Miracle-dz/UniFAR-FTUs huggingface dataset:
Exprese p53 a Ki 67 u pacientů s lokalizovaným konvenčním renálním karcinomem

Summary: Introduction: The aim was to assess the presence of p53 and Ki 67 expression in patients with localized conventional (clear cell) renal cell carcinoma. Material and methods: Seventy patients with stage pT1 and pT2 conventional (clear cell) renal cell carcinoma were included in the study. Nephrectomy was performed in 58 patients and renal resection in 12. Expression of p53 a Ki 67 was detected in both can- cerous and non-cancerous tissues of the kidney. Immunohistochemistry results were evaluated semiquantitatively using HSCORE. During a mean follow-up of 21 months tumour recurrence occurred in 8 patients. Results: Expression of p53 in tumour tissue was present in 65% and expression of Ki 67 in 96% of patients. The quantity of ex- pression of both proteins was significantly higher in cancerous tissue as compared to non-cancerous tissue. The quantity of p53 expression in the tumour did not correlate with the clinical-pathological factors nor with the course of the disease. The quantity of Ki 67 expression in the cancerous tissue increased accordingly with the tumour grade and was somewhat related to the further course of the disease, this influence, however, was not statistically significant. The high level of Ki 67 expression in the non-can- cerous tissue correlated significantly with the risk of disease recurrence. Conclusion: Expression of p53 and Ki 67 in the cancerous tissue can be detected in a high proportion of patients with localized
Caustics in Gravitational Lensing by Mixed Binary Systems

We investigated binary lenses with 1/rn potentials in the asymmetric case with two lenses with different indexes n and m. These kinds of potentials have been widely used in several contexts, ranging from galaxies with halos described by different power laws to lensing by wormholes or exotic matter. In this paper, we present a complete atlas of critical curves and caustics for mixed binaries, starting from the equal-strength case, and then exploring unequal-strength systems. We also calculate the transitions between all different topology regimes. Finally we find some useful analytic approximations for the wide binary case and for the extreme unequal-strength case.
Optimal Local and Remote Controls of Multiple Systems with Multiplicative Noises and Unreliable Uplink Channels

In this paper, the optimal local and remote linear quadratic (LQ) control problem is studied for a networked control system (NCS) which consists of multiple subsystems and each of which is described by a general multiplicative noise stochastic system with one local controller and one remote controller. Due to the unreliable uplink channels, the remote controller can only access unreliable state information of all subsystems, while the downlink channels from the remote controller to the local controllers are perfect. The difficulties of the LQ control problem for such a system arise from the different information structures of the local controllers and the remote controller. By developing the Pontyagin maximum principle, the necessary and sufficient solvability conditions are derived, which are based on the solution to a group of forward and backward difference equations (G-FBSDEs). Furthermore, by proposing a new method to decouple the G-FBSDEs and introducing new coupled Riccati equations (CREs), the optimal control strategies are derived where we verify that the separation principle holds for the multiplicative noise NCSs with packet dropouts. This paper can be seen as an important contribution to the optimal control problem with asymmetric information structures.
A Real Option Approach to Valuing the Option to Defer in a Residential Project in Melbourne, Australia

Property development feasibility evaluation is a precursor to any real estate development activity. The evaluation is predominantly calculated via the use of discounted cash flow (DCF) technique to quantify the future benefits to determine viability of the development. The DCF has been criticised on grounds of inflexibility and that it fails to evaluate real estate projects properly, leading to bias in the results. Real options theory has been proposed as a solution to the deficiencies in the DCF and to complement it. However, the real options theory has lacked practical adoption by industry due to the unavailability of comprehensive evidence to support its application in practice. The purpose of this paper is to examine a practical application of real option to a residential real estate project and compare the results with DCF to determine which of the methods deliver superior results. Dynamic programming, specifically the certainty equivalence approach of the binomial real options pricing model is applied to an actual Australian residential development project. The binomial option pricing method is a discrete time model and uses the multiplicative stochastic process to project the price evolution of the underlying real estate development value and complements the existing valuation methods to deliver better results. The real options valuation gives the developer a reason to hold uncertain projects over a period of time before commencing development. This mitigates the risks associated with projects and leads to the retention of projects that would have otherwise been abandoned by the DCF technique of evaluation. It was found out that should the developer wait until it is optimal to start development, the flexibility would add $2.58 million to the value of the development which would account for about 3.3%. This is the initial application of the certainty equivalence approach of the binomial option pricing method to evaluate an Australian residential case study pro
On the Banach spaces associated with multi-layer ReLU networks: Function representation, approximation theory and gradient descent dynamics

