Miracle-dz/UniFAR-FTUs huggingface dataset:
On topological instabilities arising in families of semilinear parabolic problems

It is known that for a broad class of one-parameter families of semilinear elliptic problems, a discontinuity in the branch of minimal solutions can be induced by arbitrarily small perturbations of the nonlinearity, when the spatial dimension is equal to one or two. In this article, we communicate, from a topological point of view, on the dynamical implications of such a sensitivity of the minimal branch, for the corresponding one-parameter family of semilinear parabolic problems. The key ingredients to do so, rely on a combination of a general continuation result from the Leray-Schauder degree theory regarding the existence of an unbounded continuum of solutions to one-parameter families of elliptic problems, and a growth property of the branch of minimal solutions to such problems. In particular, it is shown that the phase portrait of the semigroup associated with $\partial_t u -\Delta u=\lambda g(u)$, can experience a topological instability, when the function $g$ is locally perturbed. More precisely, it is shown that for all $\epsilon >0$, there exists $\widehat{g}$ such that $\|g-\widehat{g}\|_{\infty} \leq \epsilon$ ($g-\widehat{g}$ being with compact support) for which the semigroup associated with $\partial_t u -\Delta u=\lambda \widehat{g}(u)$ possesses multiple equilibria, for certain $\lambda$-values at which the semigroup associated with $\partial_t u -\Delta u=\lambda g(u)$ possesses only one equilibrium. The mechanism at the origin of such an instability is also clarified. The latter results from a local deformation of the $\lambda$-bifurcation diagram (associated with $-\Delta u=\lambda g(u)$, $u\vert_{\partial \Omega}=0$) by the creation of a multiple-point or a new fold-point on it when a small perturbation is applied. This is proved under assumptions on $g$ that prevents the use of linearization techniques.
MODELLING THE METEOROLOGICAL EFFECTS ON AIR TEMPERATURE FOR KONYA CITY IN TURKEY: THE APPROACHES OF QUANTILE REGRESSION AND QUANTILE REGRESSION NEURAL NETWORKS

In this study, we propose the use of the quantile regression and quantile regression neural networks for the relationship between monthly air temperature and various meteorological effects. Meteorological effects and air temperature differs at different points in the conditional distribution. When applied to ten years (2000-2009) of data from Konya city, results of the quantile regression and quantile regression neural networks show that the contributions of the explanatory variables to the conditional distribution of the air temperature vary significantly. Finally, computation of conditional air temperature through both of the methods for multiple regression allows the estimation of complete density distributions that can be used for forecasting next month’s air temperature under an uncertainty framework.
Challenges in anomaly and change point detection

This paper presents an introduction to the state-of-the-art in anomaly and change-point detection. On the one hand, the main concepts needed to understand the vast scientific literature on those subjects are introduced. On the other, a selection of important surveys and books, as well as two selected active research topics in the field, are presented.
The Story Of Mathematics

Mathematics and Mind: The Early Greeks and Alexandrians The Origins of Arithmetic The Origin of Abstract Mathematics: The Beginnings of Algebra From Geometry to Trigonometry Analytic Geometry: The Geometrization of Arithmetic and Algebra The Birth of the Calculus Post-Newtonian Mathematics The Golden Age of Mathematics The End of the Golden Age of Mathematics The Beginning of Modern Mathematics Modern Mathematics and the New Physics The Evolution of Modern Mathematics Whither Mathematics? Bibliography Index
Sex differences in peripheral monoamine transmitter and related hormone levels in chronic stress mice with a depression-like phenotype

