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We describe a compact calorimeter that opens ultra-low-temperature heat capacity studies of small metal crystals in moderate magnetic fields. The performance is demonstrated on the canonical heavy fermion metal YbRh 2 Si 2 . Thermometry is provided by a fast current sensing noise thermometer. This single thermometer enables us to cover a wide temperature range of interest from 175 µK to 90 mK with temperature-independent relative precision. Temperatures are tied to the international temperature scale with a single-point calibration. A superconducting solenoid surrounding the cell provides the sample field for tuning its properties and operates a superconducting heat switch. Both adiabatic and relaxation calorimetry techniques, as well as magnetic field sweeps, are employed. The design of the calorimeter results in an addendum heat capacity which is negligible for the study reported. The keys to sample and thermometer thermalisation are the lack of dissipation in the temperature measurement and the steps taken to reduce the parasitic heat leak into the cell to the tens of fW level. [ABSTRACT FROM AUTHOR]

Nonlinear solitary waves influence the Earth's crust because wave pressure on the ocean bottom contains non-hydrostatic components. Our physical-mathematical model allows us to calculate the surplus super-hydrostatic pressure on the Earth's crust. It depends on the amplitudes of solitary waves and the depth of an ocean. The surplus wave pressure averages 50% from hydrostatic pressure on the shallow ocean shelves. Thus, the solitary wave's tsunami class can provoke novel (repeated) earthquakes (or landslides) because surplus stresses affect the seismic focus. Theoretical results and experimental physical modeling of soliton waves have shown good agreement. A calculated example of the mega-tsunami in Lituya Bay and a described example of Dickson Fjord (AK, USA) indicate changes in the dynamic pressure after the onset of the tsunami. The presented studies demonstrate a first attempt at creating a numerical model of this phenomenon. [ABSTRACT FROM AUTHOR]

We present an approach to describe the group behavior of the high-frequency traders in the stock market based on Mean Field Games and optimal control theory. The problem is formalized as a system of coupled PDEs: Kolmogorov–Fokker–Planck, evolving forward in time with initial condition, and Hamilton–Jacobi–Bellman, evolving backwards in time with terminal condition. Under certain assumptions, the system of PDEs can be reduced to Riccati-type ODEs. We solve the inverse problem to describe the behavior of the high-frequency traders during the Chinese stock market crisis in 2015. [ABSTRACT FROM AUTHOR]

Electrical and thermal transport were studied in a vacuum-annealed polycrystalline silver wire with residual resistivity ratio 200 – 400, in the temperature range 0.1 - 1.2 K and in magnetic fields up to 5 T. Both at zero field and at 5 T the wire exhibits the Wiedemann–Franz law with the fundamental Lorenz number, contrary to an earlier report (Gloos et al., in Cryogenics 30:14–18, 1990). Our result demonstrates that silver is an excellent material for thermal links in ultra-low-temperature experiments operating at high magnetic fields. [ABSTRACT FROM AUTHOR]

Global warming firstly influences the permafrost regions where numerous and rich world hydrocarbon deposits are located. Permafrost thawing has caused severe problems in exploring known hydrocarbon deposits and searching for new targets. This process is also dangerous for any industrial and living regions in cold regions. Knowledge of permafrost's ice and unfrozen water content is critical for predicting permafrost behavior during the water–ice transition. This is especially relevant when ice and permafrost are melting in many regions under the influence of global warming. It is well known that only part of the formation's pore water turns into ice at 0 °C. After further lowering the temperature, the water phase transition continues, but at gradually decreasing rates. Thus, the porous space is filled with ice and unfrozen water. Laboratory data show that frozen formations' mechanical, thermal, and rheological properties strongly depend on the moisture content. Hence, porosity and temperature are essential parameters of permafrost. In this paper, it is shown that by combining research in three fields, (1) geophysical exploration, (2) numerical modeling, and (3) temperature logging, it is possible to estimate the porosity of permafrost in situ. Five examples of numerical modeling (where all input parameters are specified) are given to demonstrate the procedure. This investigation is the first attempt to quantitatively analyze permafrost's porosity in situ. [ABSTRACT FROM AUTHOR]

