Miracle-dz/UniFAR-FTUs huggingface dataset:
ATOMS: ALMA three-millimeter observations of massive star-forming regions – II. Compact objects in ACA observations and star formation scaling relations

We report studies of the relationships between the total bolometric luminosity ($L_{\rm bol}$ or $L_{\rm TIR}$) and the molecular line luminosities of $J=1-0$ transitions of H$^{13}$CN, H$^{13}$CO$^+$, HCN, and HCO$^+$ with data obtained from ACA observations in the "ATOMS" survey of 146 active Galactic star forming regions. The correlations between $L_{\rm bol}$ and molecular line luminosities $L'_{\rm mol}$ of the four transitions all appear to be approximately linear. Line emission of isotopologues shows as large scatters in $L_{\rm bol}$-$L'_{\rm mol}$ relations as their main line emission. The log($L_{\rm bol}$/$L'_{\rm mol}$) for different molecular line tracers have similar distributions. The $L_{\rm bol}$-to-$L'_{\rm mol}$ ratios do not change with galactocentric distances ($R_{\rm GC}$) and clump masses ($M_{\rm clump}$). The molecular line luminosity ratios (HCN-to-HCO$^+$, H$^{13}$CN-to-H$^{13}$CO$^+$, HCN-to-H$^{13}$CN and HCO$^+$-to-H$^{13}$CO$^+$) all appear constant against $L_{\rm bol}$, dust temperature ($T_{\rm d}$), $M_{\rm clump}$ and $R_{\rm GC}$. Our studies suggest that both the main lines and isotopologue lines are good tracers of the total masses of dense gas in Galactic molecular clumps. The large optical depths of main lines do not affect the interpretation of the slopes in star formation relations. We find that the mean star formation efficiency (SFE) of massive Galactic clumps in the "ATOMS" survey is reasonably consistent with other measures of the SFE for dense gas, even those using very different tracers or examining very different spatial scales.
Speaker and Posture Classification using Instantaneous Intraspeech Breathing Features

Acoustic features extracted from speech are widely used in problems such as biometric speaker identification and first-person activity detection. However, the use of speech for such purposes raises privacy issues as the content is accessible to the processing party. In this work, we propose a method for speaker and posture classification using intraspeech breathing sounds. Instantaneous magnitude features are extracted using the Hilbert-Huang transform (HHT) and fed into a CNN-GRU network for classification of recordings from the open intraspeech breathing sound dataset, BreathBase, that we collected for this study. Using intraspeech breathing sounds, 87% speaker classification, and 98% posture classification accuracy were obtained.
Initial solidification dynamics of spreading droplets

When a droplet is brought in contact with an undercooled surface, it wets the substrate and solidifies at the same time. The interplay between the phase transition effects and the contact-line motion, leading to its arrest, remains poorly understood. Here we reveal the early solidification patterns and dynamics of spreading hexadecane droplets. Total internal reflection (TIR) imaging is employed to temporally and spatially resolve the early solidification behaviour. With this, we determine the conditions leading to the contact-line arrest. We quantify the overall nucleation behaviour, \textit{i.e.} the nucleation rate and the crystal growth speed, and show its sensitivity to the applied undercooling of the substrate. By combining the Johnson-Mehl-Avrami-Kolmogorov nucleation theory and scaling relations for the spreading, we can calculate the temporal evolution of the solid area fraction, which is in good agreement with our observations. We also show that for strong enough undercooling it is the rapid growth of the crystals which determines the eventual arrest of the spreading contact line.
Instability, Computational Efficiency and Statistical Accuracy

Many statistical estimators are defined as the fixed point of a data-dependent operator, with estimators based on minimizing a cost function being an important special case. The limiting performance of such estimators depends on the properties of the population-level operator in the idealized limit of infinitely many samples. We develop a general framework that yields bounds on statistical accuracy based on the interplay between the deterministic convergence rate of the algorithm at the population level, and its degree of (in)stability when applied to an empirical object based on $n$ samples. Using this framework, we analyze both stable forms of gradient descent and some higher-order and unstable algorithms, including Newton's method and its cubic-regularized variant, as well as the EM algorithm. We provide applications of our general results to several concrete classes of models, including Gaussian mixture estimation, single-index models, and informative non-response models. We exhibit cases in which an unstable algorithm can achieve the same statistical accuracy as a stable algorithm in exponentially fewer steps---namely, with the number of iterations being reduced from polynomial to logarithmic in sample size $n$.
Kink-Antikink Collisions in the Periodic $\varphi^{4}$ Model

