Miracle-dz/UniFAR-FTUs huggingface dataset:
Kinetic wealth-exchange model of economic growth

En esta tesis se propone un modelo cinetico de intercambio de riqueza con crecimiento economico (KWEMEG) en el que se explora el efecto del ahorro de la produccion, los im- puestos con beneficio tributario y la aversion al intercambio, sobre las distribuciones de riqueza y de dinero. Este modelo se obtiene a partir de una extension al formalismo mi- croeconomico del modelo de Chakraborti y Chakrabarti (CCM), donde las preferencias de consumo se describen mediante funciones de utilidad tipo Cobb-Douglas. Al maximizar es- tas funciones, sujetas a ciertas restricciones sobre el consumo, se obtienen reglas generales de interaccion entre agentes economicos, caracterizadas por la emergencia de un regimen conservativo, donde la riqueza total se mantiene constante en el tiempo y las interacciones entre agentes se reducen a meros intercambios monetarios; y un regimen no conservativo, en el cual la riqueza crece exponencialmente, induciendo un efecto de crecimiento economi- co a tasa constante. Como casos particulares del KWEMEG se presentan por separado en esta tesis tres nuevos modelos en el contexto de la econofisica, que extienden el CCM. Su dinamica macroscopica se estudia analiticamente en todos los casos, utilizando la ecuacion cinetica de Boltzmann; y numericamente por medio de simulaciones de Monte Carlo. Esto permite ajustar las distribuciones emergentes a densidades de probabilidad tipo gamma, y establecer relaciones analiticas para sus parametros, asi como para el indice de Gini, con el cual se estudia el nivel de desigualdad en las distribuciones. Adicionalemente, en el regimen no conservativo es posible estudiar distribuciones auto-semejantes utilizando una aproxima- cion de campo medio, que abre la puerta a la discusion sobre la posible existencia de colas de Pareto. Como un resultado general, al agregar en el modelo un efecto relacionado con el ingreso por salario, se obtiene como una propiedad emergente la Segunda ley fundamental del capitalismo, de Piketty. Las ideas propuestas en este trabajo atan algunos de los problemas de la economia moderna con el discurso tradicional de los modelos cineticos de intercambio y proponen un puente que conecta de forma efectiva la microfundamentacion basada en la maximizacion de la funcion de utilidad y los modelos no conservativos. Ambos resultados son novedosos en el contexto de la econofisica.
Quantification of Allophane from Ecuador

Allophane is a very fine-grained clay mineral which is especially common in Andosols. Its importance in soils derives from its large reactive surface area. Owing to its short-range order, allophane cannot be quantified by powder X-ray diffraction (XRD) directly. It is commonly dissolved from the soil by applying extraction methods. In the present study the standard extraction method (oxalate) was judged to be unsuitable for the quantification of allophane in a soil/clay deposit from Ecuador, probably because of the large allophane content (>60 wt.%). This standard extraction method systematically underestimated the allophane content but the weakness was less pronounced in samples with small allophane contents. In the case of allophane-rich materials, the Rietveld XRD technique, using an internal standard to determine the sum of X-ray amorphous phases, is recommended if appropriate structural models are available for the other phases present in the sample. The allophane (+imogolite) content is measured by subtracting the amount of oxalate-soluble phases (e.g. ferrihydrite). No correction would be required if oxalate-soluble Fe were incorporated in the allophane structure. The present study, however, provides no evidence for this hypothesis. Mössbauer and scanning electron microscopy investigations indicate that goethite and poorly ordered hematite are the dominant Fe minerals and occur as very fine grains (or coatings) being dispersed in the cloud-like allophane aggregates.Allophane is known to adsorb appreciable amounts of water, depending on ambient conditions. The mass fraction of the sample attributed to this mineral thus changes accordingly; the choice of a reference hydration state is, therefore, a fundamental factor in the quantification of allophane in a sample. Results from the present study revealed that (1) drying at 105ºC produced a suitable reference state, and (2) water adsorption has no effect on quantification by XRD analysis.
The Dissipation of Kinetic Energy in the Lofoten Basin Eddy

