Miracle-dz/UniFAR-FTUs huggingface dataset:
Testing the Genomic Bottleneck Hypothesis in Hebbian Meta-Learning

Recent work has shown promising results using Hebbian meta-learning to solve hard reinforcement learning problems and adapt-to a limited degree-to changes in the environment. In previous works each synapse has its own learning rule. This allows each synapse to learn very specific learning rules and we hypothesize this limits the ability to discover generally useful Hebbian learning rules. We hypothesize that limiting the number of Hebbian learning rules through a "genomic bottleneck" can act as a regularizer leading to better generalization across changes to the environment. We test this hypothesis by decoupling the number of Hebbian learning rules from the number of synapses and systematically varying the number of Hebbian learning rules. We thoroughly explore how well these Hebbian meta-learning networks adapt to changes in their environment.
Information-theoretically secure data origin authentication with quantum and classical resources

In conventional cryptography, information-theoretically secure message authentication can be achieved by means of universal hash functions, and requires that the two legitimate users share a random secret key, which is at least twice as long as the tag. We address the question of whether quantum resources can offer any advantage over classical unconditionally secure message authentication codes. It is shown that a broad class of symmetric prepare-and-measure quantum message-authentication schemes cannot do better than their classical counterparts.
Modulation Response of Twin Optically Coupled Diode Lasers

Abstract : The modulation response of twin optically coupled diode lasers is investigated by modeling the dynamics of the system with a set of single mode rate equations. Steady states of the system are derived analytically or calculated numerically when an analytic expression is not easily available. The stability of the steady states is examined by using a linear stability analysis, which is also used in an algorithm that calculates the infinitesimal modulation response. The modulation response is also calculated by using a numerical method that directly integrates the rate equations. Typical parameters for an InGaAsP diode laser are used in the algorithms to investigate mutual coupling and evanescent coupling. It is discovered that mutually coupled lasers can be effectively modulated out to frequencies of approximately 9GHz compared to 4GHz for a solitary laser. Large regions of the parameter space for evanescent coupling produce unstable steady states, but this is remedied by introducing asymmetric DC currents through the lasers or by introducing the effects of gain saturation. With stable steady states, evanescently coupled lasers can be effectively modulated at frequencies out to about 30 GHz which is more than a seven fold improvement over a solitary laser.
A Comprehensive Survey on Deep Music Generation: Multi-level Representations, Algorithms, Evaluations, and Future Directions

The utilization of deep learning techniques in generating various contents (such as image, text, etc.) has become a trend. Especially music, the topic of this paper, has attracted widespread attention of countless researchers.The whole process of producing music can be divided into three stages, corresponding to the three levels of music generation: score generation produces scores, performance generation adds performance characteristics to the scores, and audio generation converts scores with performance characteristics into audio by assigning timbre or generates music in audio format directly. Previous surveys have explored the network models employed in the field of automatic music generation. However, the development history, the model evolution, as well as the pros and cons of same music generation task have not been clearly illustrated. This paper attempts to provide an overview of various composition tasks under different music generation levels, covering most of the currently popular music generation tasks using deep learning. In addition, we summarize the datasets suitable for diverse tasks, discuss the music representations, the evaluation methods as well as the challenges under different levels, and finally point out several future directions.
Adaptive Estimation In High-Dimensional Additive Models With Multi-Resolution Group Lasso

In additive models with many nonparametric components, a number of regularized estimators have been proposed and proven to attain various error bounds under different combinations of sparsity and fixed smoothness conditions. Some of these error bounds match minimax rates in the corresponding settings. Some of the rate minimax methods are non-convex and computationally costly. From these perspectives, the existing solutions to the high-dimensional additive nonparametric regression problem are fragmented. In this paper, we propose a multi-resolution group Lasso (MR-GL) method in a unified approach to simultaneously achieve or improve existing error bounds and provide new ones without the knowledge of the level of sparsity or the degree of smoothness of the unknown functions. Such adaptive convergence rates are established when a prediction factor can be treated as a constant. Furthermore, we prove that the prediction factor, which can be bounded in terms of a restricted eigenvalue or a compatibility coefficient, can be indeed treated as a constant for random designs under a nearly optimal sample size condition.
Efficient CNOT Synthesis for NISQ Devices

