Miracle-dz/UniFAR-FTUs huggingface dataset:
LancasterAQ: A High Resolution Street Level Dataset of Ultrafine Particles

We present a mobile dataset of ultrafine particles (UFPs) in Lancaster, UK, with measurements taken by car and bike over 5 days in May 2022. UFPs are a con-stituent of air pollution and comprise of particulate matter (PM) less than 0.1 µ m in diameter. UFPs are unregulated and less measured than larger constituents of PM, despite being harmful to health and an important part of the atmospheric and meteorological system. By making mobile UFP measurements, we have produced a street level dataset that captures the high spatial variability of UFPs at the scale an individual experiences it. The dataset is accessible through the LancasterAQ Python package and lends itself to modelling spatially or on a network. This dataset’s potential use cases include route planning under constraints of air pol-lutant exposure, identifying processes that affect air pollution at street level, and investigating the causal relationship between human activity and UFPs.
Loop spaces and representations

We introduce loop spaces (in the sense of derived algebraic geometry) into the representation theory of reductive groups. In particular, we apply the theory developed in our previous paper arXiv:1002.3636 to flag varieties, and obtain new insights into fundamental categories in representation theory. First, we show that one can recover finite Hecke categories (realized by D-modules on flag varieties) from affine Hecke categories (realized by coherent sheaves on Steinberg varieties) via S^1-equivariant localization. Similarly, one can recover D-modules on the nilpotent cone from coherent sheaves on the commuting variety. We also show that the categorical Langlands parameters for real groups studied by Adams-Barbasch-Vogan and Soergel arise naturally from the study of loop spaces of flag varieties and their Jordan decomposition (or in an alternative formulation, from the study of local systems on a Moebius strip). This provides a unifying framework that overcomes a discomforting aspect of the traditional approach to the Langlands parameters, namely their evidently strange behavior with respect to changes in infinitesimal character.
Algebraic independence of G-functions and congruences "à la Lucas"

We develop a new method for proving algebraic independence of $G$-functions. Our approach rests on the following observation: $G$-functions do not always come with a single linear differential equation, but also sometimes with an infinite family of linear difference equations associated with the Frobenius that are obtained by reduction modulo prime ideals. When these linear difference equations have order one, the coefficients of the $G$-function satisfy congruences reminiscent of a classical theorem of Lucas on binomial coefficients. We use this to derive a Kolchin-like algebraic independence criterion. We show the relevance of this criterion by proving, using p-adic tools, that many classical families of $G$-functions turn out to satisfy congruences "\`a la Lucas".
Deep Optimal Stopping

In this paper we develop a deep learning method for optimal stopping problems which directly learns the optimal stopping rule from Monte Carlo samples. As such, it is broadly applicable in situations where the underlying randomness can efficiently be simulated. We test the approach on three problems: the pricing of a Bermudan max-call option, the pricing of a callable multi barrier reverse convertible and the problem of optimally stopping a fractional Brownian motion. In all three cases it produces very accurate results in high-dimensional situations with short computing times.
Assessing anti-B7-H3 antibody and gp70 cancer vaccine therapy for TNBC

