Miracle-dz/UniFAR-FTUs huggingface dataset:
Un sous-groupe $p$-divisible de la jacobienne de $X_1(N p_r)$ comme module sur l’algèbre de Hecke

— In this paper, we détermine thé structure of a certain /? -di visible group of thé jacobian of X^ (N ff} as a module ovcr thé Hecke algebra, under rathcr weak hypothèses. This will allows us to generalize in a forthcoming paper some rcsults of Hida [7] on />-adic interpolation of spécial values of L séries. Soit E) le demi-plan de Poincaré muni de l'action habituelle de 5L^(Z) et soit b^bUP^Q), le demi-plan complété. L'élément de P^VQ est noté oo. Pour chaque entier Af^ l , on note T^(M) le sous-groupe de SL^(Z) constitué des matrices ( ) avec a=. d=\ module M et c==0 \c d ) modulo M. On peut munir l'espace quotient F, (M)\y d'une structure de surface de Riemann compacte connexe. Son corps des fonctions admet un modèle défini sur Q composé des fonctions invariantes par F, (M) dont le ^-développement au point oo est à coefficients rationnels. On note X^ (M) la courbe algébrique définie sur Q projective lisse géométriquement connexe associée à ce corps de fonctions. On note (p la projection de b* sur X^ (Af)(C). Par construction (p(oo) est rationnel sur Q. On fixe désormais un entier N^1 et p un nombre premier ^5 ne divisant pas N. Pour chaque r^ 1, on note en abrégé X^=X^ (N y^). 0 Texte reçu le 10 février 1986, révisé le 16 février 1987 S. TILOUINE, Mathématiques, Bat. n 425. Universilé Pans-Sud. 91405 Orsay Cedex. BULLETIN DE LA SOCIÉTÉ MATHÉMATIQUE DE FRANCE 0037-9484 1987/03 329 32/Î5. 20 <£) Gauthicr-Villars
The square kilometer array (SKA) antenna

One of the major approaches to satisfying future goals for radio astronomy is a wide bandwidth receiving array that forms a radio telescope with one square kilometer of collecting area. The array will operate over a decade of frequency, approximately 200 MHz to 2000 MHz, it will provide polarimetric information for at least 100 simultaneous beams, and it will form interferometric images with 0.1 arcsec angular resolution at 1.4 GHz. Such an array will be comprised of approximately 10/sup 8/ wide bandwidth elements and will require calibration, digital beamforming, and adaptive nulling. This paper describes the radiating aperture design that is currently employed for the demonstration system. It is likely that the final array design will resemble this demonstration aperture, which utilizes wide bandwidth Vivaldi tapered slot antennas for the individual elements. Design information and results for prototypes and the demonstrator are presented.
Rethinking Attentive Object Detection via Neural Attention Learning

Visual attention advances object detection by attending neural networks to object representations. While existing methods incorporate empirical modules to empower network attention, we rethink attentive object detection from the network learning perspective in this work. We propose a NEural Attention Learning approach (NEAL) which consists of two parts. During the back-propagation of each training iteration, we first calculate the partial derivatives (a.k.a. the accumulated gradients) of the classification output with respect to the input features. We refine these partial derivatives to obtain attention response maps whose elements reflect the contributions to the final network predictions. Then, we formulate the attention response maps as extra objective functions, which are combined together with the original detection loss to train detectors in an end-to-end manner. In this way, we succeed in learning an attentive CNN model without introducing additional network structures. We apply NEAL to the two-stage object detection frameworks, which are usually composed of a CNN feature backbone, a region proposal network (RPN), and a classifier. We show that the proposed NEAL not only helps the RPN attend to objects but also enables the classifier to pay more attention to the premier positive samples. To this end, the localization (proposal generation) and classification mutually benefit from each other in our proposed method. Extensive experiments on large-scale benchmark datasets, including MS COCO 2017 and Pascal VOC 2012, demonstrate that the proposed NEAL algorithm advances the two-stage object detector over state-of-the-art approaches.
Learning Hidden Markov Models using

