Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-Classifier Interactive Learning for Ambiguous Speech Emotion Recognition

In recent years, speech emotion recognition technology is of great significance in widespread applications such as call centers, social robots and health care. Thus, the speech emotion recognition has been attracted much attention in both industry and academic. Since emotions existing in an entire utterance may have varied probabilities, speech emotion is likely to be ambiguous, which poses great challenges to recognition tasks. However, previous studies commonly assigned a single-label or multi-label to each utterance in certain. Therefore, their algorithms result in low accuracies because of the inappropriate representation. Inspired by the optimally interacting theory, we address the ambiguous speech emotions by proposing a novel multi-classifier interactive learning (MCIL) method. In MCIL, multiple different classifiers first mimic several individuals, who have inconsistent cognitions of ambiguous emotions, and construct new ambiguous labels (the emotion probability distribution). Then, they are retrained with the new labels to interact with their cognitions. This procedure enables each classifier to learn better representations of ambiguous data from others, and further improves the recognition ability. The experiments on three benchmark corpora (MAS, IEMOCAP, and FAU-AIBO) demonstrate that MCIL does not only improve each classifier’s performance, but also raises their recognition consistency from moderate to substantial.
A Patient With Dengue Fever and COVID-19: Coinfection or Not?

Co-epidemics can create a burden on healthcare systems in the affected areas. The world, at present, is facing the pandemic of coronavirus disease. Nonetheless, many areas worldwide suffer from endemics that are not of less danger than the current pandemic. We presented a case of a patient diagnosed with dengue fever and was also found to have coronavirus through nasal swab, but immunoglobulin M and G were undetectable. Our case brings to notice the alarming probability of two co-epidemics happening simultaneously. However, through the presented case, our theory is that the dengue virus may cause a false-positive detection of severe acute respiratory syndrome coronavirus 2.
Lactic Acid Production in a Membrane Bioreactor System with Thermophilic Bacillus coagulans: Fouling Analysis of the Used Ceramic Membranes

In lactic acid fermentation broth systems, membrane fouling is characterized as the crucial factor in limiting ceramic membrane life-time and efficiency. This study aimed to find the limiting factor of this process and seek an effective solution. By quantitative analysis using resistance-in-series model, the cake layer resistance is demonstrated to be the dominant factor in a steady state. A cake compressibility index was measured as high as 2.09. Within the low transmembrane pressure range, the cake resistance was manipulated through crossflow velocity regulation to achieve a better membrane performance. A new correlation between flux and cell density was also introduced.
A Statistical Growth Property of Plant Root Architectures

Numerous types of biological branching networks, with varying shapes and sizes, are used to acquire and distribute resources. Here, we show that plant root and shoot architectures share a fundamental design property. We studied the spatial density function of plant architectures, which specifies the probability of finding a branch at each location in the 3-dimensional volume occupied by the plant. We analyzed 1645 root architectures from four species and discovered that the spatial density functions of all architectures are population-similar. This means that despite their apparent visual diversity, all of the roots studied share the same basic shape, aside from stretching and compression along orthogonal directions. Moreover, the spatial density of all architectures can be described as variations on a single underlying function: a Gaussian density truncated at a boundary of roughly three standard deviations. Thus, the root density of any architecture requires only four parameters to specify: the total mass of the architecture and the standard deviations of the Gaussian in the three (x, y, z) growth directions. Plant shoot architectures also follow this design form, suggesting that two basic plant transport systems may use similar growth strategies.
Global linear and nonlinear gyrokinetic modelling of Alfvén eigenmodes in ITER

Linear and nonlinear modelling of Alfvénic instabilities, most notably toroidal Alfvén eigenmodes (TAEs), obtained by using the global nonlinear electromagnetic gyrokinetic model of the code ORB5 are presented for the 15 MA scenario of the ITER tokamak. Linear simulations show that elliptic Alfvén eigenmodes and odd-parity TAEs are only weakly damped but not excited by alpha particles, whose drive favours even-parity TAEs. Low mode number TAEs are found to be global, requiring global treatment. Nonlinearly, even with double the nominal EP density, single mode simulations lead to saturation with negligible EP transport however multi-mode simulations predict that with double the nominal EP density, enhanced saturation and significant EP redistribution will occur.
3D Bounding Box Detection in Volumetric Medical Image Data: A Systematic Literature Review

