Miracle-dz/UniFAR-FTUs huggingface dataset:
The micromagnetic modeling and simulation kit MS for the simulation of the dynamic response of ferromagnets to electric currents

Microand nanostructured ferromagnetic materials are actively studied as they offer a variety of applications for microelectronics, hard disks and main memory devices. The widely accepted standard model to describe ferromagnetic systems in this regime is the micromagnetic model [1]. Recently, the interaction of electric currents with the local magnetization in a ferromagnet by transfer of spin momentum have become a focus in academic and industrial research. Hence it has become necessary to extend the micromagnetic model by current-dependent terms, known as the spin-transfer torque extensions. This work presents the micromagnetic modeling and simulation kit MS, which implements the basic micromagnetic model as well as the spin-transfer torque extensions for multilayer systems based on Slonczewski [2, 3] and the spin-transfer torque extension for continuously varying magnetization based on Zhang and Li [4]. The architecture of the MS is discussed and the validity of the implementation is proven by several test problems.
The Anatomical Edutainer

Physical visualizations (i.e., data representations by means of physical objects) have been used for many centuries in medical and anatomical education. Recently, 3D printing techniques started also to emerge. Still, other medical physicalizations that rely on affordable and easy-to-find materials are limited, while smart strategies that take advantage of the optical properties of our physical world have not been thoroughly investigated. We propose the Anatomical Edutainer, a workflow to guide the easy, accessible, and affordable generation of physicalizations for tangible, interactive anatomical edutainment. The Anatomical Edutainer supports 2D printable and 3D foldable physicalizations that change their visual properties (i.e., hues of the visible spectrum) under colored lenses or colored lights, to reveal distinct anatomical structures through user interaction.
Ant Colony-based Tabu List Optimization for Minimizing the Number of Vehicles in Vehicle Routing Problem with Time Window Constraints

The Vehicle Routing Problem consists in finding a routing plan for vehicles of identical capacity to satisfy the demands of a set of customers. Time window constraints mean that customers can only be served within a pre-defined time window. Researchers have intensively studied this problem because of its wide range of applications in logistics. In this paper, we tackle the problem on an economical point of view with a focus on capital expenditure (CAPEX), where the minimization of the number of vehicles is more important than the total traveling distance. This customization finds its applications in scenarios with limited CAPEX or seasonal/temporary operations. In these cases, the CAPEX should be minimized as much as possible to reduce the overall cost of the operation, while satisfying time window constraints. We provide an Ant Colony Optimization-based Tabu List (ACOTL). We test the proposed approach on the well-known Solomon’s benchmarks. We compare experiments results to Dynamic Programming on small size instances and later to the best-known results in the literature on large size instances. ACOTL allows to reduce the number of vehicles used sometimes up to three units, compared to the best-known results, especially for instances where customers are geographically in clusters randomly distributed with vehicles of low or medium charges.
Geothermal reservoir quality prediction from diagenesis modelling

The Danish subsurface is excellent for geothermal exploitation with its km-thick sedimentary successions of locally proved geothermal applicability. However, it is a challenge to find the geothermal reservoirs with optimum combination of the highest possible temperatures and sufficiently high porosity and permeability. The best reservoirs seem to be restricted to a depth of c. 1–3 km. The geothermal gradient is c. 25–30°C/km resulting in significant mineralogical alteration and cement precipitation in the sandstones (diagenesis) with large impact on porosity and permeability in the deeper part of the basin. Therefore, it is important to make reliable predictions of the reservoir quality before selecting locations for new geothermal plants. Diagenesis modelling is here applied to make qualified pre-drill estimates of reservoir properties. Input parameters from a comprehensive database based on analyses of well cores include stratigraphic division, depositional facies, grain-size distribution, detrital mineralogy, cement types, grain coat coverage, porosity, permeability, provenance, burial history and temperature indicators. The temperature is confined by vitrinite reflectance, homogenization temperatures from fluid inclusions in quartz overgrowths and oxygen isotope composition of carbonate cements. The diagenesis model, based on the modelling tool Touchstone, is compared with generalized mechanical compaction curves from other studies and results from the modelling software SURP. These modelling software’s can calculate the forward evolution of porosity induced by individual contributions of mechanical compaction and diagenesis. The calibrated forward diagenesis modelling is used to estimate the temporal and spatial distribution of reservoir properties while applying basin modelling. Our research has identified a number of correlations that make the modelling of the sandstone reservoirs possible, when taking into account the influences of provenance and depositional environment on mineralogy, as well as the influences of mineralogy and temperature regime on diagenesis, the influence of burial depth on temperature regime and the influence of diagenesis on the reservoir properties.
Associations of depression and anxiety with substance use and social health among older adults living with HIV