Abstract—The Baum-Welsh algorithm together with its deriva-tives and variations has been the main technique for learningHidden Markov Models (HMM) from observational data. Wepresent an HMM learning algorithm based on the non-negativematrix factorization (NMF) of higher order Markovian statisticsthat is structurally different from the Baum-Welsh and its a sso-ciated approaches. The described algorithm supports estimationof the number of recurrent states of an HMM and iterates thenon-negative matrix factorization (NMF) algorithm to improvethe learned HMM parameters. Numerical examples are providedas well.Index Terms—Hidden Markov Models, machine learning, non-negative matrix factorization. I. I NTRODUCTION Hidden Markov Models (HMM) have been successfullyused to model stochastic systems arising in a variety of appli-cations ranging from biology to engineering to ﬁnance [1], [ 2],[3], [4], [5], [6]. Following accepted notation for representingthe parameters and structure of HMM’s (see [7], [8], [9], [1],[10] for example), we will use the following terminology anddeﬁnitions:1) N is the number of states of the Markov chain underly-ing the HMM. The state space is S = {S
Optical inspection algorithm for dust defect of compact camera module

Dust is one of the most critical issues in assembly of Compact Camera Module (CCM) for mobile phones. Defect due to dust entry or dust deposit severely degrades image quality. There have been lots of literatures about the compensating of dust defect on images by image processing, but the discussion about where the dust locates is still deficient. Dust may sneak in the CCM in any step of packaging process, so the analysis of the dust location may be useful for improving of the production line. This work develops an optical inspection algorithm to detect the location of dust inside CCM based on imaging optics. A planar light source with uniformly emission is designed as the capture target. A series of defocused images are then taken and analyzed. According to the dependence of the image defect on the capture distance, the location of the dust can be well defined. This inspection algorithm provides an easy and efficient way to help manufacturers improve their packaging process.
Tied Survival Times In Survival Analysis

Survival analysis is generally defined as a set of methods for analyzing data where the outcome variable is the time until the occurrence of an event of interest. One of the methods commonly used in the survival analysis is Cox regresion model which is used to determine the factors that impact on survival times. Cox regression model has assumptions. One of them is proportional hazards assumption and the another one is there is no tied data between event times. However, in real applications, tied event times are commonly observed and Cox’s partial likelihood function needs to be modified to handle ties. It is well known methods that the Exact method, Breslow method, Efron method and Discrete method for handling tied event times. Firstly, the methods are analysed during the study, Breslow, Efron and Exact methods, which is applied on a stomach canser data set (there is tied data between event times) It was decided that Cox regression with Exact Method is the best model. Than this methods is applied Acute Myocardial Infarction data set which has no tied data between event times and it is found the same resuts at all methods.
Acoustics of palilalia: A case study

Palilalia is a rather rare speech disorder associated with a variety of etiologies including Parkinson's disease, stroke, epilepsy, and others. According to the literature, palilalia is characterized by repetitions of words, phrases, and/or sentences produced with an increasing rate and decreasing intensity. Because of the rare nature of this disorder, literature involving the characteristics is limited and somewhat contradictive. Although the original definition involves increasing rate and decreasing intensity, some researchers (i.e., Kent & Lapointe, 1982) have found different characteristics such as decreasing rate and increasing intensity when examining individual cases. This study adds to the literature regarding characteristics of palilalic speech in terms of change in rate and fundamental frequency from the original utterance to the first repeated train. This study utilized a single-subject design involving a 42 year old male who suffered an anoxic brain injury. Spontaneous speech samples were col.
Big Data of the Cosmic Web

