Miracle-dz/UniFAR-FTUs huggingface dataset:
Impact of Hydraulic Characteristics of Raw or Composted Posidonia Residues, Coir, and Their Mixtures with Pumice on Root Aeration, Water Availability, and Yield in a Lettuce Crop

The residues of the aquatic plant Posidonia oceanica that are washed ashore, thereby causing environmental problems in coastal areas, can be used as growing media in horticulture. In the present study, the hydraulic characteristics of raw or composted Posidonia residues, coir, and their 1:1 blends (v/v) with pumice were determined, and their agronomic performance was evaluated in a lettuce crop. The mixture of all three substrates with pumice reduced their effective pore space and increased their bulk density. Furthermore, the water and air capacity (determined at a suction of 10 cm) and the easily available water were also reduced by mixing the three tested media with pumice. The relative hydraulic conductivity (Kr) decreased with increasing suction (c )i n all of the tested media. The highest and the lowest rates of Kr decrease with increasing c wereobservedinthemixofnon-compostedPosidoniawithpumiceandin100%composted Posidonia, respectively. Blending composted or non-composted Posidonia with pumice at a 1:1 ratio raised the rate of Kr decrease with increasing c in comparison with 100% composted or 100% non-composted Posidonia, respectively. In contrast, blending coir with pumice reduced the rate of Kr decrease with increasing c in comparison with 100% coir. The differences in the mean fresh weight between lettuce plants grown on the six growing media were similar with those in the rate ofKrdecrease with increasing c. These results indicate that the crucial factor for the yield performance of lettuce grown on the tested growing media was not the air but the water availability. Furthermore, the present results indicate that the actual water availability to plants grown on the tested substrates depends much more on water flux toward roots and concomitantly on their hydraulic conductivitythanontheeasilyavailablewater(i.e.,thedifferenceinwatercontentbetween 10 and 50 cm suction).
Data-driven uncertainty quantification for systematic coarse-grained models

ABSTRACT In this work, we present methodologies for the quantification of confidence in bottom-up coarse-grained models for molecular and macromolecular systems. Coarse-graining methods have been extensively used in the past decades in order to extend the length and time scales accessible by simulation methodologies. The quantification, though, of induced errors due to the limited availability of fine-grained data is not yet established. Here, we employ rigorous statistical methods to deduce guarantees for the optimal coarse models obtained via approximations of the multi-body potential of mean force, with the relative entropy, the relative entropy rate minimization, and the force-matching methods. Specifically, we present and apply statistical approaches, such as bootstrap and jackknife, to infer confidence sets for a limited number of samples, i.e., molecular configurations. Moreover, we estimate asymptotic confidence intervals assuming adequate sampling of the phase space. We demonstrate the need for non-asymptotic methods and quantify confidence sets through two applications. The first is a two-scale fast/slow diffusion process projected on the slow process. With this benchmark example, we establish the methodology for both independent and time-series data. Second, we apply these uncertainty quantification approaches on a polymeric bulk system. We consider an atomistic polyethylene melt as the prototype system for developing coarse-graining tools for macromolecular systems. For this system, we estimate the coarse-grained force field and present confidence levels with respect to the number of available microscopic data.
Multi-view Frequency LSTM: An Efficient Frontend for Automatic Speech Recognition

Acoustic models in real-time speech recognition systems typically stack multiple unidirectional LSTM layers to process the acoustic frames over time. Performance improvements over vanilla LSTM architectures have been reported by prepending a stack of frequency-LSTM (FLSTM) layers to the time LSTM. These FLSTM layers can learn a more robust input feature to the time LSTM layers by modeling time-frequency correlations in the acoustic input signals. A drawback of FLSTM based architectures however is that they operate at a predefined, and tuned, window size and stride, referred to as 'view' in this paper. We present a simple and efficient modification by combining the outputs of multiple FLSTM stacks with different views, into a dimensionality reduced feature representation. The proposed multi-view FLSTM architecture allows to model a wider range of time-frequency correlations compared to an FLSTM model with single view. When trained on 50K hours of English far-field speech data with CTC loss followed by sMBR sequence training, we show that the multi-view FLSTM acoustic model provides relative Word Error Rate (WER) improvements of 3-7% for different speaker and acoustic environment scenarios over an optimized single FLSTM model, while retaining a similar computational footprint.
Targeted Measles Virus Vector Displaying Echistatin Infects Endothelial Cells via αvβ3 and Leads to Tumor Regression