Life expectancy after HIV infection dramatically increased following the development of antiretroviral therapies (ART) in 1996. As the population of older adults living with HIV increases, concerns have grown about possible disparities in mental health. We evaluated mental health outcomes, specifically depressive symptoms and anxiety, among older adults living with HIV in two large urban cities. We also examined the relationship between mental health outcomes and substance use and social health. We recruited 154 participants from infectious disease clinics in Los Angeles, CA and New Orleans, LA to complete crosssectional surveys. We assessed prevalence of depressive symptoms (CES-D-10) and anxiety (GAD-7). Bivariate analyses and multivariate linear regressions were run to examine the factors associated with depressive symptoms and anxiety. We found that 44.8% of all participants were at risk for clinical depression, 55.8% reported mild to severe levels of anxiety, and 40.0% met criteria for both. In multivariate analyses, greater depressive symptoms and anxiety were associated with greater perceived HIV-related stigma and smaller social networks. Depressive symptoms were additionally associated with younger age. Substance use was not associated with depressive symptoms or anxiety. Findings suggest older adults living with HIV may be at high risk for depression and anxiety and that the presence of these conditions may also be linked to poorer social health indicators. This highlights the importance of screening for and addressing depression and anxiety among older adults with HIV.
Generalized hydrodynamics of a class of integrable quantum field theories with non-diagonal scattering

In questo lavoro di tesi abbiamo analizzato alcuni modelli conformi con perturbazioni integrabili, in particolare il modello di Ising tri-critico e i successivi modelli minimali. Abbiamo costruito un protocollo che realizza questi modelli in un regime fuori dall’equilibrio termodinamico. Questo sistema è stato ottenuto connettendo due sistemi semi-infiniti termalizzati a due diverse temperature. In tempi e spazi grandi ci si aspetta che questo sistema evolva verso uno stato stazionario indipendente dal tempo. Le quantità fisiche di nostro interesse sono le correnti stazionarie generate in tale situazione. Per studiare questo sistema abbiamo utilizzato strumenti di integrabilità come il Bethe ansatz termodinamico, concetti di idrodinamica generalizzata e l’insieme di Gibbs generalizzato. Finora questo schema è stato formulato per le teorie di campo con un’interazione tra le particelle data da una matrice S diagonale, ovvero per i modelli con lo spettro di quasi-particelle prive di gradi di libertà interni. In questa tesi abbiamo proposto un’estensione di questo metodo a un modello dotato di uno spettro contenente quasi-particelle organizzate in multipletti di simmetrie e quindi dotate di gradi di libertà interni detti magnoni con processi d’urto descritti da matrici S non diagonali. Abbiamo quindi risolto numericamente le equazioni differenziali che descrivono il sistema di non equilibrio e abbiamo discusso questi risultati.
Confirming the Generalizability of a Chain-Based Animacy Detector

