phase-asynchronous superimposed signal. Different from the analog approach which requires additional power synchronization and phase synchronization, our MRP-based digital approach is compatible with commercial LoRa nodes and is more reliable. Different from traditional MPR approaches that are designed for the collision decoding scenario, our new MPR approach allows simultaneous transmissions with small packet arrival time offsets, and addresses a new co-located peak problem through the following components: 1) an improved channel and offset estimation algorithm that enables accurate phase tracking in each symbol, 2) a new symbol demodulation algorithm that finds the maximum likelihood sequence of nodes' data, and 3) a soft-decision packet decoding algorithm that utilizes the likelihoods of several sequences to improve decoding performance. Trace-driven simulation results show that the symbol demodulation algorithm outperforms the state-of-the-art MPR decoder by 5.3$\times$ in terms of physical-layer throughput, and the soft decoder is more robust to unavoidable adverse phase misalignment and estimation error in practice. Moreover, LoRaPDA outperforms the state-of-the-art MPR scheme by at least 2.1$\times$ for all SNRs in terms of network throughput, demonstrating quick and reliable data aggregation.
A novel approach to exploit elastic deformation to constrain regional ice mass change in Antarctica

Across the ANET GNSS network, decadal mass loss of the Antarctic ice sheet drives elastic uplift rates of up to 20 mm/yr. We explore, for the first time, the viability of using elastic deformation observed in the ANET GNSS network to constrain ice mass changes in Antarctica. We begin by estimating which regions of ice mass loss contribute to the elastic deformation observed at each ANET GNSS site. This is done using an observer-centric model of elastic deformation, which we call a Load Sensitivity Kernel (LSK). Using this approach, we find that the elastic deformation at the majority of ANET sites is dominated by far-field mass change. Over 70% of ANET sites require ice mass change within a radius of 200km or greater to be included before 90% of the site's elastic deformation is recovered. We show that the difference between LSKs of a pair of neighboring GNSS receivers can be used to localize the near-field mass change responsible for the differential displacement. Finally, we generalize this in a novel approach that uses an arbitrary number of GNSS sites. In this approach, LSKs of each site are weighted and added in a linear series. These weights are optimized such that the elastic deformation from mass change outside a Region of Interest is minimized. We demonstrate that 7 ANET GNSS sites in the Amundsen Sea region can isolate the elastic deformation, in both the vertical and horizontal components, that results exclusively from PIG's mass loss and/or the losses of the PSK glaciers. With the current GNSS receiver distribution, the east component of elastic deformation due to mass change at Thwaites can be expressed relative to mass changes at PSK. Our results demonstrate that the current ANET configuration can use elastic deformation to constrain ice mass changes in Antarctica, especially in regions with relatively dense clusters of GNSS.
Interpretable Diabetic Retinopathy Diagnosis Based on Biomarker Activation Map

Objective: Deep learning classifiers provide the most accurate means of automatically diagnosing diabetic retinopathy (DR) based on optical coherence tomography (OCT) and its angiography (OCTA). The power of these models is attributable in part to the inclusion of hidden layers that provide the complexity required to achieve a desired task. However, hidden layers also render algorithm outputs difficult to interpret. Here we introduce a novel biomarker activation map (BAM) framework based on generative adversarial learning that allows clinicians to verify and understand classifiers’ decision-making. Methods: A data set including 456 macular scans were graded as non-referable or referable DR based on current clinical standards. A DR classifier that was used to evaluate our BAM was first trained based on this data set. The BAM generation framework was designed by combing two U-shaped generators to provide meaningful interpretability to this classifier. The main generator was trained to take referable scans as input and produce an output that would be classified by the classifier as non-referable. The BAM is then constructed as the difference image between the output and input of the main generator. To ensure that the BAM only highlights classifier-utilized biomarkers an assistant generator was trained to do the opposite, producing scans that would be classified as referable by the classifier from non-referable scans. Results: The generated BAMs highlighted known pathologic features including nonperfusion area and retinal fluid. Conclusion/Significance: A fully interpretable classifier based on these highlights could help clinicians better utilize and verify automated DR diagnosis.
Verification of the Born rule via direct measurement of superposition wavefunction

The Born rule, which is one of foundational axioms of quantum theory, states that the probability of obtain outcome a for the quantum state | ψ (cid:105) is determined by P ( a ) = |(cid:104) a | ψ (cid:105)| 2 . Despite its great success in predicting the experimental outcomes, there still lacks a direct way to verify the Born rule. Here, we show that the weak value based direct measurement of superposition wavefunction is feasible, which can be used to verify the Born rule directly. The plausible experiment is suggested.
Life in a Random Universe: Sciama's Argument Reconsidered