Ocean microstructure, current, and hydrography observations from June 2016 are used to characterize the turbulence structure of the Lofoten Basin eddy (LBE), a long-lived anticyclone in the Norwegian Sea. The LBE had an azimuthal peak velocity of 0.8ms 2 1 at 950-m depth and 22-km radial distance from its center and a core relative vorticity reaching 2 0.7 f ( f is the local Coriolis parameter). When contrasted to a reference station in a relativelyquiescentpartofthebasin,theLBEwassigniﬁcantlyturbulentbetween750and2000m,exceedingthe dissipation rates « in the reference station by up to two orders of magnitude. Dissipation rates were elevated particularly in the core and at the rim below the swirl velocity maximum, reaching 10 2 8 Wkg 2 1 . The sources of energy for the observed turbulence are the background shear (gradient Richardson number less than unity) and the subinertial energy trapped by the negative vorticity of the eddy. Idealized ray-tracing calculations show that the vertical and lateral changes in stratiﬁcation, shear, and vorticity allow subinertial waves to be trapped within the LBE. Spectral analysis shows increased high-wavenumber clockwise-polarized shear variance in the core and rim regions, consistent with downward-propagating near-inertial waves (vertical wavelengths of order 100m and energy levels 3 to 10 times the canonical open-ocean level). The energetic packets with a distinct downward energy propagation are typically accompanied with an increase in dissipation levels. Based on these summer observations, the
A Details of Feature Extractor Adaptation Conventional transfer learning

Conventional transfer learning chooses the feature extractor according to the pre-training accuracy (e.g., ImageNet accuracy) and uses the same one for all transfer tasks [1, 2]. However, we find this approach sub-optimal since different target tasks may need very different feature extractors, and high pre-training accuracy does not guarantee good transferability of the pre-trained weights. This is especially critical in our case where the weights are frozen. ImageNet super net w. weight sharing Skip
Communications in Mathematical Physics T-Duality : Topology Change from H-Flux

T-duality acts on circle bundles by exchanging the first Chern class with the fiberwise integral of the H -flux, as we motivate using E8 and also using S-duality. We present known and new examples including NS5-branes, nilmanifolds, lens spaces, both circle bundles over RP, and theAdS5×S5 toAdS5×CP2×S1 with backgroundH -flux of Duff, Lü and Pope. When T-duality leads to M-theory on a non-spin manifold the gravitino partition function continues to exist due to the background flux, however the known quantization condition forG4 receives a correction. In a more general context, we use correspondence spaces to implement isomorphisms on the twisted K-theories and twisted cohomology theories and to study the corresponding Grothendieck-RiemannRoch theorem. Interestingly, in the case of decomposable twists, both twisted theories admit fusion products and so are naturally rings.
Probabilistic Face Tracking From Location and Facial Identity Information

In recent times, the advancement in object detection has induced new video processing applications. This paper explores the development of the face tracking system with a probabilistic approach where face similarities and relative positions are considered. The potential applications and further enhancement includes detecting the human faces along with the behavior analysis and video surveillance at the chaotic situation such as objects overlapping. The system includes three stages - face detection and face alignment using MTCNN, face recognition using FaceNet, and face tracking after Gibbs sampling. I evaluate the full-temporal method and three other approaches - baseline, positional, and instantaneous methods.
NLP for Stock Market Prediction with Reddit Data

Reddit and the WallStreetBet subreddit has become a very hot topic on the capital market from the beginning of 2021. The discussions on these forums show the potential to influence the stock market. My project is to build a model to forecast the market movement based on the rich text data from Reddit. Specifically, I have explored sentence embedding, document embedding, CNN-based model, and sentiment analysis methods to leverage the sentence of posts & comments information for market forecasting. This project has tested and compared several types of model architectures. So far, the performance shows that the model could slightly improve performance from the naive forecasting method
Enhanced Representations for Relations by Multi-task Learning

A relation describes the relationship between a pair of entities. Relation Extraction is the process of extracting relations from free text and converting them to structured machine-readable knowledge. This process can facilitate building and extending knowledge bases, and therefore can benefit a variety of natural language processing applications such as Question Answering and Summarization. Typical relation extraction projects start by defining a relation schema: a set of mutually-exclusive relation types. Based on these definitions, all instances of these relations in a text corpus are labeled by hand, producing a dataset which can be used to train a statistical model. Labeling relations in text is difficult and time-consuming. There only exist limited relation datasets developed in this way. New applications will give rise to new schemas, so the lack of high-quality labeled data is almost inevitable for Relation Extraction. Despite limited labeled samples in relation datasets, neural net models have been shown to be more effective than traditional methods in learning feature representations with pre-trained word embeddings. In the context of representation learning, this thesis presents multi-task learning frameworks to learn enhanced representations for relations. It shows how to learn better feature representations in vi
Title : Synergistic positive feedback underlying seizure initiation

