Miracle-dz/UniFAR-FTUs huggingface dataset:
Grid computing for Monte Carlo based intensive calculations in financial derivative pricing applications

Dans cette these, nous proposons un environnement de programmation sur grille, nomme PicsouGrid, qui cible tout particulierement le pricing de contrats derives optionnels dans le domaine de la finance. Nous nous focalisons sur le probleme de la valorisation d'options de grande dimension (c. A. D. Dont le sous-jacent est un grand panier d'actifs). Cet environnement inclut des mecanismes de tolerance aux fautes, de repartition de charge, de partage dynamique de taches, de deploiement, et ainsi, cible des environnements de calcul heterogenes tels les grilles de calcul. Nous nous interessons egalement a la parallelisation d'un algorithme nomme Classification Monte Carlo (CMC), defini a l'origine par Picazo (2002), pour calculer le prix d'options de type Americain en grande dimension, et qui passe a l'echelle. Nous decrivons les resultats d'experiences obtenus avec cet algorithme parallele implemente dans PicsouGrid avec jusqu'a 100 opportunites d'exercice et jusqu'a 40 actifs sous-jacents (pour faire reference a l'indice CAC40). Nous comparons et analysons la precision numerique des prix des ces options calcules avec cet algorithme CMC, et ce en utilisant differents algorithmes de classification, comme AdaBoost, Gradient Boost et les "Support Vector Machines". Finalement, nous definissons une suite de tests de performance, dans le domaine de la finance, pour l'evaluation et l'analyse d'intergiciels de grilles de calcul. La suite de tests de performance a ete utilisee avec succes durant le "2008 SuperQuant Monte Carlo challenge - the Fifth Grid Plugtest and Contest". Dans le cadre de ce challenge, nous avons egalement developpe une API pour faciliter la mise en œuvre d'applications ProActive necessitant des simulations Monte Carlo.
Ja n 20 00 Geometric Phase , Curvature , and Extrapotentials in Constrained Quantum Systems

We derive an effective Hamiltonian for a quantum system constrained to a submanifold (the constraint manifold) of configuration space (the ambient space) by an infinite restoring force. We pay special attention to how this Hamiltonian depends on quantities which are external to the constraint manifold, such as the external curvature of the constraint manifold, the (Riemannian) curvature of the ambient space, and the constraining potential. In particular, we find the remarkable fact that the twisting of the constraining potential appears as a gauge potential in the constrained Hamiltonian. This gauge potential is an example of geometric phase, closely related to that originally discussed by Berry. The constrained Hamiltonian also contains an effective potential depending on the external curvature of the constraint manifold, the curvature of the ambient space, and the twisting of the constraining potential. The general nature of our analysis allows applications to a wide variety of problems, such as rigid molecules, the evolution of molecular systems along reaction paths, and quantum strip waveguides.
The flow of a compressible fluid past a sphere

The flow of a compressible fluid past a sphere fixed in a uniform stream is calculated to the third order of approximation by means of the Janzen-Rayleigh method. The velocity and the pressure distribution over the surface of the sphere are computed and the terms involving the fourth power of the Mach number, neglected in Rayleigh's calculation, are shown to be of considerable importance as the local velocity of sound is approached on the sphere. The critical Mach number, that is, the value of the Mach number at which the maximum velocity of the fluid past the sphere is just equal to the local velocity of sound, is calculated for both the second and the third approximation and is found to be, respectively, Mcr=0.587 and Mcr=0.573.
Flower-Like CPW-FED Monopole Antenna for Quad-Band Operation of Mobile Handsets

A novel flower-like quad-band CPW-fed monopole antenna using a dual-inverted-L loop and four branched-inverted-L strips is proposed and investigated experimentally. The antenna that occupies area of 69 × 50 mm2 is demonstrated on a FR4 substrate including the ground plane. The proposed antenna is easy to be integrated with radio-frequency/microwave circuitry for low manufacturing cost. The proposed quad-band antenna has enough bandwidth to cover the GSM (880–960 MHz), DCS (1710–1880 MHz), PCS (1850–1990 MHz), and WLAN (2400–2483.5 MHz) bands for VSWR ≤ 2. The measured peak gains are 1.44, 3.55, 3.287, and 2.7 dBi, at the GSM, DCS, PCS, and WLAN bands, respectively. Good monopole-like radiation patterns and antenna gains have also been obtained.
Mechanizing Proof Step Evaluation for Mathematics Tutoring - The Case of Granularity

