Miracle-dz/UniFAR-FTUs huggingface dataset:
Differentiability of the $\chi$-volume function over an adelic curve

In this article, we show a differentiability property for the $\chi$-volume function on the ample cone of adelic line bundles over an adelic curve. This result is deduced from a non-Archimedean counterpart of a diffrentiability result of Witt Nystr\"om. As an application, we give a logarithmic equidistribution result over adelic curves.
Excitation and probing of low-energy nuclear states at high-energy storage rings

(Dated: 229 Th with a low-lying nuclear isomeric state is an essential candidate for a nuclear clock as well as many other applications. Laser excitation of the isomeric state has been a long-standing goal. With relativistic 229 Th ions in storage rings, high-power lasers with wavelengths in the visible range or longer can be used to achieve high production rates of 229 Th isomers. This can be realized through either direct resonant excitation, excitation via an intermediate nuclear state, or excitation via an intermediate electronic state, facilitated by the tunability of both the laser-beam and ion-bunch parameters. Notably, the expected nuclear hyperﬁne mixing eﬀect in H- or Li-like 229 Th ions oﬀers novel opportunities for exciting thorium isomers. In the direct resonant excitation scenario, the signiﬁcantly reduced isomeric-state lifetime in H- or Li-like 229 Th ions would correspond to much higher excitation rates compared to bare thorium nuclei. We also present the possibility of exciting thorium isomers via an electronic excited state in Li-like 229 Th ions, where Stimulated Raman Adiabatic Passage as well as single-laser excitation can be implemented. Finally, we discuss schemes for probing produced isomers utilizing nuclear or electronic transitions, through which the isomeric-state energy could be determined with orders of magnitude improvement in precision compared to the current value. The schemes proposed here for 229 Th could also be adapted to studying low-energy nuclear states in other nuclei, such as 229 Pa. The present discussion shall facilitate the relevant experimental eﬀorts as well as aﬀect the concepts of future trapping and storage facilities. of , to the The lower of to The that can in the irrespective the actual production method of 229 Th The “coasting” refers to unbunched ion beams, i.e., the ion beam ﬁlling the whole storage ring almost uniformly.
Seismic performance of non-ductile reinforced concrete frames subjected to vertical ground motion

Most building codes state that vertical (V) component of the earthquake is less severe than the horizontal (H) component, the V/H ratio is assumed to be in the range of 1/3 to 2/3 (Bozorgnia et al., 1998). Consequently, the current design and assessment of structures consider horizontal shaking only (e.g. Elwood 2013). However, from analysis of free-field vertical ground motion, Bozorgnia et al. (1998) have shown that the commonly assumed 2/3 value of V/H is non-conservative for near-filed earthquake at the short periods and reduces to less than 2/3 at long periods. The 1994 Northridge and 1995 Hyogo-ken Nanbu earthquakes generated V/H ratios as high as 1.79 and 1.63, respectively. Normally, the vertical component of ground motion has a lower energy content concentrated in a narrow and high frequency band. This can cause damage to structures having vertical periods within this range (Collier and Elnashai, 2001).
Exact values and improved bounds on the clique number of cyclotomic graphs

Let $q$ be an odd power of a prime $p$, and $S \subset \mathbb{F}_q^*$ such that $S=-S$ and $S/S \neq \mathbb{F}_q^*$. We show that the clique number of the Cayley graph $\operatorname{Cay}(\mathbb{F}_q^+,S)$ is at most $\sqrt{|S/S|}+\sqrt{q/p}$, improving the best-known $\sqrt{q}$ upper bound for many families of such graphs substantially. Such a new bound is strongest for cyclotomic graphs and in particular it extends the celebrated Hanson-Petridis bound on the clique number of generalized Paley graphs over prime fields to a much larger family of Cayley graphs over finite fields. Surprisingly, our new bound is asymptotically sharp for an infinite family of generalized Paley graphs, and we further discover the first nontrivial family among them for which the clique number can be exactly determined. We also obtain a new lower bound on the number of directions determined by a large Cartesian product in the affine Galois plane $AG(2,q)$, which is sharp for infinite families.
End-to-End Optimization of Metasurfaces for Imaging with Compressed Sensing