Backgrounds Chronic stress could induce depression-like phenotype in animal models. Previous data showed that sex differences exist after chronic stress model establishment, however, the detailed information about the difference of blood biochemical indexes is not clear. In this study, we aim to supply comparison of monoamine transmitters and related hormone markers in serum between male and female depressed mice, and in order to better understand the sex difference in transmitters and hormone levels in depression occurrence and development. Methods Sixty C57BL/6 mice (both male and female) were divided into two groups by gender. Same gender mice were then divided randomly into the non-treated control group and chronic stress group which was exposed to 8 weeks of chronic unpredictable mild stress (CUMS). Depression-like behavior was assessed with open-field test and sucrose preference test. Blood sample was collected and monoamine transmitter and related hormone in serum were measured by ELISA. Results The depression-like phenotype mice model was established successfully after 8 weeks of chronic stress. The locomotion activity scores in male stressed mice declined more than that in female stressed mice, while the exploratory behavior scores in female stressed mice declined more than that in male stressed mice. Compared to non-treated control group mice, mice in the chronic stress group in response to stress showed greater declines in monoamine transmitters (5-HT, dopamine, norepinephrine) and sex hormones (androgen, estrogen, oxytocin and prolactin), while stress hormones (adrenaline, corticosterone and ACTH) were significantly increased. The decrease of norepinephrine, androgen and estrogen in female stressed mice was greater than in male stressed mice, whereas the 5-HT and oxytocin in male stressed mice decreased more than in female stressed mice, and the corticosterone in male stressed mice increased more than in female stressed mice. Conclusion Sex differences of monoamine transmitter and related hormone levels in serum occurred in chronic stress induced depression-like phenotype mice model. It may provide a useful reference to guide precise antidepressant treatment in different gender population in clinical care.
Agglomeration of Productive Services, Industrial Structure Upgrading and Green Total Factor Productivity: An Empirical Analysis Based on 68 Prefectural-Level-and-Above Cities in the Yellow River Basin of China

Improving green total factor productivity (GTFP) is the inherent requirement for practicing the philosophy of green development and achieving regional high-quality development. Based on panel data for 68 prefectural-level-and-above cities in the Yellow River Basin of China from 2006 to 2019, we measured their GTFPs and degrees of productive-services agglomeration using the non-radial directional distance function and industrial agglomeration index formulas, respectively. Furthermore, we empirically investigated the interactive relationship between agglomeration of productive services, industrial-structure upgrading, and GTFP using the dual fixed-effects model, the mediating-effect model, and the moderating-effect model. The findings were as follows. (1) Both specialized and diversified agglomeration of productive services significantly improved the GTFPs of cities in the Yellow River Basin, and the promoting effect of specialized agglomeration was stronger than that of diversified agglomeration. (2) The diversified agglomeration of productive services (hereinafter referred to as diversified agglomeration) made a significant contribution to GTFP in all sample cities of the Yellow River Basin, while the specialized agglomeration of productive services (hereinafter referred to as specialized agglomeration) only significantly improved GTFP in the upstream cities and had no significant effect on the midstream and downstream cities. (3) When examined according to city size, specialized agglomeration was found to have a positive impact on the GTFPs of small and medium-sized cities in the Yellow River Basin but a non-significant negative impact on large cities, while the effect of diversified agglomeration on GTFP was found not to be significant. (4) Industrial-structure upgrading played partially mediating and negative moderating roles in the process of specialized agglomeration affecting the GTFPs of cities in the Yellow River Basin, but it did not become a mediating channel and moderating factor that influenced diversified agglomeration in relation to GTFP.
Calibrated Forecasts of Extreme Windstorms Using the Extreme Forecast Index (EFI) and Shift of Tails (SOT)

AbstractThis study presents a method that improves extreme windstorm early warning in regards to past events that hit France during the last 30 years. From a 21-member ensemble forecast, the extreme forecast index (EFI) and the shift of tails (SOT) are used to produce calibrated forecasts for a selection of 59 windstorm cases. The EFI and SOT forecasts are evaluated for windstorms of different levels of severity and for various forecast index thresholds using the Heidke skill score (HSS), hit rate (HR), and false alarm rate (FA). The HR and FA show that a “zero misses” level always goes conjointly with a high level of false alarms. The HSS shows maxima that are associated with EFI (or SOT) thresholds that could be used as a rationale for decision-makers to issue warnings. For most extreme events, it is found that a higher level of HR can be achieved using the SOT rather than the EFI. Overall, most of the windstorms are well anticipated 3–4 days ahead. To facilitate the use of EFI or SOT forecasts, it is s.
Effectiveness of selenium supplementation in children with autoimmune thyroiditis