The motif of this thesis is the application of deduction techniques to tutorial dialogs in mathematics. The aim is to characterize different degrees of “granularity” of proof steps, which designates their argumentative size. This aspect of mathematical proof steps, also referred to as grain size, is still under-investigated in the theorem proving community. However, the ability to reflect on the granularity of proof steps is important for teaching mathematics. We propose an approach towards the automatization of the granularity assessment for a given proof fragment, and perform a first analysis on an empirically collected sample of mathematical dialogs from a Wizard-of-Oz study that is also part of this thesis. We show how such an analysis is performed with a first manifest hypothesis on how a granularity measure is obtained. This hypothesis relates the granularity level of a mathematical statement to the number of inference steps required for its justification, which is tested for justifications in two different natural deduction calculi. The outcome does not provide sufficient evidence for this first hypothesis. However, it clarifies its deficiencies.
Random Walks on Graphs

We study random walks on finite graphs. The reader is introduced to general Markov chains before we move on more specifically to random walks on graphs. A random walk on a graph is just a Markov chain that is time-reversible. The main parameters we study are the hitting time, commute time and cover time. We find novel formulas for the cover time of the subdivided star graph and broom graph before looking at the trees with extremal cover times. Lastly we look at a connection between random walks on graphs and electrical networks, where the hitting time between two vertices of a graph is expressed in terms of a weighted sum of effective resistances. This expression in turn proves useful when we study the cover cost, a parameter related to the cover time. ii Stellenbosch University http://scholar.sun.ac.za
Testing high-dimensional mean vector with applications. A normal reference approach.

Summary: A centered L 2 -norm based test statistic is used for testing if a high-dimensional mean vector equals zero where the data dimension may be much larger than the sample size. Inspired by the fact that under some regularity conditions the asymptotic null distributions of the proposed test are the same as the limiting distributions of a chi-square-mixture, a three-cumulant matched chi-square-approximation is suggested to approximate this null distribution. The asymptotic power of the proposed test under a local alternative is established and the eﬀect of data non-normality is discussed. A simulation study under various settings demonstrates that in terms of size control, the proposed test performs signiﬁcantly better than some existing competitors. Several real data examples are presented to illustrate the wide applicability of the proposed test to a variety of high-dimensional data analysis problems, including the one-sample problem, paired two-sample problem, and MANOVA for correlated samples or independent samples.
Preentangling Quantum Algorithms -- the Density Matrix Renormalization Group-assisted Quantum Canonical Transformation

We propose the use of parameter-free preentanglers as initial states for quantum algorithms. We apply this idea to the electronic structure problem, combining a quantized version of the Canonical Transformation by Yanai and Chan [J. Chem. Phys. 124, 194106 (2006)] with the Complete Active Space Density Matrix Renormalization Group. This new ansatz allows to shift the computational burden between the quantum and the classical processor. In the vicinity of multi-reference points in the potential energy surfaces of H 2 O, N 2 , BeH 2 and the P4 system, we ﬁnd this strategy to require signiﬁcantly less parameters than corresponding generalized unitary coupled cluster circuits. We propose a new algorithm to prepare Matrix Product States based on the Linear Combination of Unitaries and compare it to the Sequential Unitary Algorithm proposed by Ran in [Phys. Rev. A 101, 032310 (2020)].
Different Degrees of Nitrogen and Carbon Depletion in the Warm Molecular Layers of Protoplanetary Disks