Fig. 1. The bilevel optimization problem for a metasurface imaging system in an underdetermined and potentially multi-channel imaging setting. A compressed sensing minimization problem is nested within the optimization pipeline. We find the optimal measurement matrix that is physically realizable by our metasurface optics, along with the best corresponding reconstruction hyperparameters, through end-to-end optimization of the reconstruction error.
Anomalous spin Josephson effect in spin superconductors

The spin superconductor state is the spin-polarized triplet exciton condensate, which can be viewed as a counterpart of the charge superconductor state. As an analogy of the charge Josephson effect, the spin Josephson effect can be generated in the spin superconductor/normal metal/spin superconductor junctions. Here we study the spin supercurrent in the Josephson junctions consisting of two spin superconductors with noncollinear spin polarizations. For the Josephson junctions with out-of-plane spin polarizations, the possible π-state spin supercurrent appears due to the Fermi momentum-splitting Andreev-like reflections at the normal metal/spin superconductor interfaces. For the Josephson junctions with in-plane spin polarizations, the anomalous spin supercurrent appears and is driven by the misorientation angle of the in-plane polarizations. The symmetry analysis shows that the appearance of the anomalous spin Josephson current is possible when the combined symmetry of the spin rotation and the time reversal is broken.
DEDUCE: Multi-head attention decoupled contrastive learning to discover cancer subtypes based on multi-omics data

Due to the high heterogeneity and clinical characteristics of cancer, there are significant differences in multi-omics data and clinical features among subtypes of different cancers. Therefore, the identification and discovery of cancer subtypes are crucial for the diagnosis, treatment, and prognosis of cancer. In this study, we proposed a generalization framework based on attention mechanisms for unsupervised contrastive learning to analyze cancer multi-omics data for the identification and characterization of cancer subtypes. The framework contains a symmetric unsupervised multi-head attention encoder, which can deeply extract contextual features and long-range dependencies of multi-omics data, reducing the impact of noise in multi-omics data. Importantly, the proposed framework includes a decoupled contrastive learning model (DEDUCE) based on a multi-head attention mechanism to learn multi-omics data features and clustering and identify cancer subtypes. This method clusters subtypes by calculating the similarity between samples in the feature space and sample space of multi-omics data. The basic idea is to decouple different attributes of multi-omics data features and learn them as contrasting terms. Construct a contrastive loss function to measure the difference between positive examples and negative examples, and minimize this difference, thereby encouraging the model to learn better feature representation. The DEDUCE model conducts large-scale experiments on simulated multi-omics data sets, single-cell multi-omics data sets and cancer multi-omics data sets, and the results are better than 10 deep learning models. Finally, we used the DEDUCE model to reveal six cancer subtypes of AML. By analyzing GO functional enrichment, subtype-specific biological functions and GSEA of AML,
European option pricing under generalized fractional Brownian motion

The Generalized fractional Brownian motion (gfBm) is a stochastic process that acts as a generalization for both fractional, sub-fractional, and standard Brownian motion. Here we study its application into the option pricing problem by means of the valuation of a European Call option. By the derivation of the generalized fractional Ito’s lemma and the related Fokker-Planck equation, a closed-form pricing formula for both Black-Scholes and CEV models driven by gfBm is obtained.
Causal Rule Ensemble: Interpretable Discovery and Inference of Heterogeneous Causal Effects

In health and social sciences, it is critically important to identify subgroups of the study population where a treatment has notable heterogeneity in the causal effects with respect to the average treatment effect. Data-driven discovery of heterogeneous treatment effects (HTE) via decision tree methods has been proposed for this task. Despite its high interpretability, the single-tree discovery of HTE tends to be highly unstable and to find an oversimplified representation of treatment heterogeneity. To accommodate these shortcomings, we propose Causal Rule Ensemble (CRE), a new method to discover heterogeneous subgroups through an ensemble-of-trees approach. CRE has the following features: 1) provides an interpretable representation of the HTE; 2) allows extensive exploration of complex heterogeneity patterns; and 3) guarantees high stability in the discovery. The discovered subgroups are defined in terms of interpretable decision rules, and we develop a general two-stage approach for subgroup-specific conditional causal effects estimation, providing theoretical guarantees. Via simulations, we show that the CRE method has a strong discovery ability and a competitive estimation performance when compared to state-of-the-art techniques. Finally, we apply CRE to discover subgroups most vulnerable to the effects of exposure to air pollution on mortality for 35.3 million Medicare beneficiaries across the contiguous U.S.
For One-Shot Decoding: Unsupervised Deep Learning-Based Polar Decoder