This paper discusses current methods and trends for 3D bounding box detection in volumetric medical image data. For this purpose, an overview of relevant papers from recent years is given. 2D and 3D implementations are discussed and compared and multiple identified approaches for localizing anatomical structures are presented. The results show that most research recently focuses on Deep Learning methods, such as Convolutional Neural Networks instead of methods with manual feature engineering, e.g., Random Regression Forests. 2D and 3D implementations are equally common. An overview of bounding box detection options is presented and helps researchers to select the most promising approach for their target objects.
A first comparison of Kinetic Field Theory with Eulerian Standard Perturbation Theory

We present a detailed comparison of the newly developed particle-based Kinetic Field Theory framework for cosmic large-scale structure formation with the established formalism of Eulerian Standard Perturbation Theory. We highlight the qualitative differences of both approaches by a comparative analysis of the respective equations of motion and implementation of initial conditions. A natural starting point for a first quantitative comparison is given by the non-interacting regime of free-streaming kinematics. Our results suggest that Kinetic Field Theory contains a complete resummation of Standard Perturbation Theory in this regime. We further show that the exact free-streaming solution of Kinetic Field Theory cannot be recovered in any finite order of Standard Perturbation Theory. Kinetic Field Theory should therefore provide a better starting point for perturbative treatments of non-linear structure formation.
COVID-MTL: Multitask Learning with Shift3D and Random-weighted Loss for Diagnosis and Severity Assessment of COVID-19

The outbreak of COVID-19 has resulted in over 67 million infections with over 1.5 million deaths worldwide so far. Both computer tomography (CT) diagnosis and nucleic acid test (NAT) have their pros and cons. Here we present a multitask-learning (MTL) framework, termed COVID-MTL, which is capable of simultaneously detecting COVID-19 against both radiology and NAT as well as assessing infection severity. We proposed an active-contour based method to refine lung segmentation results on COVID-19 CT scans and a Shift3D real-time 3D augmentation algorithm to improve the convergence and accuracy of state-of-the-art 3D CNNs. A random-weighted multitask loss function was then proposed, which made simultaneous learning of different COVID-19 tasks more stable and accurate. By only using CT data and extracting lung imaging features, COVID-MTL was trained on 930 CT scans and tested on another 399 cases, which yielded AUCs of 0.939 and 0.846, and accuracies of 90.23% and 79.20% for detection of COVID-19 against radiology and NAT, respectively, and outperformed state-of-the-art models. COVID-MTL yielded AUC of 0.800 $\pm$ 0.020 and 0.813 $\pm$ 0.021 (with transfer learning) for classifying control/suspected (AUC of 0.841), mild/regular (AUC of 0.808), and severe/critically-ill (AUC of 0.789) cases. Besides, we identified top imaging biomarkers that are significantly related (P < 0.001) to the positivity and severity of COVID-19.
On the Secrecy Capacity of MIMO Wiretap Channels: Convex Reformulation and Efficient Numerical Methods

This paper presents novel numerical approaches to finding the secrecy capacity of the multiple-input multiple-output (MIMO) wiretap channel subject to multiple linear transmit covariance constraints, including sum power constraint, per antenna power constraints and interference power constraint. An analytical solution to this problem is not known and existing numerical solutions suffer from slow convergence rate and/or high per-iteration complexity. Deriving computationally efficient solutions to the secrecy capacity problem is challenging since the secrecy rate is expressed as a difference of convex functions (DC) of the transmit covariance matrix, for which its convexity is only known for some special cases. In this paper we propose two low-complexity methods to compute the secrecy capacity along with a convex reformulation for degraded channels. In the first method we capitalize on the accelerated DC algorithm which requires solving a sequence of convex subproblems, for which we propose an efficient iterative algorithm where each iteration admits a closed-form solution. In the second method, we rely on the concave-convex equivalent reformulation of the secrecy capacity problem which allows us to derive the so-called partial best response algorithm to obtain an optimal solution. Notably, each iteration of the second method can also be done in closed form. The simulation results demonstrate a faster convergence rate of our methods compared to other known solutions. We carry out extensive numerical experiments to evaluate the impact of various parameters on the achieved secrecy capacity.
Universal fault-tolerant quantum computing with stabilizer codes