In the era of noisy intermediate-scale quantum (NISQ), executing quantum algorithms on actual quantum devices faces unique challenges. One such challenge is that quantum devices in this era have restricted connectivity: quantum gates are allowed to act only on specific pairs of physical qubits. For this reason, a quantum circuit needs to go through a compiling process called qubit routing before it can be executed on a quantum computer. In this study, we propose a CNOT synthesis method called the token reduction method to solve this problem. The token reduction method works for all quantum computers whose architecture is represented by connected graphs. A major difference between our method and the existing ones is that our method synthesizes a circuit to an output qubit mapping that might be different from the input qubit mapping. The final mapping for the synthesis is determined dynamically during the synthesis process. Results showed that our algorithm consistently outperforms the best publicly accessible algorithm for all of the tested quantum architectures.
Transverse instability and universal decay of spin spiral order in the Heisenberg model

We analyze the stability of spin spiral states in the two-dimensional Heisenberg model. Our analysis reveals that the SU(2) symmetric point hosts a dynamic instability that is enabled by the existence of energetically favorable transverse deformations---both in real and spin space---of the spiral order. The instability is universal in the sense that it applies to systems with any spin number, spiral wavevector, and spiral amplitude. Unlike the Landau or modulational instabilities which require impurities or periodic potential modulation of an optical lattice, quantum fluctuations alone are sufficient to trigger the transverse instability. We analytically find the most unstable mode and its growth rate, and compare our analysis with phase space methods. By adding an easy plane exchange coupling that reduces the Hamiltonian symmetry from SU(2) to U(1), the stability boundary is shown to continuously interpolate between the modulational instability and the transverse instability. This suggests that the transverse instability is an important mechanism that hinders the formation of a spin superfluid, even in the presence of strong exchange anisotropy.
Induced actions of $\mathfrak{B}$-Volterra operators on regular bounded martingale spaces

A positive operator $T:E\to E$ on a Banach lattice $E$ with an order continuous norm is said to be $\mathfrak{B}$-Volterra with respect to a Boolean algebra $\mathfrak{B}$ of order projections of $E$ if the bands canonically corresponding to elements of $\mathfrak{B}$ are left fixed by $T$. A linearly ordered sequence $\xi$ in $\mathfrak{B}$ connecting $\textbf{0}$ to $\textbf{1}$ is called a forward filtration. A forward filtration can be to used to lift the action of the $\mathfrak{B}$-Volterra operator $T$ from the underlying Banach lattice $E$ to an action of a new norm continuous operator $\hat{T}_{\xi}\colon \mathcal{M}_{r}(\xi) \to \mathcal{M}_{r}(\xi)$ on the Banach lattice $\mathcal{M}_{r}(\xi)$ of regular bounded martingales on $E$ corresponding to $\xi$. In the present paper, we study properties of these actions. The set of forward filtrations are left fixed by a function which erases the first order projection of a forward filtration and which shifts the remaining order projections towards $\textbf{0}$. This function canonically induces a norm continuous shift operator $\textbf{s}$ between two Banach lattices of regular bounded martingales. Moreover, the operators $\hat{T}_{\xi}$ and $\textbf{s}$ commute. Utilizing this fact with inductive limits, we construct a categorical limit space $\mathcal{M}_{T,\xi}$ which is called the associated space of the pair $(T,\xi)$. We present new connections between theories of Boolean algebras, abstract martingales and Banach lattices.
On Free Boundary Minimal Annuli Embedded in the Unit Ball.