We borrow the form of potential of the well-known kink-bearing $\varphi^4$ system in the range between its two vacua and paste it repeatedly into the other ranges to introduce the periodic $\varphi^4$ system. The paper is devoted to providing a comparative numerical study of the properties of the two systems. Although the two systems are quite similar for a kink (antikink) solution, they usually exhibit different behaviors throughout collisions. For instance, they have different critical velocities, different results during collisions, and a different rule in their quasi-fractal structures. Their quasi-fractal structures will be studied in the disturbed kink-antikink collisions as well. Hence, three types of scattering windows will be introduced with respect to the incoming speed, the amplitude, and initial phase of the internal mode, respectively. Moreover, a detailed comparative study of the collisions between two kinks and one antikink will be done at the end.
Review: The composition and availability of straw and chaff from small grain cereals for beef cattle in western Canada

Small cereal grain residues are heterogeneous feeds consisting of several botanical fractions: chaff, grain, leaf blade, leaf sheath, internode and node. These parts vary in composition, digestibility, resistance to comminution, intake potential and energy availability. Large differences in the nutritional quality of straw and chaff may occur from year to year and between locations due to effects of environmental conditions on botanical composition and cell anatomy. Stage of maturity, harvest method and weathering will influence composition and quality of the most nutritious parts of cereal residues, the leaf and chaff. In addition, cultivars and species differ in the proportion, anatomy and digestibility of botanical fractions. As a result, the quality of crop residues is highly variable with an economic value that is difficult to accurately assess. Cereal straw and chaff are of low economic value as they are low in nutritive value, where nutritive value is the product of nutrient intake, digestibility, .
Active Learning for Skewed Data Sets

Consider a sequential active learning problem where, at each round, an agent selects a batch of unlabeled data points, queries their labels and updates a binary classifier. While there exists a rich body of work on active learning in this general form, in this paper, we focus on problems with two distinguishing characteristics: severe class imbalance (skew) and small amounts of initial training data. Both of these problems occur with surprising frequency in many web applications. For instance, detecting offensive or sensitive content in online communities (pornography, violence, and hate-speech) is receiving enormous attention from industry as well as research communities. Such problems have both the characteristics we describe -- a vast majority of content is not offensive, so the number of positive examples for such content is orders of magnitude smaller than the negative examples. Furthermore, there is usually only a small amount of initial training data available when building machine-learned models to solve such problems. To address both these issues, we propose a hybrid active learning algorithm (HAL) that balances exploiting the knowledge available through the currently labeled training examples with exploring the large amount of unlabeled data available. Through simulation results, we show that HAL makes significantly better choices for what points to label when compared to strong baselines like margin-sampling. Classifiers trained on the examples selected for labeling by HAL easily out-perform the baselines on target metrics (like area under the precision-recall curve) given the same budget for labeling examples. We believe HAL offers a simple, intuitive, and computationally tractable way to structure active learning for a wide range of machine learning applications.
A Data-Driven Approach for Extraction of Event-Waveform: Application to Gravitational Waves

In the light of the importance of gravitational waves in astronomy, it is essential to perform independent \textit{data analysis} compared to the approach used by the LIGO team. To address this, here we develop a general data-driven, template-free {\it noise suppression method}, for extraction of the event-waveform. Using the developed method, we obtain waveforms of all reported events by LIGO. In addition, using instantaneous frequencies (derived by Hilbert transform) of the extracted waveforms, we provide the time delays between the arrival of gravitational waves to the detectors.
Macroeconomic factors for inflation in Argentine 2013-2019

The aim of this paper is to investigate the use of the Factor Analysis in order to identify the role of the relevant macroeconomic variables in driving the inflation. The Macroeconomic predictors that usually affect the inflation are summarized using a small number of factors constructed by the principal components. This allows us to identify the crucial role of money growth, inflation expectation and exchange rate in driving the inflation. Then we use this factors to build econometric models to forecast inflation. Specifically, we use univariate and multivariate models such as classical autoregressive, Factor models and FAVAR models. Results of forecasting suggest that models which incorporate more economic information outperform the benchmark. Furthermore, causality test and impulse response are performed in order to examine the short-run dynamics of inflation to shocks in the principal factors.
In vitro evaluation of antimicrobial peptides against Fusarium species.