Deficiencies of Selenium (Se) is correlated with the risk and onset of autoimmune thyroid disease (AITD). The aim of this study was to determine the association between Se supplementation and AITD. Methods: Electronic data searches of 4 databases were performed. We assessed the included studies using PRISMA for protocol assurance. Five studies met our inclusion criteria and were analyzed. The predictor covariate in the present study was Se administration. The outcome measures were levels of thyroid peroxidase antibodies (TPO-Ab), thyroglobulin antibody (Tg-Ab), free thyroxine (fT4), and thyroid volume. Results: Of the five studies that met the inclusion criteria, one randomized controlled trial (RCT) was included in the qualitative review, whereas four quasi-experimental studies were included in the meta-analysis. The results showed that Se supplementation significantly reduced TPO-Ab levels (MD 90.85; 95% CI 61.71–120.00; p<0.00001) and fT4 levels (MD 1.52; 95% CI, 0.55–2.50; p=0.002), while the RCT showed that Se supplementation significantly reduced Tg-Ab levels. Conclusions: Se supplementation significantly reduces TPO-Ab and fT4 levels in children and adolescents with AITD. The limited number of studies and population sizes emergence of further studies especially RCTS are needed to make a better meta-analysis
Keunggulan Pendugaan Model Aditif dengan Pendekatan Model Linear Campuran Dibanding dengan Algoritma Backfitting

The additive model is the generalized of multiple linear regression that expresses the mean of a reponse variable as a sum of functional form of predictors. The widely used estimation of additive models described in Hastie and Tibshirani (1990) is backfitting algorithm. However, the algorithm with linear smoothers gave some difficulties when it comes to model selection and its inference. The additive model with P-spline as smooth function admits a mixed model formulation, in which variance components control the non-linearity degree in the smooth function. This research is focused in comparing of estimation additive models using backfitting algorithm and linear mixed model approach. The research results show that estimation of additive models using linear mixed models offer simplicity in the computation, since use low-rank dimension of P-spline, and in the model inference, since based on maximum likelihood method. Estimation additive model using linear mixed model approach can be suggested as an alternative method beside backfitting algorithm
Effect of Inactivation of Mst1 and Mst2 in the Mouse Adrenal Cortex

Abstract Recent conditional knockout of core components of the Hippo signaling pathway in the adrenal gland of mice has demonstrated that this pathway must be tightly regulated to ensure proper development and maintenance of the adrenal cortex. We report herein that the most upstream kinases of the pathway, the mammalian STE20-like protein kinases 1 and 2 (MST1and MST2, respectively), are expressed in the mouse adrenal cortex with MST2 expression being restricted to the zona glomerulosa (zG). To further explore the role of Hippo signaling in adrenocortical cells, we conditionally deleted Mst1/2 in steroidogenic cells using an Nr5a1-cre strain (Mst1flox/flox; Mst2flox/flox; Nr5a1-cre). Our results show that the loss of MST1/2 leads to the premature and progressive accumulation of subcapsular GATA4+, WT1+ adrenal gonadal primordium (AGP)-like progenitor cells starting at 2 months of age without affecting aldosterone and corticosterone secretion. To help us understand this phenotype, microarray analyses were performed on adrenal glands from 2-month-old mutant and control mice. Gene expression analyses revealed that loss of Mst1/2 leads to the overexpression of known downstream target genes (Ajuba, Aqp1, Fn1, Ibsp, Igf1, Igfbp2, Mmp2, Thbs1) of the main effector of Hippo signaling, YAP; and underexpression of genes (Agtr1b, Ecgr4, Hsd3b6, Nr0b1, Tesc, Vsnl1) that are normally specifically expressed in the zG or overexpressed in the zG compared to the zona fasciculata (zF). Together, these results suggest that MST1/2 regulates Hippo signaling activity in the adrenal cortex and that these two kinases are also involved in the fine tuning of zG cell function or differentiation.
A BERT-based Language Modeling Framework