We prove that an embedded free boundary minimal annulus in the unit three-ball which is invariant under the antipodal map is the critical catenoid. Our proof combines a boundary nodal domain argument with the two-piece property to show that the first Steklov eigenspace of the annulus is the space of linear functions; by a result of Fraser-Schoen, we conclude the annulus is rotationally-invariant.
Medical symptom recognition from patient text: An active learning approach for long-tailed multilabel distributions

We study the problem of medical symptoms recognition from patient text, for the purposes of gathering pertinent information from the patient (known as history-taking). We introduce an active learning method that leverages underlying structure of a continually refined, learned latent space to select the most informative examples to label. This enables the selection of the most informative examples that progressively increases the coverage on the universe of symptoms via the learned model, despite the long tail in data distribution.
Deriving CME Density From Remote Sensing Data and Comparison to In‐Situ Measurements

We determine the three‐dimensional geometry and deprojected mass of 29 well‐observed coronal mass ejections (CMEs) and their interplanetary counterparts (ICMEs) using combined Solar Terrestrial Relations Observatory ‐ Solar and Heliospheric Observatory white‐light data. From the geometry parameters, we calculate the volume of the CME for the magnetic ejecta (flux‐rope type geometry) and sheath structure (shell‐like geometry resembling the (I)CME frontal rim). Working under the assumption that the CME mass is roughly equally distributed within a specific volume, we expand the CME self‐similarly and calculate the CME density for distances close to the Sun (15–30 Rs) and at 1 AU. Specific trends are derived comparing calculated and in‐situ measured proton densities at 1 AU, though large uncertainties are revealed due to the unknown mass and geometry evolution: (1) a moderate correlation for the magnetic structure having a mass that stays rather constant (cc ≈ 0.56 − 0.59), and (2) a weak correlation for the sheath density (cc ≈ 0.26) by assuming the sheath region is an extra mass—as expected for a mass pile‐up process—that is in its amount comparable to the initial CME deprojected mass. High correlations are derived between in‐situ measured sheath density and the solar wind density (cc ≈ −0.73) and solar wind speed (cc ≈ 0.56) as measured 24 h ahead of the arrival of the disturbance. This gives additional confirmation that the sheath‐plasma indeed stems from piled‐up solar wind material. While the CME interplanetary propagation speed is not related to the sheath density, the size of the CME may play some role in how much material could be piled up.
Mechanics and structure of carbon black gels under high-power ultrasound

Colloidal gels made of carbon black particles are "rheo-acoustic" materials: their mechanical and structural properties can be tuned using high-power ultrasound, sound waves with submicron amplitude and frequency larger than 20 kHz. The effect is demonstrated using two experiments: rheology coupled to ultrasound to test for the gel mechanical response and an ultra small-angle X-ray scattering experiment coupled to ultrasound to test for structural changes within the gel. We show that high-power ultrasound above a critical amplitude softens carbon black gels at rest due to the formation of micro-cracks in the bulk. The gel softens exponentially with a characteristic time of a few seconds. High-power ultrasound also eases the flow of the gel thanks again to the formation of micro-cracks. High shear rates however tend to select a gel structure that is less sensitive to ultrasound.
Online Object-Oriented Semantic Mapping and Map Updating

Creating and maintaining an accurate representation of the environment is an essential capability for every service robot. Especially for household robots acting in indoor environments, semantic information is important. In this paper, we present a semantic mapping framework with modular map representations. Our system is capable of online mapping and object updating given object detections from RGB-D data and provides various 2D and 3D representations of the mapped objects. To undo wrong data associations, we perform a refinement step when updating object shapes. Furthermore, we maintain an existence likelihood for each object to deal with false positive and false negative detections and keep the map updated. Our mapping system is highly efficient and achieves a run time of more than 10 Hz. We evaluated our approach in various environments using two different robots, i.e., a Toyota HSR and a Fraunhofer Care-O-Bot-4. As the experimental results demonstrate, our system is able to generate maps that are close to the ground truth and outperforms an existing approach in terms of intersection over union, different distance metrics, and the number of correct object mappings.
Analytic reproducibility in articles receiving open data badges at the journal Psychological Science: an observational study

