Miracle-dz/UniFAR-FTUs huggingface dataset:
RECENT PROGRESS IN MICROPLANE MODELLING OF PLAIN CONCRETE

Despite determined efforts in mathematical modelling of multiaxial behaviour of plain concrete by many researchers, the existing models have not achieved a full description of this complex behaviour. Among these models, the microplane models have contributed important advances in the semi-multiscale modelling of multiaxial behaviour of concrete since their inception in the early 80s. Among several versions of microplane models for plain concrete, model M4 had the greatest success in modelling both the rate-dependent dynamic and quasistatic multiaxial behaviours of concrete. Yet, some problems still have persisted, such as (1) a spurious lateral contraction under uniaxial tension, and (2) an unrealistic damage prediction in tension. These problems resulted from the difficulty in reconciling the pressure sensitive ductile behaviour in compression of concrete with its brittle tensile behaviour. A new microplane model, called M7 as a successor to the earlier microplane models for plain concrete, overcomes the aforementioned problems while retaining all of its compressive data fitting prowess. The volumetric-deviatoric split, required in the previous modelling of the pressure sensitive compressive behaviour of concrete, is now removed from the elastic strains under both compression and tension, but retained in the formulation of compressive stress-strain boundaries (i.e, strain-dependent yield limits on the generic microplane). This allows the simulation of a much more realistic tensile behaviour including the correct damage (loading/unloading slope) and correct lateral contraction. It also means that a new compressive normal boundary is needed. It is defined in terms of the existing deviatoric and volumetric boundaries, which preserves the versatility of the model in fitting a wide range of experimental data. Model M7 has been tested in finite element simulations of a wide range of compressive, tensile, mixed 1 Recent progress in microplane modelling of plain concrete
PACEMAKER: Avoiding HeART attacks in storage clusters with disk-adaptive redundancy

Data redundancy provides resilience in large-scale storage clusters, but imposes significant cost overhead. Substantial space-savings can be realized by tuning redundancy schemes to observed disk failure rates. However, prior design proposals for such tuning are unusable in real-world clusters, because the IO load of transitions between schemes overwhelms the storage infrastructure (termed transition overload). This paper analyzes traces for millions of disks from production systems at Google, NetApp, and Backblaze to expose and understand transition overload as a roadblock to disk-adaptive redundancy: transition IO under existing approaches can consume 100% cluster IO continuously for several weeks. Building on the insights drawn, we present PACEMAKER, a low-overhead disk-adaptive redundancy orchestrator. PACEMAKER mitigates transition overload by (1) proactively organizing data layouts to make future transitions efficient, and (2) initiating transitions proactively in a manner that avoids urgency while not compromising on space-savings. Evaluation of PACEMAKER with traces from four large (110K-450K disks) production clusters show that the transition IO requirement decreases to never needing more than 5% cluster IO bandwidth (0.2-0.4% on average). PACEMAKER achieves this while providing overall space-savings of 14-20% and never leaving data under-protected. We also describe and experiment with an integration of PACEMAKER into HDFS.
Scientific Realism and the Quantum: Primitive Ontology and the Pessimistic Meta Induction

In this paper I wish to connect the recent debate in the philosophy of quantum mechanics concerning the nature of the wave-function to the historical debate in the philosophy of science regarding the tenability of scientific realism. Being realist about quantum mechanics is particularly challenging when focusing on the wave-function. According to the wave-function ontology approach, the wavefunction is a concrete physical entity. In contrast, according to an alternative viewpoint, namely the primitive ontology approach, the wave-function does not represent physical entities. In this paper, I argue that the primitive ontology approach can naturally be interpreted as an instance of the so-called ‘explanationism’ realism, which has been proposed as a response to the pessimistic-meta induction argument against scientific realism. If my arguments are sound, then one could conclude that: (1) contrarily to what is commonly though, if explanationism realism is a good response to the pessimisticmeta induction argument, it can be straightforwardly extended also to the quantum domain; (2) the primitive ontology approach is in better shape than the wave-function ontology approach in resisting the pessimistic-meta induction argument against scientific realism.
Modelling the effect of temperature on shelf-life and on the interaction between the spoilage microflora and Listeria monocytogenes in cold-smoked salmon

