Miracle-dz/UniFAR-FTUs huggingface dataset:
Using Knowledge-Embedded Attention to Augment Pre-trained Language Models for Fine-Grained Emotion Recognition

Modern emotion recognition systems are trained to recognize only a small set of emotions, and hence fail to capture the broad spectrum of emotions people experience and express in daily life. In order to engage in more empathetic interactions, future AI has to perform fine-grained emotion recognition, distinguishing between many more varied emotions. Here, we focus on improving fine-grained emotion recognition by introducing external knowledge into a pre-trained self-attention model. We propose Knowledge-Embedded Attention (KEA) to use knowledge from emotion lexicons to augment the contextual representations from pre-trained ELECTRA and BERT models. Our results and error analyses outperform previous models on several datasets, and is better able to differentiate closely-confusable emotions, such as afraid and terrified.
A Unified Convergence Analysis of First Order Convex Optimization Methods via Strong Lyapunov Functions

We present a unified convergence analysis for first order convex optimization methods using the concept of strong Lyapunov conditions. Combining this with suitable time scaling factors, we are able to handle both convex and strong convex cases, and establish contraction properties of Lyapunov functions for many existing ordinary differential equation models. Then we derive prevailing first order optimization algorithms, such as proximal gradient methods, heavy ball methods (also known as momentum methods), Nesterov accelerated gradient methods, and accelerated proximal gradient methods from numerical discretizations of corresponding dynamical systems. We also apply strong Lyapunov conditions to the discrete level and provide a more systematical analysis framework. Another contribution is a novel second order dynamical system called Hessian-driven Nesterov accelerated gradient flow which can be used to design and analyze accelerated first order methods for smooth and non-smooth convex optimizations.
ANDOR and beyond: dynamically switchable logic gates as modules for flexible information processing

Understanding how complex (bio-)chemical pathways and regulatory networks may be capable of processing information in efficient, flexible, and robust ways is a key question with implications touching fields across biology, systems biology, biochemistry, synthetic biology, dynamical systems theory, and network science. Considerable effort has been focused on the identification and characterization of structural motifs in these signaling networks, and companion efforts have instead sought to cast their operation as controlled by dynamical modules that appear out of dynamical correlations during information processing. While both these approaches have been successful in many examples of biological information processing, cases in which the signaling or regulatory network exhibits multi-functionality or context dependence remain problematic. We here propose a small set of higher-order effective modules that simultaneously incorporate both network structure and the attendant dynamical landscape. In so doing, we render effective computational units that can perform different logical operations based purely on the basin of attraction in which the network dynamics resides or is steered to. These dynamically switchable biochemical logic gates require fewer chemical components or gene products overall than their traditional analogs where static, separate gates are used for each desired function. We demonstrate the applicability and limits of these flexible gates by determining a robust range of parameters over which they correctly operate and further characterize the resilience of their function against intrinsic noise of the constituent reactions using the theory of large deviations. We also show the capability of this framework for general computations by designing a binary adder/subtractor circuit composed of only six components.
Quark Sivers Function at Small-$x$: Leading contribution from the Spin-Dependent Odderon

We present the calculation of the leading contribution to the quark Sivers function at small-Bjorken x as in [1]. This calculation uses the high energy scattering approximation and operator formalism developed in [2,3] to obtain a dominant contribution to the quark Sivers function coming from the spin-dependent odderon, in agreement with the results of [4]. We then calculate this dominant contribution in the diquark model of the proton to obtain a small-x estimate for the Sivers function.
Welfare implications of noise traders

We prove the existence of incomplete Radner equilibria in two models with exponential investors and different types of noise traders: an exogenous noise trader and an endogenous noise tracker. In each model, we analyze a coupled system of ODEs and reduce it to a system of two coupled ODEs in order to establish equilibrium existence. As an application, we study the impact of the noise trader types on welfare. We show that the aggregate welfare comparison depends in a non-trivial manner on every equilibrium parameter, and there is no ordering in general. Our models suggest that care should be used when drawing conclusions about welfare effects when noise traders are used.
Nonreciprocal model swimmer at intermediate Reynolds numbers