We develop Banach spaces for ReLU neural networks of finite depth $L$ and infinite width. The spaces contain all finite fully connected $L$-layer networks and their $L^2$-limiting objects under bounds on the natural path-norm. Under this norm, the unit ball in the space for $L$-layer networks has low Rademacher complexity and thus favorable generalization properties. Functions in these spaces can be approximated by multi-layer neural networks with dimension-independent convergence rates. The key to this work is a new way of representing functions in some form of expectations, motivated by multi-layer neural networks. This representation allows us to define a new class of continuous models for machine learning. We show that the gradient flow defined this way is the natural continuous analog of the gradient descent dynamics for the associated multi-layer neural networks. We show that the path-norm increases at most polynomially under this continuous gradient flow dynamics.
A superconducting nanowire spiking element for neural networks

As the limits of traditional von Neumann computing come into view, the brain's ability to communicate vast quantities of information using low-power spikes has become an increasing source of inspiration for alternative architectures. Key to the success of these largescale neural networks is a power-efficient spiking element that is scalable and easily interfaced with traditional control electronics. In this work, we present a spiking element fabricated from superconducting nanowires that has pulse energies on the order of ~10 aJ. We demonstrate that the device reproduces essential characteristics of biological neurons, such as a refractory period and a firing threshold. Through simulations using experimentally measured device parameters, we show how nanowire-based networks may be used for inference in image recognition, and that the probabilistic nature of nanowire switching may be exploited for modeling biological processes and for applications that rely on stochasticity.
Social Choice Optimization

Social choice is the theory about collective decision towards social welfare starting from individual opinions, preferences, interests or welfare. The field of Computational Social Welfare is somewhat recent and it is gaining impact in the Artificial Intelligence Community. Classical literature makes the assumption of single-peaked preferences, i.e. there exist a order in the preferences and there is a global maximum in this order. This year some theoretical results were published about Two-stage Approval Voting Systems (TAVs), Multi-winner Selection Rules (MWSR) and Incomplete (IPs) and Circular Preferences (CPs). The purpose of this paper is three-fold: Firstly, I want to introduced Social Choice Optimisation as a generalisation of TAVs where there is a max stage and a min stage implementing thus a Minimax, well-known Artificial Intelligence decision-making rule to minimize hindering towards a (Social) Goal. Secondly, I want to introduce, following my Open Standardization and Open Integration Theory (in refinement process) put in practice in my dissertation, the Open Standardization of Social Inclusion, as a global social goal of Social Choice Optimization.
Deep Multi-View Spatiotemporal Virtual Graph Neural Network for Significant Citywide Ride-hailing Demand Prediction