Modelling the effect of temperature on shelf-life and on the interaction between the spoilage microflora and Listeria monocytogenes in cold-smoked salmon Dalgaard, P., Iturrate, E. M. & Jørgensen, L. V. 2004 Seafood Quality and Safety. Advances in the New Millennium. Shahidi, F. & Simpson, B. K. (eds.). St John's, NL: ScienceTech Publishing Company, p. 281-302 Publication: Research peer-review › Book chapter – Annual report year: 2004
Immune control and viral countermeasures in murine cytomegalovirus infection.

ix Chapter 1: Introduction 1 1.1 Overview 1 1.2 Cytomegaloviruses 2 1.3 Models of MCMV infection 9 1.4 Role of MHC class I in immunobiology 15 1.5 Recognition of presented antigen by CD8 T cells 20 1.6 Role of CD8 T cells in virus infection 31 1.7 Viral immune evasion 39 1.8 Literature concerning the impact of MHC class I immune evasion on 41 MCMV infection 1.9 Conclusions 53 Chapter 2: Materials and Methods 55 Chapter 3: The impact of non-MHC genes and MHC class I immune evasion genes 64 on the CD8 T cell response to MCMV infection 3.1 Examination of the magnitude and antigen-specific CD8 T cell 64 response to MCMV infection among different strains of mice Introduction 64 Results 66 3.2 The MCMV MHC class I immune evasion genes have no impact on the 71 priming of CD8 T cells under standard laboratory infection conditions Introduction 71 Results 72 Discussion 74 Chapter 4: The impact of MHC class I evasion in MCMV infection under natural 89 infection conditions i
Microbial β-Glucosidases: Screening, Characterization, Cloning and Applications

Cellulose is the most abundant biomaterial in the biosphere and the major component of plant biomass. Cellulase is an enzymatic system required for conversion of renewable cellulose biomass into free sugar for subsequent use in different applications. Cellulase system mainly consists of three individual enzymes namely: endoglucanase, exoglucanase and β-glucosidases. β-Glucosidases are ubiquitous enzymes found in all living organisms with great biological significance. β-Glucosidases have also tremendous biotechnological applications such as biofuel production, beverage industry, food industry, cassava detoxification and oligosaccharides synthesis. Microbial β-glucosidases are preferred for industrial uses because of robust activity and novel properties exhibited by them. This review aims at describing the various biochemical methods used for screening and evaluating β-glucosidases activity from microbial sources. Subsequently, it generally highlights techniques used for purification of β-glucosidases. It then elaborates various biochemical and molecular properties of this valuable enzyme such as pH and temperature optima, glucose tolerance, substrate specificity, molecular weight, and multiplicity. Furthermore, it describes molecular cloning and expression of bacterial, fungal and metagenomic β-glucosidases. Finally, it highlights the potential biotechnological applications of β-glucosidases.
Single-Atom Nanozymes Linked Immunosorbent Assay for Sensitive Detection of Aβ 1-40: A Biomarker of Alzheimer's Disease

Single-atom nanozymes (SANs) possess unique features of maximum atomic utilization and present highly assembled enzyme-like structure and remarkable enzyme-like activity. By introducing SANs into immunoassay, limitations of ELISA such as low stability of horseradish peroxidase (HRP) can be well addressed, thereby improving the performance of the immunoassays. In this work, we have developed novel Fe-N-C single-atom nanozymes (Fe-Nx SANs) derived from Fe-doped polypyrrole (PPy) nanotube and substituted the enzymes in ELISA kit for enhancing the detection sensitivity of amyloid beta 1-40. Results indicate that the Fe-Nx SANs contain high density of single-atom active sites and comparable enzyme-like properties as HRP, owing to the maximized utilization of Fe atoms and their abundant active sites, which could mimic natural metalloproteases structures. Further designed SAN-linked immunosorbent assay (SAN-LISA) demonstrates the ultralow limit of detection (LOD) of 0.88 pg/mL, much more sensitive than that of commercial ELISA (9.98 pg/mL). The results confirm that the Fe-Nx SANs can serve as a satisfactory replacement of enzyme labels, which show great potential as an ultrasensitive colorimetric immunoassay.
Phytochemical diversity of fennel landraces from various growth types and origins

