Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploring Lorentz Invariance Violation from Ultra-high-energy Gamma Rays Observed by LHAASO

Recently the LHAASO Collaboration published the detection of 12 ultra-high-energy gamma-ray sources above 100 TeV, with the highest energy photon reaching 1.4 PeV. The first detection of PeV gamma rays from astrophysical sources may provide a very sensitive probe of the effect of the Lorentz invariance violation (LIV), which results in decay of high-energy gamma rays in the superluminal scenario and hence a sharp cutoff of the energy spectrum. Two highest energy sources are studied in this work. No signature of the existence of LIV is found in their energy spectra, and the lower limits on the LIV energy scale are derived. Our results show that the first-order LIV energy scale should be higher than about 10^5 times the Planck scale M_{pl} and that the second-order LIV scale is>10^{-3}M_{pl}. Both limits improve by at least one order of magnitude the previous results.
ABCD: A Graph Framework to Convert Complex Sentences to a Covering Set of Simple Sentences

Atomic clauses are fundamental text units for understanding complex sentences. Identifying the atomic sentences within complex sentences is important for applications such as summarization, argument mining, discourse analysis, discourse parsing, and question answering. Previous work mainly relies on rule-based methods dependent on parsing. We propose a new task to decompose each complex sentence into simple sentences derived from the tensed clauses in the source, and a novel problem formulation as a graph edit task. Our neural model learns to Accept, Break, Copy or Drop elements of a graph that combines word adjacency and grammatical dependencies. The full processing pipeline includes modules for graph construction, graph editing, and sentence generation from the output graph. We introduce DeSSE, a new dataset designed to train and evaluate complex sentence decomposition, and MinWiki, a subset of MinWikiSplit. ABCD achieves comparable performance as two parsing baselines on MinWiki. On DeSSE, which has a more even balance of complex sentence types, our model achieves higher accuracy on the number of atomic sentences than an encoder-decoder baseline. Results include a detailed error analysis.
Cantilever-enhanced photoacoustic measurement of light-absorbing aerosols

Abstract Photoacoustic detection is a sensitive method for measurement of light-absorbing particles directly in the aerosol phase. In this article, we demonstrate a new sensitive technique for photoacoustic aerosol absorption measurements using a cantilever microphone for the detection of the photoacoustic signal. Compared to conventional diaphragm microphones, a cantilever offers increased sensitivity by up to two orders of magnitude. The measurement setup uses a photoacoustic cell from Gasera PA201 gas measurement system, which we have adapted for aerosol measurements. Here we reached a noise level of 0.013 Mm−1 (one standard deviation) with a sampling time of 20 s, using a simple single-pass design without a need for a resonant acoustic cell. The sampling time includes 10 s signal averaging time and 10 s sample exchange, since the photoacoustic cell is designed for closed cell operation. We demonstrate the method in measurements of size-selected nigrosin particles and ambient black carbon. Due to the exceptional sensitivity, the technique shows great potential for applications where low detection limits are required, for example size-selected absorption measurements and black carbon detection in ultra clean environments.
A Simple and Practical Approach to Improve Misspellings in OCR Text

The focus of our paper is the identification and correction of non-word errors in OCR text. Such errors may be the result of incorrect insertion, deletion, or substitution of a character, or the transposition of two adjacent characters within a single word. Or, it can be the result of word boundary problems that lead to run-on errors and incorrect-split errors. The traditional N-gram correction methods can handle single-word errors effectively. However, they show limitations when dealing with split and merge errors. In this paper, we develop an unsupervised method that can handle both errors. The method we develop leads to a sizable improvement in the correction rates. This tutorial paper addresses very difficult word correction problems - namely incorrect run-on and split errors - and illustrates what needs to be considered when addressing such problems. We outline a possible approach and assess its success on a limited study.
Wavenumber-Division Multiplexing in Line-of-Sight Holographic MIMO Communications

Starting from first principles of wave propagation, we consider a multiple-input multiple-output (MIMO) representation of a communication system between two spatially-continuous volumes. This is the concept of holographic MIMO communications. The analysis takes into account the electromagnetic interference, generated by external sources, and the constraint on the physical radiated power. The electromagnetic MIMO model is particularized for a pair of parallel line segments in line-of-sight conditions. Inspired by orthogonal-frequency division-multiplexing, we assume that the spatially-continuous transmit currents and received fields are represented using the Fourier basis functions. In doing so, a wavenumber-division multiplexing (WDM) scheme is obtained, which is not optimal but can be efficiently implemented. The interplay among the different system parameters (e.g., transmission range, wavelength, and sizes of source and receiver) in terms of number of communication modes and level of interference among them is studied with conventional tools of linear systems theory. Due to the non-finite support (in the spatial domain) of the electromagnetic channel, WDM cannot provide non-interfering communication modes. The interference decreases as the receiver size grows, and goes to zero only asymptotically. Different digital processing architectures, operating in the wavenumber domain, are thus used to deal with the interference. The simplest implementation provides the same spectral efficiency of a singular-value decomposition architecture with water-filling when the receiver size is comparable to the transmission range. The developed framework is also used to represent a communication scheme that performs only an integration over short spatial segments. This is equivalent to a classical MIMO system with uniform linear arrays made of electrically small dipoles. Numerical comparisons show that better performance than WDM can be achieved only when a higher number of radio-frequency chains is used.
First Step Towards EXPLAINable DGA Multiclass Classification