Targeting tumor-associated vascular endothelium by replicationcompetent viral vectors is a promising strategy for cancer gene therapy. Here we describe the development of a viral vector based on the Edmonston vaccine strain of measles virus targeted to integrin AvB3, which is expressed abundantly on activated but not quiescent vascular endothelium. We displayed a disintegrin, M28L echistatin that binds with a high affinity to integrin AvB3 on the COOH terminus of the viral attachment (H) protein and rescued the replicationcompetent recombinant virus by reverse genetics. The new targeted virus was named measles virus echistatin vector (MV-ERV). Its native binding to CD46 was purposefully retained to allow virus infection of tumor cells expressing this receptor. MV-ERV correctly displayed echistatin on the outer surface of its envelope and produced interesting ring formation phenomena due to cell detachment upon infection of susceptible Vero cells in vitro. MV-ERV grew to 10 6 plaque-forming units/mL, slightly lower than the parental Edmonston strain of measles virus (MV-Edm), but it selectively infected Chinese hamster ovary cells expressing integrin AvB3. It also selectively infected both bovine and human endothelial cells on matrigels and unlike MV-Edm, MV-ERV infected newly formed blood vessels in chorioallantoic membrane assays. In animal models, MV-ERV but not the control MV-Edm caused the regression of s.c. xenografts of resistant multiple myeloma tumors (MM1) in severe combined immunodeficient mice. The tumors were either completely eradicated or their growth was significantly retarded. The specificity, potency, and feasibility of MV-ERV infection clearly show the potential use of MV-ERV in gene therapy for targeting tumor-associated vasculature for the treatment of solid tumors. (Cancer Res 2005; 65(12): 5292-300)
Analysis and Implementation of a New Single Switch, High Voltage Gain DC-DC Converter with a Wide CCM Operation Range and Reduced Components Voltage Stress

This paper presents a single switch, high step-up, non-isolated dc-dc converter suitable for renewable energy applications. The proposed converter is composed of a coupled inductor, a passive clamp circuit, a switched capacitor and voltage lift circuits. The passive clamp recovers the leakage inductance energy of the coupled inductor and limits the voltage spike on the switch. The configuration of the passive clamp and switched capacitor circuit increases the voltage gain. A wide continuous conduction mode (CCM) operation range, a low turn ratio for the coupled inductor, low voltage stress on the switch, switch turn on under almost zero current switching (ZCS), low voltage stress on the diodes, leakage inductance energy recovery, high efficiency and a high voltage gain without a large duty cycle are the benefits of this converter. The steady state operation of the converter in the continuous conduction mode (CCM) and discontinuous conduction mode (DCM) is discussed and analyzed. A 200W prototype converter with a 28V input and a 380V output voltage is implemented and tested to verify the theoretical analysis.
Inhomogeneous XX spin chains and quasi-exactly solvable models

We establish a direct connection between inhomogeneous XX spin chains (or free fermion systems with nearest-neighbors hopping) and certain QES models on the line giving rise to a family of weakly orthogonal polynomials. We classify all such models and their associated XX chains, which include two families related to the Lamé (finite gap) quantum potential on the line. For one of these chains, we numerically compute the Rényi bipartite entanglement entropy at half filling and derive an asymptotic approximation thereof by studying the model’s continuum limit, which turns out to describe a massless Dirac fermion on a suitably curved background. We show that the leading behavior of the entropy is that of a c = 1 critical system, although there is a subleading log(log N) correction (where N is the number of sites) unusual in this type of models.
Circumventing spin glass traps by microcanonical spontaneous symmetry breaking

The planted $p$-spin interaction model is a paradigm of random-graph systems possessing both a ferromagnetic ground state and an intermediate spin glass phase. Conventional simulated annealing and message-passing algorithms could not reach the planted ground state but are trapped by an exponential number of spin glass states. Here we propose discontinuous microcanonical spontaneous symmetry breaking (MSSB) as a simple mechanism to circumvent all the spin glass traps. The existence of a discontinuous MSSB phase transition is confirmed by microcanonical Monte Carlo simulations. We conjecture that the planted ground state could be retrieved in polynomial time by applying machine-learning methods (such as perceptron-learning) to microcanonically sampled independent configurations. Three candidate algorithms are proposed.
The abundance of S- and Si-bearing molecules in O-rich circumstellar envelopes of AGB stars.