Metachronal swimming, the sequential beating of limbs with a small phase lag, is observed in many organisms at various scales, but has been studied mostly in the limits of high or low Reynolds numbers. Motivated by the swimming of brine shrimp, a mesoscale organism that operates at intermediate Reynolds numbers, we computationally studied a simple nonreciprocal 2D model that performs metachronal swimming. Our swimmer is composed of two pairs of paddles beating with a phase difference that are symmetrically attached to the sides of a flat body. We numerically solved the Navier-Stokes equations and used the immersed boundary method to model the interactions between the fluid and swimmer. To investigate the effect of inertia and geometry, we performed simulations varying the paddle spacing and the Reynolds numbers in the range Re = 0.05 − 100. In all cases, we observed back-and-forth motion during the cycle and a finite cycle-averaged swim speed at steady state. Interestingly, we found that the swim speed of the swimmer has nonmonotonic dependence on Re, with a maximum around Re ≈ 1, a flat minimum between Re = 20− 30 and an eventual increase for Re > 35. To get more insight into the mechanism behind this relationship, we first decomposed the swim stroke of the swimmer and each paddle into power and recovery strokes and characterized the forward and backward motion. Unlike for reciprocal swimmers, here, for parts of the cycle there is competition between the two strokes of the paddles the balance of which leads to net motion. We then studied the cycle-averaged, as well as, power-stroke-averaged and recoverystroke-averaged fluid flows, and related differences in the fluid field to the nonmonotonic behavior of the swim speed. Our results suggest the existence of distinct motility mechanisms that develop as inertia increases within the intermediate-Re range.
In search of local singularities in ideal potential flows with free surface

For ideal ﬂuid ﬂow with zero surface tension and gravity, it remains unknown whether local singularities on the free surface can develop in well-posed initial value problems with smooth initial data. This is so despite great advances over the last 25 years in the mathematical analysis of the Euler equations for water waves. Here we expand our earlier work ( Chin. Ann. Math. Ser. B 40 (2019) 925) and review the mathematical literature and some of the history concerning Dirichlet’s ellipsoids and related hyperboloids associated with jet formation and “ﬂip-through,” “splash singularities,” and recent constructions of singular free surfaces that however violate the Taylor sign condition for linear well-posedness. We illustrate some of these phenomena with numerical computations of 2D ﬂow based upon a conformal mapping formulation (whose derivation is detailed and discussed in an appendix). Additional numerical evidence strongly suggests that corner singularities may form in an unstable self-similar way from specially prepared initial data.
The Evolutionary Map of the Universe pilot survey

Abstract We present the data and initial results from the first pilot survey of the Evolutionary Map of the Universe (EMU), observed at 944 MHz with the Australian Square Kilometre Array Pathfinder (ASKAP) telescope. The survey covers $270 \,\mathrm{deg}^2$ of an area covered by the Dark Energy Survey, reaching a depth of 25–30 $\mu\mathrm{Jy\ beam}^{-1}$ rms at a spatial resolution of $\sim$ 11–18 arcsec, resulting in a catalogue of $\sim$ 220 000 sources, of which $\sim$ 180 000 are single-component sources. Here we present the catalogue of single-component sources, together with (where available) optical and infrared cross-identifications, classifications, and redshifts. This survey explores a new region of parameter space compared to previous surveys. Specifically, the EMU Pilot Survey has a high density of sources, and also a high sensitivity to low surface brightness emission. These properties result in the detection of types of sources that were rarely seen in or absent from previous surveys. We present some of these new results here.
Functional estimation in log-concave location families