Urban ride-hailing demand prediction is a crucial but challenging task for intelligent transportation system construction. Predictable ride-hailing demand can facilitate more reasonable vehicle scheduling and online car-hailing platform dispatch. Conventional deep learning methods with no external structured data can be accomplished via hybrid models of CNNs and RNNs by meshing plentiful pixel-level labeled data, but spatial data sparsity and limited learning capabilities on temporal long-term dependencies are still two striking bottlenecks. To address these limitations, we propose a new virtual graph modeling method to focus on significant demand regions and a novel Deep Multi-View Spatiotemporal Virtual Graph Neural Network (DMVST-VGNN) to strengthen learning capabilities of spatial dynamics and temporal long-term dependencies. Specifically, DMVST-VGNN integrates the structures of 1D Convolutional Neural Network, Multi Graph Attention Neural Network and Transformer layer, which correspond to short-term temporal dynamics view, spatial dynamics view and long-term temporal dynamics view respectively. In this paper, experiments are conducted on two large-scale New York City datasets in fine-grained prediction scenes. And the experimental results demonstrate effectiveness and superiority of DMVST-VGNN framework in significant citywide ride-hailing demand prediction.
Large-scale magnetic field in the Monoceros OB-1 East molecular cloud

We study the large-scale magnetic field structure and its interplay with the gas dynamics in the Monoceros OB1 East molecular cloud. We combine observations of dust polarised emission from the Planck telescope and CO molecular line emission observations from the Taeduk Radio Astronomy Observatory 14-metre telescope. We calculate the strength of the plane-of-the-sky magnetic field using a modified Chandrasekhar-Fermi method and estimate mass over flux ratios in different regions of the cloud. We use the comparison of the velocity and intensity gradients of the molecular line observations with the polarimetric observations to trace dynamically active regions. The molecular complex shows an ordered large-scale plane-of-the-sky magnetic field structure. In the Northern part, it is mostly orientated along the filamentary structures while the Southern part shows at least two regions with distinct magnetic field orientations. We find that in the Northern filaments the magnetic field is unlikely to provide support against fragmentation at large scales. Our analysis reveals a shock region in the Northern part of the complex right in-between two filamentary clouds which were previously suggested to be in collision. Moreover, the shock seems to extend farther towards the Western part of the complex. In the Southern part, we find that either the magnetic field guides the accretion of interstellar matter towards the cloud or it was dragged by the matter towards the densest regions. The large-scale magnetic field in Monoceros OB-1 East molecular clouds is tightly connected to the global structure of the complex and, in the Northern part, it seems to be dominated by gravity and turbulence, while in the Southern part it influences the structuring of matter.
X-Ray Spectroscopy in the Microcalorimeter Era. I. Effects of Fe xxiv Resonant Auger Destruction on Fe xxv Kα Spectra

We discuss the importance of Fe23+ in determining the line intensities of the Fe xxv Kα complex in an optically thick cloud, and investigate resonant auger destruction (RAD) with CLOUDY. Although initially motivated by the Perseus cluster, our calculations are extended to the wide range of column densities encountered in astronomy. A Fe xxv line photon can change/lose its identity upon absorption by three-electron iron as a result of “line interlocking. ” This may lead to the autoionization of the absorbing ion, ultimately destroying the Fe xxv Kα photon by RAD. Out of the four members in the Fe xxv Kα complex, a significant fraction of the x line photons are absorbed by Fe23+ and destroyed, causing the x line intensity to decrease. For example, at a hydrogen column density of 1025 cm−2, ∼32% of x photons are destroyed due to RAD while w is mostly unaffected. The line intensity of y is slightly (≤2%) reduced. z is not directly affected by RAD, but the contrasting behavior between z and x line intensities points toward the possible conversion of a tiny fraction (∼2%) of x photons into z photons. The change in line intensities due to electron scattering escape off fast thermal electrons is also discussed.
Rademacher upper bounds for cross-validation errors with an application to the lasso