Aims We aim to determine the abundances of SiO, CS, SiS, SO, and SO2 in a large sample of oxygen-rich asymptotic giant branch (AGB) envelopes covering a wide range of mass loss rates to investigate the potential role that these molecules could play in the formation of dust in these environments. Methods We surveyed a sample of 30 oxygen-rich AGB stars in the λ 2 mm band using the IRAM 30m telescope. We performed excitation and radiative transfer calculations based on the large velocity gradient (LVG) method to model the observed lines of the molecules and to derive their fractional abundances in the observed envelopes. Results We detected SiO in all 30 targeted envelopes, as well as CS, SiS, SO, and SO2 in 18, 13, 26, and 19 sources, respectively. Remarkably, SiS is not detected in any envelope with a mass loss rate below 10-6 M⊙ yr-1, whereas it is detected in all envelopes with mass loss rates above that threshold. From a comparison with a previous, similar study on C-rich sources, it becomes evident that the fractional abundances of CS and SiS show a marked differentiation between C-rich and O-rich sources, being two orders of magnitude and one order of magnitude more abundant in C-rich sources, respectively, while the fractional abundance of SiO turns out to be insensitive to the C/O ratio. The abundance of SiO in O-rich envelopes behaves similarly to C-rich sources, that is, the denser the envelope the lower its abundance. A similar trend, albeit less clear than for SiO, is observed for SO in O-rich sources. Conclusions The marked dependence of CS and SiS abundances on the C/O ratio indicates that these two molecules form more efficiently in C- than O-rich envelopes. The decline in the abundance of SiO with increasing envelope density and the tentative one for SO indicate that SiO and possibly SO act as gas-phase precursors of dust in circumstellar envelopes around O-rich AGB stars.
A Multi-Agent Reinforcement Learning Approach for Dynamic Information Flow Tracking Games for Advanced Persistent Threats

Advanced Persistent Threats (APTs) are stealthy attacks that threaten the security and privacy of sensitive information. Interactions of APTs with victim system introduce information flows that are recorded in the system logs. Dynamic Information Flow Tracking (DIFT) is a promising detection mechanism for detecting APTs. DIFT taints information flows originating at system entities that are susceptible to an attack, tracks the propagation of the tainted flows, and authenticates the tainted flows at certain system components according to a pre-defined security policy. Deployment of DIFT to defend against APTs in cyber systems is limited by the heavy resource and performance overhead associated with DIFT. In this paper, we propose a resource-efficient model for DIFT by incorporating the security costs, false-positives, and false-negatives associated with DIFT. Specifically, we develop a game-theoretic framework and provide an analytical model of DIFT that enables the study of trade-off between resource efficiency and the effectiveness of detection. Our game model is a nonzero-sum, infinite-horizon, average reward stochastic game. Our model incorporates the information asymmetry between players that arises from DIFT's inability to distinguish malicious flows from benign flows and APT's inability to know the locations where DIFT performs a security analysis. Additionally, the game has incomplete information as the transition probabilities (false-positive and false-negative rates) are unknown. We propose a multiple-time scale stochastic approximation algorithm to learn an equilibrium solution of the game. We prove that our algorithm converges to an average reward Nash equilibrium. We evaluate our proposed model and algorithm on a real-world ransomware dataset and validate the effectiveness of the proposed approach.
Deep geometric texture synthesis

Recently, deep generative adversarial networks for image generation have advanced rapidly; yet, only a small amount of research has focused on generative models for irregular structures, particularly meshes. Nonetheless, mesh generation and synthesis remains a fundamental topic in computer graphics. In this work, we propose a novel framework for synthesizing geometric textures. It learns geometric texture statistics from local neighborhoods (i.e., local triangular patches) of a single reference 3D model. It learns deep features on the faces of the input triangulation, which is used to subdivide and generate offsets across multiple scales, without parameterization of the reference or target mesh. Our network displaces mesh vertices in any direction (i.e., in the normal and tangential direction), enabling synthesis of geometric textures, which cannot be expressed by a simple 2D displacement map. Learning and synthesizing on local geometric patches enables a genus-oblivious framework, facilitating texture transfer between shapes of different genus.
Monitoring Depression Trend on Twitter during the COVID-19 Pandemic

BACKGROUND The COVID-19 pandemic has severely affected people’s daily lives and caused tremendous economic loss worldwide. Anecdotal evidence suggests that the pandemic has increased the depression level among the population. However, systematic studies of depression detection and monitoring during the depression are lacking. OBJECTIVE This study aims (1) to develop a method to accurately identify people with depression by analyzing their tweets and (2) to monitor the population-wise depression level on Twitter. METHODS To study this subject, we design an effective regular expression-based search method and create by far the largest English Twitter depression dataset containing 2,575 distinct identified depression users (N=2,575) with their past tweets. To examine the effect of depression on people’s Twitter language, we train three transformer-based depression classification models on the dataset, evaluate their performance with progressively increased training sizes, and compare the model’s “tweet chunk”-level and user-level performances. Furthermore, inspired by psychological studies, we create a fusion classifier that combines deep learning model scores with psychological text features and users’ demographic information and investigate these features’ relations to depression signals. Finally, we demonstrate our model’s capability of monitoring both group-level and population-level depression trends by presenting two of its applications during the COVID-19 pandemic. RESULTS Our fusion model demonstrates an accuracy of 78.9% on a test set containing 446 people (N=446), half of which are identified as suffering from depression. Conscientiousness, neuroticism, appearance of first-person pronouns, talking about biological processes such as eat and sleep, talking about power, and exhibiting sadness are shown to be important features in depression classification. Further, when used for monitoring the depression trend, our model shows that depressive users, in general, respond to the pandemic later than the control group based on their tweets. It is also shown that three states of the United States - New York (NY), California (CA), and Florida (FL) - share a similar depression trend as the whole US population. When compared to NY and CA, people in FL demonstrate a significantly lower level of depression. CONCLUSIONS This study proposes an efficient method that can be used to analyze the depression level of different groups of people on Twitter. We hope this study can raise awareness among researchers and the general public of COVID-19’s impact on people’s mental health. The non-invasive monitoring system can also be rapidly adapted to other big events besides COVID-19 and might be useful during future outbreaks.
Accumulation of 1-aminocyclopropane-1-carboxylic acid (ACC) in petunia pollen is associated with expression of a pollen-specific ACC synthase late in development.

