Miracle-dz/UniFAR-FTUs huggingface dataset:
Hot‐wire vapor deposition of amorphous MoS2 thin films

Amorphous, as shown by X-ray diffraction measurements, MoS2 films (a-MoS2) were deposited by heating a molybdenum wire at temperatures between 500 and 700 °C in H2S at 1 Torr. As shown by Scanning Electron Microscopy measurements, the morphology of samples depends significantly on the filament temperature; at low temperature samples are homogeneous and smooth, at intermediate temperatures they exhibit a granular microstructure and at high temperatures a columnar one. X-ray photoelectron spectroscopy measurements have shown S/Mo ratios in films varying between 2.5 and 1.5 dependent on filament temperature. Films also contain oxygen at atomic contents of 8 to 12%. As shown by XPS and Raman spectroscopy, at a filament temperature of 600 °C films are mainly composed of MoS2 also containing oxygen at an atomic ratio of 8%. Spectroscopic ellipsometry measurements made on a-MoS2 films have shown that their band gap is of the order of 1.4 eV, slightly higher than that for the bulk crystalline material. Photoluminescence spectroscopy measurements have shown that samples exhibit a doublet of peaks at 2.8 and 3 eV blue shifted relatively to MoS2 samples composed of one or two mono-layers. The above indicate that the electronic structure of crystalline atomic-layer thick MoS2 is preserved in a-MoS2 films. (© 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)
Synthesis of Poly Amide Based On Heterocyclic Rings

This research included preparation of a two heterocyclic triazole compounds, where prepared the 3,5diamino-1-phenyl-1,2,4-triazoles and 3,5-diamino-1-carbamyl-1,2,4-triazoles from reaction of Ncyanoguanidine with phenyl hydrazine hydrochloride and semicarbazide hydrochloride respectively. Then synthesis of poly amide compounds by the reaction of these triazoles diamine compounds with dicarboxylic acid (oxalic acid, malonic acid and adipic acid) after converting to their acid chlorides (oxalic chloride, malonic chloride and adipic chloride) by reaction these carboxylic acids with thionyl chloride. The compounds were purified and characterized with the analytical and spectral data such as FT-IR spectrum (400-4000 cm -1 ), and 1 HNMR spectrum (300MHz).
Hybrid Beamforming for Terahertz Wireless Communications: Challenges, Architectures, and Open Problems

Terahertz (THz) communications are regarded as a pillar technology for 6G wireless systems, by offering multi-ten-GHz bandwidth. To overcome the short transmission distance and huge propagation loss, ultra-massive (UM) MIMO systems that employ sub-millimeter wavelength antenna arrays are proposed to enable an enticingly high array gain. In UM-MIMO systems, hybrid beamforming stands out for its great potential in promisingly high data rate and reduced power consumption. In this article, challenges and features of the THz hybrid beamforming design are investigated, in light of the distinctive THz peculiarities. Specifically, we investigate that the spatial degree-of-freedom is poor, which is caused by the extreme sparsity of the THz channel. The blockage problem caused by the huge reflection and scattering losses is studied. We analyze the challenges led by the array containing 1024 or more antennas, including the requirement for dynamic subarray architecture, strict energy efficiency, and propagation characterization based on spherical-wave propagation mechanisms. Owning to multi-ten-GHz bandwidth, beam squint effect could cause tens of dB array gain loss. Inspired by these facts, three novel THz-specific hybrid beamforming architectures are presented, including widely-spaced multi-subarray, dynamic array-of-subarrays, and dynamic-subar-ray with fixed-true-time-delay architectures. We also demonstrate the potential data rate, power consumption, and array gain capabilities for THz communications. As a roadmap of THz hybrid beamforming design, multiple open problems and potential research directions are elaborated.
Adsorption removal of chloroform and bromoform by activated carbon fiber