Immunotherapy has emerged as a promising approach for addressing TNBC, an aggressive breast cancer subtype for which few targeted therapies exist. TNBC immunotherapy, however, is dominated by PD-1 immune checkpoint blockade (ICB), which only benefits a small minority of patients. To improve upon these initial efforts, we sought to target B7-H3 with TNBC immunotherapy, as this marker is expressed in a vast majority of TNBCs. In searching for an immunotherapy strategy, we decided to develop and assess a B7-H3-targeting antibody with cancer vaccine combination, as this regimen has recently shown resounding therapeutic benefits for another breast cancer type in the clinic (NCT00524277). To this end, we adapted NCT00524277’s treatment components for murine studies, creating M-m276, a B7-H3-targeting antibody, and a gp70-targeting cancer vaccine; both B7-H3 and gp70 are human/mouse TNBC-specific biomarkers. M-m276 and gp70 vaccine were administered in a B7-H3 + murine in vivo model of TNBC (i.e., Balb/C mice with lung-seeded 4T1). Significant survival extension was observed in mice treated with the combination therapy, relative to the monotherapies alone. Given these results, we next sought to better understand the mechanism of this combination therapy. Upon finding that M-m276 mediates antibody-dependent cellular phagocytosis (ADCP), we hypothesized that M-m276 augments the efficacy of cancer vaccines by increasing tumor antigen presentation to cytotoxic T lymphocytes (CTLs) through either cross-presentation or trogocytosis, two ADCP-linked processes. Although M-m276 was found to have no impact on cross-presentation, we found that M-m276 significantly increases the trogocytosis of tumor membrane by antigen-presenting cells (APCs) in vitro , enabling these APCs to present greater amounts of tumor antigen to CTLs. Altogether, our results support B7-H3-targeting antibody with cancer vaccine as a TNBC treatment strategy and propose a potential mechanism for Cell lines culture. L5178Y, L5178Y-S (LY-S), E.G7-OVA, 4T1, and L929 cell lines were from ATCC. RF3370 cells were a kind Massachusetts. L5178Y, LY-S, and RF3370 cells were cultured in R10 medium, which is RPMI-1640 (Gibco) supplemented with 10% fetal bovine serum (FBS), 100 U ml −1 penicillin (Gibco), 100 μg ml −1 streptavidin (Gibco), 2 mM L-glutamine (Gibco), and 20 mM HEPES (Gibco). E.G7-OVA, 4T1, and L929 cells were cultured in DMEM (Gibco) supplemented with 10% FBS, 100 U ml −1 penicillin, 100 μg ml −1 streptomycin (Gibco) and 250 ng ml −1 amphotericin B (Gibco). All cells were grown in a humidified incubator at 37 °C, with 5% CO 2
Job Creation during Korea’s Transition to a Knowledge Economy †

This paper analyzes job creation when the Korean economy transitioned to a knowledge economy from the 1990s to the 2010s. During this period, the ratio of service to manufacturing jobs increased, knowledge intensive industries grew, and job creation became geographically concentrated around Seoul. The changes slowed down in the 2010s, and overall job growth weakened. To analyze the effect of job creation driver industries during this period, the main part of which are knowledge intensive tradable service industries, on local service job creation, I use a modified version of the local labor market of Moretti (2010). I analyze the job changes during 1995-2005 and during 2006-2016 in 237 Si-Gun-Gu areas in the Census on Establishments datasets. I find that one manufacturing job creates 0.5 local service jobs and that one tradable service job creates 1.1 jobs within Gu areas of metro cities and 2.3 jobs in Si-Gun areas. The job creation relationship between the tradable and local service sectors was not altered in this period. As more jobs were created in the tradable sector driven by the transition to a knowledge economy, job creation overall remained active, with the opposite also being true.
Impact of Transport Infrastructure on Gross Regional Products: A Study on Chinese Economic Regions

Purpose: This study examines the impact of transport infrastructure on the Gross Regional Products in Chinese economic regions. Methods: The study analyzes the impact of transport infrastructure on the Gross Regional Products according to the eight economic regions in China by using descriptive statistics and regression analysis methods. Results: The findings are that a) densities of railways and highways have strong impacts on Gross Regional Products per capita according to Chinese economic regions, b) impacts of densities of railways and highways on Gross Regional Products per capita differ among Chinese economic regions and c) construction of new transport infrastructure has a more positive impact on Gross Regional Products per capita compared to regions with already developed transport infrastructure. Implications : Region-specific features ought to be considered in making and implementing the economic development policy according to economic regions. Northeast Region has the weakest correlation of the density of highways with GRP per capita (0.839). And North Coastal Area has the same correlations of two independent variables with GRP per capita (0.984). There is no region that has the strongest correlations of two independent variables with GRP per capita. Correlation coefficients are statistically significant at one percent. These correlations show that there exists a strong interrelation between GRP per capita and densities of railways and highways, but this does not mean that GRP per capita entirely depends on these factors.
Application of Geometric measure Theory in Continuum Mechanics: The Configuration Space, Principle of Virtual Power and Cauchy's Stress Theory for Rough Bodies