Observations have revealed that the elemental abundances of carbon and oxygen in the warm molecular layers of some protoplanetary disks are depleted compared to those in the interstellar medium by a factor of ∼10–100. Meanwhile, little is known about nitrogen. To investigate the time evolution of nitrogen, carbon, and oxygen elemental abundances in disks, we develop a one-dimensional plane-parallel model that incorporates dust settling, turbulent diffusion of dust and ices, as well as gas-ice chemistry including the chemistry driven by stellar UV/X-rays and galactic cosmic rays. We find that gaseous CO in the warm molecular layer is converted to CO2 ice and locked up near the midplane via the combination of turbulent mixing (i.e., the vertical cold finger effect) and ice chemistry driven by stellar UV photons. On the other hand, gaseous N2, the main nitrogen reservoir in the warm molecular layer, is less processed by ice chemistry and exists as it is. Then, nitrogen depletion occurs solely through the vertical cold finger effect of N2. As the binding energy of N2 is lower than that of CO and CO2, the degree of nitrogen depletion is smaller than that of carbon and oxygen depletion, leading to higher elemental abundance of nitrogen than that of carbon and oxygen. This evolution occurs within 1 Myr and proceeds further, when the α parameter for the diffusion coefficient is ≳10−3. Consequently, the N2H+/CO column density ratio increases with time. How the vertical transport affects the midplane ice composition is briefly discussed.
Langevin Autoencoders for Learning Deep Latent Variable Models

Markov chain Monte Carlo (MCMC), such as Langevin dynamics, is valid for approximating intractable distributions. However, its usage is limited in the context of deep latent variable models owing to costly datapoint-wise sampling iterations and slow convergence. This paper proposes the amortized Langevin dynamics (ALD), wherein datapoint-wise MCMC iterations are entirely replaced with updates of an encoder that maps observations into latent variables. This amortization enables efficient posterior sampling without datapoint-wise iterations. Despite its efficiency, we prove that ALD is valid as an MCMC algorithm, whose Markov chain has the target posterior as a stationary distribution under mild assumptions. Based on the ALD, we also present a new deep latent variable model named the Langevin autoencoder (LAE). Interestingly, the LAE can be implemented by slightly modifying the traditional autoencoder. Using multiple synthetic datasets, we first validate that ALD can properly obtain samples from target posteriors. We also evaluate the LAE on the image generation task, and show that our LAE can outperform existing methods based on variational inference, such as the variational autoencoder, and other MCMC-based methods in terms of the test likelihood.
Graph-to-Text Generation with Dynamic Structure Pruning

Most graph-to-text works are built on the encoder-decoder framework with cross-attention mechanism. Recent studies have shown that explicitly modeling the input graph structure can significantly improve the performance. However, the vanilla structural encoder cannot capture all specialized information in a single forward pass for all decoding steps, resulting in inaccurate semantic representations. Meanwhile, the input graph is flatted as an unordered sequence in the cross attention, ignoring the original graph structure. As a result, the obtained input graph context vector in the decoder may be flawed. To address these issues, we propose a Structure-Aware Cross-Attention (SACA) mechanism to re-encode the input graph representation conditioning on the newly generated context at each decoding step in a structure aware manner. We further adapt SACA and introduce its variant Dynamic Graph Pruning (DGP) mechanism to dynamically drop irrelevant nodes in the decoding process. We achieve new state-of-the-art results on two graph-to-text datasets, LDC2020T02 and ENT-DESC, with only minor increase on computational cost.
Using Genetic Algorithms to Simulate Evolution

Evolution is the theory that plants and animals today have come from kinds that have existed in the past. Scientists such as Charles Darwin and Alfred Wallace dedicate their life to observe how species interact with their environment, grow, and change. We are able to predict future changes as well as simulate the process using genetic algorithms. Genetic Algorithms give us the opportunity to present multiple variables and parameters to an environment and change values to simulate different situations. By optimizing genetic algorithms to hold entities in an environment, we are able to assign varying characteristics such as speed, size, and cloning probability, to the entities to simulate real natural selection and evolution in a shorter period of time. Learning about how species grow and evolve allows us to find ways to improve technology, help animals going extinct to survive, and figure* out how diseases spread and possible ways of making an environment uninhabitable for them. Using data from an environment including genetic algorithms and parameters of speed, size, and cloning percentage, the ability to test several changes in the environment and observe how the species interacts within it appears. After testing different environments with a varied amount of food while keeping the number of starting population at 10 entities, it was found that an environment with a scarce amount of food was not sustainable for small and slow entities. All environments displayed an increase in speed, but the environments that were richer in food allowed for the entities to live for the entire duration of 50 generations, as well as allowed the population to grow significantly.
The Controllability and Structural Controllability of Laplacian Dynamics