Adsorption characteristics of chloroform and bromoform onto activated carbon fibers (ACFs) for the safety drinking water were investigated. The amounts of chloroform and bromoform adsorbed onto ACFs decreased with their grade number that is the surface area and pore volume. Bromoform was more easily adsorbed onto ACFs than chloroform due to the difference in water solubility. The dominant factor of chloroform and bromoform adsorption onto ACFs may be the surface hydrophobicity of adsorbent rather than the surface area or pore volume.
Van der Waals cascade in supercritical turbulence near a critical point

We investigate a quite strong turbulence in a supercritical ﬂuid near a gas-liquid critical point. Speciﬁcally, we consider a case in which the Kolmogorov scale is much smaller than the equilibrium correlation length ξ . Although equilibrium critical ﬂuctuations are destroyed by turbulence, ξ still provides a crossover length scale between two types of energy cascade. At scales much larger than ξ , the Richardson cascade becomes dominant, whereas at scales much smaller than ξ , another type of cascade, which we call the van der Waals cascade, is induced by density ﬂuctuations. Experimental conditions required to observe the van der Waals cascade are also discussed.
Monitoring nonstationary processes based on recursive cointegration analysis and elastic weight consolidation

This paper considers the problem of nonstationary process monitoring under frequently varying operating conditions. Traditional approaches generally misidentify the normal dynamic deviations as faults and thus lead to high false alarms. Besides, they generally consider single relatively steady operating condition and suffer from the catastrophic forgetting issue when learning successive operating conditions. In this paper, recursive cointegration analysis (RCA) is first proposed to distinguish the real faults from normal systems changes, where the model is updated once a new normal sample arrives and can adapt to slow change of cointegration relationship. Based on the long-term equilibrium information extracted by RCA, the remaining short-term dynamic information is monitored by recursive principal component analysis (RPCA). Thus a comprehensive monitoring framework is built. When the system enters a new operating condition, the RCA-RPCA model is rebuilt to deal with the new condition. Meanwhile, elastic weight consolidation (EWC) is employed to settle the `catastrophic forgetting' issue inherent in RPCA, where significant information of influential parameters is enhanced to avoid the abrupt performance degradation for similar modes. The effectiveness of the proposed method is illustrated by a practical industrial system.
A Joint Diagonalization Based Efficient Approach to Underdetermined Blind Audio Source Separation Using the Multichannel Wiener Filter

Blind source separation (BSS) of audio signals aims to separate original source signals from their mixtures recorded by microphones. The applications include automatic speech recognition in a noisy/multi-speaker environment, hearing aids, and music analysis. Independent component analysis (ICA) can perform BSS efficiently, but it is basically inapplicable to the underdetermined case—the number of sources $\boldsymbol{>}$ the number of microphones. In contrast, a BSS approach using the multichannel Wiener filter (MWF) is applicable even to the underdetermined case, but conventional methods based on this approach—including full-rank spatial covariance analysis (FCA)—are highly inefficient. This is because these methods require massive numbers of matrix inversions to design the MWF. To obtain the best of both worlds, we take a joint diagonalization approach: We restrict spatial covariance matrices of all sources to the class of jointly diagonalizable matrices. This enables the above matrix inversions to be replaced by mere scalar inversions of the diagonal elements of diagonal matrices. Based on this, we present FastFCA and FastMNMF—efficient methods for underdetermined BSS. In an experiment, FastFCA was several orders of magnitude faster than FCA without sacrificing separation performance. We also present a unified framework for underdetermined and determined BSS, which highlights theoretical connections between various methods including ours. The efficiency of our BSS methods makes them suitable for large data (e.g., data augmentation for machine learning) or limited computational resources encountered in, e.g., hearing aids, distributed microphone arrays, and online BSS.
The Gospel According to Q: Understanding the QAnon Conspiracy from the Perspective of Canonical Information