In this work, the principles of Homological Integration Theory are applied to the mathematical formulation of continuum mechanics. A central guideline in the currently acceptable formulation of continuum mechanics is that an admissible body is represented by a set of finite perimeter. The proposed framework is shown to enable the inclusion of a class of generalized bodies for which a corresponding stress theory is properly formulated and a generalized principle of virtual power is presented.
Improving Large-Scale Conversational Assistants using Model Interpretation based Training Sample Selection

Natural language understanding (NLU) models are a core component of large-scale conversational assistants. Collecting training data for these models through manual annotations is slow and expensive that impedes the pace of model improvement. We present a three stage approach to address this challenge: First, we identify a large set of relatively infrequent utterances from live trafﬁc where the users implicitly communicated satisfaction with a response (such as by not interrupting), along with the existing model outputs as candidate annotations. Second, we identify a small sub-set of these utterances usings Integrated Gra-dients based importance scores computed with the current models. Finally, we augment our training sets with these utterances and retrain our models. We demonstrate the effectiveness of our approach in a large-scale conversational assistant, processing billions of utterances every week. By augmenting our training set with just 0 . 05% more utterances through our approach, we observe statistically signiﬁcant improvements for infrequent tail utterances: a 0 . 45% reduction in semantic error rate (Se-mER) in ofﬂine experiments, and a 1 . 23% reduction in defect rates in online A/B tests.
A New Boundary Scheme for BGK Model

In this paper, we proposed a new boundary condition scheme for the BGK model which has second order accuracy and can be developed to higher accuracy. Numerical tests show that the numerical solutions of the BGK model applied to the new boundary scheme have great agreement with analytical solutions. It is also found that the numerical accuracy of the present schemes is much better than that of the original extrapolation schemes proposed by Guo et al at small grid.
Completeness of rank one perturbations of normal operators with lacunary spectrum

Suppose $\mathcal{A}$ is a compact normal operator on a Hilbert space $H$ with certain lacunarity condition on the spectrum (which means, in particular, that its eigenvalues go to zero exponentially fast), and let $\mathcal{L}$ be its rank one perturbation. We show that either infinitely many moment equalities hold or the linear span of root vectors of $\mathcal{L}$, corresponding to non-zero eigenvalues, is of finite codimension in $H$. In contrast to classical results, we do not assume that the perturbation is weak.
UWS Academic Portal Lime binders for the repair of historic buildings

Lime binders are utilised worldwide and are associated with a considerable scale of production and corresponding CO2 emissions. The relevance of this review is therefore international in scope, with production transcending geographical boundaries and construction practices. An holistic view of lime binders, considering their provenance, production and utilisation offers the potential for significant CO2 savings urgently required globally. Importantly, the technical aspects of lime materials production is critical, but the alteration of behaviour of both specifier and those undertaking the construction processes is also essential in achieving meaningful CO2 emission reduction. This review paper investigates the life cycle stages of lime binders in line with the features highlighted in the Scottish Building Alliance (SBA) ‘building life cycle stages model’. It attempts to determine what can be learnt from our understanding of the manufacture and use of binders from historic, current and future perspectives in the context of reconciling the production of historically authentic materials in a decarbonising environment. The production and use of such authentic historic lime binders initially appears highly carbon intensive through its utilisation of relatively inefficient kiln technologies and loss of economies of scale associated with larger operations. However, this review shows numerous benefits in the production of such binders; including their CO2 sequestration capability, lower potential processing energy, and a reduction in excessive ‘carbon miles’ associated with transportation. Importantly, we show how historic production and on-site manufacture approaches with lime-based materials also offers exciting potential for carbon savings. Importantly, hot mixed lime mortars offer the promise of higher durability materials compared to their modern cold manufactured counterparts, thereby reducing the frequency of repeat maintenance interventions, again yielding CO2 savings. We suggest hot mixing, whether in an innovative contemporary materials realm or within traditional contexts should be explored. In addition, hot mixed materials can concomitantly satisfy conservation requirements for repairs through better reflecting the aspired to building conservation requirement of like for like materials replacement reflected with historically produced binders. We suggest future paths for the industry that would simultaneously reconcile demands for authentic materials and production methods for the conservation sector with the necessity of achieving cleaner production in a decarbonizing world.
Constraining supersymmetry from the satellite experiments