Deep learning has brought considerable changes and created a new paradigm in many research areas, including computer vision, speech processing, and natural language processing. In the context of language modeling, recurrent-based language models and word embedding methods have been pivotal studies in the past decade. Recently, pre-trained language models have attracted widespread attention due to their enormous success in many challenging tasks. However, only a dearth of works concentrates on creating novel language models based on the pre-trained models. In order to bridge the research gap, we take the bidirectional encoder representations from Transformers (BERT) model as an example to explore novel uses of a pre-trained model for language modeling. More formally, this paper proposes a set of BERT-based language models, and a neural-based dynamic adaptation method is also introduced to combine these language models systematically and methodically. We conduct comprehensive studies on three datasets for the perplexity evaluation. Experiments show that the proposed framework achieves 11%, 39%, and 5% relative improvements over the baseline model for Penn Treebank, Wikitext 2, and Tedlium Release 2 corpora, respectively. Besides, when applied to rerank n -best lists from a speech recognizer, our framework also yields promising results compared with baseline systems.
Artemis: A Latency-Oriented Naming and Routing System

Today, Internet service deployment is typically implemented with server replication at multiple locations. Domain name system (DNS), which translates human-readable domain names into network-routable IP addresses, is typically used for distributing users to different server replicas. However, DNS relies on several network-based queries and the queries delay the connection setup process between the client and the server replica. In this article, we propose Artemis, a practical low-latency naming and routing system that supports optimal server (replica) selection based on user-defined policies and provides lower query latencies than DNS. Artemis uses a DNS-like domain name-IP mapping for replica selection and achieves low query latency by combining the name resolution process with the transport layer handshake process. In Artemis, all server replicas at different locations share the same anycast IP address, called Service Address. Clients use the Service Address to establish a transport layer connection with the server. The client's initial handshake packet is routed over an overlay network to reach the optimal server. Then the server migrates the transport layer connection to its original unicast IP address after finishing the handshake process. After that, service discovery is completed, and the client communicates with the server directly via IP addresses. To validate the effectiveness of Artemis, we evaluate its performance via both real trace-driven simulation and real-world deployment. The result shows that Artemis can handle a large number of connections and reduce the connection setup latency compared with state-of-the-art solutions. More specifically, our deployment across 11 Google data centers shows that Artemis reduces the connection setup latency by 39.4% compared with DNS.
Purity and entropy evolution speed limits for open quantum systems

We derive generic upper bounds on the rate of purity change and entropy increase for open quantum systems. These bounds depend solely on the generators of the nonunitary dynamics and are independent of the particular states of the systems. They are thus perfectly suited to investigate dephasing and thermalization processes of arbitrary systems. We apply these results to single and multiple dephasing channels, to a problem of quantum control in the presence of noise, and to cooling.
Detailed study of galaxies with the stellar counter-rotation phenomenon

The process of galactic disc growing is still not fully understood. In the majority of disk galaxies the gas and stars are located in the same plane and rotate in the same direction. However, there are kinematically peculiar galaxies hosting two counter-rotating stellar discs. Their origin is believed to be the result of a past event of accretion of gas followed by star formation. By studying such galaxies we can learn how much material, when, and how, have fallen onto the progenitor galaxy. We identiﬁed a sample of 56 counter-rotating galaxies in the MaNGA IFU survey and initiated a follow-up observing campaign at the 6-m telescope (BTA) aiming to determine the stellar population properties of both stellar discs. Our preliminary results suggest the dichotomy of the sample of counter-rotating galaxies. We found that most massive galaxies have extended counter-rotating disks, whose contribution to luminosity is higher than in the less massive galaxies suggestive of diﬀerent evolutionary paths.
HErZ: The German Hans-Ertel Centre for Weather Research