A new extremely simple ensemble-based model with the uniformly generated axis-parallel hyper-rectangles as base models (HRBM) is proposed. Two types of HRBMs are studied: closed rectangles and corners. The main idea behind HRBM is to consider training examples inside and outside each rectangle. It is proposed to incorporate HRBMs into the gradient boosting machine (GBM) to construct effective ensemble-based models and to avoid overfitting. A simple method for calculating optimal regularization parameters of the model, which can be modified in the explicit way at each iteration of GBM, is considered. Moreover, a new regularization called the "step height penalty" is studied in addition to the L1 and L2 regularizations. An extremely simple approach to the proposed ensemble-based model prediction interpretation by using the well-known method SHAP is proposed. It is shown that GBM with HRBM can be regarded as a model extending a set of interpretable models for explaining black-box models. Numerical experiments with real datasets illustrate the proposed GBM with HRBMs for regression and classification problems. The best p-values in the t-test comparing the proposed model with the well-known ensemble-based regression and classification models are 0.004 and 0.0031, respectively. Experiments also illustrate computational efficiency of the proposed SHAP modifications. The code of proposed algorithms implementing GBM with HRBM is publicly available. [ABSTRACT FROM AUTHOR]

A modulation method is proposed for generating identical UWB chaotic radio pulses using an analog generator of chaotic oscillations. The problem is on the edge of two contradicting requirements: (1) theoretical ability to produce a huge number of various-shape signals, because of high sensitivity to the initial conditions of the generator; (2) the necessity to reproduce oscillations of the same shape both in the receiver and in the transmitter for the implementation of coherent methods of signal processing. The considered method allows us to resolve this contradiction. A single-transistor chaotic oscillator with single power supply and frequency range 100 to 500 MHz is proposed. A mathematical model of the generator (a system of ODEs) was derived. A method of generating chaotic radio pulses with a reproducible shape that could be varied in a manner that is controlled and natural for UWB radio by means of changing the supply voltage of the chaotic oscillator is shown. The mathematical model of the generator is simulated numerically and proves the proposed ideas. The shaping and the replicability of UWB pulses was experimentally proven in an analog domain on a testbed with four instances of the chaotic generator. [ABSTRACT FROM AUTHOR]

A new approach called ABRF (the attention-based random forest) and its modifications for applying the attention mechanism to the random forest (RF) for regression and classification are proposed. The main idea behind the proposed ABRF models is to assign attention weights with trainable parameters to decision trees in a specific way. The attention weights depend on the distance between an instance, which falls into a corresponding leaf of a tree, and training instances, which fall in the same leaf. This idea stems from representation of the Nadaraya–Watson kernel regression in the form of a RF. Three modifications of the general approach are proposed. The first one is based on applying the Huber's contamination model and on computing the attention weights by solving quadratic or linear optimization problems. The second and the third modifications use the gradient-based algorithms for computing an extended set of the attention trainable parameters. Numerical experiments with various regression and classification datasets illustrate the proposed method. The code implementing the approach is publicly available. [ABSTRACT FROM AUTHOR]

The concept of infinitesimal elastic deformation and the theory of elastic seismic waves was formed in the first part of the 19th century and was based mainly on the Fermat, Huygens and Snell developments in the theory of optics. At the same time, seismic wave propagation (utilized in geophysical prospecting) and optic wave propagation through defined media are based on the same physical-mathematical principles, making it possible to transfer nonconventional procedures developed in the first domain to the second one and back. In this investigation, we propose transferring advanced methodologies established in seismic prospecting to practical optics. We selected two advanced approaches with the following aims: (a) homeomorphic imaging; (b) novel description of boundary conditions. The first approach is established with the utilization of the revealed local theoretical relationship between the geometrical features of two fundamental beams and the geometrical properties of hidden geological targets of the media under study. The employed geometrical characteristics of the fundamental beams are spreading functions and curvatures of the singular wavefronts. The second approach is based on a novel description of the boundary conditions. It enables the determination of a faultless seismic (optical) system with the preassigned focusing and imaging assets when any aberrations are absent. An optimal optical system is usually determined as some arrangement agreeing to some perfect system with acceptable correctness. Employment of the developed procedures in the optical design will permit the application of a description of the optical surface using: (1) parametric functions, (2) differential equations, and (3) mixed (parametric-differential). On this basis, optical systems with a minimal number of optical features with complicated shapes can be promptly computed. Another important application field of the suggested methods is the design of optical systems with diffractive elements. [ABSTRACT FROM AUTHOR]

