Miracle-dz/UniFAR-FTUs huggingface dataset:
Commodity Fuels from Biomass through Pretreatment and Torrefaction: Effects of Mineral Content on Torrefied Fuel Characteristics and Quality

Torrefaction of biomass is rapidly gaining popularity as a viable pretreatment for use with co-combustion with coal or with other thermochemical conversion processes. This work explores the effects of combining pretreatment washing techniques using water, ammonium acetate, and hydrochloric acid. Four biomasses were studied, short-rotation coppiced willow, eucalyptus, Miscanthus, and wheat straw, all in chipped or chopped form. The resultant fuels, after the pretreatments, were characterized for ultimate analysis, proximate analysis, heating value, and pyrolysis behavior (via thermogravimetric analysis), and mass and energy yields in a fixed-bed torrefier were measured. The ease of removal of certain metals, Na, K, Mg, and Ca, as well as PO43–, SO42–, and Cl– was assessed by ion chromatography on the leachates from the water washing, and influences on fouling behavior were predicted. Fuel ashes (both prior to and after torrefaction) were also assessed in the ash fusion test, a probe for slagging behavior.
Gas Dynamics in the Galaxy: Total Mass Distribution and the Bar Pattern Speed

Gas morphology and kinematics in the Milky Way contain key information for understanding the formation and evolution of our Galaxy. We present hydrodynamical simulations based on realistic barred Milky Way potentials constrained by recent observations. Our model can reproduce most features in the observed longitude–velocity diagram, including the Central Molecular Zone, the Near and Far 3 kpc arms, the Molecular Ring, and the spiral arm tangents. It can also explain the noncircular motions of masers from the recent BeSSeL2 survey. The central gas kinematics are consistent with a mass of 6.9 × 108 M ⊙ in the Nuclear Stellar Disk. Our model predicts the formation of an elliptical gaseous ring surrounding the bar, which is composed of the 3 kpc arms, the Norma arm, and the bar-spiral interfaces. This ring is similar to those “inner” rings in some Milky Way analogs with a boxy/peanut-shaped bulge (e.g., NGC 4565 and NGC 5746). The kinematics of gas near the solar neighborhood are governed by the Local arm. The bar pattern speed constrained by our gas model is 37.5–40 km s−1 kpc−1, corresponding to a corotation radius of R CR = 6.0–6.4 kpc. The rotation curve of our model rises gently within the central ∼ 5 kpc, significantly less steep than those predicted by some recent zoom-in cosmological simulations.
Geometric constructions for Ramsey-Tur\'an theory

Combining two classical notions in extremal combinatorics, the study of Ramsey-Turán theory seeks to determine, for integers m ≤ n and p ≤ q, the number RTp(n,Kq,m), which is the maximum size of an n-vertex Kq-free graph in which every set of at least m vertices contains a Kp. Two major open problems in this area from the 80s ask: (1) whether the asymptotic extremal structure for the general case exhibits certain periodic behaviour, resembling that of the special case when p = 2; (2) constructing analogues of Bollobás-Erdős graphs with densities other than 1/2. We refute the first conjecture by witnessing asymptotic extremal structures that are drastically different from the p = 2 case, and address the second problem by constructing Bollobás-Erdős-type graphs using high dimensional complex spheres with all rational densities. Some matching upper bounds are also provided.
Chemical modeling of the complex organic molecules in the extended region around Sagittarius B2

