Miracle-dz/UniFAR-FTUs huggingface dataset:
Random lasing actions in self-assembled perovskite nanoparticles

Abstract. Solution-based perovskite nanoparticles have been intensively studied in the past few years due to their applications in both photovoltaic and optoelectronic devices. Here, based on the common ground between solution-based perovskite and random lasers, we have studied the mirrorless lasing actions in self-assembled perovskite nanoparticles. After synthesis from a solution, discrete lasing peaks have been observed from optically pumped perovskites without any well-defined cavity boundaries. We have demonstrated that the origin of the random lasing emissions is the scattering between the nanostructures in the perovskite microplates. The obtained quality (Q) factors and thresholds of random lasers are around 500 and 60 μJ/cm2, respectively. Both values are comparable to the conventional perovskite microdisk lasers with polygon-shaped cavity boundaries. From the corresponding studies on laser spectra and fluorescence microscope images, the lasing actions are considered random lasers that are generated by strong multiple scattering in random gain media. In additional to conventional single-photon excitation, due to the strong nonlinear effects of perovskites, two-photon pumped random lasers have also been demonstrated for the first time. We believe this research will find its potential applications in low-cost coherent light sources and biomedical detection.
On Best-Arm Identification with a Fixed Budget in Non-Parametric Multi-Armed Bandits

We lay the foundations of a non-parametric theory of best-arm identification in multi-armed bandits with a fixed budget T. We consider general, possibly non-parametric, models D for distributions over the arms; an overarching example is the model D = P(0,1) of all probability distributions over [0,1]. We propose upper bounds on the average log-probability of misidentifying the optimal arm based on information-theoretic quantities that correspond to infima over Kullback-Leibler divergences between some distributions in D and a given distribution. This is made possible by a refined analysis of the successive-rejects strategy of Audibert, Bubeck, and Munos (2010). We finally provide lower bounds on the same average log-probability, also in terms of the same new information-theoretic quantities; these lower bounds are larger when the (natural) assumptions on the considered strategies are stronger. All these new upper and lower bounds generalize existing bounds based, e.g., on gaps between distributions.
Factor-Augmented Regularized Model for Hazard Regression

A prevalent feature of high-dimensional data is the dependence among covariates, and model selection is known to be challenging when covariates are highly correlated. To perform model selection for the high-dimensional Cox proportional hazards model in presence of correlated covariates with factor structure, we propose a new model, Factor-Augmented Regularized Model for Hazard Regression (FarmHazard), which builds upon latent factors that drive covariate dependence and extends Cox’s model. This new model generates procedures that operate in two steps by learning factors and idiosyncratic components from high-dimensional covariate vectors and then using them as new predictors. Cox’s model is a widely used semi-parametric model for survival analysis, where censored data and time-dependent covariates bring additional technical challenges. We prove model selection consistency and estimation consistency under mild conditions. We also develop a factor-augmented variable screening procedure to deal with strong correlations in ultra-high dimensional problems. Extensive simulations and real data experiments demonstrate that our procedures enjoy good performance and achieve better results on model selection, out-of-sample C-index and screening than alternative methods.
A Micromegas-based low-background x-ray detector coupled to a slumped-glass telescope for axion research

We report on the design, construction and operation of a low background x-ray detection line composed of a shielded Micromegas detector of the microbulk technology. The detector is made from radiopure materials and is placed at the focal point of a ∼ 5 cm diameter, 1.5 m focal-length, cone-approximation Wolter I x-ray telescope (XRT) assembled from thermally-formed (or “slumped”) glass substrates deposited with multilayer coatings. The system has been conceived as a technological pathfinder for the future International Axion Observatory (IAXO), as it combines two of the techniques (optic and detector) proposed in the conceptual design of the project. It is innovative for two reasons: it is the first time an x-ray optic has been designed and fabricated specifically for axion research, and the first time a Micromegas detector has been operated with an x-ray optic. The line has been installed at one end of the CERN Axion Solar Telescope (CAST) magnet and is currently looking for solar axions. The combination of the XRT and Micromegas detector provides the best signal-to-noise ratio obtained so far by any detection system of the CAST experiment with a background rate of 5.4×10−3 counts per hour in the energy region-of-interest and signal spot area.
Perturbation theory with quantum signal processing