Abstract: Let {Pθ : θ ∈ R} be a log-concave location family with Pθ(dx) = e −V (x−θ)dx, where V : R 7→ R is a known convex function and let X1, . , Xn be i.i. d. r.v. sampled from distribution Pθ with an unknown location parameter θ. The goal is to estimate the value f(θ) of a smooth functional f : R 7→ R based on observations X1, . , Xn. In the case when V is sufficiently smooth and f is a functional from a ball in a Hölder space C, we develop estimators of f(θ) with minimax optimal error rates measured by the L2(Pθ)-distance as well as by more general Orlicz norm distances. Moreover, we show that if d ≤ n and s > 1 1−α , then the resulting estimators are asymptotically efficient in Hájek-LeCam sense with the convergence rate √ n. This generalizes earlier results on estimation of smooth functionals in Gaussian shift models. The estimators have the form fk(θ̂), where θ̂ is the maximum likelihood estimator and fk : R d 7→ R (with k depending on s) are functionals defined in terms of f and designed to provide a higher order bias reduction in functional estimation problem. The method of bias reduction is based on iterative parametric bootstrap and it has been successfully used before in the case of Gaussian models.
Delta Lenses as Coalgebras for a Comonad

Delta lenses are a kind of morphism between categories which are used to model bidirectional transformations between systems. Classical state-based lenses, also known as very well-behaved lenses, are both algebras for a monad and coalgebras for a comonad. Delta lenses generalise state-based lenses, and while delta lenses have been characterised as certain algebras for a semi-monad, it is natural to ask if they also arise as coalgebras. This short paper establishes that delta lenses are coalgebras for a comonad, through showing that the forgetful functor from the category of delta lenses over a base, to the category of cofunctors over a base, is comonadic. The proof utilises a diagrammatic approach to delta lenses, and clarifies several results in the literature concerning the relationship between delta lenses and cofunctors. Interestingly, while this work does not generalise the corresponding result for state-based lenses, it does provide new avenues for exploring lenses as coalgebras.
Low-loss wavelength-selective integrated waveguide coupler based on tilted Bragg gratings

We propose and investigate numerically a new kind of integrated optical waveguide coupler. Two waveguides with tilted Bragg gratings couple selectively to a discrete counter-propagating cladding mode which acts as an intermediary to transfer power between the waveguides. This gives a highly versatile platform that can act as a low-loss coupler with arbitrary coupling ratio between 0 and 100%, as a narrowbandwidth wavelength filter, and as a phase-selective switch that transmits two light waves of a fixed relative phase but reflects the orthogonal phase within a single device. © 2019 Optical Society of America
NUM ERICAL ANALYSIS OF OVERFLOW TRAFFIC AND A DECOMPOSITION TECHNIQUE FOR THE PERFORMANCE EVALUATION OF OVERFLOW SYSTEMS

In order to achieve good and efficient approximations in the performance analysis of overflow systems a decomposition technique based on the numerical . evaluation of transient Markov chains is introduced. The basic technique is the construction of substitute processes which approximate the original overflow traffic. Due to decomposition the evaluation of large realistic models can be mastered very efficiently.
The effect of inorganic nitrate and nitrite on the production and function of extracellular vesicles

Extracellular vesicles (EVs) are spherical, submicron particles enclosed in a phospholipid bilayer, shown to have pathophysiological roles in a plethora of disease states, including cardiovascular disease (CVD). The development of an atherosclerotic plaque can lead to downstream hypoxia, which is known to stimulate the production of EVs. Nitric oxide (NO) plays a pivotal role in vascular homeostasis, highlighted by the deficiency of NO in CVD states. The inorganic anions nitrate (NO3-) and nitrite (NO2-) represent bioactive reservoirs of NO, particularly under hypoxic conditions. Therefore, the aim of this thesis was to explore the effect of inorganic NO3-/ NO2- on the production and function of EVs in CVD. In vitro, hypoxia-inducible factor-1α (HIF-1α) was shown to mediate hypoxic EV release in endothelial cells. Furthermore, NO2- derived NO increased HIF-1α degradation, and subsequently reduced EV production. This effect was attenuated by inhibition of xanthine oxidoreductase, preventing NO2- conversion to NO. Following this, hypoxic endothelial-derived EVs were shown to enhance pro-coagulant and pro-inflammatory responses in comparison to EVs derived from normoxia. Treatment of hypoxic cells with NO2- reversed the pro-coagulant effects of the EVs produced, but did not alter their effect on inflammation. In order to determine whether modulation of EV production was also possible in vivo, healthy volunteers were given a dietary NO3- supplement daily for 6 days. However, there was no change in circulating EVs over the course of this treatment. Finally, a NO3- supplement was given to CVD patients, which significantly reduced circulating EVs only in patients on clopidogrel, suggesting the formation of a thienopyridine-nitrosothiol derivative. In conclusion, the NO metabolites NO3- and NO2- appear capable of reducing the production of pathogenic EVs, representing a novel therapeutic approach which may be of interest in the future treatment of CVD.
Backhaul-Aware Carrier Selection and Scheduling Algorithms for GPRS/EDGE Networks