A new multi-attention based method for solving the MIL problem (MAMIL), which takes into account the neighboring patches or instances of each analyzed patch in a bag, is proposed. In the method, one of the attention modules takes into account adjacent patches or instances, several attention modules are used to get a diverse feature representation of patches, and one attention module is used to unite different feature representations to provide an accurate classification of each patch (instance) and the whole bag. Due to MAMIL, a combined representation of patches and their neighbors in the form of embeddings of a small dimensionality for simple classification is realized. Moreover, different types of patches are efficiently processed, and a diverse feature representation of patches in a bag by using several attention modules is implemented. A simple approach for explaining the classification predictions of patches is proposed. Numerical experiments with various datasets illustrate the proposed method. [ABSTRACT FROM AUTHOR]

The pressure dependence of electrical resistance of single-crystal magnetite (Fe3O4) was measured under quasi-hydrostatic conditions to 100 GPa using low-temperature, megabar diamond-anvil cell techniques in order to gain insight into the anomalous behavior of this material that has been reported over the years in different high-pressure experiments. The measurements under nearly hydrostatic pressure conditions allowed us to detect the clear Verwey transition and the highpressure structural phase. The appearance of a metallic ground state after the suppression of the Verwey transition around 20 GPa and the concomitant enhancement of the electrical resistance caused by the structural transformation to the high-pressure phase form reentrant semiconductingmetallic- semiconducting behavior, although the appearance of the metallic phase is highly sensitive to stress conditions and details of the measurement technique. [ABSTRACT FROM AUTHOR]

An extension of the Neural Additive Model (NAM) called SurvNAM and its modifications are proposed to explain predictions of a black-box machine learning survival model. The method is based on applying the original NAM to solving the explanation problem in the framework of survival analysis. The basic idea behind SurvNAM is to train the network by means of a specific expected loss function which takes into account peculiarities of the survival model predictions. Moreover, the loss function approximates the black-box model by the extension of the Cox proportional hazards model, which uses the well-known Generalized Additive Model (GAM) in place of the simple linear relationship of covariates. The proposed method SurvNAM allows performing local and global explanations. The global explanation uses the whole training dataset. In contrast to the global explanation, a set of synthetic examples around the explained example are randomly generated for the local explanation. The proposed modifications of SurvNAM are based on using the Lasso-based regularization for functions from GAM and for a special representation of the GAM functions using their weighted linear and non-linear parts, which is implemented as a shortcut connection. Many numerical experiments illustrate efficiency of SurvNAM. [ABSTRACT FROM AUTHOR]

A new software reliability growth model (SRGM) called RBoostSRGM is proposed in this paper. It can be regarded as a modification of the boosting SRGMs through the use of a reduced set of weights to take into account the behavior of the software reliability during the debugging process and to avoid overfitting. The main idea underlying the proposed model is to take into account that training data at the end of the debugging process may be more important than data from the beginning of the process. This is modeled by taking a set of weights which are assigned to the elements of training data, i.e., to the series of times to software failures. The second important idea is that this large set is restricted by the imprecise ε-contaminated model. The obtained RBoostSRGM is a parametric model because it is tuned in accordance with the contamination parameter ε. As a variation to this model, we also consider the use of the Kolmogorov-Smirnov bounds for the restriction of the set of weights. Various numerical experiments with data sets from the literature illustrate the proposed model and compare it with the standard non parametric SRGM and the standard boosting SRGM. [ABSTRACT FROM AUTHOR]

Recent studies demonstrate the effectiveness of integrated archaeo-geophysical tools in resolving various geological-environmental challenges. This involves combining geophysical methods in archaeological fieldwork or remote sensing methods for preliminary survey and analysis of archaeological sites, potentially enhanced by machine learning techniques to estimate object shapes and characteristics. This study highlights the potential of employing informational and probabilistic approaches as optimal tools for evaluating and integrating critical information for archaeological research. Our proposed procedure for assessing the reliability of tools or toolsets is based on improved methodologies utilizing conditional probability, which were suggested in previous authors' publications. We illustrate examples of combining remote sensing, known for its low cost, portability, and effectiveness in initial archaeological site identification, with machine learning methods to locate and discover new sites in archaeologically well-studied areas in Israel. Subsequently, we conduct an informational assessment of remote sensing data and propose steps to correlate this data with other geophysical information probabilistically. • Applying an information-probabilistic approach increases the quality, quantity, and associated parameters of geoscientific information. • Based on the information approach, Remote Sensing analysis can be numerically integrated with other geophysical and environmental studies. • Advanced Remote Sensing applications in Northern and Southern Israel enabled the discovery of earlier unknown archaeological targets. [ABSTRACT FROM AUTHOR]