For any scientific report, repeating the original analyses upon the original data should yield the original outcomes. We evaluated analytic reproducibility in 25 Psychological Science articles awarded open data badges between 2014 and 2015. Initially, 16 (64%, 95% confidence interval [43,81]) articles contained at least one ‘major numerical discrepancy' (>10% difference) prompting us to request input from original authors. Ultimately, target values were reproducible without author involvement for 9 (36% [20,59]) articles; reproducible with author involvement for 6 (24% [8,47]) articles; not fully reproducible with no substantive author response for 3 (12% [0,35]) articles; and not fully reproducible despite author involvement for 7 (28% [12,51]) articles. Overall, 37 major numerical discrepancies remained out of 789 checked values (5% [3,6]), but original conclusions did not appear affected. Non-reproducibility was primarily caused by unclear reporting of analytic procedures. These results highlight that open data alone is not sufficient to ensure analytic reproducibility.
FACTS2: Extended simulation framework for ATM communication demand analysis of Europe

The modernization of the European air transportation system will increase the need for data communication. This creates the need for large-scale simulations to ascertain that new aeronautical communication systems fulfil the requirements of future air traffic management communication. The German Aerospace Center (DLR) created a framework for such simulations based on service-oriented software architecture. The Framework for Aeronautical Communications and Traffic Simulations 2 (FACTS2) is based on the concept of service oriented simulation: Creating complex simulations from simple software building blocks called “services”. In this paper we describe the extension of the FACTS2 simulation framework by a communication load simulation suitable to feed aeronautical communication technology evaluations. We create a simulation toolchain from existing and new FACTS2 simulation services and interconnect them using XML and the UNIX pipe interface. We demonstrate our method with the air traffic management communication demand analysis of the continental European airspace. Our results indicate that the expected air traffic management communication load for the ESRA08 area is below 3,000 kbps in the ground-to-air direction and less than 1,000 kbps in the air-to ground direction until 2035.
Forecasting Value-at-Risk under Temporal and Portfolio Aggregation*

We examine the impact of temporal and portfolio aggregation on the quality of Value-at-Risk (VaR) forecasts over a horizon of 10 trading days for a well-diversified portfolio of stocks, bonds and alternative investments. The VaR forecasts are constructed based on daily, weekly, or biweekly returns of all constituent assets separately, gathered into portfolios based on asset class, or into a single portfolio. We compare the impact of aggregation with that of choosing a model for the conditional volatilities and correlations, the distribution for the innovations, and the method of forecast construction. We find that the level of temporal aggregation is most important. Daily returns form the best basis for VaR forecasts. Modeling the portfolio at the asset or asset class level works better than complete portfolio aggregation, but differences are smaller. The differences from the model, distribution, and forecast choices are also smaller compared with temporal aggregation.
Early-Onset Basal Cell Carcinoma in Young Female Endurance Athletes: A Report of Three Cases

Basal cell carcinomas (BCC) are the most prevalent cancer in the United States. While the vast majority of BCCs afflict those older than 55, recent data point to a rise in the incidence of sporadic BCCs in young Caucasian women less than 40 years of age. To date, cigarette smoking, tanning bed use, and a history of blistering sunburns have been linked to this trend. 1,2 However, the potential role of intense, intermittent outdoor ultraviolet radiation (UVR) exposure as acquired through outdoor athletics has yet to be examined.
Synthetic Development of the Tri- and Pentamethine Cyanine Chromophore for Biomolecular Interactions