The chemical differentiation of seven COMs in the extended region around Sgr B2 has been observed: CH$_2$OHCHO, CH$_3$OCHO, t-HCOOH, C$_2$H$_5$OH, and CH$_3$NH$_2$ were detected both in the extended region and near the hot cores Sgr B2(N) and Sgr B2(M), while CH$_3$OCH$_3$ and C$_2$H$_5$CN were only detected near the hot cores. The density and temperature in the extended region are relatively low. Different desorption mechanisms have been proposed to explain the observed COMs in cold regions but fail to explain the deficiency of CH$_3$OCH$_3$ and C$_2$H$_5$CN. We explored under what physical conditions the chemical simulations can fit the observations and explain the different spatial distribution of these species. We used the Monte Carlo method to perform a detailed parameter space study. We investigated how different mechanisms affect the results. All gas-grain chemical models based on static physics cannot fit the observations. The results based on evolving physical conditions can fit six COMs when $T\sim30-60$ K, but the best-fit temperature is still higher than the observed dust temperature of 20 K. The best agreement at $T\sim27$ K is achieved by considering a short-duration $\sim 10^2$ yr X-ray burst with $\zeta_{\mathrm{CR}}=1.3\times10^{-13}$ s$^{-1}$ when the temperature is 20 K. The reactive desorption is the key mechanism for producing these COMs and inducing the low abundances of CH$_3$OCH$_3$ and C$_2$H$_5$CN. The evolution of the extended region around Sgr~B2 may have begun with a cold, $T\le10$ K phase followed by a warm-up phase. When its temperature reached $T\sim20$ K, an X-ray flare from Sgr A* with a short duration of no more than 100 years was acquired, affecting strongly the Sgr B2 chemistry. The observed COMs retain their observed abundances only several hundred years after such a flare, which could imply that such short-term X-ray flares occur relatively often.
Decomposing and Recomposing Event Structure

We present an event structure classification empirically derived from inferential properties annotated on sentence- and document-level Universal Decompositional Semantics (UDS) graphs. We induce this classification jointly with semantic role, entity, and event-event relation classifications using a document-level generative model structured by these graphs. To support this induction, we augment existing annotations found in the UDS1.0 dataset, which covers the entirety of the English Web Treebank, with an array of inferential properties capturing fine-grained aspects of the temporal and aspectual structure of events. The resulting dataset (available at decomp.io) is the largest annotation of event structure and (partial) event coreference to date.
Quinductor: a multilingual data-driven method for generating reading-comprehension questions using Universal Dependencies

We propose a multilingual data-driven method for generating reading comprehension questions using dependency trees. Our method provides a strong, deterministic and inexpensive-to-train baseline for less-resourced languages. While a language-specific corpus is still required, its size is nowhere near those required by modern neural question generation (QG) architectures. Our method surpasses QG baselines previously reported in the literature in terms of automatic evaluation metrics and shows a good performance in terms of human evaluation.
Device-independent bounds from Cabello's nonlocality argument

Hardy-type arguments manifest Bell nonlocality in one of the simplest possible ways. Except for demonstrating nonclassical signature of entangled states in question, they can also serve for device-independent self-testing of states, as shown, e.g., in Phys. Rev. Lett. 109, 180401 (2012). Here we develop and broaden these results to an extended version of Hardy's argument, often referred to as Cabello's nonlocality argument. We show that, as in the simpler case of Hardy's nonlocality argument, the maximum quantum value for Cabello's nonlocality is achieved by a pure two-qubit state and projective measurements that are unique up to local isometries. We also examine the properties of a more realistic case when small errors in the ideal constraints are accepted within the probabilities obtained and prove that also in this case the two-qubit state and measurements are sufficient for obtaining the maximum quantum violation of the classical bound.
Transformer-based ASR Incorporating Time-reduction Layer and Fine-tuning with Self-Knowledge Distillation

End-to-end automatic speech recognition (ASR), unlike conventional ASR, does not have modules to learn the semantic representation from speech encoder. Moreover, the higher frame-rate of speech representation prevents the model to learn the semantic representation properly. Therefore, the models that are constructed by the lower frame-rate of speech encoder lead to better performance. For Transformer-based ASR, the lower frame-rate is not only important for learning better semantic representation but also for reducing the computational complexity due to the self-attention mechanism which has O(n^2) order of complexity in both training and inference. In this paper, we propose a Transformer-based ASR model with the time reduction layer, in which we incorporate time reduction layer inside transformer encoder layers in addition to traditional sub-sampling methods to input features that further reduce the frame-rate. This can help in reducing the computational cost of the self-attention process for training and inference with performance improvement. Moreover, we introduce a fine-tuning approach for pre-trained ASR models using self-knowledge distillation (S-KD) which further improves the performance of our ASR model. Experiments on LibriSpeech datasets show that our proposed methods outperform all other Transformer-based ASR systems. Furthermore, with language model (LM) fusion, we achieve new state-of-the-art word error rate (WER) results for Transformer-based ASR models with just 30 million parameters trained without any external data.
A Soft-Aided Staircase Decoder Using Three-Level Channel Reliabilities