A new robust algorithm based on the explanation method SurvLIME called SurvLIME-KS is proposed for explaining machine learning survival models. The algorithm is developed to ensure robustness to cases of a small amount of training data or outliers of survival data. The first idea behind SurvLIME-KS is to apply the Cox proportional hazards model to approximate the black-box survival model at the local area around a test example due to the linear relationship of covariates in the model. The second idea is to incorporate the well-known Kolmogorov–Smirnov bounds for constructing sets of predicted cumulative hazard functions. As a result, the robust maximin strategy is used, which aims to minimize the average distance between cumulative hazard functions of the explained black-box model and of the approximating Cox model, and to maximize the distance over all cumulative hazard functions in the interval produced by the Kolmogorov–Smirnov bounds. The maximin optimization problem is reduced to the quadratic program. Various numerical experiments with synthetic and real datasets demonstrate the SurvLIME-KS efficiency. [ABSTRACT FROM AUTHOR]

The process of earthquake appearance in its focus is analyzed on the basis of the oscillation theory. The earthquake focus consisting for simplicity of two blocks (granitic and basaltic) is studied mathematically and physically. The block sizes, density and Young's modulus of the rocks composing these blocks are considered to be known. We assume that the blocks are located on an edge of a regional tectonic fault. The tectonic plate or subplate, moving with a given speed u of shearing, is on the other edge of the fault. The mechanical interaction of the fault edges is due to the friction, which depends on the relative velocity V = u − dx/dt, where x is a coordinate of the concrete block. Physical-mathematical equations of block motion are solved using analytical methods. As a result, we find complete information about seismic vibrations in the focus and their characteristics. The evolutions of kinetic, potential and total energy as well as the function of dissipation in the focus and magnitude of the earthquake are calculated. The computations were carried out at different speeds of movement u. This allowed us to study the dependence of the earthquake magnitude on the velocity u of the main plate. The constructed original model of the earthquake focus unmasks the mechanism of seismic oscillations and their properties. [ABSTRACT FROM AUTHOR]

A new meta-algorithm for estimating the conditional average treatment effects is pro-posed in the paper. The basic idea behind the algorithm is to consider a new dataset consisting of feature vectors produced by means of concatenation of examples from control and treatment groups, which are close to each other. Outcomes of new data are defined as the difference between outcomes of the corresponding examples comprising new feature vectors. The second idea is based on the assumption that the number of controls is rather large and the control outcome function is precisely determined. This assumption allows us to augment treatments by generating feature vectors which are closed to available treatments. The outcome regression function constructed on the augmented set of concatenated feature vectors can be viewed as an estimator of the conditional average treatment effects. A simple modification of the Co-learner based on the random subspace method or the feature bagging is also proposed. Various numerical simulation experiments illustrate the proposed algorithm and show its outperformance in comparison with the well-known T-learner and X-learner for several types of the control and treatment outcome functions. [ABSTRACT FROM AUTHOR]

