Miracle-dz/UniFAR-FTUs huggingface dataset:
Establishing Coherent Momentum-Space Electronic States in Locally Ordered Materials

In our understanding of solids, the formation of highly spatially coherent electronic states, fundamental to command the quantum behavior of materials, relies on the existence of discrete translational symmetry of the crystalline lattice. In contrast, in the absence of long-range order, as in the case of non-crystalline materials, the electronic states are localized and electronic coherence does not develop. This brings forward the fundamental question whether long range order is necessary condition to establish coherence and structured momentum-dependent electronic state, and how to characterize it in the presence of short-range order. Here we study Bi$_2$Se$_3$, a material that exists in its crystalline form with long range order, in amorphous form, with short and medium range order, and in its nanocrystalline form, with reduced short range order. By using angle resolved photoemission spectroscopy to directly access the electronic states in a momentum resolved manner, we reveal that, even in the absence of long-range order, a well-defined real-space length scale is sufficient to produce dispersive band structures. Moreover, we observe for the first time a repeated Fermi surface structure of duplicated annuli, reminiscent of Brillouin zone-like repetitions. These results, together with our simulations using amorphous Hamiltonians, reveal that the typical momentum scale where coherence occurs is the inverse average nearest-neighbor distance, the direct fingerprint of the local order of the underlying atomic structure. These results, not only lead the way to a new understanding of electronic coherence in solids, but also open the way to the realization of novel momentum-dependent quantum phenomena such as momentum pairing and spin-orbit coupling, in a much broader class of materials than the currently studied ones, lacking long range crystalline translational symmetry.
Quantum Key Distribution Using an Integrated Quantum Emitter in Hexagonal Boron Nitride

Quantum Key Distribution (QKD) is considered the most immediate application to be widely implemented amongst a variety of potential quantum technologies. QKD enables sharing secret keys between distant users, using photons as information carriers. The current challenge is to implement these protocols in practice, for real-world conditions, in a robust, and compact manner. Single Photon Sources (SPS) in solid-state materials are prime candidates in this respect. Here, we demonstrate a room temperature, discrete-variable quantum key distribution system using a bright single photon source in hexagonal-boron nitride, operating in free-space. Employing an integrated, “plug and play” photon source system, we have generated keys with one million bits length, and demonstrated a secret key of approximately 70,000 bits, at a quantum bit error rate of 6%, with ε -security of 10 − 10 . Emphasis was put on the inclusion of all known eﬀects impacting the derived security level, thereby demonstrating the most trustworthy QKD system realised with SPSs to date. Our results will be important to achieve meaningful progress with deterministic room-temperature QKD systems.
Will Admins Cope? Decentralized Moderation in the Fediverse

As an alternative to Twitter and other centralized social networks, the Fediverse is growing in popularity. The recent, and polemical, takeover of Twitter by Elon Musk has exacerbated this trend. The Fediverse includes a growing number of decentralized social networks, such as Pleroma or Mastodon, that share the same subscription protocol (ActivityPub). Each of these decentralized social networks is composed of independent instances that are run by different administrators. Users, however, can interact with other users across the Fediverse regardless of the instance they are signed up to. The growing user base of the Fediverse creates key challenges for the administrators, who may experience a growing burden. In this paper, we explore how large that overhead is, and whether there are solutions to alleviate the burden. We study the overhead of moderation on the administrators. We observe a diversity of administrator strategies, with evidence that administrators on larger instances struggle to find sufficient resources. We then propose a tool, WatchGen, to semi-automate the process.
Parameterizable Acoustical Modeling and Auralization of Cultural Heritage Sites based on Photogrammetry

