Miracle-dz/UniFAR-FTUs huggingface dataset:
On the choice of the splitting ratio for the split likelihood ratio test

The recently introduced framework of universal inference provides a new approach to constructing hypothesis tests and confidence regions that are valid in finite samples and do not rely on any specific regularity assumptions on the underlying statistical model. At the core of the methodology is a split likelihood ratio statistic, which is formed under data splitting and compared to a cleverly selected universal critical value. As this critical value can be very conservative, it is interesting to mitigate the potential loss of power by careful choice of the ratio according to which data are split. Motivated by this problem, we study the split likelihood ratio test under local alternatives and introduce the resulting class of noncentral split chi-square distributions. We investigate the properties of this new class of distributions and use it to numerically examine and propose an optimal choice of the data splitting ratio for tests of composite hypotheses of different dimensions.
Effects of an Immortal Stellar Population in AGN Disks

Stars are likely embedded in the gas disks of active galactic nuclei (AGN). Theoretical models predict that in the inner regions of the disk, these stars accrete rapidly, with fresh gas replenishing hydrogen in their cores faster than it is burned into helium, effectively stalling their evolution at hydrogen burning. We produce order-of-magnitude estimates of the number of such stars in a fiducial AGN disk. We find numbers of order 102–4, confined to the inner r cap ∼ 3000r s ∼ 0.03 pc. These stars can profoundly alter the chemistry of AGN disks, enriching them in helium and depleting them in hydrogen, both by order-unity amounts. We further consider mergers between these stars and other disk objects, suggesting that star–star mergers result in rapid mass loss from the remnant to restore an equilibrium mass, while star–compact object mergers may result in exotic outcomes and even host binary black hole mergers within themselves. Finally, we examine how these stars react as the disk dissipates toward the end of its life, and find that they may return mass to the disk fast enough to extend its lifetime by a factor of several and/or may drive powerful outflows from the disk. Post-AGN, these stars rapidly lose mass and form a population of stellar mass black holes around 10M ⊙. Due to the complex and uncertain interactions between embedded stars and the disk, their plausible ubiquity, and their order-unity impact on disk structure and evolution, they must be included in realistic disk models.
Supercurrent-induced resonant optical response

The optical conductivity encodes the current response to a time dependent electric ﬁeld. We develop a theory of the optical conductivity σ ( ω ) in presence of a dc supercurrent. Current induced optical response is prohibited by Galilean invariance from occurring in systems with a single parabolic band. However, we show that lattice eﬀects give rise to a pronounced current dependent peak in σ ( ω ) at the gap edge ω = 2∆ , which diverges in the clean limit. We demonstrate this in a model of a multi-band superconductor. Our theory explains the recent observation of a current induced peak in the optical conductivity in NbN by Nakamura et al. [1]
Optimization‐based approach for scalable small‐signal and noise model extraction of GaN‐on‐SiC HEMTs

An optimization‐based method allowing a straightforward extraction of scalable small‐signal equivalent‐circuit models for high‐frequency active devices is proposed. The approach only requires a set of devices with a fixed number of fingers and scaling unit gate widths: no dummy structures or bias conditions potentially harmful for the devices are needed. The extraction method is then demonstrated by presenting in full a sample extraction, carried out on a 0.25 GaN‐on‐SiC HEMT technology provided by Selex‐ES. The example also includes the extraction of a noise model, by means of a well‐known noise‐temperature approach. Both the small‐signal and noise models agree well with the experimental data. Copyright © 2015 John Wiley & Sons, Ltd.
Hybrid Active-Passive Reconfigurable Intelligent Surface-Assisted Multi-User MISO Systems