In this paper I study the detectability of gamma rays from dark matter annihilation in the subhalos of the Milky Way by the satellite-based experiments EGRET and GLAST. I work in the frame of supersymmetric extension of the standard model and assume the lightest neutralino being the dark matter particles. Based on the N-body simulation of the evolution of dark matter subhalos I first calculate the average intensity distribution of this new class of gamma-ray sources by neutralino annihilation. It is possible to detect these gamma-ray sources by EGRET and GLAST. Conversely, if these sources are not detected the nature of the dark matter particles will be constrained by these experiments, depending, however, on the uncertainties of the subhalo profile.
Generalized q-gaussian von Neumann algebras
with coefficients, I : Relative strong solidity

We define $\Gamma_q(B,S \otimes H)$, the generalized $q$-gaussian von Neumann algebras associated to a sequence of symmetric independent copies $(\pi_j,B,A,D)$ and to a subset $1 \in S = S^* \subset A$ and, under certain assumptions, prove their strong solidity relative to $B$. We provide many examples of strongly solid generalized $q$-gaussian von Neumann algebras. We also obtain non-isomorphism and non-embedability results about some of these von Neumann algebras.
Automatic Fish Identification Using Single Shot Detector

The vast sea conditions and the long coastline make Bengkulu one of the provinces with a high diversity of marine fish. Although it is predicted to have high diversity, data on the diversity of marine fish on the Bengkulu coast is still very limited, especially in the process of fish species detection. With the development and expansion of computer capabilities, the ability to classify fish can be done with the help of computer equipment. The research presents a new method of automating the detection of marine fish with a Single Shot Detector method. It is a relatively simple algorithm to detect an object with the help of a MobileNet architecture. In the research, the Single Shot Detector used is six extra convolution layers. Three of the extra layers can generate six predictions for each cell. The Single Shot Detector model, in total, can generate 8,732 predictions. The research succeeds in identifying seven from ten genera of marine fish with a total dataset of 1,000 images, with 90% training data and 10% validation data. Each fish genus has 100 images with different shooting angles and backgrounds. The results show that the Single Shot Detector model with MobileNet architecture gets an accuracy value of 52.48% for the identification of 10 genera of marine fish.
On divergence of expectations of the Feynman-Kac type with singular potentials

Motivated by the work of Baras-Goldstein (1984), we discuss when expectations of the Feynman-Kac type with singular potentials are divergent. Underlying processes are Brownian motion and $\alpha$-stable process. In connection with the work of Ishige-Ishiwata (2012) concerned with the heat equation in the half-space with a singular potential on the boundary, we also discuss the same problem in the half-space for the case of Brownian motion.
MIT Open Access Articles Mapping 3D Underwater Environments with Smoothed Submaps

This paper presents a technique for improved mapping of complex underwater environments. Autonomous underwater vehicles (AUVs) are becoming valuable tools for inspection of underwater infrastructure, and can create 3D maps of their environment using high-frequency proﬁling sonar. However, the quality of these maps is limited by the drift in the vehicle’s navigation system. We have developed a technique for simultaneous localization and mapping (SLAM) by aligning point clouds gathered over a short time scale using the iterative closest point (ICP) algorithm. To improve alignment, we have developed a system for smoothing these “submaps” and removing outliers. We integrate the constraints from submap alignment into a 6-DOF pose graph, which is optimized to estimate the full vehicle trajectory over the duration of the inspection task. We present real-world results using the Blueﬁn Hovering AUV, as well as analysis of a synthetic data set.
Quantification of Airfoil Geometry-Induced Aerodynamic Uncertainties - Comparison of Approaches

Uncertainty quantification in aerodynamic simulations calls for efficient numerical methods to reduce computational cost, especially for uncertainties caused by random geometry variations which involve a large number of variables. This paper compares five methods, including quasi-Monte Carlo quadrature, polynomial chaos with coefficients determined by sparse quadrature and by point collocation, radial basis function and a gradient-enhanced version of kriging, and examines their efficiency in estimating statistics of aerodynamic performance upon random perturbation to the airfoil geometry which is parameterized by independent Gaussian variables. The results show that gradient-enhanced surrogate methods achieve better accuracy than direct integration methods with the same computational cost.
On $L$-functions of quadratic $\mathbb{Q}$-curves