The presence of certain secondary metabolites in fennel essential oil is the cause of its pharmacological and flavoring properties. In this study phytochemical diversity including essential oil content and compositions of 26 fennel landraces from various growth types and geographical originations were assessed. Essential oil content of the fennel landraces varied from 1.1 to 4.8%, with late and medium maturities showing higher essential oil contents than early maturities. According to the Gas Chromatography Mass Spectrometry (GCMS) results, the main essential oil components were trans-anethole (1.2–88.4%), methyl chavicol (0.2–59.1%), fenchone (1.1–14.7%) and limonene (5.3–15.7%). According to the clustering results, it was noticed that all the fennel landraces originated from arid climates were trans-anethole chemotype with an average concentration of 76%. The early and late maturity fennels from humid climates were methyl chavicol chemotype with an average concentration of 54%. The late maturities from humid and moderate climates were fenchone chemotypes with 12% concentration, and finally the early and late maturities from semi-arid climates were limonene chemotype with 12% concentration. Our results confirm that climate is a major evolutionary determining factor on the phytochemical diversity of fennel landraces.
Interspeech 2020, 21st Annual Conference of the International Speech Communication Association, Virtual Event, Shanghai, China, 25-29 October 2020

Semantically-aligned $(speech, image)$ datasets can be used to explore "visually-grounded speech". In a majority of existing investigations, features of an image signal are extracted using neural networks "pre-trained" on other tasks (e.g., classification on ImageNet). In still others, pre-trained networks are used to extract audio features prior to semantic embedding. Without "transfer learning" through pre-trained initialization or pre-trained feature extraction, previous results have tended to show low rates of recall in $speech \rightarrow image$ and $image \rightarrow speech$ queries. Choosing appropriate neural architectures for encoders in the speech and image branches and using large datasets, one can obtain competitive recall rates without any reliance on any pre-trained initialization or feature extraction: $(speech,image)$ semantic alignment and $speech \rightarrow image$ and $image \rightarrow speech$ retrieval are canonical tasks worthy of independent investigation of their own and allow one to explore other questions---e.g., the size of the audio embedder can be reduced significantly with little loss of recall rates in $speech \rightarrow image$ and $image \rightarrow speech$ queries.
Self-Supervised Video Representation Using Pretext-Contrastive Learning

Pretext tasks and contrastive learning have been successful in self-supervised learning for video retrieval and recognition. In this study, we analyze their optimization targets and utilize the hyper-sphere feature space to explore the connections between them, indicating the compatibility and consistency of these two different learning methods. Based on the analysis, we propose a self-supervised training method, referred as Pretext-Contrastive Learning (PCL), to learn video representations. Extensive experiments based on different combinations of pretext task baselines and contrastive losses confirm the strong agreement with their self-supervised learning targets, demonstrating the effectiveness and the generality of PCL. The combination of pretext tasks and contrastive losses showed significant improvements in both video retrieval and recognition over the corresponding baselines. And we can also outperform current state-of-the-art methods in the same manner. Further, our PCL is flexible and can be applied to almost all existing pretext task methods.
Revision of the Lerista muelleri species-group (Lacertilia: Scincidae) in Western Australia, with a redescription of L. muelleri (Fischer, 1881) and the description of nine new species