We consider a multi-user multiple-input single-output (MISO) communications system which is assisted by a hybrid active-passive reconfigurable intelligent surface (RIS). Unlike conventional passive RISs, hybrid RIS is equipped with a few active elements with the ability to reflect and amplify incident signals to significantly improve the system performance. Towards a fairness-oriented design, we maximize the minimum rate among all users through jointly optimizing the transmit beamforming vectors and RIS reflecting/amplifying coefficients. Combining tools from block coordinate ascent and successive convex approximation, the challenging nonconvex problem is efficiently solved by a low-complexity iterative algorithm. The numerical results show that a hybrid RIS with 4 active elements out of a total of 50 elements with a power budget of −1 dBm offers an improvement of up to 80% to the considered system, while that achieved by a fully passive RIS is only 27%.
Cross-View-Prediction: Exploring Contrastive Feature for Hyperspectral Image Classification

This paper presents a self-supervised feature learning method for hyperspectral image classification. Our method tries to construct two different views of the raw hyperspectral image through a cross-representation learning method. And then to learn semantically consistent representation over the created views by contrastive learning method. Specifically, four cross-channel-prediction based augmentation methods are naturally designed to utilize the high dimension characteristic of hyperspectral data for the view construction. And the better representative features are learned by maximizing mutual information and minimizing conditional entropy across different views from our contrastive network. This 'Cross-View-Predicton' style is straightforward and gets the state-of-the-art performance of unsupervised classification with a simple SVM classifier.
On the Implicit Bias Towards Depth Minimization in Deep Neural Networks

We study the implicit bias of gradient based training methods to favor low-depth solutions when training deep neural networks. Recent results in the literature suggest that penultimate layer representations learned by a classiﬁer over multiple classes exhibit a clustering property, called neural collapse. We demonstrate empirically that neural collapse extends beyond the penultimate layer and emerges in intermediate layers as well. In this regards, we hypothesize and empirically show that gradient based methods are implicitly biased towards selecting neural networks of minimal depth for achieving this clustering property.
Implicit Data Augmentation Using Feature Interpolation for Low-Shot Image Generation

Training of generative models especially Generative Adversarial Networks can easily diverge in low-data setting. To mitigate this issue, we propose a novel implicit data augmentation approach which facilitates stable training and synthesize high-quality samples without need of label information. Specifically, we view the discriminator as a metric embedding of the real data manifold, which offers proper distances be-tween real data points. We then utilize information in the feature space to develop a data-driven augmentation method. Experiments on few-shot generation tasks show the proposed method significantly improve results from strong baselines, and allows generating high-quality images with around 100 training samples.
Self-Supervised Graph Neural Networks for Improved Electroencephalographic Seizure Analysis

Automated seizure detection and classification from electroencephalography (EEG) can greatly improve seizure diagnosis and treatment. However, several modeling challenges remain unaddressed in prior automated seizure detection and classification studies: (1) representing non-Euclidean data structure in EEGs, (2) accurately classifying rare seizure types, and (3) lacking a quantitative interpretability approach to measure model ability to localize seizures. In this study, we address these challenges by (1) representing the spatiotemporal dependencies in EEGs using a graph neural network (GNN) and proposing two EEG graph structures that capture the electrode geometry or dynamic brain connectivity, (2) proposing a self-supervised pre-training method that predicts preprocessed signals for the next time period to further improve model performance, particularly on rare seizure types, and (3) proposing a quantitative model interpretability approach to assess a model's ability to localize seizures within EEGs. When evaluating our approach on seizure detection and classification on a large public dataset, we find that our GNN with self-supervised pre-training achieves 0.875 Area Under the Receiver Operating Characteristic Curve on seizure detection and 0.749 weighted F1-score on seizure classification, outperforming previous methods for both seizure detection and classification. Moreover, our self-supervised pre-training strategy significantly improves classification of rare seizure types. Furthermore, quantitative interpretability analysis shows that our GNN with self-supervised pre-training precisely localizes 25.4% focal seizures, a 21.9 point improvement over existing CNNs. Finally, by superimposing the identified seizure locations on both raw EEG signals and EEG graphs, our approach could provide clinicians with an intuitive visualization of localized seizure regions.
A new flavor of correlation and superconductivity in small twist-angle trilayer graphene