Numerous malware families rely on domain generation algorithms (DGAs) to establish a connection to their command and control (C2) server. Counteracting DGAs, several machine learning classifiers have been proposed enabling the identification of the DGA that generated a specific domain name and thus triggering targeted remediation measures. However, the proposed state-of-the-art classifiers are based on deep learning models. The black box nature of these makes it difficult to evaluate their reasoning. The resulting lack of confidence makes the utilization of such models impracticable. In this paper, we propose EXPLAIN, a feature-based and contextless DGA multiclass classifier. We comparatively evaluate several combinations of feature sets and hyperparameters for our approach against several state-of-the-art classifiers in a unified setting on the same real-world data. Our classifier achieves competitive results, is real-time capable, and its predictions are easier to trace back to features than the predictions made by the DGA multiclass classifiers proposed in related work.
End-to-End Lexically Constrained Machine Translation for Morphologically Rich Languages

Lexically constrained machine translation allows the user to manipulate the output sentence by enforcing the presence or absence of certain words and phrases. Although current approaches can enforce terms to appear in the translation, they often struggle to make the constraint word form agree with the rest of the generated output. Our manual analysis shows that 46% of the errors in the output of a baseline constrained model for English to Czech translation are related to agreement. We investigate mechanisms to allow neural machine translation to infer the correct word inflection given lemmatized constraints. In particular, we focus on methods based on training the model with constraints provided as part of the input sequence. Our experiments on English-Czech language pair show that this approach improves translation of constrained terms in both automatic and manual evaluation by reducing errors in agreement. Our approach thus eliminates inflection errors, without introducing new errors or decreasing overall quality of the translation.
Improving Transformer-based Sequential Recommenders through Preference Editing

One of the key challenges in sequential recommendation is how to extract and represent user preferences. Traditional methods rely solely on predicting the next item. But user behavior may be driven by complex preferences. Therefore, these methods cannot make accurate recommendations when the available information user behavior is limited. To explore multiple user preferences, we propose a transformer-based sequential recommendation model, named MrTransformer (Multi-preference Transformer). For training MrTransformer, we devise a preference-editing-based self-supervised learning (SSL) mechanism that explores extra supervision signals based on relations with other sequences. The idea is to force the sequential recommendation model to discriminate between common and unique preferences in different sequences of interactions. By doing so, the sequential recommendation model is able to disentangle user preferences into multiple independent preference representations so as to improve user preference extraction and representation. We carry out extensive experiments on five benchmark datasets. MrTransformer with preference editing significantly outperforms state-of-the-art sequential recommendation methods in terms of Recall, MRR, and NDCG. We find that long sequences of interactions from which user preferences are harder to extract and represent benefit most from preference editing.
Random Effect Bandits

This paper studies regret minimization in a multi-armed bandit. It is well known that side information, such as the prior distribution of arm means in Thompson sampling, can improve the statistical efficiency of the bandit algorithm. While the prior is a blessing when correctly specified, it is a curse when misspecified. To address this issue, we introduce the assumption of a random-effect model to bandits. In this model, the mean arm rewards are drawn independently from an unknown distribution, which we estimate. We derive a random-effect estimator of the arm means, analyze its uncertainty, and design a UCB algorithm ReUCB that uses it. We analyze ReUCB and derive an upper bound on its $n$-round Bayes regret, which improves upon not using the random-effect structure. Our experiments show that ReUCB can outperform Thompson sampling, without knowing the prior distribution of arm means.
Global 3D radiation hydrodynamic simulations of proto-Jupiter’s convective envelope