The quantum logic gates used in the design of a quantum computer should be both universal, meaning arbitrary quantum computations can be performed, and fault-tolerant, meaning the gates keep errors from cascading out of control. A number of no-go theorems constrain the ways in which a set of fault-tolerant logic gates can be universal. These theorems are very restrictive, and conventional wisdom holds that a universal fault-tolerant logic gate set cannot be implemented natively, requiring us to use costly distillation procedures for quantum computation. Here, we present a general framework for universal fault-tolerant logic with stabiliser codes, together with a no-go theorem that reveals the very broad conditions constraining such gate sets. Our theorem applies to a wide range of stabiliser code families, including concatenated codes and conventional topological stabiliser codes such as the surface code. The broad applicability of our no-go theorem provides a new perspective on how the constraints on universal fault-tolerant gate sets can be overcome. In particular, we show how non-unitary implementations of logic gates provide a general approach to circumvent the no-go theorem, and we present a rich landscape of constructions for logic gate sets that are both universal and fault-tolerant. That is, rather than restricting what is possible, our no-go theorem provides a signpost to guide us to new, efficient architectures for fault-tolerant quantum computing.
Infusing Finetuning with Semantic Dependencies

Abstract For natural language processing systems, two kinds of evidence support the use of text representations from neural language models “pretrained” on large unannotated corpora: performance on application-inspired benchmarks (Peters et al., 2018, inter alia), and the emergence of syntactic abstractions in those representations (Tenney et al., 2019, inter alia). On the other hand, the lack of grounded supervision calls into question how well these representations can ever capture meaning (Bender and Koller, 2020). We apply novel probes to recent language models— specifically focusing on predicate-argument structure as operationalized by semantic dependencies (Ivanova et al., 2012)—and find that, unlike syntax, semantics is not brought to the surface by today’s pretrained models. We then use convolutional graph encoders to explicitly incorporate semantic parses into task-specific finetuning, yielding benefits to natural language understanding (NLU) tasks in the GLUE benchmark. This approach demonstrates the potential for general-purpose (rather than task-specific) linguistic supervision, above and beyond conventional pretraining and finetuning. Several diagnostics help to localize the benefits of our approach.1
Quantum algorithms for open lattice field theory

Certain aspects of some unitary quantum systems are well-described by evolution via a non-Hermitian effective Hamiltonian, as in the Wigner-Weisskopf theory for spontaneous decay. Conversely, any non-Hermitian Hamiltonian evolution can be accommodated in a corresponding unitary system + environment model via a generalization of Wigner-Weisskopf theory. This demonstrates the physical relevance of novel features such as exceptional points in quantum dynamics, and opens up avenues for studying many body systems in the complex plane of coupling constants. In the case of lattice field theory, sparsity lends these channels the promise of efficient simulation on standardized quantum hardware. We thus consider quantum operations that correspond to Suzuki-Lee-Trotter approximation of lattice field theories undergoing non-Hermitian time evolution, with potential applicability to studies of spin or gauge models at finite chemical potential, with topological terms, to quantum phase transitions - a range of models with sign problems. We develop non-Hermitian quantum circuits and explore their promise on a benchmark, the quantum one-dimensional Ising model with complex longitudinal magnetic field, showing that observables can probe the Lee-Yang edge singularity. The development of attractors past critical points in the space of complex couplings indicates a potential for study on near-term noisy hardware.
Interactive Fusion of Multi-level Features for Compositional Activity Recognition