The synthetic methodology of triand pentamethine carbocyanines and their interactions with biomolecules will be discussed in two chapters. The first chapter describes the preparation of halogenated carbocyanine dyes that display multiple charges; furthermore, these particular compounds were examined for their ability to bind G-quadruplex DNA with selectivity over duplex DNA and have potential for developing novel chemotherapeutic agents. The second section discusses the synthetic methods utilized to prepare trimethine cyanine fluorophores. This chapter will show how varying the N-indolenyl substituients’ hydrophobicity from ethyl to phenylpropyl influences the binding to Human Serum Albumin (HSA); additionally, alternating the terminal heterocyclic moieties of the cyanine dye has a direct quantitative effect on the biomolecular interaction. These identical compounds were recognized to be structurally analogous to agents that commonly interact with Protein Arginine Methyl Transferase (PRMT) and these compounds display low IC50 values toward inhibition of PRMT1 with unique NIR imaging properties. INDEX WORDS: Carbocyanine, Synthesis, Near-infrared, Chromophore, Dye, Halogenated, Tricationic, Fluorescence, G-quadruplex, Human serum albumin, Protein arginine methyl transferase SYNTHETIC DEVELOPMENT OF THE TRIAND PENTAMETHINE CYANINE CHROMOPHORE FOR BIOMOLECULAR INTERACTIONS
Mitochondrial dysfunction and pancreatic islet β-cell failure (Review)

Pancreatic β-cells are the only source of insulin in humans. Mitochondria uses pyruvate to produce ATP as an intermediate link between glucose intake and insulin secretion in β-cells, in a process known as glucose-stimulated insulin secretion (GSIS). Previous studies have demonstrated that GSIS is negatively regulated by various factors in the mitochondria, including tRNALeu mutations, high p58 expression, reduced nicotinamide nucleotide transhydrogenase activity, abnormal levels of uncoupling proteins and reduced expression levels of transcription factors A, B1 and B2. Additionally, oxidative stress damages mitochondria and impairs antioxidant defense mechanisms, leading to the increased production of reactive oxygen species, which induces β-cell dysfunction. Inflammation in islets can also damage β-cell physiology. Inflammatory cytokines trigger the release of cytochrome c from the mitochondria via the NF-κB pathway. The present review examined the potential factors underlying mitochondrial dysfunction and their association with islet β-cell failure, which may offer novel insights regarding future strategies for the preservation of mitochondrial function and enhancement of antioxidant activity for individuals with diabetes mellitus.
Effect of magnesium sulfate combined with labetalol on serum sFlt-1/PlGF ratio in patients with early-onset severe pre-eclampsia

The aim of the present study was to investigate the therapeutic effect of magnesium sulfate combined with labetalol on the early-onset severe pre-eclampsia (ES-PE) and explore the role of soluble fms-like tyrosine kinase-1 (sFlT-1), placental growth factor (PlGF), and sFlt-1/PlGF ratio in the treatment. A total of 164 ES-PE patients admitted to the Maternity and Child Health Care Hospital of Hubei (Wuhan, China) were assigned to this observational study. Among them, 83 patients were enrolled in group A and treated with magnesium sulfate combined with labetalol hydrochloride, and 81 patients were enrolled in group B and treated with magnesium sulfate. The therapeutic effect, adverse reactions and pregnancy outcomes in the two groups were analyzed. Serum sFlt-1 and PlGF concentrations, before and after treatment, were measured by enzyme-linked immunosorbent assay (ELISA). Receiver operating characteristic (ROC) curve analysis was performed to assess the predictive value of pre-treatment serum sFlt-1/PlGF ratio for the clinical outcome. The effective rate was significantly higher in group A than that in group B. Group A presented superior pregnancy outcomes over group B. The serum sFlt-1 concentration and sFlt-1/PlGF ratio after treatment were significantly lower than those before treatment in groups A and B, whereas PlGF concentration was significantly higher after treatment in both groups. After treatment, group A had markedly lower serum sFlt-1 concentration and sFlt-1/PlGF ratio than group B, and markedly higher PlGF concentration than group B. The area under curve (AUC) of serum sFlt-1/PlGF ratio before treatment for the prediction of the clinical efficacy was 0.737. In conclusion, magnesium sulfate combined with labetalol could be effectively used for the treatment of ES-PE. The results of ELISA revealed that the balance of sFlT-1 and PlGF was improved after treatment and the sFlT-1/PlGF ratio was decreased. The assessment of sFlt-1/PlGF ratio before treatment was shown to have a certain predictive value for the efficacy of ES-PE treatment.