The soft-aided bit-marking (SABM) algorithm is based on the idea of marking bits as highly reliable bits (HRBs), highly unreliable bits (HUBs), and uncertain bits to improve the performance of hard-decision (HD) decoders. The HRBs and HUBs are used to assist the HD decoder to prevent miscorrections and to decode those originally uncorrectable cases via bit flipping (BF), respectively. In this paper, an improved SABM algorithm (called iSABM) is proposed for staircase codes (SCCs). Similar to the SABM, iSABM marks bits with the help of channel reliabilities, i.e., using the absolute values of the log-likelihood ratios. The improvements offered by iSABM include: (i) HUBs being classified using a reliability threshold, (ii) BF randomly selecting HUBs, and (iii) soft-aided decoding over multiple SCC blocks. The decoding complexity of iSABM is comparable of that of SABM. This is due to the fact that on the one hand no sorting is required (lower complexity) because of the use of a threshold for HUBs, while on the other hand multiple SCC blocks use soft information (higher complexity). The gains achieved by iSABM are up to 0.53 dB with respect to SABM and up to 0.91 dB with respect to standard decoding of SCCs at a bit error rate of $10^{-6}$. Furthermore, it is shown that using 1-bit reliability marking, i.e., only having HRBs and HUBs, only causes a gain penalty of up to 0.25 dB with a significantly reduced memory requirement.
Using latent space regression to analyze and leverage compositionality in GANs

In recent years, Generative Adversarial Networks have become ubiquitous in both research and public perception, but how GANs convert an unstructured latent code to a high quality output is still an open question. In this work, we investigate regression into the latent space as a probe to understand the compositional properties of GANs. We find that combining the regressor and a pretrained generator provides a strong image prior, allowing us to create composite images from a collage of random image parts at inference time while maintaining global consistency. To compare compositional properties across different generators, we measure the trade-offs between reconstruction of the unrealistic input and image quality of the regenerated samples. We find that the regression approach enables more localized editing of individual image parts compared to direct editing in the latent space, and we conduct experiments to quantify this independence effect. Our method is agnostic to the semantics of edits, and does not require labels or predefined concepts during training. Beyond image composition, our method extends to a number of related applications, such as image inpainting or example-based image editing, which we demonstrate on several GANs and datasets, and because it uses only a single forward pass, it can operate in real-time. Code is available on our project page: https://chail.github. io/latent-composition/.
The Cauchy dual subnormality problem via de Branges–Rovnyak spaces

The Cauchy dual subnormality problem (for short, CDSP) asks whether the Cauchy dual of a 2-isometry is subnormal. In this paper, we address this problem for cyclic 2-isometries. In view of some recent developments in operator theory on function spaces (see [4, 22]), one may recast CDSP as the problem of subnormality of the Cauchy dual M ′ z of the multiplication operator Mz acting on a de BrangesRovnyak space H(B), where B is a vector-valued rational function. The main result of this paper characterizes the subnormality of M ′ z on H(B) provided B is a vector-valued rational function with simple poles. As an application, we provide affirmative solution to CDSP for the Dirichlettype spaces D(μ) associated with measures μ supported on two antipodal points of the unit circle. 1. Cauchy dual subnormality problem for 2-isometries The Cauchy dual subnormality problem (for short, CDSP) for 2-isometries can be seen as the manifestation of the rich interplay between positive definite and negative definite functions on abelian semigroups. Indeed, CDSP can be considered as the non-commutative variant of the fact from the harmonic analysis on semigroups that the reciprocal of a Bernstein function f : [0,∞) → (0,∞) is completely monotone (see [31, Theorem 3.6]). This fact turns out to be somewhat equivalent to the solution of CDSP for completely hyperexpansive weighted shifts (see [7, Proposition 6] for a generalization). Another early result towards the solution of CDSP asserts that the Cauchy dual of any concave operator is a hyponormal contraction (see [33, Equation (26)]). Later this fact was generalized in [13, Theorem 3.1] by deducing power hyponormality of the Cauchy dual of any concave operator. Around the same time CDSP was settled affirmatively for ∆T -regular 2-isometries in [8, Theorem 3.4] and for 2-isometric operator-valued weighted shifts in [5, Theorems 2.5 and 3.3] (see also [14, Corollary 6.2] for the solution for yet another subclass of 2-isometries). Further, it was shown in [5, Examples 6.6 and 7.10] that there exist 2-isometric weighted shifts on directed trees (that include adjacency operators) whose Cauchy dual is not necessarily subnormal. Recently, a class of cyclic 2-isometric composition operators without subnormal Cauchy dual has been exhibited in [6, Theorem 4.4]. 2000 Mathematics Subject Classification. Primary 47B32, 47B38; Secondary 44A60, 31C25.
Exploring theoretical uncertainties in the hydrodynamic description of relativistic heavy-ion collisions