Random sampling in high dimensions has successfully been applied to phenomena as diverse as nuclear resonances, neural networks, and black hole evaporation. Here we revisit an elegant argument by the British physicist Dennis Sciama, who demonstrated that were our Universe random, it would almost certainly have a negligible chance for life. Under plausible assumptions, we show that a random universe can masquerade as “intelligently designed,” with the fundamental constants instead appearing to be fined tuned to achieve the highest probability for life to occur. For our Universe, this mechanism may only require there to be around a dozen currently unknown fundamental constants. We speculate on broader applications for the mechanism we uncover.
HighMMT: Quantifying Modality & Interaction Heterogeneity for High-Modality Representation Learning

Many real-world problems are inherently multimodal, from the communicative modalities humans use to express social and emotional states such as spoken language, gestures, and paralinguistics to the force, proprioception, and visual sensors ubiquitous on robots. While there has been an explosion of interest in multimodal representation learning, these methods are still largely focused on a small set of modalities, primarily in the language, vision, and audio space. In order to accelerate generalization towards diverse and understudied modalities, this paper studies eﬃcient representation learning for high-modality scenarios involving a large set of diverse modalities. Since adding new models for every new modality or task becomes prohibitively expensive, a critical technical challenge is heterogeneity quantiﬁcation : how can we measure which modalities encode similar information and interactions in order to permit parameter sharing with previous modalities? This paper proposes two new information theoretic metrics for heterogeneity quantiﬁcation: (1) modality heterogeneity studies how similar 2 modalities { X 1 ,X 2 } are by measuring how much information can be transferred from X 1 to X 2 , while (2) interaction heterogeneity studies how similarly pairs of modalities { X 1 ,X 2 } , { X 3 ,X 4 } interact by measuring how much interaction information can be transferred from { X 1 ,X 2 } to { X 3 ,X 4 } . We show the importance of these 2 proposed metrics in high-modality scenarios as a way to automatically prioritize the fusion of modalities that contain unique information or unique interactions. The result is a single model, HighMMT , that scales up to 10 modalities (text, image, audio, video, sensors, proprioception, speech, time-series, sets, and tables) and 15
An efﬁcient differential privacy deep learning method based on noise screening

Differential privacy (DP) provides a formal privacy guarantee that prevents adversaries with access to machine learning models from extracting information about individual training points. Differentially private stochastic gradient descent (DPSGD) is the most popular training method with differential privacy in image recognition. However, existing DPSGD schemes lead to signiﬁcant performance degradation, which prevents the application of differential privacy. In this paper, we propose an efﬁcient differential privacy deep learning which accepts a candidate update with a probability that depends both on the update quality and on the number of iterations. Through this random update screening, we make the differentially private gradient descent proceed in the right direction in each iteration, and result in a more accurate model ﬁ-nally. In our experiments, under the same hyperparameters, our scheme achieves test accuracies 98.35%, 87.41% and 60.92% on datasets MNIST, FashionMNIST and CI-FAR10, respectively, compared to the state-of-the-art result of 98.12%, 86.33% and 59.34%. Under the freely adjusted hyperparameters, our scheme achieves even higher accuracies, 98.89%, 88.50% and 64.17%. We believe that our method has a great contribution for closing the accuracy gap between private and non-private image classiﬁcation.
A Modified Derivation of the Correlation Factor in Tracer Diffusion via Vacancy Mechanism

The average number of site exchanges made by a particular atom-vacancy pair has been calculated. It is found that the number of exchanges is equal to the number of visits to the origin of a random walker; for an infinite fcc lattice the number of exchanges is about 1.345, including the first exchange. This result seems invalidate the assumption of an infinite number of exchanges, which is usually made in the derivation of the correlation factor in diffusion via the vacancy mechanism. A modified derivation of the correlation factor, which takes into account the number of exchanges explicitly, is presented.
Off-diagonal properties of the Feynman propagator and the Green function for a bare Coulomb field

Blinder [Phys. Rev. A, 43, 13 (1991)] has given an explicit diagonal form for the nonrelativistic Coulomb propagator. His result is an infinite series involving Whittaker, Laguerre, Hermite, and error functions. However, he notes that at the nucleus, only error functions remain in the infinite series. Here, an off-diagonal generalization of Blinder's result at the nucleus is constructed. By relating this result to the Green function, it is then shown that one is led in this latter representation to an explicit sum of Blinder's infinite series. Finally, contact is established between the present off-diagonal study and the recent work of Chibisov et al. [Phys. Rev. Lett. 84, 451 (2000)] on the Coulomb Green function.
Replication concerns in sports and exercise science: a narrative review of selected methodological issues in the field

Known methodological issues such as publication bias, questionable research practices and studies with underpowered designs are known to decrease the replicability of study findings. The presence of such issues has been widely established across different research fields, especially in psychology. Their presence raised the first concerns that the replicability of study findings could be low and led researchers to conduct large replication projects. These replication projects revealed that a significant portion of original study findings could not be replicated, giving rise to the conceptualization of the replication crisis. Although previous research in the field of sports and exercise science has identified the first warning signs, such as an overwhelming proportion of significant findings, small sample sizes and lack of data availability, their possible consequences for the replicability of our field have been overlooked. We discuss the consequences of the above issues on the replicability of our field and offer potential solutions to improve replicability.