Phum Siriviboon1∗, Jiang-Xiazi Lin1∗, Xiaoxue Liu1∗, Harley D. Scammell, Song Liu, Daniel Rhodes, K. Watanabe, T. Taniguchi, James Hone, Mathias S. Scheurer, and J.I. A. Li1∗ Department of Physics, Brown University, Providence, RI 02912, USA School of Physics, the University of New South Wales, Sydney, NSW, 2052 Australia Department of Mechanical Engineering, Columbia University, New York, NY 10027, USA Research Center for Functional Materials, National Institute for Materials Science, 1-1 Namiki, Tsukuba 305-0044, Japan International Center for Materials Nanoarchitectonics, National Institute for Materials Science, 1-1 Namiki, Tsukuba 305-0044, Japan 6 Institute for Theoretical Physics, University of Innsbruck, Innsbruck, A-6020, Austria and ∗These authors contributed equally in this work. (Dated: March 15, 2022)
Delta Tuning: A Comprehensive Study of Parameter Efficient Methods for Pre-trained Language Models

Despite the success, the process of fine-tuning large-scale PLMs brings prohibitive adaptation costs. In fact, fine-tuning all the parameters of a colossal model and retaining separate instances for different tasks are practically infeasible. This necessitates a new branch of research focusing on the parameter-efficient adaptation of PLMs, dubbed as delta tuning in this paper. In contrast with the standard fine-tuning, delta tuning only fine-tunes a small portion of the model parameters while keeping the rest untouched, largely reducing both the computation and storage costs. Recent studies have demonstrated that a series of delta tuning methods with distinct tuned parameter selection could achieve performance on a par with full-parameter fine-tuning, suggesting a new promising way of stimulating large-scale PLMs. In this paper, we first formally describe the problem of delta tuning and then comprehensively review recent delta tuning approaches. We also propose a unified categorization criterion that divide existing delta tuning methods into three groups: addition-based, specification-based, and reparameterization-based methods. Though initially proposed as an efficient method to steer large models, we believe that some of the fascinating evidence discovered along with delta tuning could help further reveal the mechanisms of PLMs and even deep neural networks. To this end, we discuss the theoretical principles underlying the effectiveness of delta tuning and propose frameworks to interpret delta tuning from the perspective of optimization and optimal control, respectively. Furthermore, we provide a holistic empirical study of representative methods, where results on over 100 NLP tasks demonstrate a comprehensive performance comparison of different approaches. The experimental results also cover the analysis of combinatorial, scaling and transferable properties of delta tuning.
On the $g_{z}$-Kato decomposition and generalization of Koliha Drazin invertibility

In [24], Koliha proved that T ∈ L(X) (X is a complex Banach space) is generalized Drazin invertible operator equivalent to there exists an operator S commuting with T such that STS = S and σ(T S − T ) ⊂ {0} which is equivalent to say that 0 6∈ accσ(T ). Later, in [14, 34] the authors extended the class of generalized Drazin invertible operators and they also extended the class of pseudo-Fredholm operators introduced by Mbekhta [27] and other classes of semi-Fredholm operators. As a continuation of these works, we introduce and study the class of gz-invertible (resp., gz-Kato) operators which generalizes the class of generalized Drazin invertible operators (resp., the class of generalized Kato-meromorphic operators introduced by Živković-Zlatanović and Duggal in [35]). Among other results, we prove that T is gz-invertible if and only if T is gz-Kato with p̃(T ) = q̃(T ) < ∞ which is equivalent to there exists an operator S commuting with T such that STS = S and accσ(T S− T ) ⊂ {0} which in turn is equivalent to say that 0 6∈ acc (accσ(T )). As application and using the concept of the Weak SVEP introduced at the end of this paper, we give new characterizations of Browder-type theorems.
Toward deconstructing the simplest seesaw mechanism