Abstract One of the main goals in cosmology is to understand how the Universe evolves, how it forms structures, why it expands, and what is the nature of dark matter and dark energy. Next decade large and expensive observational projects will bring information on the structure and the distribution of many millions of galaxies at different redshifts enabling us to make great progress in answering these questions. However, these data require a very special and complex set of analysis tools to extract the maximum valuable information. Statistical inference techniques are being developed, bridging the gaps between theory, simulations, and observations. In particular, we discuss the efforts to address the question: What is the underlying nonlinear matter distribution and dynamics at any cosmic time corresponding to a set of observed galaxies in redshift space? An accurate reconstruction of the initial conditions encodes the full phase-space information at any later cosmic time (given a particular structure formation model and a set of cosmological parameters). We present advances to solve this problem in a self-consistent way with Big Data techniques of the Cosmic Web.
Statistical model for diffusion-mediated recovery of dislocation and point-defect microstructure

The evolution of the defect microstructure in materials at high temperature is dominated by diffusion-mediated interactions between dislocations, cavities, and surfaces. This gives rise to complex nonlinear couplings between interstitial and vacancy-type dislocation loops, cavities, and the field of diffusing vacancies that adiabatically follows the evolution of microstructure. In our previous work, we developed a nonlocal model for the climb of curved dislocations and the morphological evolution of cavities during postirradiation annealing of structural components in nuclear reactors. We now expand the formalism to include the treatment of population of very small defects and dislocation loops that are below the experimental detection limit. These are taken into account through a mean field approach coupled with an explicit real-space treatment of larger-scale discrete defect clusters. We find that randomly distributed small defects screen diffusive interactions between larger discrete clusters, renormalizing the free diffusion Green’s functions and transforming them into Yukawa-type propagators. The evolution of the coupled system is modelled self-consistently, showing how the defect microstructure evolves through a nonmonotonic variation of the distribution of sizes of dislocation loops and cavities, treated as discrete real-space objects.
Mesh Density Adaptation for Template-based Shape Reconstruction

In 3D shape reconstruction based on template mesh deformation, a regularization, such as smoothness energy, is employed to guide the reconstruction into a desirable direction. In this paper, we highlight an often overlooked property in the regularization: the vertex density in the mesh. Without careful control on the density, the reconstruction may suffer from under-sampling of vertices near shape details. We propose a novel mesh density adaptation method to resolve the under-sampling problem. Our mesh density adaptation energy increases the density of vertices near complex structures via deformation to help reconstruction of shape details. We demonstrate the usability and performance of mesh density adaptation with two tasks, inverse rendering and non-rigid surface registration. Our method produces more accurate reconstruction results compared to the cases without mesh density adaptation. Our code is available at https://github.com/ycjungSubhuman/density-adaptation.
CSA Based Radix-4 Gemmini Systolic Array for Machine Learning Applications

Systolic arrays are becoming the backbone of machine learning accelerators due to high computational parallelism and data re-usability. This paper presents a novel fully factored systolic array architecture: it extracts out the booth encoding logic which is common across rows and carry propagate adder which is common across columns of the array. It results in significant reduction in area, power and delay. We have demonstrated the proposed scheme in open source Gemmini systolic array. The proposed systolic array fully integrates into the Gemmini accelerator without effecting it’s functionality. The proposed systolic array architecture achieves up to 54%, 8.9% and 42.5% reduction in area, delay and power respectively, as compared to baseline Gemmini systolic array. The ADP and PDP of the proposed SA improves by 58.2% and 47.6% respectively.
Evaluation of Users’ Participation in Value Co-creation of Open Government Data Platform

During the covid-19 epidemic, open government data (OGD) has become the focus of the world, and users’ participation in value co-creation of open government data platform(OGDP) will help to release the value of data. The purpose of this paper is to evaluate the outcome of users’ participation in value co-creation of OGDP, then it follows analytic hierarchy process (AHP) to construct a hierarchical model from three perspectives, government to users, users to government, users to users, which totally includes 7 dimensions and 22 criteria. TOPSIS is used to rank effect of users’ participation in value co-creation of 4 OGDPs. Finally, it puts forward some suggestions for open government data platform, such as data reuse, promoting cross-border cooperation of government data, and encouraging users to become co-creators of the platform.
User-irrelevant Cross-domain Association Analysis for Cross-domain Recommendation with Transfer Learning