The in vitro activity of five antimicrobial peptides was evaluated against several Fusarium species that affect wheat in Canada. Among the peptides tested (10R, 11R, BMAP-18, MsrA2 and MsrA3) 10R was most effective against conidial germination, whereas MsrA2 showed activity against mycelial growth. Antimicrobial peptides 10R and MsrA2 were identified as potential candidates for engineering resistance against common species causing fusarium head blight in wheat. Key words: Antimicrobial peptides, fusarium head blight, conidia, mycelia
Finding small satisfying assignments faster than brute force: a fine-grained perspective into boolean constraint satisfaction

To study the question under which circumstances small solutions can be found faster than by exhaustive search (and by how much), we study the fine-grained complexity of Boolean constraint satisfaction with size constraint exactly k. More precisely, we aim to determine, for any finite constraint family, the optimal running time f(k)ng(k) required to find satisfying assignments that set precisely k of the n variables to 1. Under central hardness assumptions on detecting cliques in graphs and 3-uniform hypergraphs, we give an almost tight characterization of g(k) into four regimes: 1. Brute force is essentially best-possible, i.e., [MATH HERE], 2. the best algorithms are as fast as current k-clique algorithms, i.e., [MATH HERE], 3. the exponent has sublinear dependence on k with [MATH HERE] or 4. the problem is fixed-parameter tractable, i.e., g(k) = O(1). This yields a more fine-grained perspective than a previous FPT/W[1]-hardness dichotomy (Marx, Computational Complexity 2005). Our most interesting technical contribution is a f(k)n4[MATH HERE]-time algorithm for SubsetSum with precedence constraints parameterized by the target k - particularly the approach, based on generalizing a bound on the Frobenius coin problem to a setting with precedence constraints, might be of independent interest.
Rethinking Blockchains in the Internet of Things Era from a Wireless Communication Perspective

Due to the rapid development of the internet of Things (ioT), a massive number of devices are connected to the internet. For these distributed devices in ioT networks, how to ensure their security and privacy becomes a significant challenge. Blockchain technology provides a promising solution to protect the data integrity, provenance, privacy, and consistency for ioT networks. in blockchains, communication is a prerequisite for participants, which are distributed in the system, to reach consensus. However, in ioT networks, most of the devices communicate through wireless links, which are not always reliable. Hence, the communication reliability of ioT devices influences the system security. in this article, we rethink the roles of communication and computing in blockchains by accounting for communication reliability. We analyze the trade-off between communication reliability and computing power in blockchain security, and present a lower bound to the computing power that is needed to conduct an attack with a given communication reliability. Simulation results show that adversarial nodes can succeed in tampering with a block with less computing power by hindering the propagation of blocks from other nodes.
Species Interactions and Community Structure in Alaskan Seabird Colonies

Previous studies of colonial seabirds suggest that similarities among geographically distinct seabird communities, in terms of ecologically matched species, are strong evidence for struc- ture in these bird assemblages. We quantify species associations and community structure within five nesting guilds of Alaskan seabirds using multiway contingency tables and log-linear models. The observed species combinations differ in frequency from those expected using an independent assort- ment model. Simple models that adequately describe the data structure contain mostly positive two- way interactions between species. Higher order interactions, although uncommon, may suggest a type of diffuse competition. A ranked measure of resource overlap is uncorrelated with the magnitude of species interactions within guilds, but is positively correlated with deviations from independence among all species pairs.
Interaction-Aware Trajectory Prediction of Connected Vehicles using CNN-LSTM Networks

Predicting the future trajectory of a surrounding vehicle in congested traffic is one of the necessary abilities of an autonomous vehicle. In congestion, a vehicle’s future movement is the result of its interaction with surrounding vehicles. A vehicle in congestion may have many neighbors in a relatively short distance, while only a small part of neighbors affect its future trajectory mostly. In this work, An interaction-aware method that predicts the future trajectory of an ego vehicle considering its interaction with eight surrounding vehicles is proposed. The dynamics of vehicles are encoded by LSTMs with shared weights, and the interaction is extracted with a simple CNN. The proposed model is trained and tested on trajectories extracted from the publicly accessible NGSIM US-101 dataset. Quantitative experimental results show that the proposed model outperforms previous models in root-mean-square error (RMSE). Results visualization shows that the model is able to predict future trajectory induced by lane change before the vehicle operates noticeable lateral movement to initiate lane changing.
Short-term Load Forecasting Based on Hybrid Strategy Using Warm-start Gradient Tree Boosting

A deep-learning based hybrid strategy for short-term load forecasting is presented. The strategy proposes a novel tree-based ensemble method Warm-start Gradient Tree Boosting (WGTB). Current strategies either ensemble submodels of a single type, which fail to take advantage of statistical strengths of different inference models. Or they simply sum the outputs from completely different inference models, which doesn't maximize the potential of ensemble. WGTB is thus proposed and tailored to the great disparity among different inference models in accuracy, volatility and linearity. The complete strategy integrates four different inference models (i.e., auto-regressive integrated moving average, nu support vector regression, extreme learning machine and long short-term memory neural network), both linear and nonlinear models. WGTB then ensembles their outputs by hybridizing linear estimator ElasticNet and nonlinear estimator ExtraTree via boosting algorithm. It is validated on the real historical data of a grid from State Grid Corporation of China of hourly resolution. The result demonstrates the effectiveness of the proposed strategy that hybridizes statistical strengths of both linear and nonlinear inference models.
Fault-tolerant quantum speedup from constant depth quantum circuits