We explore theoretical uncertainties in the hydrodynamic description of relativistic heavy-ion collisions by examining the full non-linear causality conditions and quantifying the second-order transport coefficients’ role on flow observables. The causality conditions impose physical constraints on the maximum allowed values of inverse Reynolds numbers during the hydrodynamic evolution. Including additional second-order gradient terms in the Denicol-Niemi-Molnár-Rischke (DNMR) theory significantly shrinks the casual regions compared to those in the Israel-Stewart hydrodynamics. For Au+Au collisions, we find the variations of flow observables are small with and without imposing the necessary causality conditions, suggesting a robust extraction of the Quark-Gluon Plasma’s transport coefficients in previous model-to-data comparisons. However, sizable sensitivity is present in small p+Au collisions, which poses challenges to study the small systems’ collectivity.
Stochastic bandits for multi-platform budget optimization in online advertising

We study the problem of an online advertising system that wants to optimally spend an advertiser’s given budget for a campaign across multiple platforms, without knowing the value for showing an ad to the users on those platforms. We model this challenging practical application as a Stochastic Bandits with Knapsacks problem over T rounds of bidding with the set of arms given by the set of distinct bidding m-tuples, where m is the number of platforms. We modify the algorithm proposed in Badanidiyuru et al., [11] to extend it to the case of multiple platforms to obtain an algorithm for both the discrete and continuous bid-spaces. Namely, for discrete bid spaces we give an algorithm with regret , where OPT is the performance of the optimal algorithm that knows the distributions. For continuous bid spaces the regret of our algorithm is . When restricted to this special-case, this bound improves over Sankararaman and Slivkins [34] in the regime OPT < < T, as is the case in the particular application at hand. Second, we show an lower bound for the discrete case and an Ω(m1/3B2/3) lower bound for the continuous setting, almost matching the upper bounds. Finally, we use a real-world data set from a large internet online advertising company with multiple ad platforms and show that our algorithms outperform common benchmarks and satisfy the required properties warranted in the real-world application.
Challenges of 3D Surface Reconstruction in Capsule Endoscopy

Essential for improving the accuracy and reliability of bowel cancer screening, three-dimensional (3D) surface reconstruction using capsule endoscopy (CE) images remains challenging due to CE hardware and software limitations. This report generally focuses on challenges associated with 3D visualization and specifically investigates the impact of the indeterminate selection of the angle of the line–of–sight on 3D surfaces. Furthermore, it demonstrates that impact through 3D surfaces viewed at the same azimuth angles and different elevation angles of the line–of–sight. The report concludes that 3D printing of reconstructed 3D surfaces can potentially overcome line–of–sight indeterminate selection and 2D screen visual restriction-related errors.
A Location-Sensitive Local Prototype Network For Few-Shot Medical Image Segmentation

Despite the tremendous success of deep neural networks in medical image segmentation, they typically require a large amount of costly, expert-level annotated data. Few-shot segmentation approaches address this issue by learning to transfer knowledge from limited quantities of labeled examples. Incorporating appropriate prior knowledge is critical in designing high-performance few-shot segmentation algorithms. Since strong spatial priors exist in many medical imaging modalities, we propose a prototype-based method—namely, the location-sensitive local prototype network—that leverages spatial priors to perform few-shot medical image segmentation. Our approach divides the difficult problem of segmenting the entire image with global prototypes into easily solvable subproblems of local region segmentation with local prototypes. For organ segmentation experiments on the VISCERAL CT image dataset, our method outperforms the state-of-the-art approaches by 10% in the mean Dice coefficient. Extensive ablation studies demonstrate the substantial benefits of incorporating spatial information and confirm the effectiveness of our approach.
Danish Fungi 2020 – Not Just Another Image Recognition Dataset