AbstractIn 2011, the German Federal Ministry of Transport, Building and Urban Development laid the foundation of the Hans-Ertel Centre for Weather Research [Hans-Ertel-Zentrum fur Wetterforschung (HErZ)] in order to better connect fundamental meteorological research and teaching at German universities and atmospheric research centers with the needs of the German national weather service Deutscher Wetterdienst (DWD). The concept for HErZ was developed by DWD and its scientific advisory board with input from the entire German meteorological community. It foresees core research funding of about €2,000,000 yr−1 over a 12-yr period, during which time permanent research groups must be established and DWD subjects strengthened in the university curriculum. Five priority research areas were identified: atmospheric dynamics and predictability, data assimilation, model development, climate monitoring and diagnostics, and the optimal use of information from weather forecasting and climate monitoring for the benefit .
When Worlds Collide

We analyze the cosmological signatures visible to an observer in a Coleman–de Luccia bubble when another such bubble collides with it. We use a gluing procedure to generalize the results of Freivogel, Horowitz and Shenker to the case of a general cosmological constant in each bubble and study the resulting spacetimes. The collision breaks the isotropy and homogeneity of the bubble universe and provides a cosmological ‘axis of evil’ which can affect the cosmic microwave background in several unique and potentially detectable ways. Unlike more conventional perturbations to the inflationary initial state, these signatures can survive even relatively long periods of inflation. In addition, we find that for a given collision the observers in the bubble with smaller cosmological constant are safest from collisions with domain walls, possibly providing another anthropic selection principle for small positive vacuum energy.
A Volunteer Computing Architecture for Computational Workflows on Decentralized Web

The amount of accessible computational devices over the Internet offers an enormous but latent computational power. Nonetheless, the complexity of orchestrating and managing such devices requires dedicated architectures and tools and hinders the exploitation of this vast processing capacity. Over the last years, the paradigm of (Browser-based) Volunteer Computing emerged as a unique approach to harnessing such computational capabilities, leveraging the idea of voluntarily offering resources. This article proposes VFuse, a groundbreaking architecture to exploit the Browser-based Volunteer Computing paradigm via a ready-to-access volunteer network. VFuse offers a modern multi-language programming environment for developing scientific workflows using WebAssembly technology without requiring the user any local installation or configuration. We equipped our architecture with a secure and transparent rewarding mechanism based on blockchain technology (Ethereum) and distributed P2P file system (IPFS). Further, the use of Non-Fungible Tokens provides a unique, secure, and transparent methodology for recognizing the users’ participation in the network. We developed a prototype of the proposed architecture and four example applications implemented with our system. All code and examples are publicly available on GitHub.
Neural Synthesis as a Methodology for Art-Anthropology in Contemporary Music

This article investigates the use of machine learning within contemporary experimental music as a methodology for anthropology, as a transformational engagement that might shape knowing and feeling. In Midlands (2019), Sam Salem presents an (auto)ethnographical account of his relationship to the city of Derby, UK. By deriving musical materials from audio generated by the deep neural network WaveNet, Salem creates an uncanny, not-quite-right representation of his childhood hometown. Similarly, in her album A Late Anthology of Early Music Vol. 1: Ancient to Renaissance (2020), Jennifer Walshe uses the neural network SampleRNN to create a simulated narrative of Western art music. By mapping her own voice onto selected canonical works, Walshe presents both an autoethnographic and anthropological reimagining of a musical past and questions practices of historiography. These works are contextualised within the practice and theory of filmmaker-ethnographer Trinh T. Minh-ha and her notion of ‘speaking nearby’. In extension of Tim Ingold’s conception of anthropology, it is shown that both works make collaborative human and non-human inquiries into the possibilities of human (and non-human) life.