The triplet or type-II seesaw mechanism is the simplest way to endow neutrinos with mass in the Standard Model (SM). Here we review its associated theory and phenomenology, including restrictions from $S$, $T$, $U$ parameters, neutrino experiments, charged lepton flavour violations as well as collider searches. We also examine restrictions coming from requiring consistency of electroweak symmetry breaking, i.e. perturbative unitarity and stability of the vacuum. Finally, we discuss novel effects associated to the scalar mediator of neutrino mass generation namely, (i) rare processes, e.g. $l_\alpha \to l_\beta \gamma$ decays, at the intensity frontier, and also (ii) four-lepton signatures in colliders at the high-energy frontier. These can be used to probe neutrino properties in an important way, providing a test of the absolute neutrino mass and mass-ordering, as well as of the atmospheric octant. They may also provide the first evidence for charged lepton flavour violation in nature. In contrast, neutrino non-standard interaction strengths are found to lie below current detectability.
Semi-Discrete Normalizing Flows through Differentiable Tessellation

Mapping between discrete and continuous distributions is a difficult task and many have had to resort to heuristical approaches. We propose a tessellation-based approach that directly learns quantization boundaries in a continuous space, complete with exact likelihood evaluations. This is done through constructing normalizing flows on convex polytopes parameterized using a simple homeomorphism with an efficient log determinant Jacobian. We explore this approach in two application settings, mapping from discrete to continuous and vice versa. Firstly, a Voronoi dequantization allows automatically learning quantization boundaries in a multidimensional space. The location of boundaries and distances between regions can encode useful structural relations between the quantized discrete values. Secondly, a Voronoi mixture model has near-constant computation cost for likelihood evaluation regardless of the number of mixture components. Empirically, we show improvements over existing methods across a range of structured data modalities.
Colossal transverse magnetoresistance due to nematic superconducting phase fluctuations in a copper oxide

Abstract Electronic anisotropy (“nematicity”) has been detected in cuprate superconductors by various experimental techniques. Using angle-resolved transverse resistance (ARTR) measurements, a very sensitive and background-free technique that can detect 0.5% anisotropy in transport, we have observed it also in La2-xSrxCuO4 (LSCO) for 0.02 ≤ x ≤ 0.25. A central enigma in LSCO is the rotation of the nematic director (orientation of the largest longitudinal resistance) with temperature; this has not been seen before in any material. Here, we address this puzzle by measuring the angle-resolved transverse magnetoresistance (ARTMR) in LSCO. We report the discovery of colossal transverse magnetoresistance (CTMR)—an order-of-magnitude drop in the transverse resistivity in the magnetic field of 6 T. We show that the apparent rotation of the nematic director is caused by anisotropic superconducting fluctuations, which are not aligned with the normal electron fluid, consistent with coexisting bond-aligned and diagonal nematic orders. We quantify this by modeling the (magneto-)conductivity as a sum of normal (Drude) and paraconducting (Aslamazov–Larkin) channels but extended to contain anisotropic Drude and Cooper-pair effective mass tensors. Strikingly, the anisotropy of Cooper-pair stiffness is much larger than that of the normal electrons. It grows dramatically on the underdoped side, where the fluctuations become quasi-one-dimensional. Our analysis is general rather than model dependent. Still, we discuss some candidate microscopic models, including coupled strongly-correlated ladders where the transverse (interladder) phase stiffness is low compared with the longitudinal intraladder stiffness, as well as the anisotropic superconducting fluctuations expected close to the transition to a pair-density wave state.
MarkBERT: Marking Word Boundaries Improves Chinese BERT

We present a Chinese BERT model dubbed MarkBERT that uses word information in this work. Existing word-based BERT models regard words as basic units, however, due to the vocabulary limit of BERT, they only cover high-frequency words and fall back to character level when encountering out-of-vocabulary (OOV) words. Different from existing works, MarkBERT keeps the vocabulary being Chinese characters and inserts boundary markers between contiguous words. Such design enables the model to handle any words in the same way, no matter they are OOV words or not. Besides, our model has two additional benefits: first, it is convenient to add word-level learning objectives over markers, which is complementary to traditional character and sentence-level pretraining tasks; second, it can easily incorporate richer semantics such as POS tags of words by replacing generic markers with POS tag-specific markers. With the simple markers insertion, MarkBERT can improve the performances of various downstream tasks including language understanding and sequence labeling. \footnote{All the codes and models will be made publicly available at \url{https://github.com/daiyongya/markbert}}
Snowmass2021 Computational Frontier White Paper: Cosmological Simulations and Modeling