The photogrammetric and reconstructive modeling of cultural heritage sites is mostly focused on visually perceivable aspects, but if their intended purpose is the performance of cultural acts with a sonic emphasis, it is important to consider the preservation of their acoustical behaviour to make them audible in an authentic way. This applies in particular to sacral and concert environments as popular objects for photogrammetric models, which contain geometrical and textural information that can be used to locate and classify acoustically relevant surface properties. With the advancing conversion or destruction of historical acoustical spaces, it becomes even more important to preserve their unique sonic characters, while three-dimensional auralizations become widely applicable. The proposed study presents the current state of a new methodological approach to acoustical modeling using photogrammetric data and introduces a parameterizable pipeline that will be accessible as an open-source software with a graphical user interface.
The Construction of Reality in an AI: A Review

AI constructivism as inspired by Jean Piaget, described and surveyed by Frank Guerin, and representatively implemented by Gary Drescher seeks to create algorithms and knowledge structures that enable agents to acquire, maintain, and apply a deep understanding of the environment through sensorimotor interactions. This paper aims to increase awareness of constructivist AI implementations to encourage greater progress toward enabling lifelong learning by machines. It builds on Guerin's 2008"Learning Like a Baby: A Survey of AI approaches. "After briefly recapitulating that survey, it summarizes subsequent progress by the Guerin referents, numerous works not covered by Guerin (or found in other surveys), and relevant efforts in related areas. The focus is on knowledge representations and learning algorithms that have been used in practice viewed through lenses of Piaget's schemas, adaptation processes, and staged development. The paper concludes with a preview of a simple framework for constructive AI being developed by the author that parses concepts from sensory input and stores them in a semantic memory network linked to episodic data. Extensive references are provided.
Homophily-oriented Heterogeneous Graph Rewiring

With the rapid development of the World Wide Web (WWW), heterogeneous graphs (HG) have explosive growth. Recently, heterogeneous graph neural network (HGNN) has shown great potential in learning on HG. Current studies of HGNN mainly focus on some HGs with strong homophily properties (nodes connected by meta-path tend to have the same labels), while few discussions are made in those that are less homophilous. Recently, there have been many works on homogeneous graphs with heterophily. However, due to heterogeneity, it is non-trivial to extend their approach to deal with HGs with heterophily. In this work, based on empirical observations, we propose a meta-path-induced metric to measure the homophily degree of a HG. We also find that current HGNNs may have degenerated performance when handling HGs with less homophilous properties. Thus it is essential to increase the generalization ability of HGNNs on non-homophilous HGs. To this end, we propose HDHGR, a homophily-oriented deep heterogeneous graph rewiring approach that modifies the HG structure to increase the performance of HGNN. We theoretically verify HDHGR. In addition, experiments on real-world HGs demonstrate the effectiveness of HDHGR, which brings at most more than 10% relative gain.
Nonminimally assisted inflation: a general analysis

The effects of a scalar field, known as the “assistant field,” which nonminimally couples to gravity, on single-field inflationary models are studied. The analysis provides analytical expressions for inflationary observables such as the spectral index (ns ), the tensor-to-scalar ratio (r), and the local-type nonlinearity parameter (f NL (local)). The presence of the assistant field leads to a lowering of ns and r in most of the parameter space, compared to the original predictions. In some cases, ns may increase due to the assistant field. This revives compatibility between ruled-out single-field models and the latest observations by Planck-BICEP/Keck. The results are demonstrated using three example models: loop inflation, power-law inflation, and hybrid inflation.
Fixing Overconfidence in Dynamic Neural Networks

Dynamic neural networks are a recent technique that promises a remedy for the increasing size of modern deep learning models by dynamically adapting their computational cost to the difficulty of the inputs. In this way, the model can adjust to a limited computational budget. However, the poor quality of uncertainty estimates in deep learning models makes it difficult to distinguish between hard and easy samples. To address this challenge, we present a computationally efficient approach for post-hoc uncertainty quantification in dynamic neural networks. We show that adequately quantifying and accounting for both aleatoric and epistemic uncertainty through a probabilistic treatment of the last layers improves the predictive performance and aids decision-making when determining the computational budget. In the experiments, we show improvements on CIFAR-100, ImageNet, and Caltech-256 in terms of accuracy, capturing uncertainty, and calibration error.
Sources of richness and ineffability for phenomenally conscious states