In this paper, we consider the problem of carrier selection and scheduling for GPRS and EDGE networks, where the backhaul bandwidth is aggregated across all data timeslots in a carrier. This is in contrast to a circuit backhaul where the bandwidth is provisioned for each individual data timeslot. To achieve statistical multiplexing gains, the aggregate backhaul bandwidth for each carrier is allowed to be smaller than that needed for all the data timeslots to operate at the highest modulation and coding scheme. We design carrier selection and scheduling algorithms that take into account the provisioned backhaul bandwidth. We show that not taking the backhaul bandwidth into account can yield sub-optimal performance. Our algorithms variously trade oﬀ performance, computational complexity, and the amount and accuracy of information used.
Optimizing Spray-Dried Porous Particles for High Dose Delivery with a Portable Dry Powder Inhaler

This manuscript critically reviews the design and delivery of spray-dried particles for the achievement of high total lung doses (TLD) with a portable dry powder inhaler. We introduce a new metric termed the product density, which is simply the TLD of a drug divided by the volume of the receptacle it is contained within. The product density is given by the product of three terms: the packing density (the mass of powder divided by the volume of the receptacle), the drug loading (the mass of drug divided by the mass of powder), and the aerosol performance (the TLD divided by the mass of drug). This manuscript discusses strategies for maximizing each of these terms. Spray drying at low drying rates with small amounts of a shell-forming excipient (low Peclet number) leads to the formation of higher density particles with high packing densities. This enables ultrahigh TLD (>100 mg of drug) to be achieved from a single receptacle. The emptying of powder from capsules is directly proportional to the mass of powder in the receptacle, requiring an inhaled volume of about 1 L for fill masses between 40 and 50 mg and up to 3.2 L for a fill mass of 150 mg.
Identification of ZmNF-YC2 and its regulatory network in maize flowering time.

Flowering time is an important agronomic trait that determines the distribution and adaptation of plants. The accurate prediction of flowering time in elite germplasm is very critical for maize breeding. However, the molecular mechanisms underlying photoperiod response remain elusive in maize. Here we cloned the flowering time controlling gene, ZmNF-YC2, by map-based cloning and confirmed that ZmNF-YC2 is the protein of nuclear transcription factor Y subunit C-2 and a positive regulator of flowering time in maize under long-day conditions. Our results show that ZmNF-YC2 promotes the expression of ZmNF-YA3. ZmNF-YA3 negatively regulates the transcription of ZmAP2. ZmAP2 suppresses the expression of ZMM4 to delay flowering time. Then we developed a gene regulatory model of flowering time in maize using ZmNF-YC2, ZmNF-YA3, ZmAP2, ZMM4 and other key genes. The cascading regulation of ZmNF-YC2 on maize flowering time has not been reported in other species.
A UNIFIED APPROACH TOWARDS CHARACTERIZATION OF STRUCTURAL STABILITIES IN TELECOMMUNICATION NETWORKS