—We propose an unsupervised deep learning-based decoding scheme that enables one-shot decoding of polar codes. In the proposed scheme, rather than using the information bit vectors as labels for training the neural network (NN) through supervised learning as the conventional scheme did, the NN is trained to function as a bounded distance decoder by leveraging the generator matrix of polar codes through self-supervised learning. This approach eliminates the reliance on predeﬁned labels, empowering the potential to train directly on the actual data within communication systems and thereby enhancing the applicability. Furthermore, computer simulations demonstrate that (i) the bit error rate (BER) and block error rate (BLER) performances of the proposed scheme can approach those of the maximum a posteriori (MAP) decoder for very short packets and (ii) the proposed NN decoder exhibits much superior generalization ability compared to the conventional one.
Chapter 12 Depth model of the Barents and Kara seas according to geophysical surveys results

Abstract In 1995–2006 FSUE ‘Sevmorgeo’ within the framework of the Federal Program of state survey baselines network development performed geophysical works in the Barents and Kara seas along four regional profiles: 1-AR (Kola Peninsula–Heysa Island of Franz-Joseph Land Archipelago); 2-AR (Central part of the Barents region–Novaya Zemlya – Yamal Peninsula); 3-AR (White Sea–Severnaya Zemlya Archipelago); and 4-AR (Taimyr Peninsula–Franz-Joseph Land Archipelago). Geophysical surveys included works using seismic refraction–deep seismic sounding technique, seismic reflection–common-depth point technique, seismic acoustic profiling and gravimetric and magnetic measurements. Integrated geophysical surveys along the regional profiles enabled more exact definition of the Earth deep crustal structure and the sedimentary cover of the main tectonic elements.
Local algorithms for the maximum flow and minimum cut in bounded-degree networks

We show a deterministic constant-time local algorithm for constructing an approximately maximum flow and minimum fractional cut in multisource-multitarget networks with bounded degrees and bounded edge capacities. Locality means that the decision we make about each edge only depends on its constant radius neighborhood. We show two applications of the algorithms: one is related to the Aldous-Lyons Conjecture, and the other is about approximating the neighborhood distribution of graphs by bounded-size graphs. The scope of our results can be extended to unimodular random graphs and networks. As a corollary, we generalize the Maximum Flow Minimum Cut Theorem to unimodular random flow networks.
QTSumm: Query-Focused Summarization over Tabular Data

People primarily consult tables to conduct data analysis or answer specific questions. Text generation systems that can provide accurate table summaries tailored to users' information needs can facilitate more efficient access to relevant data insights. Motivated by this, we define a new query-focused table summarization task, where text generation models have to perform human-like reasoning and analysis over the given table to generate a tailored summary. We introduce a new benchmark named QTSumm for this task, which contains 7,111 human-annotated query-summary pairs over 2,934 tables covering diverse topics. We investigate a set of strong baselines on QTSumm, including text generation, table-to-text generation, and large language models. Experimental results and manual analysis reveal that the new task presents significant challenges in table-to-text generation for future research. Moreover, we propose a new approach named ReFactor, to retrieve and reason over query-relevant information from tabular data to generate several natural language facts. Experimental results demonstrate that ReFactor can bring improvements to baselines by concatenating the generated facts to the model input. Our data and code are publicly available at https://github.com/yale-nlp/QTSumm.
Unambiguous discrimination of high harmonic generation mechanisms in solids