Let $K$ be a quadratic number field of discriminant $\Delta_K$, let $E$ be a $\mathbb Q$-curve without CM completely defined over $K$ and let $\omega_E$ be an invariant differential on $E$. Let $L(E,s)$ be the $L$-function of $E$. In this setting, it is known that $L(E,s)$ possesses an analytic continuation to $\mathbb C$. The period of $E$ can be written (up to a power of $2$) as the product of the Tamagawa numbers of $E$ with $\Omega_E/\sqrt{|\Delta_K|}$, where $\Omega_E$ is a quantity, independent of $\omega_E$, which encodes the real periods of $E$ when $K$ is real and the covolume of the period lattice of $E$ when $K$ is imaginary. In this paper we compute, under the generalized Manin conjecture, an effective nonzero integer $Q=Q(E,\omega_E)$ such that if $L(E,1)\neq 0$ then $L(E,1)\cdot Q\cdot\sqrt{|\Delta_K|}/\Omega_E$ is an integer. Computing $L(E,1)$ up to sufficiently high precision, our result allows us to prove that $L(E,1)=0$ whenever this is the case and to compute the $L$-ratio $L(E,1)\cdot\sqrt{|\Delta_K|}/\Omega_E$ when $L(E,1)\neq 0$. An important ingredient is an algorithm to compute a newform $f$ of weight $2$ level $\Gamma_1(N)$ such that $L(E,s)=L(f,s)\cdot L({}^{\sigma\! } f,s)$, for ${}^{\sigma\! } f$ the unique Galois conjugate of $f$. As an application of these results, we verify the validity of the weak BSD conjecture for some $\mathbb Q$-curves of rank $2$ and we will compute the $L$-ratio of a curve of rank $0$.
A Deep Convolutional Neural Network Framework based Model for IoT based Healthcare Monitoring System

-Since the usage of internet is increasing every day, the IoT (Internet of Things) plays an important role in improving a lot of new developments in the medical and health fields. IoT Health Wearable device is an effective and easy technique and is very convenient in the medical sector for monitoring health. The wearable devices like smartphones or smart-watches, which are connected through Electrocardiograms (ECG) sensors, are used to collect data from the patients. These data are accessed and categorized to analyze the disease or forecast if any abnormality is calculated in the conditions of health patients. This data is applied to the Deep Convolutional Neural Network (DCNN) method for monitoring the patients’ health condition. This methodology is efficient and the performance is evaluated by existing methodologies. The output shows that the proposed Deep Convolutional Neural Network (DCNN) method is precise in detecting and monitoring the conditions of health patients.
A distance for probability spaces, and long-term values in Markov Decision Processes and Repeated Games

Given a finite set $K$, we denote by $X=\Delta(K)$ the set of probabilities on $K$ and by $Z=\Delta_f(X)$ the set of Borel probabilities on $X$ with finite support. Studying a Markov Decision Process with partial information on $K$ naturally leads to a Markov Decision Process with full information on $X$. We introduce a new metric $d_*$ on $Z$ such that the transitions become $1$-Lipschitz from $(X, \|.\|_1)$ to $(Z,d_*)$. In the first part of the article, we define and prove several properties of the metric $d_*$. Especially, $d_*$ satisfies a Kantorovich-Rubinstein type duality formula and can be characterized by using disintegrations. In the second part, we characterize the limit values in several classes of ''compact non expansive" Markov Decision Processes. In particular we use the metric $d_*$ to characterize the limit value in Partial Observation MDP with finitely many states and in Repeated Games with an informed controller with finite sets of states and actions. Moreover in each case we can prove the existence of a generalized notion of uniform value where we consider not only the Cesaro mean when the number of stages is large enough but any evaluation function $\theta \in \Delta(\N^*)$ when the impatience $I(\theta)=\sum_{t\geq 1} |\theta_{t+1}-\theta_t|$ is small enough.