The QAnon conspiracy theory claims that a cabal of (literally) blood-thirsty politicians and media personalities are engaged in a war to destroy society. By interpreting cryptic “drops” of information from an anonymous insider calling themself Q, adherents of the conspiracy theory believe that Donald Trump is leading them in an active fight against this cabal. QAnon has been covered extensively by the media, as its adherents have been involved in multiple violent acts, including the January 6th, 2021 seditious storming of the US Capitol building. Nevertheless, we still have relatively little understanding of how the theory evolved and spread on the Web, and the role played in that by multiple platforms. To address this gap, we study QAnon from the perspective of “Q” themself. We build a dataset of 4,949 canonical Q drops collected from six “aggregation sites,” which curate and archive them from their original posting to anonymous and ephemeral image boards. We expose that these sites have a relatively low (overall) agreement, and thus at least some Q drops should probably be considered apocryphal. We then analyze the Q drops’ contents to identify topics of discussion and find statistically significant indications that drops were not authored by a single individual. Finally, we look at how posts on Reddit are used to disseminate Q drops to wider audiences. We find that dissemination was (initially) limited to a few sub-communities and that, while heavy-handed moderation decisions have reduced the overall issue, the “gospel” of Q persists on the Web.
Validating Label Consistency in NER Data Annotation

Data annotation plays a crucial role in ensuring your named entity recognition (NER) projects are trained with the right information to learn from. Producing the most accurate labels is a challenge due to the complexity involved with annotation. Label inconsistency between multiple subsets of data annotation (e.g., training set and test set, or multiple training subsets) is an indicator of label mistakes. In this work, we present an empirical method to explore the relationship between label (in-)consistency and NER model performance. It can be used to validate the label consistency (or catches the inconsistency) in multiple sets of NER data annotation. In experiments, our method identified the label inconsistency of test data in SCIERC and CoNLL03 datasets (with 26.7% and 5.4% label mistakes). It validated the consistency in the corrected version of both datasets.
Optimal full ranking from pairwise comparisons

We consider the problem of ranking n players from partial pairwise comparison data under the Bradley-Terry-Luce model. For the first time in the literature, the minimax rate of this ranking problem is derived with respect to the Kendall’s tau distance that measures the difference between two rank vectors by counting the number of inversions. The minimax rate of ranking exhibits a transition between an exponential rate and a polynomial rate depending on the magnitude of the signal-to-noise ratio of the problem. To the best of our knowledge, this phenomenon is unique to full ranking and has not been seen in any other statistical estimation problem. To achieve the minimax rate, we propose a divide-and-conquer ranking algorithm that first divides the n players into groups of similar skills and then computes local MLE within each group. The optimality of the proposed algorithm is established by a careful approximate independence argument between the two steps.
Conformal Killing forms on 2-step nilpotent Riemannian Lie groups

Abstract We study left-invariant conformal Killing 2- or 3-forms on simply connected 2-step nilpotent Riemannian Lie groups. We show that if the center of the group is of dimension greater than or equal to 4, then every such form is automatically coclosed (i.e. it is a Killing form). In addition, we prove that the only Riemannian 2-step nilpotent Lie groups with center of dimension at most 3 and admitting left-invariant non-coclosed conformal Killing 2- and 3-forms are the following: The Heisenberg Lie groups and their trivial 1-dimensional extensions, endowed with any left-invariant metric, and the simply connected Lie group corresponding to the free 2-step nilpotent Lie algebra on 3 generators, with a particular 1-parameter family of metrics. The explicit description of the space of conformal Killing 2- and 3-forms is provided in each case.
Unifying cardiovascular modelling with deep reinforcement learning for uncertainty aware control of sepsis treatment