Using real-space view of high harmonic generation (HHG) in solids, we develop a physically transparent and gauge-invariant approach for distinguishing intraband and interband HHG mechanisms. Our approach relies on resolving the harmonic emission according to the separation between Wannier states involved in radiative transitions. We show that the intra- and inter-band HHG emission exhibit striking qualitative differences in their dependence on this separation and can be clearly distinguished using the Wannier basis.
Adapting Contrastive Language-Image Pretrained (CLIP) Models for Out-of-Distribution Detection

We present a comprehensive experimental study on pretrained feature extractors for visual out-of-distribution (OOD) detection, focusing on adapting contrastive language-image pretrained (CLIP) models. Without fine-tuning on the training data, we are able to establish a positive correlation ($R^2\geq0.92$) between in-distribution classification and unsupervised OOD detection for CLIP models in $4$ benchmarks. We further propose a new simple and scalable method called \textit{pseudo-label probing} (PLP) that adapts vision-language models for OOD detection. Given a set of label names of the training set, PLP trains a linear layer using the pseudo-labels derived from the text encoder of CLIP. To test the OOD detection robustness of pretrained models, we develop a novel feature-based adversarial OOD data manipulation approach to create adversarial samples. Intriguingly, we show that (i) PLP outperforms the previous state-of-the-art \citep{ming2022mcm} on all $5$ large-scale benchmarks based on ImageNet, specifically by an average AUROC gain of 3.4\% using the largest CLIP model (ViT-G), (ii) we show that linear probing outperforms fine-tuning by large margins for CLIP architectures (i.e. CLIP ViT-H achieves a mean gain of 7.3\% AUROC on average on all ImageNet-based benchmarks), and (iii) billion-parameter CLIP models still fail at detecting adversarially manipulated OOD images. The code and adversarially created datasets will be made publicly available.
Partial Binarization of Neural Networks for Budget-Aware Efficient Learning

Binarization is a powerful compression technique for neural networks, significantly reducing FLOPs, but often results in a significant drop in model performance. To address this issue, partial binarization techniques have been developed, but a systematic approach to mixing binary and full-precision parameters in a single network is still lacking. In this paper, we propose a controlled approach to partial binarization, creating a budgeted binary neural network (B2NN) with our MixBin strategy. This method optimizes the mixing of binary and full-precision components, allowing for explicit selection of the fraction of the network to remain binary. Our experiments show that B2NNs created using MixBin outperform those from random or iterative searches and state-of-the-art layer selection methods by up to 3% on the ImageNet-1K dataset. We also show that B2NNs outperform the structured pruning baseline by approximately 23% at the extreme FLOP budget of 15%, and perform well in object tracking, with up to a 12.4% relative improvement over other baselines. Additionally, we demonstrate that B2NNs developed by MixBin can be transferred across datasets, with some cases showing improved performance over directly applying MixBin on the downstream data.
Flor de Mayo Dolores: new dry bean cultivar for irrigated and rainfall conditions in Guanajuato

RESUMEN El frijol de tipo Flor de Mayo es de alta demanda en la region del Altiplano Centro-Occidental de Mexico. Se describe la nueva variedad de frijol Flor de Mayo Dolores (FMD); esta se derivo de la cruza simple entre Flor de Mayo Anita (FMA) y FM 99016. En promedio de 14 ensayos conducidos de 2008 a 2010 bajo condiciones de riego y temporal en Guanajuato, el rendimiento de FMD y FMA resulto similar (2 167 vs 2 118 kg ha -1 ). El peso de 100 semillas promedio de diferentes localidades de FMD es ligeramente superior al de FMA en ambas condiciones, riego y temporal, y su forma de semilla es ovalada; mientras que el de FMA es alargado-aplanada. El tiempo promedio de coccion de FMD en cocedor tipo Mattson es de 95 min; mientras que FMA es de 110 min; el contenido promedio de proteina del grano de ambas variedades es 20%, con base a peso seco; mientras que el contenido de hierro de FMD resulto superior ( p < 0.01) al de FMA con 6.1 vs 5 mg/100 g, FMD es resistente a las razas de roya y del Virus del Mosaico Comun (BCMV y BCMNV) presentes en Guanajuato, a la raza 292 de antracnosis y tolerante a los tizones comun, de halo y a las pudriciones de raiz. FMD puede utilizarse bajo condiciones de riego y temporal en los estados de la region Centro-Occidente de Mexico.
Overleaf Example