To understand a complex action, multiple sources of information, including appearance, positional, and semantic features, need to be integrated. However, these features are difficult to be fused since they often differ significantly in modality and dimensionality. In this paper, we present a novel framework that accomplishes this goal by interactive fusion, namely, projecting features across different spaces and guiding it using an auxiliary prediction task. Specifically, we implement the framework in three steps, namely, positional-to-appearance feature extraction, semantic feature interaction, and semantic-to-positional prediction. We evaluate our approach on two action recognition datasets, Something-Something and Charades. Interactive fusion achieves consistent accuracy gain beyond off-the-shelf action recognition algorithms. In particular, on Something-Else, the compositional setting of Something-Something, interactive fusion reports a remarkable gain of 2.9% in terms of top-1 accuracy.
Kinetic Study of the Adsorption of Natural Organic Matter From Aqueous Solution by Surfactant Modified Zeolite

The presence of natural organic matter (NOM) in raw water sources is unfavorable and has long been a problem for water supply industries. Natural zeolite is a low cost material that can be found in nature. Zeolite can be modifed and used for removal of NOM from aqueous solutions. Objectives: In this study, for the frst time, the efciency of modifed natural Iranian zeolite obtained from Semnan mines was investigated for removal of NOM from aqueous solution. Materials and Methods: In this study, batch studies were set up to evaluate the eﬀects of initial pH, contact time and also the initial NOM concentrations on adsorption behavior. The eﬀective initial pH values were in acidic ranges for adsorption of the NOM by surfactant modifed zeolite (SMZ). Kinetic studies showed that the adsorption of NOM by SMZ was a gradual process. Results: The batch kinetics experiments showed that NOM adsorption by the adsorbents followed the pseudo-second order model. Results showed that the acidic pH is optimum for adsorption of NOM by SMZ. Conclusions: The fndings of this study show that natural zeolite modifed with sodium dodecyl sulfate, as a cationic surfactant, is a promising adsorbent for removal of NOM
From particle swarm optimization to consensus based optimization: stochastic modeling and mean-field limit

In this paper, we consider a continuous description based on stochastic differential equations of the popular particle swarm optimization (PSO) process for solving global optimization problems and derive in the large particle limit the corresponding mean-field approximation based on Vlasov–Fokker–Planck-type equations. The disadvantage of memory effects induced by the need to store the local best position is overcome by the introduction of an additional differential equation describing the evolution of the local best. A regularization process for the global best permits to formally derive the respective mean-field description. Subsequently, in the small inertia limit, we compute the related macroscopic hydrodynamic equations that clarify the link with the recently introduced consensus based optimization (CBO) methods. Several numerical examples illustrate the mean field process, the small inertia limit and the potential of this general class of global optimization methods.
Constraining the magnetic field properties of Bok globule B335 using SOFIA/HAWC+

Thanks to their well-defined shape and mostly isolated locations, Bok globules are suitable objects for studying the physics of low-mass star formation. To study the magnetic field of the prototypical Bok globule B335, we obtained a spatially resolved polarization map with SOFIA/HAWC+ at a wavelength of 214 μm. For the first time, these observations reveal that polarization holes in Bok globules, that is, the decrease in polarization degree towards their dense centers, also occur in the far-infrared wavelength regime. The observed polarization pattern is uniform with a mean polarization angle of 48° ± 26° and a magnetic field strength of ~142 μG. Moreover, we use complementary polarimetic data for B335 obtained at near-infrared to millimeter wavelengths to analyze and constrain the magnetic field across different scales. By applying the 3D Monte-Carlo radiative transfer code POLARIS, we developed a model for the density and magnetic field structure as well as for the dust properties of this globule. We conclude that the column density towards the center of B335 is too low to cause the observed polarization hole in B335 via dichroic absorption. Furthermore, we conclude that the effect of self-scattering has no significant impact on the observed polarization. Adopting dust-grain alignment via the radiative torque mechanism, a combination of the interstellar radiation field and the central star as radiation sources is consistent with the decrease in polarization degree at the outer regions of B335 (≈104 au from the core). However, the model fails to explain the low polarization degree within the inner 5000 au.
CBX6 Promotes HCC Metastasis Via Transcription Factors Snail/Zeb1-Mediated EMT Mechanism