Sepsis is a potentially life-threatening inflammatory response to infection or severe tissue damage. It has a highly variable clinical course, requiring constant monitoring of the patient’s state to guide the management of intravenous fluids and vasopressors, among other interventions. Despite decades of research, there’s still debate among experts on optimal treatment. Here, we combine for the first time, distributional deep reinforcement learning with mechanistic physiological models to find personalized sepsis treatment strategies. Our method handles partial observability by leveraging known cardiovascular physiology, introducing a novel physiology-driven recurrent autoencoder, and quantifies the uncertainty of its own results. Moreover, we introduce a framework for uncertainty-aware decision support with humans in the loop. We show that our method learns physiologically explainable, robust policies, that are consistent with clinical knowledge. Further our method consistently identifies high-risk states that lead to death, which could potentially benefit from more frequent vasopressor administration, providing valuable guidance for future research.
Multi-robot energy autonomy with wind and constrained resources

One aspect of the ever-growing need for long term autonomy of multi-robot systems, is ensuring energy sufficiency. In particular, in scenarios where charging facilities are limited, battery-powered robots need to coordinate to share access. In this work we extend previous results by considering robots that carry out a generic mission while sharing a single charging station, while being affected by air drag and wind fields. Our mission-agnostic framework based on control barrier functions (CBFs) ensures energy sufficiency (i.e., maintaining all robots above a certain voltage threshold) and proper coordination (i.e., ensuring mutually exclusive use of the available charging station). Moreover, we investigate the feasibility requirements of the system in relation to individual robots' properties, as well as air drag and wind effects. We show simulation results that demonstrate the effectiveness of the proposed framework.
From Local Pseudorandom Generators to Hardness of Learning

We prove hardness-of-learning results under a well-studied assumption on the existence of local pseudorandom generators. As we show, this assumption allows us to surpass the current state of the art, and prove hardness of various basic problems, with no hardness results to date. Our results include: hardness of learning shallow ReLU neural networks under the Gaussian distribution and other distributions; hardness of learning intersections of $\omega(1)$ halfspaces, DNF formulas with $\omega(1)$ terms, and ReLU networks with $\omega(1)$ hidden neurons; hardness of weakly learning deterministic finite automata under the uniform distribution; hardness of weakly learning depth-$3$ Boolean circuits under the uniform distribution, as well as distribution-specific hardness results for learning DNF formulas and intersections of halfspaces. We also establish lower bounds on the complexity of learning intersections of a constant number of halfspaces, and ReLU networks with a constant number of hidden neurons. Moreover, our results imply the hardness of virtually all improper PAC-learning problems (both distribution-free and distribution-specific) that were previously shown hard under other assumptions.
Extremal Laws for Laplacian Random Matrices

For an n × n Laplacian random matrix L with Gaussian entries it is proven that the fluctuations of the largest eigenvalue and the largest diagonal entry of L/ √ n− 1 are Gumbel. We first establish suitable non-asymptotic estimates and bounds for the largest eigenvalue of L in terms of the largest diagonal element of L. An expository review of existing results for the asymptotic spectrum of a Laplacian random matrix is also presented, with the goal of noting the differences from the corresponding classical results for Wigner random matrices. Extensions to Laplacian block random matrices are indicated.
Noisy intermediate-scale quantum (NISQ) algorithms

A universal fault-tolerant quantum computer that can solve efficiently problems such as integer factorization and unstructured database search requires millions of qubits with low error rates and long coherence times. While the experimental advancement towards realizing such devices will potentially take decades of research, noisy intermediate-scale quantum (NISQ) computers already exist. These computers are composed of hundreds of noisy qubits, i.e. qubits that are not error-corrected, and therefore perform imperfect operations in a limited coherence time. In the search for quantum advantage with these devices, algorithms have been proposed for applications in various disciplines spanning physics, machine learning, quantum chemistry and combinatorial optimization. The goal of such algorithms is to leverage the limited available resources to perform classically challenging tasks. In this review, we provide a thorough summary of NISQ computational paradigms and algorithms. We discuss the key structure of these algorithms, their limitations, and advantages. We additionally provide a comprehensive overview of various benchmarking and software tools useful for programming and testing NISQ devices.
Learn to Dance with AIST++: Music Conditioned 3D Dance Generation