Perturbation theory is an important technique for reducing computational cost and providing physical insights in simulating quantum systems with classical computers. Here, we provide a quantum algorithm to obtain perturbative energies on quantum computers. The benefit of using quantum computers is that we can start the perturbation from a Hamiltonian that is classically hard to solve. The proposed algorithm uses quantum signal processing (QSP) to achieve this goal. Along with the perturbation theory, we construct a technique for ground state preparation with detailed computational cost analysis, which can be of independent interest. We also estimate a rough computational cost of the algorithm for simple chemical systems such as water clusters and polyacene molecules. To the best of our knowledge, this is the first of such estimates for practical applications of QSP. Unfortunately, we find that the proposed algorithm, at least in its current form, does not exhibit practical numbers despite of the efficiency of QSP compared to conventional quantum algorithms. However, perturbation theory itself is an attractive direction to explore because of its physical interpretability; it provides us insights about what interaction gives an important contribution to the properties of systems. This is in sharp contrast to the conventional approaches based on the quantum phase estimation algorithm, where we can only obtain values of energy. From this aspect, this work is a first step towards “explainable'' quantum simulation on fault-tolerant quantum computers.
EAPruning: Evolutionary Pruning for Vision Transformers and CNNs

Structured pruning greatly eases the deployment of large neural networks in resource-constrained environments. However, current methods either involve strong domain expertise, require extra hyperparameter tuning, or are restricted only to a specific type of network, which prevents pervasive industrial applications. In this paper, we undertake a simple and effective approach that can be easily applied to both vision transformers and convolutional neural networks. Specifically, we consider pruning as an evolution process of sub-network structures that inherit weights through reconstruction techniques. We achieve a 50% FLOPS reduction for ResNet50 and MobileNetV1, leading to 1.37x and 1.34x speedup respectively. For DeiT-Base, we reach nearly 40% FLOPs reduction and 1.4x speedup. Our code will be made available.
Blindly Deconvolving Super-noisy Blurry Image Sequences

Image blur and image noise are imaging artifacts intrinsically arising in image acquisition. In this paper, we consider multi-frame blind deconvolution (MFBD), where image blur is described by the convolution of an unobservable, undeteriorated image and an unknown ﬁlter, and the objective is to recover the undeteriorated image from a sequence of its blurry and noisy observations. We present two new methods for MFBD, which, in contrast to previous work, do not require the estimation of the unknown ﬁlters. The ﬁrst method is based on likelihood maximization and requires careful initialization to cope with the non-convexity of the loss function. The second method circumvents this requirement and exploits that the solution of likelihood maximization emerges as an eigenvector of a speciﬁcally constructed matrix, if the signal subspace spanned by the observations has a sufﬁciently large dimension. We describe a pre-processing step, which increases the dimension of the signal subspace by artiﬁcially generating additional observations. We also propose an extension of the eigenvector method, which copes with insufﬁcient dimensions of the signal subspace by estimating a footprint of the unknown ﬁlters (that is a vector of the size of the ﬁlters, only one is required for the whole image sequence). We have applied the eigenvector method to synthetically generated image sequences and performed a quantitative comparison with a previous method, obtaining strongly improved results.
DFA: Dynamic Feature Aggregation for Efficient Video Object Detection

Video object detection is a fundamental yet challenging task in computer vision. One practical solution is to take advantage of temporal information from the video and apply feature aggregation to enhance the object features in each frame. Though effective, those existing methods always suffer from low inference speeds because they use a fixed number of frames for feature aggregation regardless of the input frame. Therefore, this paper aims to improve the inference speed of the current feature aggregation-based video object detectors while maintaining their performance. To achieve this goal, we propose a vanilla dynamic aggregation module that adaptively selects the frames for feature enhancement. Then, we extend the vanilla dynamic aggregation module to a more effective and reconfigurable deformable version. Finally, we introduce inplace distillation loss to improve the representations of objects aggregated with fewer frames. Extensive experimental results validate the effectiveness and efficiency of our proposed methods: On the ImageNet VID benchmark, integrated with our proposed methods, FGFA and SELSA can improve the inference speed by 31% and 76% respectively while getting comparable performance on accuracy.
DecAF: Joint Decoding of Answers and Logical Forms for Question Answering over Knowledge Bases

Question answering over knowledge bases (KBs) aims to answer natural language questions with factual information such as entities and relations in KBs. Previous methods either generate logical forms that can be executed over KBs to obtain final answers or predict answers directly. Empirical results show that the former often produces more accurate answers, but it suffers from non-execution issues due to potential syntactic and semantic errors in the generated logical forms. In this work, we propose a novel framework DecAF that jointly generates both logical forms and direct answers, and then combines the merits of them to get the final answers. Moreover, different from most of the previous methods, DecAF is based on simple free-text retrieval without relying on any entity linking tools -- this simplification eases its adaptation to different datasets. DecAF achieves new state-of-the-art accuracy on WebQSP, FreebaseQA, and GrailQA benchmarks, while getting competitive results on the ComplexWebQuestions benchmark.
A SPITZER SEARCH FOR SUBSTELLAR COMPANIONS TO LOW-MASS WHITE DWARFS