Remote sensing techniques provide crucial insights into ancient settlement patterns in various regions by uncovering previously unknown archaeological sites and clarifying the topological features of known ones. Meanwhile, in the northern part of the Southern Levant, megalithic structures remain largely underexplored with these methods. This study addresses this gap by analyzing the landscape around Rujm el-Hiri, one of the most prominent Southern Levantine megaliths dated to the Chalcolithic/Early Bronze Age, for the first time. We discuss the type and extent of the archaeological remains identified in satellite images within a broader context, focusing on the relationships between landscapes and these objects and the implications of their possible function. Our analysis of multi-year satellite imagery covering the 30 km region surrounding the Sea of Galilee reveals several distinct patterns: 40–90-m-wide circles and thick walls primarily constructed along streams, possibly as old as Rujm el-Hiri itself; later-period linear thin walls forming vast rectangular fields and flower-like clusters of ~ 20 m diameter round-shaped fences found in wet areas; tumuli, topologically linked to the linear walls and flower-like fences. Although tumuli share similar forms and likely construction techniques, their spatial distribution, connections to other archaeological features, and the statistical distribution in their sizes suggest that they might serve diverse functions. The objects and patterns identified may be used for further training neural networks to analyze their spatial properties and interrelationships. Most archaeological structures in the region were reused long after their original construction. This involved adding new features, building walls over older ones, and reshaping the landscape with new objects. Rujm el-Hiri is a prime example of such a complex sequence. Geomagnetic analysis shows that since the entire region has rotated over time, the Rujm el-Hiri's location shifted from its original position for tens of meters for the thousands of years of the object's existence, challenging theories of the alignment of its walls with astronomical bodies and raising questions regarding its possible identification as an observatory. [ABSTRACT FROM AUTHOR]

The theory of catastrophes and synergetics, developed by mathematicians in the second half of the twentieth century (Haken in Synergetics. Introduction and advances topics. Springer, Berlin, 1978; Arnol'd VI in Catastrophic theory. Springer, Berlin 2004), is used to analyze earthquake processes in active faults. It is shown that the oscillators of the seismic radiation are vibrating blocks at the border of the fault, and the source of the energy supply is the margin of the fault moving with the velocity V0. The input or loss of energy is regulated by a nonlinear element, the frictional force F(V), which depends on the difference V between the velocity of the fault margin V0 and the velocity of the seismic block vibration: V = V0 − dx/dt. Precise conditions for the occurrence of an earthquake, its attenuation, or the generation of a tectonic tremor in its source are formulated. [ABSTRACT FROM AUTHOR]

A weighted random survival forest is presented in the paper. It can be regarded as a modification of the random forest improving its performance. The main idea underlying the proposed model is to replace the standard procedure of averaging used for estimation of the random survival forest hazard function by weighted averaging where the weights are assigned to every tree and can be viewed as training parameters which are computed in an optimal way by solving a standard quadratic optimization problem maximizing Harrell's C-index. Numerical examples with real data illustrate the outperformance of the proposed model in comparison with the original random survival forest. [ABSTRACT FROM AUTHOR]

Abstract A modification of the Deep Forest or gcForest proposed by Zhou and Feng for solving classification problems is proposed in the paper and called as PM-DF. The main idea for improving classification performance of the Deep Forest is to assign weights to subsets of the class probability distributions at the leaf nodes computed for every training example. The subsets of probability distributions are defined by using Walley's imprecise pari-mutuel model which compactly divides the unit simplex of probabilities into subsets and allows us to simplify the algorithm of the weight calculation. The weights of the distribution subsets can be viewed in this case as second-order probabilities over subsets of the probability simplex. The optimal weights are computed by solving the standard quadratic optimization problem. The numerical experiments illustrate PM-DF. [ABSTRACT FROM AUTHOR]

A Discriminative Deep Forest (DisDF) as a metric learning algorithm is proposed in the paper. It is based on the Deep Forest or gcForest proposed by Zhou and Feng and can be viewed as a gcForest modification. The case of the fully supervised learning is studied when the class labels of individual training examples are known. The main idea underlying the algorithm is to assign weights to decision trees in random forest in order to reduce distances between objects from the same class and to increase them between objects from different classes. The weights are training parameters. A specific objective function which combines Euclidean and Manhattan distances and simplifies the optimization problem for training the DisDF is proposed. The numerical experiments illustrate the proposed distance metric algorithm. [ABSTRACT FROM AUTHOR]

Abstract We start by comparing two explicatory approaches, which we call law-centered and "instructivistic". Although the latter dominates in modern biology, we find it inappropriate for treating developmental problems, especially those related to morphogenesis. As an example of a law-centered approach we suggest a simple morphomechanical rule based upon the idea of the hyper-restoration of mechanical stresses. We show that this rule not only provides a general framework for morphogenesis, but can also reproduce, via parametric modulations, quite specific developmental events. [ABSTRACT FROM AUTHOR]