The Australian scincid genus Lerista comprises more than 80 species of small semifossorial and fossorial lizards. Most species are found in arid and semiarid regions. These species are divided into species groups which are largely diagnosed by their digital formulae. Most L. muelleri species group members have a digital formula of three fingers and three toes, some four fingers and four toes. Only one species has two fingers and three toes. Hitherto L. muelleri has been considered a single pancontinental species which, together with L. aIlochira and L. ham/di, comprised the relatively small L. muelleri species group within the speciose genus Lerista. A detailed examination of morphological and genetic variation in Western Australian specimens has indicated that species-level diversity within the L. muelleri species group has been greatly underestimated. Here, 13 species are recognised: the previously described L. allochira (Kendrick 1989), L. hamldi (Storr 1983), L. muelleri (Fischer 1881) and L. rhodonoides (Lucas and Frost 1896; resurrected from the synonymy of L. muelleri) plus nine new species: L. amicorum sp. nov., L. clara sp. nov., L. jacksoni sp. nov., L. kingi sp. nov., L. micra sp. nov., L. nevinae sp. nov., L. occulta sp. nov., L. rolfei sp. novo and L. verhmens sp. novo Lerista goerlingi (Ahl 1935) is retained in the synonymy of L. rhodonoides. Support for the validity of eleven of the thirteen species is demonstrated using allozyme analysis of 114 specimens at 46 loci. Lerista mueIleri (sensu stricto) is shown to be restricted to the Pilbara plateau and its rocky outliers in northwest Western Australia. One of the two syntypes of L. muelleri that were considered lost has recently been rediscovered and is nominated as lectotype.
Turbidimetric evaluation of the solubilization rate: dissolution of dodecane nanodrops in 7.5 mM sodium dodecylsulfate solutions at selected sodium chloride concentrations

The rate of micelle solubilization (SR) can be appraised following the decrease of the radius of a macroscopic drop of oil in contact with a surfactant solution [Todorov, 2002]. Alternatively, the time required for the dissolution of a liquid dispersion can be used for this purpose. Here, the decrease of the turbidity of a dodecane-in-water (d/w) nanoemulsion in 7.5 mM sodium dodecylsulfate (SDS) is studied at sodium chloride concentrations of 100, 300, 500, 700, 900, and 1000 mM NaCl. These salinities correspond to non-aggregating ( 700). It is found that SR is equal to 2.3 x 10^-11, half the value observed in the absence of salt for a neat aqueous surfactant solution above its critical micelle concentration (7.0 < cmc < 8.7 mM SDS [Deodhar, 2020]).
Solving Sparse Linear Inverse Problems in Communication Systems: A Deep Learning Approach With Adaptive Depth

Sparse signal recovery problems from noisy linear measurements appear in many areas of wireless communications. In recent years, deep learning (DL) based approaches have attracted interests of researchers to solve the sparse linear inverse problem by unfolding iterative algorithms as neural networks. Typically, research concerning DL assume a fixed number of network layers. However, it ignores a key character in traditional iterative algorithms, where the number of iterations required for convergence changes with varying sparsity levels. By investigating on the projected gradient descent, we unveil the drawbacks of the existing DL methods with fixed depth. Then we propose an end-to-end trainable DL architecture, which involves an extra halting score at each layer. Therefore, the proposed method learns how many layers to execute to emit an output, and the network depth is dynamically adjusted for each task in the inference phase. We conduct experiments using both synthetic data and applications including random access in massive MTC and massive MIMO channel estimation, and the results demonstrate the improved efficiency for the proposed approach.
Measuring the Mass and Concentration of Dark Matter Halos from the Velocity Dispersion Profile of their Stars

We use the IllustrisTNG (TNG) simulations of galaxy formation to measure the velocity dispersion profiles of dark matter and stars in Milky Way–mass, galaxy group, and cluster-scale dark matter halos. The mean profiles calculated from both tracers are similar in shape, exhibiting a large halo-to-halo scatter around the average profile. The so-called “splashback” radius demarcates the outer boundary of the halo, and manifests as a kink in the velocity dispersion profile, located on average between ∼1.0–1.5r 200m , where r 200m is the radius within which the density of the halo equals 200 times the background density of the universe. We find that this location may also be identified as the radius at which the (stacked) dispersion profile drops to 60% of its peak value (for line-of-sight motions in TNG halos). We further show that the scatter in the dispersion profiles may be attributed to the variations in the assembly history of the host halos. In particular, this segregates the profile into two regimes: one within ∼0.1r 200m , where the scatter is set by the early assembly history of the halo; and the other beyond this radius, where the scatter is influenced more strongly by its late-time assembly. Finally, we show that a two-parameter model can be used to fit the measured velocity dispersion profiles and the fit parameters can be related directly to two fundamental halo properties: mass and concentration. We describe a simple model that allows us to express the stellar velocity dispersion profile in terms of these halo properties only.
Multitask Bandit Learning through Heterogeneous Feedback Aggregation