The core accretion model of giant planet formation has been challenged by the discovery of recycling flows between the planetary envelope and the disc that can slow or stall envelope accretion. We carry out 3D radiation hydrodynamic simulations with an updated opacity compilation to model the proto-Jupiter’s envelope. To isolate the 3D effects of convection and recycling, we simulate both isolated spherical envelopes and envelopes embedded in discs. The envelopes are heated at given rates to achieve steady states, enabling comparisons with 1D models. We vary envelope properties to obtain both radiative and convective solutions. Using a passive scalar, we observe significant mass recycling on the orbital time-scale. For a radiative envelope, recycling can only penetrate from the disc surface until ∼0.1–0.2 planetary Hill radii, while for a convective envelope, the convective motion can ‘dredge up’ the deeper part of the envelope so that the entire convective envelope is recycled efficiently. This recycling, however, has only limited effects on the envelopes’ thermal structure. The radiative envelope embedded in the disc has identical structure as the isolated envelope. The convective envelope has a slightly higher density when it is embedded in the disc. We introduce a modified 1D approach which can fully reproduce our 3D simulations. With our updated opacity and 1D model, we recompute Jupiter’s envelope accretion with a 10 M⊕ core, and the time-scale to runaway accretion is shorter than the disc lifetime as in prior studies. Finally, we discuss the implications of the efficient recycling on the observed chemical abundances of the planetary atmosphere (especially for super-Earths and mini-Neptunes).
Reduction of Transition Metal Salts by SFS: Synthesis of Copper and Silver Sulphides

Simple one‐step formation of copper and silver sulphide has been achieved from direct reaction of respective metal salts in N,N′‐dimethylformamide (DMF) by use of sodium formaldehyde sulfoxylate (SFS) as a reducing agent. It is believed that the SFS plays a dual role, i.e., of a reducing agents as well as of a surfactant to keep control on the particles growth and generates nano size powders of these sulphides. Such a method has not been tested to date for formation of these semiconductors.
Biosynthetic studies on alkaloids in Lunasia amara

Benzoic acid[14CO2H] and sodium acetate[2-14C] are incorporated into 4- methoxy-2-phenylquinoline,the major alkaloid in the leaves of L. amara. No radioactivity is found in the quinoline when sodium acetate[1-14C] or phenylalanine[3-14C] are administered; this suggests the alkaloid is derived from anthranilic acid by the pathway suggested by Leete.1 Mevalonic acid[2-14C] is incorporated into the furanoquinoline alkaloid lunacrine.
Optimal Exponents in Cascaded Hypothesis Testing under Expected Rate Constraints

Cascaded binary hypothesis testing is studied in this paper with two decision centers at the relay and the receiver. All terminals have their own observations, where we assume that the observations at the transmitter, the relay, and the receiver form a Markov chain in this order. The communication occurs over two hops, from the transmitter to the relay, and from the relay to the receiver. Expected rate constraints are imposed on both communication links. In this work, we characterize the optimal type-II error exponents at the two decision centers under constraints on the allowed type-I error probabilities. Our recent work characterized the optimal type-II error exponents in the special case when the two decision centers have same type-I error constraints and provided an achievability scheme for the general setup. To obtain the exact characterization for the general case, in this paper we provide a new converse proof as well as a new matching achievability scheme. Our results indicate that under unequal type-I error constraints at the relay and the receiver, a tradeoff arises between the maximum type-II error probabilities at these two terminals. Previous results showed that such a tradeoff does not exist under equal type-I error constraints or under general type-I error constraints when a maximum rate constraint is imposed on the communication links.
Real-time neural radiance caching for path tracing

We present a real-time neural radiance caching method for path-traced global illumination. Our system is designed to handle fully dynamic scenes, and makes no assumptions about the lighting, geometry, and materials. The data-driven nature of our approach sidesteps many difficulties of caching algorithms, such as locating, interpolating, and updating cache points. Since pretraining neural networks to handle novel, dynamic scenes is a formidable generalization challenge, we do away with pretraining and instead achieve generalization via adaptation, i.e. we opt for training the radiance cache while rendering. We employ self-training to provide low-noise training targets and simulate infinite-bounce transport by merely iterating few-bounce training updates. The updates and cache queries incur a mild overhead---about 2.6ms on full HD resolution---thanks to a streaming implementation of the neural network that fully exploits modern hardware. We demonstrate significant noise reduction at the cost of little induced bias, and report state-of-the-art, real-time performance on a number of challenging scenarios.
Classical and Quantum f(R) Cosmology: The Big Rip, the Little Rip and the Little Sibling of the Big Rip

The big rip, the little rip and the little sibling of the big rip are cosmological doomsdays predicted by some phantom dark-energy models that could describe the future evolution of our universe. When the universe evolves towards either of these future cosmic events, all bounded structures and, ultimately, space–time itself are ripped apart. Nevertheless, it is commonly believed that quantum gravity effects may smooth or even avoid these classically predicted singularities. In this review, we discuss the classical and quantum occurrence of these riplike events in the scheme of metric f(R) theories of gravity. The quantum analysis is performed in the framework of f(R) quantum geometrodynamics. In this context, we analyze the fulfilment of the DeWitt criterion for the avoidance of these singular fates. This review contains as well new unpublished work (the analysis of the equation of state for the phantom fluid and a new quantum treatment of the big rip and the little sibling of the big rip events).
Synthetic Benchmarks for Scientific Research in Explainable Machine Learning