Abstract Conscious states—state that there is something it is like to be in—seem both rich or full of detail and ineffable or hard to fully describe or recall. The problem of ineffability, in particular, is a longstanding issue in philosophy that partly motivates the explanatory gap: the belief that consciousness cannot be reduced to underlying physical processes. Here, we provide an information theoretic dynamical systems perspective on the richness and ineffability of consciousness. In our framework, the richness of conscious experience corresponds to the amount of information in a conscious state and ineffability corresponds to the amount of information lost at different stages of processing. We describe how attractor dynamics in working memory would induce impoverished recollections of our original experiences, how the discrete symbolic nature of language is insufficient for describing the rich and high-dimensional structure of experiences, and how similarity in the cognitive function of two individuals relates to improved communicability of their experiences to each other. While our model may not settle all questions relating to the explanatory gap, it makes progress toward a fully physicalist explanation of the richness and ineffability of conscious experience—two important aspects that seem to be part of what makes qualitative character so puzzling.
Graph Neural Network-Inspired Kernels for Gaussian Processes in Semi-Supervised Learning

Gaussian processes (GPs) are an attractive class of machine learning models because of their simplicity and flexibility as building blocks of more complex Bayesian models. Meanwhile, graph neural networks (GNNs) emerged recently as a promising class of models for graph-structured data in semi-supervised learning and beyond. Their competitive performance is often attributed to a proper capturing of the graph inductive bias. In this work, we introduce this inductive bias into GPs to improve their predictive performance for graph-structured data. We show that a prominent example of GNNs, the graph convolutional network, is equivalent to some GP when its layers are infinitely wide; and we analyze the kernel universality and the limiting behavior in depth. We further present a programmable procedure to compose covariance kernels inspired by this equivalence and derive example kernels corresponding to several interesting members of the GNN family. We also propose a computationally efficient approximation of the covariance matrix for scalable posterior inference with large-scale data. We demonstrate that these graph-based kernels lead to competitive classification and regression performance, as well as advantages in computation time, compared with the respective GNNs.
Languages given by Finite Automata over the Unary Alphabet

This paper studies the complexity of operations with finite automata and the complexity of their decision problems when the alphabet is unary. (1) This paper improves the upper bound of the equality problem of unary nondeterministic automata from an exponential in the second root to an exponential in the third root of the number of states. This almost matches a known lower bound based on the exponential time hypothesis by Fernau and Krebs. (2) It is established that the standard regular operations of union, intersection, complementation and Kleene star cause either only a polynomial or a quasipolynomial blow-up. Concatenation of two $n$-state ufas, in worst case, causes a blow-up from $n$ to a function with an exponent of sixth root of $n$ or worse. Decision problems of finite formulas using regular operations and comparing languages given by $n$-state unambiguous automata, in worst case, require an exponential-type of time under the Exponential Time hypothesis and this complexity goes down to quasipolynomial time in the case that the concatenation of languages is not used in the formula. Merely comparing two languages given by $n$-state ufas in Chrobak Normal Form is in LOGSPACE. (3) Starting from this research, membership of the infinite word given by a unary alphabet language in a fixed regular language of infinite words is shown to be as difficult as constructing the dfa of that language from the given automaton.
Understanding Multimodal Contrastive Learning and Incorporating Unpaired Data