In this paper, classic controllability and structural controllability under two protocols are investigated. For classic controllability, the multiplicity of eigenvalue zero of general Laplacian matrix L ∗ is shown to be determined by the sum of the numbers of zero circles, identical nodes and opposite pairs, while it is always simple for the Laplacian L with diagonal entries in absolute form. For a ﬁxed structurally balanced topology, the controllable subspace is proved to be invariant even if the antagonistic weights are selected diﬀerently under the corresponding protocol with L . For a graph expanded from a star graph rooted from a single leader, the dimension of controllable subspace is two under the protocol associated with L ∗ . In addition, the system is structurally controllable under both protocols if and only if the topology without unaccessible nodes is connected. As a reinforcing case of structural controllability, strong structural controllability requires the system to be controllable for any choice of weights. The connection between father nodes and child nodes aﬀects strong structural controllability because it determines the linear relationship of the control information from father nodes. This discovery is a major factor in es-tablishing the suﬃcient conditions on strong structural controllability for multi-agent systems under both protocols, rather than for complex networks, about latter results are already abundant.
ESSumm: Extractive Speech Summarization from Untranscribed Meeting

In this paper, we propose a novel architecture for direct extractive speech-to-speech summarization, ESSumm, which is an unsupervised model without dependence on intermediate transcribed text. Different from previous methods with text presentation, we are aimed at generating a summary directly from speech without transcription. First, a set of smaller speech segments are extracted based on speech signal's acoustic features. For each candidate speech segment, a distance-based summarization confidence score is designed for latent speech representation measure. Specifically, we leverage the off-the-shelf self-supervised convolutional neural network to extract the deep speech features from raw audio. Our approach automatically predicts the optimal sequence of speech segments that capture the key information with a target summary length. Extensive results on two well-known meeting datasets (AMI and ICSI corpora) show the effectiveness of our direct speech-based method to improve the summarization quality with untranscribed data. We also observe that our unsupervised speech-based method even performs on par with recent transcript-based summarization approaches, where extra speech recognition is required.
On the Origin of the Strong Optical Variability of Emission-line Galaxies

Emission-line galaxies (ELGs) are crucial for understanding the formation and evolution of galaxies, while little is known about their variability. Here we report on the optical variability of a sample of ELGs selected in the COSMOS field, which has narrowband observations in two epochs separated by ≳12 yr. This sample was observed with the Suprime-Cam (SC) and Hyper Suprime-Cam (HSC) on the Subaru telescope in NB816 and i′/i bands, respectively. After carefully removing the wing effect of a narrowband filter, we check the optical variability in a sample of 181 spectroscopically confirmed ELGs. We find that 0 (0/68) Hα emitters, 11.9% (5/42) [O iii] emitters, and 0 (0/71) [O ii] emitters show significant variability ( ∣ΔmNB∣≥3σΔmNB,AGN=0.20mag ) in the two-epoch narrowband observations. We investigate the presence of active galactic nuclei (AGN) in this variable ELG (var-ELG) sample with three methods, including X-ray luminosity, mid-infrared activity, and radio excess. We find zero bright AGN in this var-ELG sample but cannot rule out the contribution from faint AGN. We find that supernovae explosions (SNe) could also dominate the variability of the var-ELG sample. The merger morphology shown in the HST/F814W images of the entire var-ELG sample is in agreement with the enhancement of star formation, i.e., the SNe activity.
Vectorized Adjoint Sensitivity Method for Graph Convolutional Neural Ordinary Differential Equations