Purpose Hepatocellular carcinoma (HCC) is the most common malignant tumor worldwide with high morbidity and mortality rates. We aimed to examine the expression of chromobox 6 (CBX6) in HCC and analyze its correlation with clinicopathological features of HCC patients. Moreover, the role of CBX6 in the HCC cell proliferation, invasion and metastasis and the potential mechanism underlying HCC metastasis were also investigated. Methods We used quantitative polymerase chain reaction (qRT-PCR) and Western blot to evaluate the expression levels of CBX6 in HCC cell lines. Furthermore, the expression of CBX6 in HCC and the adjacent non-tumor tissues was assessed by immunohistochemistry (IHC). Cell proliferation was evaluated using MTT assay, cell migration and invasion were measured using wound healing and transwell assays. Finally, we detected the expression of target proteins in HCC cell lines transfected with CBX6 overexpression plasmid or CBX6 shRNA plasmid by Western blot. Results We found that the expression of CBX6 was increased in 280 cases of HCC tissues compared that in adjacent non-tumor tissues. HCC patients with high CBX6 expression had a higher tendency to have high growth rate, strong invasion ability, high clinical stage and poor tumor differentiation. Functional study demonstrated that the upregulation of CBX6 promotes proliferation, migration and invasion of HCC cells while silencing CBX6 in HCC cells significantly inhibited cell proliferation, migration and invasion. Furthermore, CBX6 could accelerate the EMT process in HCC cells by upregulating the expression of snail and zeb1. Conclusion CBX6 played an important role in the process of tumorigenesis and progression in HCC and enhanced the invasion and metastasis ability of HCC cells through regulating transcription factors snail/zeb1-mediated EMT mechanism, which indicated that the protein could serve as a novel therapeutic target for the treatment of HCC.
Large Non-Stationary Noisy Covariance Matrices: A Cross-Validation Approach

We introduce a novel covariance estimator that exploits the heteroscedastic nature of financial time series by employing exponential weighted moving averages and shrinking the in-sample eigenvalues through cross-validation. Our estimator is model-agnostic in that we make no assumptions on the distribution of the random entries of the matrix or structure of the covariance matrix. Additionally, we show how Random Matrix Theory can provide guidance for automatic tuning of the hyperparameter which characterizes the time scale for the dynamics of the estimator. By attenuating the noise from both the cross-sectional and time-series dimensions, we empirically demonstrate the superiority of our estimator over competing estimators that are based on exponentially-weighted and uniformly-weighted covariance matrices.
Dark Matter as Gravitational Solitons in the Weak Field Limit

In this paper, we will describe the idea that dark matter partly consists of gravitational solitons (gravisolitons). The corresponding solution is valid for weak gravitational fields (weak field limit) with respect to a background metric. The stability of this soliton is connected with the existence of a special foliation and amazingly with the smoothness properties of spacetime. Gravisolitons have many properties of dark matter, such as no interaction with light but act on matter via gravitation. In this paper, we showed that the gravitational lensing effect of gravisolitons agreed with the lensing effect of usual matter. Furthermore, we obtained the same equation of state w=0 as matter.
Theoretical study of the intermolecular hydrogen bond interaction for furan-HCl and furan-CHCl3 complexes

The nature of the intermolecular hydrogen bond for the furan-HCl and furan-CHCI3 complexes has been studied using ab initio calculations with MP2 level of theory. The new hydrogen bond type of C(CI)-H.O and π interactions are studied also. It is shown that, for the optimized geometries of furan-CHCI3, C-H bond lengths contract and vibrational frequencies are blue-shifted, while for the furan-HCl complex, H-CI bond lengths elongate and vibrational frequencies are red-shifted. In addition, the NBO analysis indicates that, for the furan-CHCI3 complex, the charge transfers from the lone pair of the proton acceptor to both σ *(CH) antibonding MO and lone pairs of CI atom.