Animacy is the characteristic of a referent being able to independently carry out actions in a story world (e.g., movement, communication). It is a necessary property of characters in stories, and so detecting animacy is an important step in automatic story understanding; it is also potentially useful for many other natural language processing tasks such as word sense disambiguation, coreference resolution, character identiﬁcation, and semantic role labeling. Recent work by Jahan et al. [2018] demonstrated a new approach to detecting animacy where animacy is considered a direct property of coreference chains (and referring expressions) rather than words. In Jahan et al. , they combined hand-built rules and machine learning (ML) to identify the an-imacy of referring expressions and used majority voting to assign the animacy of coreference chains, and reported high performance of up to 0.90 F 1 . In this short report we verify that the approach generalizes to two different corpora (OntoNotes and the Corpus of English Novels) and we conﬁrmed that the hybrid model performs best, with the rule-based model in second place. Our tests apply the animacy classiﬁer to almost twice as much data as Jahan et al. ’s initial study. Our results also strongly suggest, as would be expected, the dependence of the models on coreference chain quality. We release our data and code to enable reproducibility.
Generalized Points-to Graphs: A Precise and Scalable Abstraction for Points-to Analysis

Computing precise (fully ﬂow- and context-sensitive) and exhaustive (as against demand-driven) points-to information is known to be expensive. Top-down approaches require repeated analysis of a procedure for separate contexts. Bottom-up approaches need to model unknown pointees accessed indirectly through pointers that may be deﬁned in the callers and hence do not scale while preserving precision. Hence, most approaches to precise points-to analysis begin with a scalable but imprecise method and then seek to increase its precision. We take the opposite approach in that we begin with a precise method and increase its scalability. In a nutshell, we create naive but possibly non-scalable procedure summaries and then use novel optimizations to compact them while retaining their soundness and precision. For this purpose,we proposea novel abstraction calledthe Generalized Points-to Graph (GPG) which views points-to relations as memory updates and generalizes them using the counts of indirection levels leaving the unknown pointees implicit. This allows us to construct GPGs as compact representations of bottom-up procedure summaries in terms of memory updates and control ﬂow between them. Their compactness is ensured by strength reduction (which reduces the indirection levels), control ﬂow minimization (which removes control ﬂow edges while preserving soundness and precision), and call inlining (which enhances the opportunities of these optimizations). The eﬀectiveness of GPGs lies in the fact that they discard as much control ﬂow as possible without losing precision . This is the reason why the GPGs are very small even for main procedures that contain the eﬀect of the entire program. This allows our implementation to scale to 158kLoC for C programs. At a more general level, GPGs provide a convenient abstraction to represent and transform memory in the presence of pointers. Future investigations can try to combine it with other abstractions for static analyses that can beneﬁt from points-to information. ; Software veriﬁcation and validation
The diet of Bonelli's Eagle Hieraaetus fasciatus in Cyprus

The feeding habits of 14 Bonelli's Eagle pairs nesting in Calabrian Pine forest in Cyprus during 1999-2001 is described. In total, 1734 prey items were identified from 612 pellets and 528 prey remai collected during the breeding period. Birds were found to be the main prey category, followed by mammals and reptiles. Birds always formed >50% of the diet over the years and breeding stages. However, the proportion of the prey species in the diet varied seasonally with Chukar predominating in all stages, while Columbidae formed an increasing proportion in the pre-laying and incubation stages (November to February). Mammals (mainly Black Rati) formed a constant proportion (>30%) of the diet during the breeding period, whilst reptiles (mainly Slingtailed Agama) increased in importance during the period of brooding, both in pellets and prey remains analysed. Bonelli's Eagle seems to be an opportunistic predator, which may be explained by the seasonal variation of food availability in the island of Cyprus. Its influence on game species also is discussed.
Monocular Depth Estimation using Adversarial Training A THESIS SUBMITTED TO THE FACULTY OF THE GRADUATE SCHOOL OF THE UNIVERSITY OF MINNESOTA BY Pallavi Mitra IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE

Monocular depth estimation is a fundamentally challenging problem in Computer Vision and a widely researched topic. It is particularly useful for Robotics applications where design constraints prohibit the use of multiple cameras. Monocular depth estimation finds widespread use in autonomous driving applications. Since the task is to estimate the depth associated with each pixel from a single image, rather than two or more, a global perspective of the scene is required to estimate it correctly. Various pixel-wise losses like reconstruction loss, left-right consistency loss, disparity smoothness loss, etc., capture the local scene information. However, they do not take into account the global consistency of the scene. Generative Adversarial Networks(GANs) are highly effective in capturing the global structure of the scene and producing real-looking images, so they have the potential of depth estimation from a single image. This work focuses on using adversarial training for a supervised monocular depth estimation task in combination with various pixel-wise losses. Some variants of GANs like Vanilla GAN, LSGAN, Wasserstein GAN, etc. have been explored and their associated advantages and drawbacks for the training have been presented. We observe that with minimal depth-supervised training in both the generator and discriminator, there is a significant reduction of error in depth estimation in a number of GAN variants.
Studying Optimality Bounds for Reinforcement Learning from a KWIK Perspective

One of the key problems in Reinforcement Learning (RL) [6] is the exploration-exploitation trade-oﬀ, i.e. for an agent interacting in a unknown environment, how should the agent thrive a balance between using its current knowledge to maximize its reward (exploit) and sacriﬁcing the short-term rewards to increase information about environment in hope of increasing future reward (explore). The main motivation of this study is to explore algorithms in RL setting which can provide theoretical bounds on maintaing this balance. The main diﬀerence between RL and supervised setting is that the observations are not-i.i. d. , i.e., the behaviour of the environment changes with the actions of the learner and thus analysing these algorithms with the standard PAC style frameworks doesn’t feel right. In this paper, we’ll explore another framework for studying the RL learning task, known as Knows What It Knows (KWIK) [1]. When evaluating RL algorithms, there are three essential traits to consider: space complexity, computational complexity, and sample complexity. In this paper our focus will mostly on sample complexity (we’ll formally deﬁne sample complexity in the next section). The general theme of the paper is as follows: in Section 2 we’ll deﬁne the underlying notations of RL and markov Decision Processes (MDPs), in Section 3, we’ll summarize the KWIK framework, in Section 4 we’ll study a bound for model-based RL algorithms and in Section 5 we’ll go over a bound for model-free setting. Note that this work doesn’t contains any original material, therefore the general structure of each section is as follows: a brief summary (of the original work), important highlights and ﬁnally the my remark/opinion on that. Finally, in Section 6 we’ll conclude with some discussion and future prospects .
A mathematical programming model for location-routing problem and solve it using an efficient meta-heuristic method

Locational decisions, routing and allocation in supply chain management philosophy is undoubtedly one of the most important issues that affect the frequency of the supply chain cost reduction and customer satisfaction. Selection of suitable sites for construction of the facility from a specific location and design of distribution network in the levels of the supply chain is very important, and life. In recent years the issue of location and allocation decisions in the supply chain has been stressed. Attention to both categories of vehicle routing and placement within the urban distribution depots optimal solution to both problems can provide. This paper, a new mathematical model location routing consider a facility with multiple product types to minimize total costs related to facility location and transportation costs and maximizing population coverage of the demand has been presented. Because in the real world as well as some of the model parameters are uncertain in nature, in terms of the fuzzy model is developed. Also, genetic algorithms NSGA-II and NRGA was used to solve the proposed model and the results of each algorithm to solve the model through comparison and analysis are presented.
Towards Automated Deep Learning: Analysis of the AutoDL challenge series 2019

We present the design and results of recent competitions in Automated Deep Learning (AutoDL). In the AutoDL challenge series 2019, we organized 5 machine learning challenges: AutoCV, AutoCV2, AutoNLP, AutoSpeech and AutoDL. The ﬁrst 4 challenges concern each a speciﬁc application domain, such as computer vision, natural language processing and speech recognition. At the time of March 2020, the last challenge AutoDL is still on-going and we only present its design. 1 Some highlights of this work include: (1) a benchmark suite of baseline AutoML solutions, with emphasis on domains for which Deep Learning methods have had prior
Timbre and Affect in Electronic Dance Music Discourse