Abstract Several important morphogenetic processes belong to the category of collective cell movements (CCM), by which we mean coordinated rearrangements of many neighboring cells. The causes of the dynamic order established during CCM are still unclear. We performed statistical studies of rates and angular orientations of cell rearrangements in two kinds of embryonic tissues, which we categorized as "committed" (in the sense of being capable of autonomous CCM) as opposed to "naïve" tissues, which are those that require external forces in order to exhibit full scale CCM. In addition, we distinguished two types of cell rearrangements: first, those in which mutual cell-cell shifts characterizing the local dynamics (LD); and, second, those which moved in reference to common external coordinates (global dynamics, GD). We observed that in most cases LD rates deviated from normal distributions and do so by creating excesses of extensively converging and moderately diverging cells. In contrast, GD was characterized by nearly random behavior of slowly moving cells, combined with increased angular focusing of the fast cells trajectories as well as bimodal distribution of cell rates. When committed tissues were opposed by external mechanical forces, then they tended to preserve the inherent CCM patterns. On the other hand, the naïve ones reacted by creating two orthogonal cells flows, one of these coinciding with the force direction. We consider CCM as a self-organizing process based on feedbacks between converging and diverging cell shifts, which is able to focus the trajectories imposed by external forces. [ABSTRACT FROM AUTHOR]

This paper proposes a new software reliability growth model (SRGM), which can be regarded as an extension of the non-parametric SRGMs using support vector regression to predict probability measures of time to software failure. The first novelty underlying the proposed model is the use of a set of weights instead of precise weights as done in the established non-parametric SRGMs, and to minimize the expected risk in the framework of robust decision making. The second novelty is the use of the intersection of two specific sets of weights, produced by the imprecise ε-contaminated model and by pairwise comparisons, respectively. The sets are chosen in accordance to intuitive conceptions concerning the software reliability behaviour during a debugging process. The proposed model is illustrated using several real data sets and it is compared to the standard non-parametric SRGM. [ABSTRACT FROM AUTHOR]

The symmetry-breaking first-order phase transition between superfluid phases 3 He-A and 3 He-B can be triggered extrinsically by ionising radiation or heterogeneous nucleation arising from the details of the sample cell construction. However, the role of potential homogeneous intrinsic nucleation mechanisms remains elusive. Discovering and resolving the intrinsic processes may have cosmological consequences, since an analogous first-order phase transition, and the production of gravitational waves, has been predicted for the very early stages of the expanding Universe in many extensions of the Standard Model of particle physics. Here we introduce a new approach for probing the phase transition in superfluid 3 He. The setup consists of a novel stepped-height nanofluidic sample container with close to atomically smooth walls. The 3 He is confined in five tiny nanofabricated volumes and assayed non-invasively by NMR. Tuning of the state of 3 He by confinement is used to isolate each of these five volumes so that the phase transitions in them can occur independently and free from any obvious sources of heterogeneous nucleation. The small volumes also ensure that the transitions triggered by ionising radiation are strongly suppressed. Here we present the preliminary measurements using this setup, showing both strong supercooling of 3 He-A and superheating of 3 He-B, with stochastic processes dominating the phase transitions between the two. The objective is to study the nucleation as a function of temperature and pressure over the full phase diagram, to both better test the proposed extrinsic mechanisms and seek potential parallel intrinsic mechanisms. [ABSTRACT FROM AUTHOR]

The most important data on the thermal regime of the Earth’s interior come from temperature measurements in deep boreholes. The drilling process greatly alters the temperature field of formations surrounding the wellbore. In permafrost regions, due to thawing of the formation surrounding the wellbore during drilling, representative data can be obtained only by repeated observations over a long period of time (up to 10 years). Usually a number of temperature logs (3–10) are taken after the well's shut-in. Significant expenses are required to monitor the temperature regime of deep wells. In this paper, we introduce a new approach in predicting the undisturbed formations temperatures from shut-in temperature logs in deep wells. The main features of the suggested method are following: the refreezing of the thawed formations (around the wellbore) is completed, the temperature logs are taken after refreezing, the starting point in the well thermal recovery is moved from the end of well completion to the moment of time when the first shut-in temperature log was conducted. It is shown that after refreezing the further cooling of a well can be approximated by a constant (per unit of length) linear heat source. Hence, the Horner equation can be used for predicting the temperature of frozen formations for estimation of the formation temperature. A simple method to process field temperature data is presented. To demonstrate this approach, temperature shut-in time data for four depths from four wells in Alaska were successfully used. [ABSTRACT FROM AUTHOR]