Language-supervised vision models have recently attracted great attention in computer vision. A common approach to build such models is to use contrastive learning on paired data across the two modalities, as exemplified by Contrastive Language-Image Pre-Training (CLIP). In this paper, under linear representation settings, (i) we initiate the investigation of a general class of nonlinear loss functions for multimodal contrastive learning (MMCL) including CLIP loss and show its connection to singular value decomposition (SVD). Namely, we show that each step of loss minimization by gradient descent can be seen as performing SVD on a contrastive cross-covariance matrix. Based on this insight, (ii) we analyze the performance of MMCL. We quantitatively show that the feature learning ability of MMCL can be better than that of unimodal contrastive learning applied to each modality even under the presence of wrongly matched pairs. This characterizes the robustness of MMCL to noisy data. Furthermore, when we have access to additional unpaired data, (iii) we propose a new MMCL loss that incorporates additional unpaired datasets. We show that the algorithm can detect the ground-truth pairs and improve performance by fully exploiting unpaired datasets. The performance of the proposed algorithm was verified by numerical experiments.
A Statistical Analysis of Galactic Radio Supernova Remnants

We present a revised table of 390 Galactic radio supernova remnants (SNRs) and their basic parameters. Statistical analyses are performed on SNR diameters, ages, spectral indices, Galactic heights, and spherical symmetries. Furthermore, the accuracy of distances estimated using the Σ–D relation is examined. The arithmetic mean of the Galactic SNR diameters is 30.5 pc with standard error 1.7 pc and standard deviation 25.4 pc. The geometric mean and geometric standard deviation factor of Galactic SNR diameters is 21.9 pc and 2.4, respectively. We estimate ages of 97 SNRs and find a supernova (SN) birth rate lower than, but within 2σ of, currently accepted values for the SN birth rate. The mean spectral index of shell-type SNRs is −0.51 ± 0.01 and no correlations are found between spectral indices and the SNR parameters of molecular cloud association, SN type, diameter, Galactic height, and surface brightness. The Galactic height distribution of SNRs is best described by an exponential distribution with a scale height of 48 ± 4 pc. The spherical symmetry measured by the ovality of radio SNRs is not correlated to any other SNR parameters considered here or to explosion type.
Identification of deep trap levels from thermally stimulated current spectra of semi-insulating CdZnTe detector material

Deep trap levels in the semi-insulating (SI) CdZnTe detector material were characterized by simultaneous multiple peak analysis based on thermally stimulated current (TSC) measurements. In our TSCs that have been published previously electron hole pairs were created through the use of proton beam irradiation. Charge carriers were captured in deep traps and afterward released by thermal emission, which was recorded in the 90–300 K range. We showed that the obtained TSC spectra could be well fitted with a unique set of 14 different deep traps, which were all simultaneously and completely characterized. The obtained trap data are in good accordance with earlier deep trap characterizations of the other authors obtained on similar SI CdZnTe materials using different methods.
Improving Accuracy of Interpretability Measures in Hyperparameter Optimization via Bayesian Algorithm Execution

Despite all the benefits of automated hyperparameter optimization (HPO), most modern HPO algorithms are black-boxes themselves. This makes it difficult to understand the decision process which leads to the selected configuration, reduces trust in HPO, and thus hinders its broad adoption. Here, we study the combination of HPO with interpretable machine learning (IML) methods such as partial dependence plots. These techniques are more and more used to explain the marginal effect of hyperparameters on the black-box cost function or to quantify the importance of hyperparameters. However, if such methods are naively applied to the experimental data of the HPO process in a post-hoc manner, the underlying sampling bias of the optimizer can distort interpretations. We propose a modified HPO method which efficiently balances the search for the global optimum w.r. t. predictive performance \emph{and} the reliable estimation of IML explanations of an underlying black-box function by coupling Bayesian optimization and Bayesian Algorithm Execution. On benchmark cases of both synthetic objectives and HPO of a neural network, we demonstrate that our method returns more reliable explanations of the underlying black-box without a loss of optimization performance.
Elementary Particle Physics Vision for EPP2024

In the fall of 2022, the decadal survey committee on Elementary Particle Physics of the US National Academies requested 2000 word Vision Papers, giving personal interpretations of the results of the Snowmass 2021 study and the future of the field. This is my contribution, which emphasizes the central role of the Higgs boson and its associated mysteries. I encourage the authors of other Vision Papers to make them widely available on the arXiv.
The Structure of Multibody Dynamics Equations