Different notions of affect are often portrayed as the singular motivating force behind and distinguishing feature of electronic dance music and culture (e.g. Gilbert & Pearson, 1999; Jasen, 2017). Especially prominent is the Deleuze-Guattari-Massumi strand of affect theory, which locates affect in the midst of things and uses it to probe ‘how the “outside” realms of the pre-/para-linguistic intersect with “lower” proximal senses’ (Seigworth & Gregg, 2010, p. 8). Ruth Leys (2011, p. 449) summarises the prized attributes of post-Deleuzean affect theorists as including ‘the nonsemantic, the nonlinear, [. ] the vital, the singular, [. ] the indeterminate, [. ] and the disruption of fixed or “conventional” meanings’, all of which privilege nonconscious bodily becomings over conscious deliberation. Such emphases on ineffability, inbetweenness and vitality also mark some recent musicological work on timbre, which conceives of timbre as ‘vital relationality’ which supersedes binarisms between the acoustic and the perceptual, the material and the ideal, and between timbre and tone (Van Elferen, 2018, p. 18). At the same time, studies of usergenerated EDM discourse (e.g. Jóri, 2020) have noted that EDM fans show a keen awareness and understanding of timbre in their music, despite the language used to express it being marked—like the timbres of EDM—by ‘nonspecificity’ (Fales, 2018, p. 25) and affectivity. In this paper, I conduct a discourse analysis of what appear to be nonspecific affective verbal descriptors used in EDM record reviews on the online record retailer Boomkat.com, in order to investigate whether the vernacular affective terms used by EDM scene participants really are nonspecific, or whether and to what extent they correlate with timbral features in the tracks they are reviewing. Boomkat’s record reviews are well-known for their inventive use of language which plays upon the tacit scene knowledge of their customers, and as such represent an interesting case study into the natural language of the EDM discourse community.
Effects of partial defoliation on the growth, ion relations and photosynthesis of Lycium chinense Mill. under salt stress

In this study, we investigated the effects of artificial defoliation on the growth and physiological response of Lycium chinense Mill. to salt stress. Our results show that partial defoliation increases the plant relative growth rate, leaf water content and dry weight-based leaf Na+ content, and reduces the fresh weight-based leaf Na+ content under salt stress. In response to defoliation, the leaf Na+/Ca2+ and Na+/Mg2+ ratios were decreased, but the K+ content remained unchanged. The contents of ROS and MDA were decreased in defoliated plants. Net The photosynthetic rate (PN), stomatal conductance (gs), electron transport rate (ETR), actual photochemical quenching (ΦPSII) and photochemical quenching (qp) were enhanced by defoliation. Together, these findings indicate that partial defoliation mitigates the salt-induced growth inhibition and physiological damage in L. chinense.
Simple Purification and Characterization of an Extracellular Dextrin Dextranase from Acetobacter capsulatum ATCC 11894

Only a few reports concerning dextrin dex tranase (DDase, EC 2.4.1.2) have been published so f ar.1-8) In 1947, Shimwell found an acetic acid bacteria grown in a ropy beer and isolated it carefully. ' In 1951, Hehre and Hamilton reported that (1) a viscous material was pro duced from dextrin as glucosyl donor by DDase from Acetobacter capsulatum ATCC 11894 or A. viscosum ATCC 11895, and (2) DDase mainly reacted with a series of malto oligosaccharides except maltose and did not react with amylose, amylopectin, glycogen, cyclodextrin, sucrose, glucose and others.2-4) But, at that time, the enzyme itself was only a cell-free extract and a high level of enzyme purification was not done. In 1992-1994, Yamamoto et al. reported the purification procedures of intracellular DDase from A. capsulatus ATCC 118945) and some of its physico chemical and enzymatic properties. 6,7) They also referred (1) that the molecular structure of dextran produced by DDase was quite different from that of Leuconostoc dextran,5) and (2) the effective production of glucosyl-steviosides by a-1,6 transglucosylation of DDase.8) Recently, we found that A. capsulatum ATCC 11894 effectively produced extracellular DDase. In this paper, we report the simple. purification