Cross-domain recommendation (CDR) is an effective approach to boost user experience and expand business. Traditional CDR methods generally rely on sharing user-relevant data between domains (e.g., user-item interaction data or user-overlap information). However, this approach is unrealistic in many practical applications, due to user data policies. Some works attempt to circumvent this limitation by leveraging other forms of overlapped data (e.g., item-, content-, or tag-overlap). However, such a solution is not always possible, especially if these forms of overlap are either non-existent or unknown. Until now, there have been limited studies focusing on the intractable CDR task without relying on sharing user-relevant data or using other types of overlap information. Transfer learning techniques can be used to mine and exploit explicit or implicit relationships between domains and then, transfer knowledge learned from one domain to another. Such techniques can prove to be efficient to address the aforementioned intractable CDR task. In this work, we first propose a user-irrelevant cross-domain (CD) association analysis method to mine the CD co-occurrence relationship among the items of different domains. Then, using the mined CD co-occurrence items, we generate transferable item embeddings across domains and realize CDR. We evaluate our proposal and confirm its effectiveness on public datasets.
Detecting Cheapfakes using Self-Query Adaptive-Context Learning

Detecting Cheapfakes often requires identifying contextual changes in media resulting from misleading captions. Cheapfake media can be created either by manipulating the content using image or video editing software, or by altering the context of an image or video through misleading claims, without relying on any software. While previous research has shown promising results, these approaches are limited to the data used during training. To overcome this limitation, we propose a Self-Query Adaptive-Context Learning method that is flexible and capable of adapting to new contexts during inference by using image search engine queries to enrich its knowledge. By verifying the context of captions based on the collected information, our approach extends knowledge in a flexible manner. Despite achieving an experimental accuracy of 59.70% on the IEEE ICME 2023 Cheapfakes Challenge dataset, our work has opened up new avenues for detecting out-of-context misuses.
A Contrarian Trading Strategy: Mean vs. Median Reversion

To optimize the total cumulative wealth, it needs to rebalance the portfolio on a period-by-period basis, using previously published portfolio values. The median and mean reversion techniques are two contrarian trading strategies. Mean reversion is a common trading strategy in portfolio theory. If it is used properly, it could outperform a benchmark. Using a strong -median estimator, median reversion explicitly predicts the next price vector. However, current mean and median reversion methods have several limitations: First, the mean reversion method does not consider noise and outliers; It suffers from estimate mistakes, resulting in suboptimal portfolios and poor performance. Second, although a median reversion method works well in the presence of noise and outliers, it also suffers the same issues when the dataset contains worthless data. As a result, the two methods may not work well on real-world datasets, which may contain both noise and worthless data. To address the issues mentioned above, we provide two methods for selecting an online portfolio. Also, we propose a hybrid reversion approach with a weighted scheme. To evaluate the effectiveness of our method, extensive experiments have been conducted on real-world datasets. It can improve performance and reduce the impact of noise and outliers.
NeRF-Texture: Texture Synthesis with Neural Radiance Fields

Texture synthesis is a fundamental problem in computer graphics that would benefit various applications. Existing methods are effective in handling 2D image textures. In contrast, many real-world textures contain meso-structure in the 3D geometry space, such as grass, leaves, and fabrics, which cannot be effectively modeled using only 2D image textures. We propose a novel texture synthesis method with Neural Radiance Fields (NeRF) to capture and synthesize textures from given multi-view images. In the proposed NeRF texture representation, a scene with fine geometric details is disentangled into the meso-structure textures and the underlying base shape. This allows textures with meso-structure to be effectively learned as latent features situated on the base shape, which are fed into a NeRF decoder trained simultaneously to represent the rich view-dependent appearance. Using this implicit representation, we can synthesize NeRF-based textures through patch matching of latent features. However, inconsistencies between the metrics of the reconstructed content space and the latent feature space may compromise the synthesis quality. To enhance matching performance, we further regularize the distribution of latent features by incorporating a clustering constraint. Experimental results and evaluations demonstrate the effectiveness of our approach.
A Determination of the Level of Adoption of eGovernment-based Conversational Systems in Nigeria