In this paper, we present a transformer-based learning framework for 3D dance generation conditioned on music. We carefully design our network architecture and empirically study the keys for obtaining qualitatively pleasing results. The critical components include a deep cross-modal transformer, which well learns the correlation be-tween the music and dance motion; and the full-attention with future-N supervision mechanism which is essential in producing long-range non-freezing motion. In addition, we propose a new dataset of paired 3D motion and music called AIST++, which we reconstruct from the AIST multi-view dance videos. This dataset contains 1.1M frames of 3D dance motion in 1408 sequences, covering 10 gen-res of dance choreographies and accompanied with multi-view camera parameters. To our knowledge it is the largest dataset of this kind. Rich experiments on AIST++ demonstrate our method produces much better results than the state-of-the-art methods both qualitatively and quantitatively. Please watch the video project page and dataset at https://google.github. io/aichoreographer.
Robust Differential Abundance Test in Compositional Data

Differential abundance tests in compositional data are essential and fundamental tasks in various biomedical applications, such as single-cell, bulk RNA-seq, and microbiome data analysis. However, because of the compositional constraint and the prevalence of zero counts in the data, differential abundance analysis in compositional data remains a complicated and unsolved statistical problem. This study introduces a new differential abundance test, the robust differential abundance test, to address these challenges. Compared with existing methods, the robust differential abundance test is simple and computationally efficient, is robust to prevalent zero counts in compositional datasets, can take the data's compositional nature into account, and has a theoretical guarantee of controlling false discoveries in a general setting. Furthermore, in the presence of observed covariates, the robust differential abundance test can work with the covariate balancing techniques to remove the potential confounding effects and draw reliable conclusions. Finally, we apply the new test to several numerical examples using simulated and real datasets to demonstrate its practical merits.
Digging into the Interior of Hot Cores with ALMA (DIHCA). I. Dissecting the High-mass Star-forming Core G335.579-0.292 MM1

We observed the high-mass star-forming region G335.579–0.292 with the Atacama Large Millimeter/submillimeter Array (ALMA) at 226 GHz with an angular resolution of 0. ″3 (∼1000 au resolution at the source distance). G335.579–0.292 hosts one of the most massive cores in the Galaxy (G335–MM1). The continuum emission shows that G335–MM1 fragments into at least five sources, while molecular line emission is detected in two of the continuum sources (ALMA1 and ALMA3). We found evidence of large- and small-scale infall in ALMA1 revealed by an inverse P-Cygni profile and the presence of a blueshifted spot at the center of the first moment map of the CH3CN emission. In addition, hot gas expansion in the innermost region is unveiled by a redshifted spot in the first moment map of HDCO and (CH3)2CO (both with E u > 1100 K). Our modeling reveals that this expansion motion originates close to the central source, likely due to reversal of the accretion flow induced by the expansion of the H ii region, while infall and rotation motions originate in the outer regions. ALMA3 shows clear signs of rotation, with a rotation axis inclination with respect to the line of sight close to 90°, and a system mass (disk + star) in the range of 10–30 M ☉.
A multimodal approach for individual tracking of people and their belongings

Abstract In this study, a fully automatic surveillance system for indoor environments which is capable of tracking multiple objects using both visible and thermal band images is proposed. These two modalities are fused to track people and the objects they carry separately using their heat signatures and the owners of the belongings are determined. Fusion of complementary information from different modalities (for example, thermal images are not affected by shadows and there is no thermal reflection or halo effect in visible images) is shown to result in better object detection performance. We use adaptive background modeling and local intensity operation for object detection and the mean-shift tracking algorithm for fully automatic tracking. Trackers are refreshed to resolve potential problems which may occur due to the changes in object’s size, shape and to handle occlusion-split and to detect newly emerging objects as well as objects that leave the scene. The proposed scheme is applied to the abandoned object detection problem and the results are compared with the state of art methods. The results show that the proposed method facilitate individual tracking of objects for various applications, and provide lower false alarm rates compared to the state of art methods when applied to the abandoned object detection problem.