We establish a general upper bound for $K$-fold cross-validation ($K$-CV) errors that can be adapted to many $K$-CV-based estimators and learning algorithms. Based on Rademacher complexity of the model and the Orlicz-$\Psi_{\nu}$ norm of the error process, the CV error upper bound applies to both light-tail and heavy-tail error distributions. We also extend the CV error upper bound to $\beta$-mixing data using the technique of independent blocking. We provide a Python package (\texttt{CVbound}, \url{this https URL}) for computing the CV error upper bound in $K$-CV-based algorithms. Using the lasso as an example, we demonstrate in simulations that the upper bounds are tight and stable across different parameter settings and random seeds. As well as accurately bounding the CV errors for the lasso, the minimizer of the new upper bounds can be used as a criterion for variable selection. Compared with the CV-error minimizer, simulations show that tuning the lasso penalty parameter according to the minimizer of the upper bound yields a more sparse and more stable model that retains all of the relevant variables.
Water-Stress, Carbon-Dioxide, and Light Effects on Sucrose-Phosphate Synthase Activity in Phaseolus-Vulgaris

The characteristics of sucrose-phosphate synthase (SPS; EC 2.4.1.14) activity in leaves of Phaseolus vulgaris L. cv. Linden was studied in plants subjected to water stress and various CO2 and light treatments. When water was withheld for 3 days causing mild water stress (–0.9 MPa), the activity of SPS measured in crude extracts was reduced ca 50%. The effect of water stress was most evident when the enzyme was assayed with saturating amounts of its substrates fructose 6-phosphate and UDP glucose. Placing a water-stressed plant in an atmosphere containing 1% CO2 reversed the effect of water stress on SPS activity over 5 h even though the water stress was not relieved. Holding unstressed leaves in low CO2 partial pressure reduced the extractable activity of SPS. After 1 h of low CO2 treatment the effect of low CO2 could be reversed by 20 min of 5% CO2. However, after 24 h of low CO2 treatment, less SPS activity was recovered by the 20 min treatment. The cytosolic protein synthesis inhibitor cycloheximide prevented the slow recovery of SPS activity, but did not affect the rapid recovery of SPS. We conclude that the effect of water stress on SPS activity was a consequence of the inhibition of photosynthesis caused by stomatal closure. Responses of Phaseolus vulgaris SPS to light were similar to the response to low CO2 in that the effects were most pronounced under Vmax assay conditions. This is the first report of this type of light response of SPS in a dicotyledonous species.
Synchronized Planarity with Applications to Constrained Planarity Problems

We introduce the problem Synchronized Planarity. Roughly speaking, its input is a loop-free multi-graph together with synchronization constraints that, e.g., match pairs of vertices of equal degree by providing a bijection between their edges. Synchronized Planarity then asks whether the graph admits a crossing-free embedding into the plane such that the orders of edges around synchronized vertices are consistent. We show, on the one hand, that Synchronized Planarity can be solved in quadratic time, and, on the other hand, that it serves as a powerful modeling language that lets us easily formulate several constrained planarity problems as instances of Synchronized Planarity. In particular, this lets us solve Clustered Planarity in quadratic time, where the most efficient previously known algorithm has an upper bound of O(n8).
Revisiting the AdS boson stars: The mass-charge relations

Motivated by the recent progress in solving the large charge sector of conformal field theories, we revisit the mass-charge relation of boson stars asymptotic to global AdS. We construct and classify a large number of electrically charged boson star solutions in a toy model and two supergravity models arising from the $SU(3)$ and $U(1)^4$ truncation of $D=4$ $SO(8)$ gauged maximal supergravity. We find a simple ansatz for the chemical potential that can fit the numerical data in striking accuracy for the full range of charge. Combining with the first law of thermodynamics, we can then evaluate the mass as a function of the charge and obtain the free energy in the fixed charge ensemble. We show that in the toy model, the ground state can be either the extremal RN black hole or the boson stars depending on the parameter region. For the $SU(3)$ truncation, there always exists a boson star that has smaller free energy than the extremal RN black hole, in contrast to the $U(1)^4$ model where the extremal RN black hole is always the ground state. In all models, for boson star solutions with arbitrarily large charge, we show that the large charge expansion of the mass reproduces the same structure exhibited in the CFT side.
Resolution of track association ambiguities using expectation-maximization algorithm