In many real-world applications, multiple agents seek to learn how to perform highly related yet slightly different tasks in an online bandit learning protocol. We formulate this problem as the $\epsilon$-multi-player multi-armed bandit problem, in which a set of players concurrently interact with a set of arms, and for each arm, the reward distributions for all players are similar but not necessarily identical. We develop an upper confidence bound-based algorithm, RobustAgg$(\epsilon)$, that adaptively aggregates rewards collected by different players. In the setting where an upper bound on the pairwise similarities of reward distributions between players is known, we achieve instance-dependent regret guarantees that depend on the amenability of information sharing across players. We complement these upper bounds with nearly matching lower bounds. In the setting where pairwise similarities are unknown, we provide a lower bound, as well as an algorithm that trades off minimax regret guarantees for adaptivity to unknown similarity structure.
Orientation-dependent stabilization of 
MgCr2O4
 spinel thin films

$A{B}_{2}{\mathrm{O}}_{4}$ normal spinels with a magnetic $B$ site can host a variety of magnetic and orbital frustrations leading to spin-liquid phases and field-induced phase transitions. Here, we report the epitaxial growth of (111)-oriented $\mathrm{Mg}{\mathrm{Cr}}_{2}{\mathrm{O}}_{4}$ thin films. By characterizing the structural and electronic properties of films grown along the (001) and (111) directions, the influence of growth orientation has been studied. Despite distinctly different growth modes observed during deposition, the comprehensive characterization reveals no measurable disorder in the cation distribution nor multivalency issue for Cr ions in either orientation. Contrary to a naive expectation, the (111) stabilized films exhibit a smoother surface and a higher degree of crystallinity than (001)-oriented films. The preference in growth orientation is explained within the framework of heteroepitaxial stabilization in connection to a significantly lower (111) surface energy. These findings open broad opportunities in the fabrication of two-dimensional kagome-triangular heterostructures with emergent magnetic behavior inaccessible in bulk crystals.
Energy-balance for the incompressible Euler equations with stochastic forcing

We establish energy-balance for weak solutions of the stochastically forced incompressible Euler equations, enjoying H\"older regularity $C^{\alpha}$, $\alpha>1/3$. It is well known as the Onsager's conjecture for the deterministic incompressible Euler equations, which describes the energy conservation of weak solutions having H\"older regularity $C^{\alpha}$, $\alpha>1/3$. Additionally, we obtain energy-balance for the inhomogeneous incompressible Euler system driven by a cylindrical Wiener process.
CopyNext: Explicit Span Copying and Alignment in Sequence to Sequence Models

Copy mechanisms are employed in sequence to sequence (seq2seq) models to generate reproductions of words from the input to the output. These frameworks, operating at the lexical type level, fail to provide an explicit alignment that records where each token was copied from. Further, they require contiguous token sequences from the input (spans) to be copied individually. We present a model with an explicit token-level copy operation and extend it to copying entire spans. Our model provides hard alignments between spans in the input and output, allowing for nontraditional applications of seq2seq, like information extraction. We demonstrate the approach on Nested Named Entity Recognition, achieving near state-of-the-art accuracy with an order of magnitude increase in decoding speed.
On k-para-Kähler Lie algebras, a subclass of k-symplectic Lie algebras

Abstract k-Para-Kähler Lie algebras are a generalization of para-Kähler Lie algebras (k = 1) and constitute a subclass of k-symplectic Lie algebras. In this paper, we show that the characterization of para-Kähler Lie algebras as left symmetric bialgebras can be generalized to k-para-Kähler Lie algebras leading to the introduction of two new structures which are different but both generalize the notion of left symmetric algebra. This permits also the introduction of generalized S-matrices. We determine then all the k-symplectic Lie algebras of dimension and all the six dimensional 2-para-Kähler Lie algebras.