Seizure onset is a critically important brain state transition that has proved very difficult to predict accurately from recordings of brain activity. Here we show that an intermittent, optogenetic, stimulation paradigm reveals a latent change in dendritic excitability that is tightly correlated to the onset of seizure activity. Our data show how the precipitous 15 nature of the transition can be understood in terms of multiple, synergistic positive feedback mechanisms: raised intracellular Cl and extracellular K, coupled to a reduced threshold for dendritic plateau potentials, and which in turn leads to a switch to pyramidal burst firing. Notably, the stimulation paradigm also delays the evolving epileptic activity, meaning that not only can one monitor seizure risk safely, it may even have an additional 20 anti-epileptic benefit. One Sentence Summary: Rapid transitions into seizures arise from mutually accelerating feedback loops, involving changes in dendritic excitability 25
An Improved Method for Monitoring Fine Particulate Matter Mass Concentrations via Satellite Remote Sensing

Ground level monitoring of Particulate Matter (PM) is limited by spatial coverage and resolution, in spite of possessing high temporal resolution and accuracy. Atmospheric Aerosol Optical Depth (AOD), a product of space-borne remote sensing, has shown significant potential for estimating ground level PM concentrations. Several approaches have been used to improve the correlation between AOD-PM by providing corrections for the aerosol vertical profile and ground level humidity. However, the effects of the vertical profile of humidity and aerosol size on the AOD-PM relationship requires further study. In this paper, we propose a method for developing an AOD-PM_(2.5) relationship by retrieving the vertical profile of relative humidity via ground observation data and aerosol size distribution in Beijing. Moreover, a series of Hanel growth coefficients (γ) are applied to determine the specific value, which maximizes the correlation. The results show that applying our proposed method can improve the correlation from R = 0.610 to R = 0.707 for Terra and R = 0.707 to 0.752 for Aqua. The best correlations were obtained for γ = 1.2 and 1.3 for Terra and Aqua, respectively. A good correlation (R = 0.8) between ground based and MODIS based PM_(2.5) measurements, together with employing MODIS to predict true air pollution levels (65% accuracy), suggests that the vertical profile of RH derived via ground level observation and aerosol size should be considered and applied to models in future studies, which utilize satellite data for air pollution monitoring and controlling.
Planning Multimodal Exploratory Actions for Online Robot Attribute Learning

Robots frequently need to perceive object attributes, such as"red,""heavy,"and"empty,"using multimodal exploratory actions, such as"look,""lift,"and"shake. "Robot attribute learning algorithms aim to learn an observation model for each perceivable attribute given an exploratory action. Once the attribute models are learned, they can be used to identify attributes of new objects, answering questions, such as"Is this object red and empty? "Attribute learning and identification are being treated as two separate problems in the literature. In this paper, we first define a new problem called online robot attribute learning (On-RAL), where the robot works on attribute learning and attribute identification simultaneously. Then we develop an algorithm called information-theoretic reward shaping (ITRS) that actively addresses the trade-off between exploration and exploitation in On-RAL problems. ITRS was compared with competitive robot attribute learning baselines, and experimental results demonstrate ITRS' superiority in learning efficiency and identification accuracy.
Neural dSCA: demixing multimodal interaction among brain areas during naturalistic experiments

Multi-regional interaction among neuronal populations underlies the brain's processing of rich sensory information in our daily lives. Recent neuroscience and neuroimaging studies have increasingly used naturalistic stimuli and experimental design to identify such realistic sensory computation in the brain. However, existing methods for cross-areal interaction analysis with dimensionality reduction, such as reduced-rank regression and canonical correlation analysis, have limited applicability and interpretability in naturalistic settings because they usually do not appropriately 'demix' neural interactions into those associated with different types of task parameters or stimulus features (e.g., visual or audio). In this paper, we develop a new method for cross-areal interaction analysis that uses the rich task or stimulus parameters to reveal how and what types of information are shared by different neural populations. The proposed neural demixed shared component analysis combines existing dimensionality reduction methods with a practical neural network implementation of functional analysis of variance with latent variables, thereby efficiently demixing nonlinear effects of continuous and multimodal stimuli. We also propose a simplifying alternative under the assumptions of linear effects and unimodal stimuli. To demonstrate our methods, we analyzed two human neuroimaging datasets of participants watching naturalistic videos of movies and dance movements. The results demonstrate that our methods provide new insights into multi-regional interaction in the brain during naturalistic sensory inputs, which cannot be captured by conventional techniques.
Tensor Normal Training for Deep Learning Models