Localizing mobile robotic nodes in indoor and GPS-denied environments is a complex problem, particularly in dynamic, unstructured scenarios where traditional cameras and LIDAR-based sensing and localization modalities may fail. Alternatively, wireless signal-based localization has been extensively studied in the literature yet primarily focuses on fingerprinting and feature-matching paradigms, requiring dedicated environment-specific offline data collection. We propose an online robot localization algorithm enabled by collaborative wireless sensor nodes to remedy these limitations. Our approach’s core novelty lies in obtaining the Collaborative Direction of Arrival (CDOA) of wireless signals by exploiting the geometric features and collaboration between wireless nodes. The CDOA is combined with the Expectation Maximization (EM) and Particle Filter (PF) algorithms to calculate the Gaussian probability of the node’s location with high efficiency and accuracy. The algorithm relies on RSSI-only data, making it ubiquitous to resourceconstrained devices. We theoretically analyze the approach and extensively validate the proposed method’s consistency, accuracy, and computational efficiency in simulations, real-world public datasets, as well as real robot demonstrations. The results validate the method’s real-time computational capability and demonstrate considerably-high centimeter-level localization accuracy, outperforming relevant state-of-the-art localization approaches.
Loss Dynamics of Temporal Difference Reinforcement Learning

Reinforcement learning has been successful across several applications in which agents have to learn to act in environments with sparse feedback. However, despite this empirical success there is still a lack of theoretical understanding of how the parameters of reinforcement learning models and the features used to represent states interact to control the dynamics of learning. In this work, we use concepts from statistical physics, to study the typical case learning curves for temporal difference learning of a value function with linear function approximators. Our theory is derived under a Gaussian equivalence hypothesis where averages over the random trajectories are replaced with temporally correlated Gaussian feature averages and we validate our assumptions on small scale Markov Decision Processes. We find that the stochastic semi-gradient noise due to subsampling the space of possible episodes leads to significant plateaus in the value error, unlike in traditional gradient descent dynamics. We study how learning dynamics and plateaus depend on feature structure, learning rate, discount factor, and reward function. We then analyze how strategies like learning rate annealing and reward shaping can favorably alter learning dynamics and plateaus. To conclude, our work introduces new tools to open a new direction towards developing a theory of learning dynamics in reinforcement learning.
HIGHER ORDER ESTIMATING EQUATIONS FOR HIGH-DIMENSIONAL MODELS.

We introduce a new method of estimation of parameters in semi-parametric and nonparametric models. The method is based on estimating equations that are U-statistics in the observations. The U-statistics are based on higher order influence functions that extend ordinary linear influence functions of the parameter of interest, and represent higher derivatives of this parameter. For parameters for which the representation cannot be perfect the method leads to a bias-variance trade-off, and results in estimators that converge at a slower than n -rate . In a number of examples the resulting rate can be shown to be optimal. We are particularly interested in estimating parameters in models with a nuisance parameter of high dimension or low regularity, where the parameter of interest cannot be estimated at n -rate , but we also consider efficient n -estimation using novel nonlinear estimators. The general approach is applied in detail to the example of estimating a mean response when the response is not always observed.
Testing primordial black hole and measuring the Hubble constant with multiband gravitational-wave observations

There exist two kinds of stochastic gravitational-wave backgrounds associated with the primordial curvature perturbations. One is called induced gravitational wave due to the nonlinear coupling of curvature perturbations to tensor perturbations, while the other is produced by coalescences of binary primordial black holes formed when the large amplitude curvature perturbations reenter the horizon in the radiation dominant era. In this paper we find a quite useful relation for the peak frequencies of these two stochastic gravitational-wave backgrounds. This relation can not only offer a smoking-gun criterion for the existence of primordial black holes, but also provide a method for measuring the Hubble constant H 0 by multiband observations of the stochastic gravitational-wave backgrounds.