The formation scenarios for single low-mass (M < 0.45 M☉) white dwarfs (WDs) include enhanced mass loss from a metal-rich progenitor star or a common envelope phase of a solar-like star with a close-in massive planet or a brown dwarf. Both scenarios suggest that low-mass WDs may have planets. Here, we present a Spitzer IRAC search for substellar and planetary mass companions to 14 low-mass WDs. One of our targets, HS 1653+7753, displays near- and mid-infrared flux excess. However, follow-up MMT observations show that this excess is due to a nearby resolved source, which is mostly likely a background object. Another target, PG 2257+162, shows flux excess compatible with a late-type stellar companion. We do not detect substellar companions to any of the remaining targets. In addition, eight of these stars do not show any radial velocity variations, ruling out stellar mass companions including other WDs. We conclude that a significant fraction of the low-mass WDs in our sample do not have stellar or massive brown dwarf companions.
Masked Supervised Learning for Semantic Segmentation

Self-attention is of vital importance in semantic segmentation as it enables modeling of long-range context, which translates into improved performance. We argue that it is equally important to model short-range context, especially to tackle cases where not only the regions of interest are small and ambiguous, but also when there exists an imbalance between the semantic classes. To this end, we propose Masked Supervised Learning (MaskSup), an effective single-stage learning paradigm that models both short- and long-range context, capturing the contextual relationships between pixels via random masking. Experimental results demonstrate the competitive performance of MaskSup against strong baselines in both binary and multi-class segmentation tasks on three standard benchmark datasets, particularly at handling ambiguous regions and retaining better segmentation of minority classes with no added inference cost. In addition to segmenting target regions even when large portions of the input are masked, MaskSup is also generic and can be easily integrated into a variety of semantic segmentation methods. We also show that the proposed method is computationally efficient, yielding an improved performance by 10\% on the mean intersection-over-union (mIoU) while requiring $3\times$ less learnable parameters.
Ensembling improves stability and power of feature selection for deep learning models

With the growing adoption of deep learning models in different real-world domains, including computational biology, it is often necessary to understand which data features are essential for the model's decision. Despite extensive recent efforts to define different feature importance metrics for deep learning models, we identified that inherent stochasticity in the design and training of deep learning models makes commonly used feature importance scores unstable. This results in varied explanations or selections of different features across different runs of the model. We demonstrate how the signal strength of features and correlation among features directly contribute to this instability. To address this instability, we explore the ensembling of feature importance scores of models across different epochs and find that this simple approach can substantially address this issue. For example, we consider knockoff inference as they allow feature selection with statistical guarantees. We discover considerable variability in selected features in different epochs of deep learning training, and the best selection of features doesn't necessarily occur at the lowest validation loss, the conventional approach to determine the best model. As such, we present a framework to combine the feature importance of trained models across different hyperparameter settings and epochs, and instead of selecting features from one best model, we perform an ensemble of feature importance scores from numerous good models. Across the range of experiments in simulated and various real-world datasets, we demonstrate that the proposed framework consistently improves the power of feature selection.
Learning Equivariant Segmentation with Instance-Unique Querying

Prevalent state-of-the-art instance segmentation methods fall into a query-based scheme, in which instance masks are derived by querying the image feature using a set of instance-aware embeddings. In this work, we devise a new training framework that boosts query-based models through discriminative query embedding learning. It explores two essential properties, namely dataset-level uniqueness and transformation equivariance, of the relation between queries and instances. First, our algorithm uses the queries to retrieve the corresponding instances from the whole training dataset, instead of only searching within individual scenes. As querying instances across scenes is more challenging, the segmenters are forced to learn more discriminative queries for effective instance separation. Second, our algorithm encourages both image (instance) representations and queries to be equivariant against geometric transformations, leading to more robust, instance-query matching. On top of four famous, query-based models ($i.e.,$ CondInst, SOLOv2, SOTR, and Mask2Former), our training algorithm provides significant performance gains ($e.g.,$ +1.6 - 3.2 AP) on COCO dataset. In addition, our algorithm promotes the performance of SOLOv2 by 2.7 AP, on LVISv1 dataset.
Axioms for Constant Function AMMs

We study axiomatic foundations for diﬀerent classes of constant function automated market makers (AMMs). We focus particularly on independence, homogeneity and convexity properties and give axiomatic characterizations of a natural generalization of constant product AMMs that has not been considered so far in the decentralized ﬁnance context. Our results add to a formal analysis of mechanisms that are currently used for decentralized exchanges and introduces tools of decision theory to the topic.
Comparison of three equations for predicting stress wave velocity as a function of grain angle