Despite the predominant use of first-order methods for training deep learning models, second-order methods, and in particular, natural gradient methods, remain of interest because of their potential for accelerating training through the use of curvature information. Several methods with non-diagonal preconditioning matrices, including KFAC, Shampoo, and K-BFGS, have been proposed and shown to be effective. Based on the so-called tensor normal (TN) distribution, we propose and analyze a brand new approximate natural gradient method, Tensor Normal Training (TNT), which like Shampoo, only requires knowledge of the shape of the training parameters. By approximating the probabilistically based Fisher matrix, as opposed to the empirical Fisher matrix, our method uses the block-wise covariance of the sampling based gradient as the pre-conditioning matrix. Moreover, the assumption that the sampling-based (tensor) gradient follows a TN distribution, ensures that its covariance has a Kronecker separable structure, which leads to a tractable approximation to the Fisher matrix. Consequently, TNT's memory requirements and per-iteration computational costs are only slightly higher than those for first-order methods. In our experiments, TNT exhibited superior optimization performance to state-of-the-art first-order methods, and comparable optimization performance to the state-of-the-art second-order methods KFAC and Shampoo. Moreover, TNT demonstrated its ability to generalize as well as first-order methods, while using fewer epochs.
A Comprehensive Assessment of Dialog Evaluation Metrics

Automatic evaluation metrics are a crucial component of dialog systems research. Standard language evaluation metrics are known to be ineffective for evaluating dialog. As such, recent research has proposed a number of novel, dialog-specific metrics that correlate better with human judgements. Due to the fast pace of research, many of these metrics have been assessed on different datasets and there has as yet been no time for a systematic comparison between them. To this end, this paper provides a comprehensive assessment of recently proposed dialog evaluation metrics on a number of datasets. In this paper, 23 different automatic evaluation metrics are evaluated on 10 different datasets. Furthermore, the metrics are assessed in different settings, to better qualify their respective strengths and weaknesses. This comprehensive assessment offers several takeaways pertaining to dialog evaluation metrics in general. It also suggests how to best assess evaluation metrics and indicates promising directions for future work.
Revisiting Hilbert-Schmidt Information Bottleneck for Adversarial Robustness

We investigate the HSIC (Hilbert-Schmidt independence criterion) bottleneck as a regularizer for learning an adversarially robust deep neural network classifier. In addition to the usual cross-entropy loss, we add regularization terms for every intermediate layer to ensure that the latent representations retain useful information for output prediction while reducing redundant information. We show that the HSIC bottleneck enhances robustness to adversarial attacks both theoretically and experimentally. In particular, we prove that the HSIC bottleneck regularizer reduces the sensitivity of the classifier to adversarial examples. Our experiments on multiple benchmark datasets and architectures demonstrate that incorporating an HSIC bottleneck regularizer attains competitive natural accuracy and improves adversarial robustness, both with and without adversarial examples during training. Our code and adversarially robust models are publicly available.
A Shape-Newton Approach to the Problem of Covering with Identical Balls

The problem of covering a region of the plane with a fixed number of minimum-radius identical balls is studied in the present work. An explicit construction of bi-Lipschitz mappings is provided to model small perturbations of the union of balls. This allows us to obtain analytical expressions for first- and second-order derivatives using nonsmooth shape optimization techniques under appropriate regularity assumptions. Singular cases are also studied using asymptotic analysis. For the case of regions given by the union of disjoint convex polygons, algorithms based on Voronoi diagrams that do not rely on approximations are given to compute the derivatives. Extensive numerical experiments illustrate the capabilities and limitations of the introduced approach.
Real-Time Cognitive Evaluation of Online Learners through Automatically Generated Questions

With the increased adoption of E-learning platforms, keeping online learners engaged throughout a lesson is challenging. One approach to tackle this challenge is to probe learners periodically by asking questions. The paper presents an approach to generate questions from a given video lecture automatically. The generated questions are aimed to evaluate learners' lower-level cognitive abilities. The approach automatically extracts text from video lectures to generate wh-kinds of questions. When learners respond with an answer, the proposed approach further evaluates the response and provides feedback. Besides enhancing learner's engagement, this approach's main benefits are that it frees instructors from designing questions to check the comprehension of a topic. Thus, instructors can spend this time productively on other activities.
Making CNNs Interpretable by Building Dynamic Sequential Decision Forests with Top-down Hierarchy Learning

In this paper, we propose a generic model transfer scheme to make Convlutional Neural Networks (CNNs) interpretable, while maintaining their high classification accuracy. We achieve this by building a differentiable decision forest on top of CNNs, which enjoys two characteristics: 1) During training, the tree hierarchies of the forest are learned in a top-down manner under the guidance from the category semantics embedded in the pre-trained CNN weights; 2) During inference, a single decision tree is dynamically selected from the forest for each input sample, enabling the transferred model to make sequential decisions corresponding to the attributes shared by semantically-similar categories, rather than directly performing flat classification. We name the transferred model deep Dynamic Sequential Decision Forest (dDSDF). Experimental results show that dDSDF not only achieves higher classification accuracy than its conuterpart, i.e., the original CNN, but has much better interpretability, as qualitatively it has plausible hierarchies and quantitatively it leads to more precise saliency maps.