Towards reaping the benefits provided by conversational systems, conversational systems in form of chatbots have been deployed to provide different kinds of services in different countries of the world. However, government-based services to the citizens are limited. Consequently, there is a need to know the level of the adoption of this technology in Nigeria so as for the stakeholders to be guided as per the line of action to be taken in reaping the benefit of the technology being usable to bridge the digital divide prevalent in the country. This work therefore investigated the level of adoption of conversational systems - both text-based and voice-based by the various governments in Nigeria. In carrying out the investigations, the websites of the various state governments were visited and searched, for text-based and voice-based conversational systems as well as the availability of any chatting facilities. The results obtained show that even though the vast majority of the state governments have web presence, conversational systems are generally not available. Only three states have chatting facilities; two are text-based and one has a chatting interface that is really not a conversational system. None has voice-based conversational system and two states from the North Central zone have no web presence at all. Consequently, governments need to adopt the usage of conversational systems in order to make services more available in different modes for the benefits of the mases and to reach the unreached. The North central states specifically also need to fully have web presence like the others in the remaining geo-political zones.
Polynomial 2D Green Coordinates for Polygonal Cages

Cage coordinates are a powerful means to define 2D deformation fields from sparse control points. We introduce Conformal polynomial Coordinates for closed polyhedral cages, enabling segments to be transformed into polynomial curves of any order. Extending classical 2D Green coordinates, our coordinates result in conformal harmonic deformations that are cage-aware. We demonstrate the usefulness of our technique on a variety of 2D deformation scenarios where curves allow artists to perform intuitive deformations with few input parameters. Our method combines the texture preservation property of conformal deformations together with the expressiveness offered by Bezier controls.
Dissipation of environmental DDT and its metabolites in selected rivers of south-western Kenya

Rivers are the main source of domestic and industrial water supplies in Kenya. In this study, water sampling was done at eight upstream sites in the Kuja River catchment, Kenya, between October 2016 and April 2017, specifically to test for residue levels of the organochlorine pesticide, dichlorodiphenyltrichloroethane (p,p′-DDT), and its degradation products, dichlorodiphenyldichloroethylene (p,p′-DDE) and dichlorodiphenyldichloroethane (p,p′-DDD). The analysis was performed with gas chromatography coupled with electron-capture detection in an autosampler. Concentrations of these residues in the surface water samples ranged from below detection limit (BDL) to 1.1113 μg l-1 in the wet season, and from BDL to 2.007 μg l-1 in the dry season. Overall, the highest mean residue concentration was for p,p′-DDT (0.9669 ± 0.2994 μg l-1). The lowest mean concentration in the dry season was that of p,p′-DDE (0.1824 ± 0.0964 μg l-1), and in the wet season it was p,p′-DDD (0.0610 ± 0.0038 μg l-1). From our results it is evident that DDT is still in use and detectable in the study area, though the mean levels detected were below the acceptable thresholds for natural water as suggested by the World Health Organization (WHO). Except for a slightly higher mean spatial distribution of the metabolite p,p′-DDT, the resides were recorded as below the maximum admissible concentrations of pesticide residues in drinking water, as set out in WHO guidelines and by Kenya’s National Environment Management Authority (NEMA). Monitoring organochlorine pesticide contamination levels in Kenya’s water resources should be scaled up to leverage potential for a sustainable blue economy and in safeguarding human and environmental health.