Several alternative formulations for the dynamics of multibody systems are described. These alternatives include momentum and velocity formulations with decoupling or coupling of constraints. The presentation of equations is facilitated by the introduction of a path matrix and a reference matrix that describe the topology of the H-body configurations. The final equations of motion are obtained from the equations of motion for a single body via the transformation operator formalism. The equations of motion presented herein are compared with 10 /z-body dynamics formulations in the spacecraft dynamics literature.
The Conformal Laplacian and Positive Scalar Curvature Metrics on Manifolds with Boundary

We give examples of spin $4$-manifolds with boundary that do not admit metrics of positive scalar curvature and nonnegative mean curvature. These manifolds in fact have the stronger property that the conformal Laplacian with appropriate boundary conditions is never positive. The obstruction to the positivity of the conformal Laplacian is given by a real-valued $\xi$-invariant associated to the APS theorem for the twisted Dirac operator. We use analytic techniques related to the prescribed scalar curvature problem in conformal geometry to move beyond earlier work where the metric is a product near the boundary.
Low‐frequency noise in silicon‐gate metal‐oxide‐silicon capacitors before oxide breakdown

Measurements of the fluctuations in the tunneling current It through a thin SiO2 insulating layer, by means of an ultralow noise measurement set, showed that after a first time interval in which its power spectral density is stationary and proportional to I2t, an on‐off modulation of It arises, just before oxide breakdown. This bistable noise seems to be related to localized phenomena controlled by trapping‐detrapping processes within the oxide. Two possible mechanisms which could give rise to this bistable noise are discussed.
Experiments on the Surging of a Blower

This paper deals with experiments on surging phenomena. The results may be summarized as follows : 1. The air column in the pipe-line vibrates in a certain form resembling that of free vibration. 2. The more rapid revolutions of a blower become, the more violent the surging grows, and the more important role the characteristic of a blower plays in determining the amplitude of the surging. 3. When the length and area of a cross section of the pipe-line are fixed, the surging is apt to be built up according to the volume of the tank. 4. The feature of the surging is considerably influenced by the conditions under which the energy is dissipated from vibrating air column.
New developments in multimodal clinical multiphoton tomography

80 years ago, the PhD student Maria Goeppert predicted in her thesis in Goettingen, Germany, two-photon effects. It took 30 years to prove her theory, and another three decades to realize the first two-photon microscope. With the beginning of this millennium, first clinical multiphoton tomographs started operation in research institutions, hospitals, and in the cosmetic industry. The multiphoton tomograph MPTflexTM with its miniaturized flexible scan head became the Prism-Award 2010 winner in the category Life Sciences. Multiphoton tomographs with its superior submicron spatial resolution can be upgraded to 5D imaging tools by adding spectral time-correlated single photon counting units. Furthermore, multimodal hybrid tomographs provide chemical fingerprinting and fast wide-field imaging. The world's first clinical CARS studies have been performed with a hybrid multimodal multiphoton tomograph in spring 2010. In particular, nonfluorescent lipids and water as well as mitochondrial fluorescent NAD(P)H, fluorescent elastin, keratin, and melanin as well as SHG-active collagen have been imaged in patients with dermatological disorders. Further multimodal approaches include the combination of multiphoton tomographs with low-resolution imaging tools such as ultrasound, optoacoustic, OCT, and dermoscopy systems. Multiphoton tomographs are currently employed in Australia, Japan, the US, and in several European countries for early diagnosis of skin cancer (malignant melanoma), optimization of treatment strategies (wound healing, dermatitis), and cosmetic research including long-term biosafety tests of ZnO sunscreen nanoparticles and the measurement of the stimulated biosynthesis of collagen by anti-ageing products.