We introduce a novel fine-grained dataset and bench-mark, the Danish Fungi 2020 (DF20). The dataset, constructed from observations submitted to the Atlas of Danish Fungi, is unique in its taxonomy-accurate class labels, small number of errors, highly unbalanced long-tailed class distribution, rich observation metadata, and well-defined class hierarchy. DF20 has zero overlap with ImageNet, al-lowing unbiased comparison of models fine-tuned from publicly available ImageNet checkpoints. The proposed evaluation protocol enables testing the ability to improve classification using metadata – e.g. precise geographic location, habitat, and substrate, facilitates classifier calibration testing, and finally allows to study the impact of the device settings on the classification performance. Experiments using Convolutional Neural Networks (CNN) and the recent Vision Transformers (ViT) show that DF20 presents a challenging task. Interestingly, ViT achieves results su-perior to CNN baselines with 80.45% accuracy and 0.743 macro F1 score, reducing the CNN error by 9% and 12% respectively. A simple procedure for including metadata into the decision process improves the classification accuracy by more than 2.95 percentage points, reducing the error rate by 15%. The source code for all methods and experiments is available at https://sites.google. com/view/danish-fungi-dataset.
Intelligent Reflecting Surface Enabled Random Rotations Scheme for the MISO Broadcast Channel

The current literature on intelligent reflecting surface (IRS) focuses on optimizing the IRS phase shifts to yield coherent beamforming gains, under the assumption of perfect channel state information (CSI) of individual IRS-assisted links, which is highly impractical. This work, instead, considers the random rotations scheme at the IRS in which the reflecting elements only employ random phase rotations without requiring any CSI. The only CSI then needed is at the base station (BS) of the overall channel to implement the beamforming transmission scheme. Under this framework, we derive the sum-rate scaling laws in the large number of users regime for the IRS-assisted multiple-input single-output (MISO) broadcast channel, with optimal dirty paper coding (DPC) scheme and the lower-complexity random beamforming (RBF) and deterministic beamforming (DBF) schemes at the BS. The random rotations scheme increases the sum-rate by exploiting multi-user diversity, but also compromises the gain to some extent due to correlation. Finally, energy efficiency maximization problems in terms of the number of BS antennas, IRS elements and transmit power are solved using the derived scaling laws. Simulation results show the proposed scheme to improve the sum-rate, with performance becoming close to that under coherent beamforming for a large number of users.
Detection of two interstellar polycyclic aromatic hydrocarbons via spectral matched filtering

Identifying PAHs in space Midinfrared spectroscopy has shown that polycyclic aromatic hydrocarbons (PAHs) are abundant in many astronomical objects, but this technique cannot determine which specific PAH molecules are present. Radio astronomy could provide individual identifications if the molecule is sufficiently abundant and has a large dipole moment, but PAHs are expected to produce large numbers of very weak lines. McGuire et al. performed a stacking and matched filter analysis to search for PAHs in radio observations of TMC-1, located within the interstellar Taurus Molecular Cloud. They identified emission from two isomers of the small PAH cyanonapthalene, two fused benzene rings with a CN group attached. Science, this issue p. 1265 Radio astronomy is used to identify specific polycyclic aromatic hydrocarbon molecules in the interstellar medium. Unidentified infrared emission bands are ubiquitous in many astronomical sources. These bands are widely, if not unanimously, attributed to collective emissions from polycyclic aromatic hydrocarbon (PAH) molecules, yet no single species of this class has been identified in space. Using spectral matched filtering of radio data from the Green Bank Telescope, we detected two nitrile-group–functionalized PAHs, 1- and 2-cyanonaphthalene, in the interstellar medium. Both bicyclic ring molecules were observed in the TMC-1 molecular cloud. In this paper, we discuss potential in situ gas-phase PAH formation pathways from smaller organic precursor molecules.