Space object observations cannot always be unambiguously associated to catalogued objects. Multiple objects, orbiting in close proximity to each other, can pass an assignment criterion and can therefore become likely candidates for the association. If the state uncertainty of the catalogued objects is large, this ambiguity can only be resolved when new observations arrive again. In order to screen for conjunctions between space objects, the most likely associations have to be identified at all time steps. This works studies an approach where multiple objects with unresolved associations are grouped and then their orbits are determined simultaneously. The measurements are loosely associated first with a weighting factor depending on the statistical distance to the current reference trajectory. The factors are iteratively updated using an expectation-maximization approach with repeated orbit determination and association runs. The method is presented along with practicality considerations and recommendations for an operational implementation.
Future prospects for the treatment of rapidly evolving viral pathogens: insights from evolutionary biology

The evolutionary dynamics of viruses must be taken into consideration in designing preventive and therapeutic treatments. Here we review mechanisms by which viruses adapt in response to antiviral interventions. We propose combination therapy and multiepitopic vaccines as adequate to circumvent virus adaptability. An alternative design, termed lethal mutagenesis or virus entry into error catastrophe is presented. It exploits the high error rates inherent in RNA virus replication, to provoke virus extinction through excess of mutations.
The incidence of dark cutting in southern Australian beef production systems fluctuates between months

Dark cutting is detrimental to meat quality and therefore is the major cause of carcass downgrades under the Meat Standards Australia grading system. This study quantified the variation between months in the incidence of dark cutting, in southern Australia. Four years of Meat Standards Australia grading data, from nine individual beef processors in Western Australia, South Australia, Victoria and Tasmania, was utilised for the analysis. The dataset contained 42162 slaughter groups, of 10 or more grass-fed cattle, which allowed for the percentage of dark cutters per slaughter group to be analysed. The interaction between month, year and state was significant (P < 0.001). The lowest risk of dark cutting for South Australia and Western Australia was in October (1.53% ± 0.75 and 6.96% ± 0.76) and November in Tasmania and Victoria (7.34% ± 0.9 and 5.27% ± 0.81) potentially when feed availability and quality is highest. The incidence of dark cutting was highest for all states during the period from February to June. Lower pasture availability and quality in combination with higher levels of stress due to extreme high or low temperatures during this time could all contribute to the higher incidences. The findings of this study show that procurement and management decisions made by cattle buyers, producers and processors need to change throughout the year to help mitigate the incidence of dark cutting carcasses and reduce financial loss.
Zariski Density of Points with Maximal Arithmetic Degree

Given a dominant rational self-map on a projective variety over a number field, we can define the arithmetic degree at a rational point. It is known that the arithmetic degree at any point is less than or equal to the first dynamical degree. In this article, we show that there are densely many $\overline{\mathbb Q}$-rational points with maximal arithmetic degree (i.e. whose arithmetic degree is equal to the first dynamical degree) for self-morphisms on projective varieties. For unirational varieties and abelian varieties, we show that there are densely many rational points with maximal arithmetic degree over a sufficiently large number field. We also give a generalization of a result of Kawaguchi and Silverman in the appendix.
Development cryptography protocol based on Magic Square and Linear Algebra System

Information security cryptographic protocols are very important in the modern era due to the development and advanced technology in internet applications and networks communications. In this paper, we proposed a protocol to save information from passive attacks when sending between two nodes over an insecure channel. This proposed protocol relies on magic square of size 3*3, linear equation system and finite field.
Gray Assessment of Equipment Maintenance Support Capability Based on the Structure Entropy Weight Method

The influencing factors which about equipment maintenance support abilities are analysed and the index system that is used to evaluate equipment support abilities are constructed in the paper. Then, the weight of the index is determined by the structure entropy weight method which is a combination of qualitative and quantitative. The comprehensive evaluation model of equipment maintenance support abilities are constructed by combining gray clustering evaluation theory. Finally, the feasibility and validity of the model are verified by an example.