Mature pollen from Petunia hybrida contains significant levels of 1-aminocyclopropane-1-carboxylic acid (ACC), and this ACC is thought to play a role in pollination-induced ethylene by the pistil. We investigated the developmental accumulation of ACC in anthers and pollen. The level of ACC in anthers was very low until the day before anthesis, at which time it increased 100-fold. A 1.1-kb partial ACC synthase cDNA clone (pPHACS2) was amplified from total RNA isolated from mature anthers by reverse transcriptase, followed by polymerase chain reaction using oligonucleotide primers synthesized to conserved amino acid sequences in ACC synthases. The expression of pPHACS2 mRNA during anther development was correlated with the accumulation of ACC and was localized to the pollen grain. The pPHACS2 cDNA was used to identify the PH-ACS2 gene from a library of genomic DNA fragments from Petunia hybrida. PH-ACS2 encoded an ACC synthase transcript of four exons interrupted by three introns. The ACC synthase protein encoded by the PH-ACS2 gene shared >80% homology with ACC synthases from tomato (LE-ACS3) and potato (ST-ACS1a). A chimeric PH-ACS2 promoter-β-glucuronidase (GUS) gene was used to transform petunia and transgenic plants were analyzed for GUS activity. GUS staining was localized to mature pollen grains and was not detected in other tissues. Despite similarities to LE-ACS3, we did not detect GUS activity under conditions of anaerobic stress or in response to auxin. A series of 5-prime-flanking DNA deletions revealed that sequences within the PH-ACS2 promoter were responsible for pollen-specific expression.
A Dataset for Evaluating Multi-spectral Motion Estimation Methods

Visible images have been widely used for indoor motion estimation. Thermal images, in contrast, are more challenging to be used in motion estimation since they typically have lower resolution, less texture, and more noise. In this paper, a novel dataset for evaluating the performance of multi-spectral motion estimation systems is presented. The dataset includes both multi-spectral and dense depth images with accurate ground-truth camera poses provided by a motion capture system. All the sequences are recorded from a handheld multi-spectral device, which consists of a standard visible-light camera, a long-wave infrared camera, and a depth camera. The multi-spectral images, including both color and thermal images in full sensor resolution (640 $\times$ 480), are obtained from the hardware-synchronized standard and long-wave infrared camera at 32Hz. The depth images are captured by a Microsoft Kinect2 and can have benefits for learning cross-modalities stereo matching. In addition to the sequences with bright illumination, the dataset also contains scenes with dim or varying illumination. The full dataset, including both raw data and calibration data with detailed specifications of data format, is publicly available.
Superconductivity, correlated insulators, and Wess–Zumino–Witten terms in twisted bilayer graphene

Significance When the twist angle between two layers of graphene is close to 1.1°, the energy of an electron becomes nearly independent of its momentum, and the dominant Coulomb repulsion between the electrons leads to remarkable effects. Experiments have shown novel superconducting and insulating states. We propose here that these states are linked by a topological quantal phase, which is an analog of the Wess–Zumino–Witten term of high-energy physics. This term links spatiotemporal textures of one order with quanta of another order. We classify such orders in the superconducting and insulating states. Our work can help guide experiments studying twisted graphene on the microscopic scale and lead to a fundamental understanding of its remarkable and complex phase diagram. Recent experiments on twisted bilayer graphene have shown a high-temperature parent state with massless Dirac fermions and broken electronic flavor symmetry; superconductivity and correlated insulators emerge from this parent state at lower temperatures. We propose that the superconducting and correlated insulating orders are connected by Wess–Zumino–Witten terms, so that defects of one order contain quanta of another order and skyrmion fluctuations of the correlated insulator are a “mechanism” for superconductivity. We present a comprehensive listing of plausible low-temperature orders and the parent flavor symmetry-breaking orders. The previously characterized topological nature of the band structure of twisted bilayer graphene plays an important role in this analysis.