As machine learning models grow more complex and their applications become more high-stakes, tools for explaining model predictions have become increasingly important. This has spurred a flurry of research in model explainability and has given rise to feature attribution methods such as LIME and SHAP. Despite their widespread use, evaluating and comparing different feature attribution methods remains challenging: evaluations ideally require human studies, and empirical evaluation metrics are often data-intensive or computationally prohibitive on real-world datasets. In this work, we address this issue by releasing XAI-Bench: a suite of synthetic datasets along with a library for benchmarking feature attribution algorithms. Unlike real-world datasets, synthetic datasets allow the efficient computation of conditional expected values that are needed to evaluate ground-truth Shapley values and other metrics. The synthetic datasets we release offer a wide variety of parameters that can be configured to simulate real-world data. We demonstrate the power of our library by benchmarking popular explainability techniques across several evaluation metrics and across a variety of settings. The versatility and efficiency of our library will help researchers bring their explainability methods from development to deployment. Our code is available at https://github.com/abacusai/xai-bench.
Sentinel-1 and Sentinel-2 Spatio-Temporal Data Fusion for Clouds Removal

—The abundance of clouds, located both spatially and temporally, often makes remote sensing applications with optical images difﬁcult or even impossible. In this manuscript a novel method for clouds-corrupted optical images restoration has been presented and developed, based on a joint data fusion paradigm, where three deep neural networks have been combined in order to fuse spatio-temporal features extracted from Sentinel-1 and Sentinel-2 time-series of data. It is worth to highlight that both the code and the dataset have been implemented from scratch and made available to interested research for further analysis and investigation.
A Light Mediator Relating Neutrino Reactions

The extension of the standard model with a multiplicative U(1)R factor is consistent with a light vector boson. In its simplest realization, only right-handed particles carry charges of the new group. In this model, there is a residual τ3R symmetry and one new coupling constant which correlates neutrino interactions. We compute new contributions to antineutrino–electron scattering and coherent scattering on nuclei, and compare them with the XENON1T result.
Electrical control of the hole spin qubit in Si and Ge nanowire quantum dots

Strong, direct Rashba spin-orbit coupling in Si, Ge, and the Ge/Si core/shell nanowire quantum dot (QD) allows for all electrical manipulation of the hole spin qubit. Motivated by this fact, we analyze different fabrication-dependent properties of nanowires, such as orientation, cross section, and the presence of strain, with the goal being to find the material and geometry that enables the fastest qubit manipulation, whose speed can be identified using the Rabi frequency. We show that QD in nanowires with a circular cross section (cNWs) enables much weaker driving of the hole spin qubit than QDs embedded in square profile nanowires (sNWs). Assuming the orientation of the Si nanowire that maximizes the spin-orbit effects, our calculations predict that the Rabi frequencies of the hole spin qubits inside Ge and Si sNW QD have comparable strengths for weak electric fields. The global maximum of the Rabi frequency is found in Si sNW QD for strong electric fields, putting this setup ahead of others in creating the hole spin qubit. Finally, we demonstrate that strain in the Si/Ge core/shell nanowire QD decreases the Rabi frequency. In cNW QD, this effect is weak; in sNW QD, it is possible to optimize the impact of strain with the appropriate tuning of the electric field strength.
A partial information decomposition for discrete and continuous variables

Conceptually, partial information decomposition (PID) is concerned with separating the information contributions several sources hold about a certain target by decomposing the corresponding joint mutual information into contributions such as synergistic, redundant, or unique information. Despite PID conceptually being defined for any type of random variables, so far, PID could only be quantified for the joint mutual information of discrete systems. Recently, a quantification for PID in continuous settings for two or three source variables was introduced. Nonetheless, no ansatz has managed to both quantify PID for more than three variables and cover general measure-theoretic random variables, such as mixed discrete-continuous, or continuous random variables yet. In this work we will propose an information quantity, defining the terms of a PID, which is well-defined for any number or type of source or target random variable. This proposed quantity is tightly related to a recently developed local shared information quantity for discrete random variables based on the idea of shared exclusions. Further, we prove that this newly proposed information-measure fulfills various desirable properties, such as satisfying a set of local PID axioms, invariance under invertible transformations, differentiability with respect to the underlying probability density, and admitting a target chain rule.