This paper reviews some of the recent results on studies on the properties of stability asso ciated with the dynamic flows in different network schemes such as Input Buffer Limiting, Non hierarchical Alternate Routing and Combined Random-reservation Multiple Access schemes. Results from catastrophe theory are applied on the potential function to characterize the instablli ty involved in each case. It is also found that, apart form the prediction and characterization of instability, the models concurrently lend themselves for quantitative performance estimates. These methods promise a unifying approach in the characterization of structural stabilities and in the identification of stable regions in telecommunication networks
Presence of Leptospira spp. and absence of Bartonella spp. in urban rodents of Buenos Aires province, Argentina

ABSTRACT Big cities of Argentina are characterized by a strong social and economic fragmentation. This context enables the presence of urban rodents in close contact to the human population, mostly in the peripheral areas of the cities. Urban rodents can harbor a large variety of zoonotic pathogens. The aim of this study was to molecularly characterize Leptospira spp. and Bartonella spp. in urban rodents from the area of Gran La Plata, Buenos Aires province, Argentina. The species of urban rodents captured and tested were Rattus norvegicus, Rattus rattus, and Mus musculus. Leptospira interrogans and L. borgpetersenii were detected in R. norvegicus and M. musculus respectively. Bartonella spp. DNA was not detected in any of the kidney samples tested. No significant differences were observed between the prevalence of bacteria and rodent and environmental variables such as host sex, presence of stream and season by Generalized Linear Model analysis. These results confirm the role of urban rodents as infection sources of Leptospira spp. , suggesting the need to implement public health measures to prevent the transmission of Leptospira spp. and other zoonotic pathogens from rodents to humans. Bartonella was not detected in this set of samples. Graphical abstract
Oocyte quality and aging

It is well known that female reproduction ability decreases during the forth decade of life due to age-related changes in oocyte quality and quantity; although the number of women trying to conceive has today increased remarkably between the ages of 36 to 44. The causes of reproductive aging and physiological aspects of this phenomenon are still elusive. With increase in the women’s age, during Assisted Reproductive Technologies (ART) we have perceived a significant decline in the number and quality of retrieved oocytes, as well as in ovarian follicle reserves. This is because of increased aneuploidy due to factors such as spindle apparatus disruption; oxidative stress and mitochondrial damage. The aim of this review paper is to study data on the potential role of the aging process impacting oocyte quality and female reproductive ability. We present the current evidence that show the decreased oocyte quality with age, related to reductions in female reproductive outcome. The aging process is complicated and it is caused by many factors that control cellular and organism life span. Although the factors responsible for reduced oocyte quality remain unknown, the present review focuses on the potential role of ovarian follicle environment, oocyte structure and its organelles. To find a way to optimize oocyte quality and ameliorate clinical outcomes for women with aging-related causes of infertility.
Necdin: A purposive integrator of molecular interaction networks for mammalian neuron vitality

Necdin was originally found in 1991 as a hypothetical protein encoded by a neural differentiation‐specific gene transcript in murine embryonal carcinoma cells. Virtually all postmitotic neurons and their precursor cells express the necdin gene (Ndn) during neuronal development. Necdin mRNA is expressed only from the paternal allele through genomic imprinting, a placental mammal‐specific epigenetic mechanism. Necdin and its homologous MAGE (melanoma antigen) family, which have evolved presumedly from a subcomplex component of the SMC5/6 complex, are expressed exclusively in placental mammals. Paternal Ndn‐mutated mice totally lack necdin expression and exhibit various types of neuronal abnormalities throughout the nervous system. Ndn‐null neurons are vulnerable to detrimental stresses such as DNA damage. Necdin also suppresses both proliferation and apoptosis of neural stem/progenitor cells. Functional analyses using Ndn‐manipulated cells reveal that necdin consistently exerts antimitotic, anti‐apoptotic and prosurvival effects. Necdin interacts directly with a number of regulatory proteins including E2F1, p53, neurotrophin receptors, Sirt1 and PGC‐1α, which serve as major hubs of protein–protein interaction networks for mitosis, apoptosis, differentiation, neuroprotection and energy homeostasis. This review focuses on necdin as a pleiotropic protein that integrates molecular interaction networks to promote neuronal vitality in modern placental mammals.