A defining feature in the field of quantum computing is the potential of a quantum device to outperform its classical counterpart for a specific computational task. By now, several proposals exist showing that certain sampling problems can be done efficiently quantumly, but are not possible efficiently classically, assuming strongly held conjectures in complexity theory. A feature dubbed quantum speedup. However, the effect of noise on these proposals is not well understood in general, and in certain cases it is known that simple noise can destroy the quantum speedup. Here we develop a fault-tolerant version of one family of these sampling problems, which we show can be implemented using quantum circuits of constant depth. We present two constructions, each taking $poly(n)$ physical qubits, some of which are prepared in noisy magic states. The first of our constructions is a constant depth quantum circuit composed of single and two-qubit nearest neighbour Clifford gates in four dimensions. This circuit has one layer of interaction with a classical computer before final measurements. Our second construction is a constant depth quantum circuit with single and two-qubit nearest neighbour Clifford gates in three dimensions, but with two layers of interaction with a classical computer before the final measurements. For each of these constructions, we show that there is no classical algorithm which can sample according to its output distribution in $poly(n)$ time, assuming two standard complexity theoretic conjectures hold. The noise model we assume is the so-called local stochastic quantum noise. Along the way, we introduce various new concepts such as constant depth magic state distillation (MSD), and constant depth output routing, which arise naturally in measurement based quantum computation (MBQC), but have no constant-depth analogue in the circuit model.
Temporal Network Motifs: Models, Limitations, Evaluation

Investigating the frequency and distribution of small subgraphs with a few nodes/edges, i.e., motifs, is an effective analysis method for static networks. Motif-driven analysis is also useful for temporal networks where the spectrum of motifs is significantly larger due to the additional temporal information on edges. This variety makes it challenging to design a temporal motif model that can consider all aspects of temporality. In the literature, previous works have introduced various models that handle different characteristics. In this work, we compare the existing temporal motif models and evaluate the facets of temporal networks that are overlooked in the literature. We first survey four temporal motif models and highlight their differences. Then, we evaluate the advantages and limitations of these models with respect to the temporal inducedness and timing constraints. In addition, we suggest a new lens, event pairs, to investigate temporal correlations. We believe that our comparative survey and extensive evaluation will catalyze the research on temporal network motif models.
Joint Training Capsule Network for Cold Start Recommendation

This paper proposes a novel neural network, joint training capsule network (JTCN), for the cold start recommendation task. We propose to mimic the high-level user preference other than the raw interaction history based on the side information for the fresh users. Specifically, an attentive capsule layer is proposed to aggregate high-level user preference from the low-level interaction history via a dynamic routing-by-agreement mechanism. Moreover, JTCN jointly trains the loss for mimicking the user preference and the softmax loss for the recommendation together in an end-to-end manner. Experiments on two publicly available datasets demonstrate the effectiveness of the proposed model. JTCN improves other state-of-the-art methods at least 7.07% for CiteULike and 16.85% for Amazon in terms of Recall@100 in cold start recommendation.
A Bayesian-inspired, deep learning, semi-supervised domain adaptation technique for land cover mapping

Land cover maps are a vital input variable to many types of environmental research and management. While they can be produced automatically by machine learning techniques, these techniques require substantial training data to achieve high levels of accuracy, which are not always available. One technique researchers use when labelled training data are scarce is domain adaptation (DA) -- where data from an alternate region, known as the source domain, are used to train a classifier and this model is adapted to map the study region, or target domain. The scenario we address in this paper is known as semi-supervised DA, where some labelled samples are available in the target domain. In this paper we present Sourcerer, a Bayesian-inspired, deep learning-based, semi-supervised DA technique for producing land cover maps from SITS data. The technique takes a convolutional neural network trained on a source domain and then trains further on the available target domain with a novel regularizer applied to the model weights. The regularizer adjusts the degree to which the model is modified to fit the target data, limiting the degree of change when the target data are few in number and increasing it as target data quantity increases. Our experiments on Sentinel-2 time series images compare Sourcerer with two state-of-the-art semi-supervised domain adaptation techniques and four baseline models. We show that on two different source-target domain pairings Sourcerer outperforms all other methods for any quantity of labelled target data available. In fact, the results on the more difficult target domain show that the starting accuracy of Sourcerer (when no labelled target data are available), 74.2%, is greater than the next-best state-of-the-art method trained on 20,000 labelled target instances.