A Siamese Deep Forest (SDF) is proposed in the paper. It is based on the Deep Forest or gcForest proposed by Zhou and Feng and can be viewed as a gcForest modification. It can be also regarded as an alternative to the well-known Siamese neural networks. The SDF uses a modified training set consisting of concatenated pairs of vectors. Moreover, it defines the class distributions in the deep forest as the weighted sum of the tree class probabilities such that the weights are determined in order to reduce distances between similar pairs and to increase them between dissimilar points. We show that the weights can be obtained by solving a quadratic optimization problem. The SDF aims to prevent overfitting which takes place in neural networks when only limited training data are available. The numerical experiments illustrate the proposed distance metric method. [ABSTRACT FROM AUTHOR]

A new robust SVM-based algorithm of the binary classification is proposed. It is based on the so-called uncertainty trick when training data with the interval uncertainty are transformed to training data with the weight or probabilistic uncertainty. Every interval is replaced by a set of training points with the same class label such that every point inside the interval has an unknown weight from a predefined set of weights. The robust strategy dealing with the upper bound of the interval-valued expected risk produced by a set of weights is used in the SVM. An extension of the algorithm based on using the imprecise Dirichlet model is proposed for its additional robustification. Numerical examples with synthetic and real interval-valued training data illustrate the proposed algorithm and its extension. [ABSTRACT FROM AUTHOR]

A modification of the well-known one-class classification support vector machine (OCC SVM) dealing with interval-valued or set-valued training data is proposed. Its main idea is to represent every interval of training data by a finite set of precise data with imprecise weights. This representation is based on replacement of the interval-valued expected risk produced by interval-valued data with the interval-valued expected risk produced by imprecise weights or sets of weights. In other words, the interval uncertainty is replaced with the imprecise weight or probabilistic uncertainty. It is shown how constraints for the imprecise weights are incorporated into dual quadratic programming problems which can be viewed as extensions of the well-known OCC SVM models. Numerical examples with synthetic and real interval-valued training data illustrate the proposed approach and investigate its properties. [ABSTRACT FROM AUTHOR]

Classification algorithms based on different forms of support vector machines (SVMs) for dealing with interval-valued training data are proposed in the paper. L 2 -norm and L ∞ -norm SVMs are used for constructing the algorithms. The main idea allowing us to represent the complex optimization problems as a set of simple linear or quadratic programming problems is to approximate the Gaussian kernel by the well-known triangular and Epanechnikov kernels. The minimax strategy is used to choose an optimal probability distribution from the set and to construct optimal separating functions. Numerical experiments illustrate the algorithms. [ABSTRACT FROM AUTHOR]

The biodiversity paradox has been a long-standing enigma in theoretical ecology. It emerged as a contradiction between the competitive exclusion principle and the natural species richness. There are two competing ecological theories which try to explain this issue: niche theory and neutral theory. The problem is that both theories are based on nontransparent models which ignore local interactions between individuals and cannot provide an understanding of interspecific competition mechanisms. Mathematical models of complex systems may be of three general types: black-box, grey-box and white-box models. Classical ecological models – Malthusian, Verhulst and Lotka–Volterra models are of black-box type. Black-box models are nonmechanistic. They cannot help to create a mechanistic ecological theory as they do not provide a direct insight into individual-based mechanisms. Here we make some critical notes on the recent attempts to resolve the paradox by black-box and grey-box approaches. We critically discuss a contribution of the neutral theory and the attempts to solve the paradox by methods of the classical quantum mechanics. These attempts are rather ineffective due to the lack of mechanicalness and likely lead to more confusion than clarity in understanding of biodiversity mechanisms. We also discuss our solution to the biodiversity paradox through a verification of the competitive exclusion principle with using the white-box approach to mathematical modelling of competitive coexistence. [ABSTRACT FROM AUTHOR]

Abstract The late Lev V. Beloussov wrote a 2005 textbook The Foundations of General Embryology which is available in Russian. In 2003 he prepared an excellent, annotated translation of the part of his manuscript for this book on distinguishing positional information models from morphogenetic field models of embryogenesis, which is reproduced here verbatim. He concluded: "...the PI [positional information] concept has no predictive value, and cannot be thus regarded as a scientific theory". [ABSTRACT FROM AUTHOR]