Assessment of a nondestructive test system for detecting defects in the gluelines of edge-glued hardwood panels required development of a mathematical relationship for predicting stress wave velocity as a function of grain angle. This relationship was necessary to understand better how stress waves propagated around gaps or flaws in a glueline. In addition, the relationship was needed to assess the influence of specimen geometry upon the effectiveness of the stress wave technique. Equations were generated by a statistical regression analysis software package and compared to Hankinson's equation. Equations were based upon measured velocity of stress waves traveling at angles between 0 and 90 degrees to the grain at 15 degree intervals in birch, black cherry, red oak, yellow-poplar, and western white pine boards. Regression analyses indicated that the best correlations were found with second order hyperbolic and parabolic equations. The two equations were compared to Hankinson's equation and to each other by using Absolute Average Error (AAE) for each equation for each species and for all species combined at each of the grain angles for which data were collected. Hankinson's equation produces the least AAE of the three equations although the hyperbolic and parabolic equations must also be considered reasonable predictors of stress wave velocity at most angles to the grain.
Local Computation of Maximal Independent Set

We present a randomized Local Computation Algorithm (LCA) with query complexity poly $(\Delta) \cdot \log n$ for the Maximal Independent Set (MIS) problem. That is, the algorithm determines whether each node is in the computed MIS or not using poly $(\Delta)\cdot\log n$ queries to the adjacency lists of the graph, with high probability, and this can be done for different nodes simultaneously and independently. Here $\Delta$ and n denote the maximum degree and the number of nodes. This algorithm resolves a key open problem in the study of local computations and sublinear algorithms (attributed to Rubinfeld).
ModelAngelo: Automated Model Building in Cryo-EM Maps

Electron cryo-microscopy (cryo-EM) produces three-dimensional (3D) maps of the electrostatic potential of biological macromolecules, including proteins. At sufﬁcient resolution, the cryo-EM maps, along with some knowledge about the imaged molecules, allow de novo atomic modelling. Typically, this is done through a laborious manual process. Recent advances in machine learning applications to protein structure prediction show potential for automating this process. Taking inspiration from these techniques, we have built ModelAngelo 1 for automated model building of proteins in cryo-EM maps. ModelAngelo ﬁrst uses a residual convolutional neural network (CNN) to initialize a graph representation with nodes assigned to individual amino acids of the proteins in the map and edges representing the protein chain. The graph is then reﬁned with a graph neural network (GNN) that combines the cryo-EM data, the amino acid sequence data and prior knowledge about protein geometries. The GNN reﬁnes the geometry of the protein chain and classiﬁes the amino acids for each of its nodes. The ﬁnal graph is post-processed with a hidden Markov model (HMM) search to map each protein chain to entries in a user provided sequence ﬁle. Application to 28 test cases shows that ModelAngelo outperforms the state-of-the-art and approximates manual building for cryo-EM maps with resolutions better than 3.5 ˚A.
The JWST Hubble Sequence: The Rest-Frame Optical Evolution of Galaxy Structure at $1.5

We present results on the morphological and structural evolution of a total of 4265 galaxies observed with JWST at 1 . 5 < z < 8 in the JWST CEERS observations that overlap with the CANDELS EGS ﬁeld. This is the biggest visually classiﬁed sample observed with JWST yet, ∼ 20 times larger than previous studies, and allows us to examine in detail how galaxy structure has changed over this critical epoch. All sources were classiﬁed by six individual classiﬁers using a simple classiﬁcation scheme aimed to produce disk/spheroid/peculiar classiﬁcations, whereby we determine how the relative number of these morphologies evolves since the Universe’s ﬁrst billion years. Additionally, we explore structural and quantitative morphology measurements using Morfometryka , and show that galaxies at z > 3 are not dominated by irregular and peculiar structures, either visually or quantitatively, as previously thought. We ﬁnd a strong dominance of morphologically selected disk galaxies up to z = 8, a far higher redshift than previously thought possible. We also ﬁnd that the stellar mass and star formation rate densities are dominated by disk galaxies up to z ∼ 6, demonstrating that most stars in the universe were likely formed in a disk galaxy. We compare our results to theory to show that the fraction of types we ﬁnd is predicted by cosmological simulations, and that the Hubble Sequence was already in place as early as one billion years after the Big Bang. Additionally, we make our visual classiﬁcations public for the community.
Two new classes of exponential Runge-Kutta integrators for efficiently solving stiff systems or highly oscillatory problems

We note a fact that stiff systems or differential equations that have highly oscillatory solutions cannot be solved efficiently using conventional methods. In this paper, we study two new classes of exponential Runge-Kutta (ERK) integrators for efficiently solving stiff systems or highly oscillatory problems. We first present a novel class of explicit modified version of exponential Runge-Kutta (MVERK) methods based on the order conditions. Furthermore, we consider a class of explicit simplified version of exponential Runge-Kutta (SVERK) methods. Numerical results demonstrate the high efficiency of the explicit MVERK integrators and SVERK methods derived in this paper compared with the well-known explicit ERK integrators for stiff systems or highly oscillatory problems in the literature.