Powerful new observational facilities will come online over the next decade, enabling a number of discovery opportunities in the"Cosmic Frontier", which targets understanding of the physics of the early universe, dark matter and dark energy, and cosmological probes of fundamental physics, such as neutrino masses and modifications of Einstein gravity. Synergies between different experiments will be leveraged to present new classes of cosmic probes as well as to minimize systematic biases present in individual surveys. Success of this observational program requires actively pairing it with a well-matched state-of-the-art simulation and modeling effort. Next-generation cosmological modeling will increasingly focus on physically rich simulations able to model outputs of sky surveys spanning multiple wavebands. These simulations will have unprecedented resolution, volume coverage, and must deliver guaranteed high-fidelity results for individual surveys as well as for the cross-correlations across different surveys. The needed advances are as follows: (1) Development of scientifically rich and broadly-scoped simulations, which capture the relevant physics and correlations between probes (2) Accurate translation of simulation results into realistic image or spectral data to be directly compared with observations (3) Improved emulators and/or data-driven methods serving as surrogates for expensive simulations, constructed from a finite set of full-physics simulations (4) Detailed and transparent verification and validation programs for both simulations and analysis tools. (Abridged)
Combinatorial results for order-preserving partial injective contraction mappings

Let In be the symmetric inverse semigroup on Xn = {1, 2, . , n}. Let OCIn be the subsemigroup of In consisting of all order-preserving injective partial contraction mappings, and let ODCIn be the subsemigroup of In consisting of all order-preserving and order-decreasing injective partial contraction mappings of Xn. In this paper, we investigate the cardinalities of some equivalences on OCIn and ODCIn which lead naturally to obtaining the order of these semigroups. Then, we relate the formulae obtained to Fibonacci numbers. Similar results about ORCIn, the semigroup of order-preserving or orderreversing injective partial contraction mappings, are deduced.
End-to-End Multi-Tab Website Fingerprinting Attack: A Detection Perspective

Website fingerprinting attack (WFA) aims to deanonymize the website a user is visiting through anonymous networks channels (e.g., Tor). Despite of remarkable progress in the past years, most existing methods make implicitly a couple of artificial assumptions that (1) only a single website (i.e., single-tab) is visited each time, and (2) website fingerprinting data are pre-trimmed into a single trace per website manually. In reality, a user often open multiple tabs for multiple websites spontaneously. Indeed, this multi-tab WFA (MT-WFA) setting has been studied in a few recent works, but all of them still fail to fully respect the real-world situations. In particular, the overlapping challenge between website fingerprinting has never been investigated in depth. In this work, we redefine the problem of MT-WFA as detecting multiple monitored traces, given a natural untrimmed traffic data including monitored traces, unmonitored traces, and potentially unconstrained overlapping between them. This eliminates the above assumptions, going beyond the conventional single website fingerprint classification perspective taken by all previous WFA methods. To tackle this realistic MT-WFA problem, we formulate a novel Website Fingerprint Detection (WFD) model capable of detecting accurately the start and end points of all the monitored traces and classifying them jointly, given long, untrimmed raw traffic data. WFD is end-to-end, with the trace localization and website classification integrated in a single unified pipeline. To enable quantitative evaluation in our MT-WFA setting, we introduce new performance metrics. Extensive experiments on several newly constructed benchmarks show that our WFD outperforms the state-of-the-art alternative methods in both accuracy and efficiency by a large margin, even with a very small training set. Code is available at https://github.com/WFDetector/WFDetection