This document, as the title stated, is meant to provide a vectorized implementation of adjoint dynamics calculation for Graph Convolutional Neural Ordinary Differential Equations (GCDE). The adjoint sensitivity method is the gradient approximation method for neural ODEs that replaces the back propagation. When implemented on libraries such as PyTorch or Tensorflow, the adjoint can be calculated by autograd functions without the need for a hand-derived formula. In applications such as edge computing and in memristor crossbars, however, autograds are not available, and therefore we need a vectorized derivation of adjoint dynamics to efficiently map the system on hardware. This document will go over the basics, then move on to derive the vectorized adjoint dynamics for GCDE.
Generative Visual Prompt: Unifying Distributional Control of Pre-Trained Generative Models

Generative models (e.g., GANs, diffusion models) learn the underlying data distribution in an unsupervised manner. However, many applications of interest require sampling from a particular region of the output space or sampling evenly over a range of characteristics. For efficient sampling in these scenarios, we propose Generative Visual Prompt (PromptGen), a framework for distributional control over pre-trained generative models by incorporating knowledge of other off-the-shelf models. PromptGen defines control as energy-based models (EBMs) and samples images in a feed-forward manner by approximating the EBM with invertible neural networks, avoiding optimization at inference. Our experiments demonstrate how PromptGen can efficiently sample from several unconditional generative models (e.g., StyleGAN2, StyleNeRF, diffusion autoencoder, NVAE) in a controlled or/and de-biased manner using various off-the-shelf models: (1) with the CLIP model as control, PromptGen can sample images guided by text, (2) with image classifiers as control, PromptGen can de-bias generative models across a set of attributes or attribute combinations, and (3) with inverse graphics models as control, PromptGen can sample images of the same identity in different poses. (4) Finally, PromptGen reveals that the CLIP model shows a"reporting bias"when used as control, and PromptGen can further de-bias this controlled distribution in an iterative manner. The code is available at https://github.com/ChenWu98/Generative-Visual-Prompt.
NAAP-440 Dataset and Baseline for Neural Architecture Accuracy Prediction

Neural architecture search (NAS) has become a common approach to developing and discovering new neural architectures for different target platforms and purposes. However, scanning the search space is comprised of long training processes of many candidate architectures, which is costly in terms of computational resources and time. Regression algorithms are a common tool to predicting a candidate architecture's accuracy, which can dramatically accelerate the search procedure. We aim at proposing a new baseline that will support the development of regression algorithms that can predict an architecture's accuracy just from its scheme, or by only training it for a minimal number of epochs. Therefore, we introduce the NAAP-440 dataset of 440 neural architectures, which were trained on CIFAR10 using a fixed recipe. Our experiments indicate that by using off-the-shelf regression algorithms and running up to 10% of the training process, not only is it possible to predict an architecture's accuracy rather precisely, but that the values predicted for the architectures also maintain their accuracy order with a minimal number of monotonicity violations. This approach may serve as a powerful tool for accelerating NAS-based studies and thus dramatically increase their efficiency. The dataset and code used in the study have been made public.
ProAPT: Projection of APT Threats with Deep Reinforcement Learning

The highest level in the Endsley situation awareness model is called projection when the status of elements in the environment in the near future is predicted. In cybersecurity situation awareness, the projection for an Advanced Persistent Threat (APT) requires predicting the next step of the APT. The threats are constantly changing and becoming more complex. As supervised and unsupervised learning methods require APT datasets for projecting the next step of APTs, they are unable to identify unknown APT threats. In reinforcement learning methods, the agent interacts with the environment, and so it might project the next step of known and unknown APTs. So far, reinforcement learning has not been used to project the next step for APTs. In reinforcement learning, the agent uses the previous states and actions to approximate the best action of the current state. When the number of states and actions is abundant, the agent employs a neural network which is called deep learning to approximate the best action of each state. In this paper, we present a deep reinforcement learning system to project the next step of APTs. As there exists some relation between attack steps, we employ the Long- Short-Term Memory (LSTM) method to approximate the best action of each state. In our proposed system, based on the current situation, we project the next steps of APT threats.