Magnetic prospecting is one of the most widely used methods for archaeological prospection in the world. Noise both of natural [main factors are inclined magnetization, complex geological (archaeological) structure of investigated sites, and uneven terrain relief] and artificial origin (different iron-containing targets, electric power lines, etc.) strongly obscure interpretation of observed magnetic anomalies. For quantitative analysis of magnetic anomalies produced by archaeological targets under aforementioned conditions a non-conventional interpreting system has been developed. Methodology of magnetic anomalies interpretation from models of thin bed and horizontal circular cylinder (sphere) in conditions of oblique magnetization, rugged relief and unknown level of the total magnetic field by the use of improved versions of characteristic point and tangents has been earlier suggested. However, many archaeological targets have geometrical form of thick bed, thin horizontal plate and intermediate between these two models. In this paper methodology of magnetic anomalies produced by thick bed models in complex environments is explicitly described. It is shown that quantitative analysis of magnetic anomalies due to intermediate (between the thick bed and thin horizontal plate) targets could be successfully carried out by the use of methodology developed for the thick bed model. In the case of thin horizontal plate with a large horizontal extent, the two measured anomalies may be interpreted as anomalies from thin beds. The interpretation methodology was successfully tested both on typical models and on real archaeological targets with some success. [ABSTRACT FROM AUTHOR]

A robust one-class classification model as an extension of Campbell and Bennett’s (C–B) novelty detection model on the case of interval-valued training data is proposed in the paper. It is shown that the dual optimization problem to a linear program in the C–B model has a nice property allowing to represent it as a set of simple linear programs. It is proposed also to replace the Gaussian kernel in the obtained linear support vector machines by the well-known triangular kernel which can be regarded as an approximation of the Gaussian kernel. This replacement allows us to get a finite set of simple linear optimization problems for dealing with interval-valued data. Numerical experiments with synthetic and real data illustrate performance of the proposed model. [ABSTRACT FROM AUTHOR]

This paper presents an investigation into generalised Bayesian analysis of warranty contracts, using sets of prior distributions within the theory of imprecise probability. Explicit expressions are derived for optimal lower and upper bounds for the expected profit for the manufacturer of a product, corresponding to an imprecise negative binomial model for which two sets of prior distributions are studied. The results can be used to set a maximum value of compensation such that the manufacturer׳s expected profit remains positive, under vague prior knowledge. [ABSTRACT FROM AUTHOR]

A new algorithm for ensemble construction based on adapted restricting a set of weights of examples in training data to avoid overfitting and to reduce a number of iterations is proposed in the paper. The algorithm called IDMBoost (Imprecise Dirichlet Model Boost) applies Walley׳s imprecise Dirichlet model for modifying the restricted sets of weights depending on the number and location of classification errors. Updating of weights within the restricted set (simplex) is carried out by using its extreme points. The proposed algorithm has a double adaptation procedure. The first adaptation is carried out within every restricted simplex like the AdaBoost. The second adaptation reduces and changes the restricted sets of possible weights of examples. Various numerical experiments with real data illustrate the proposed algorithm. [ABSTRACT FROM AUTHOR]

A framework for constructing robust classification models is proposed in the paper. An assumption about importance of one of the classes in comparison with other classes is incorporated into the models. It often takes place in the real application, for example, in reliability, in medical diagnostic, etc. A main idea underlying the models is to consider a set of probability distributions on training examples produced by the imprecise probability models such as linear-vacuous mixture and constant odd-ratio contaminated models. Extreme points of the sets of probability distributions are a main tool for constructing the robust classifiers. It is shown that algorithms for computing optimal classification parameters are reduced to a finite number of weighted support vector machines with weights determined by the extreme points. Experimental results with synthetic and real data illustrate the proposed models. [ABSTRACT FROM AUTHOR]

Abstract: Robust classification models based on the ensemble methodology are proposed in the paper. The main feature of the models is that the precise vector of weights assigned for examples in the training set at each iteration of boosting is replaced by a local convex set of weight vectors. The minimax strategy is used for building weak classifiers at each iteration. The local sets of weights are constructed by means of imprecise statistical models. The proposed models are called RILBoost (Robust Imprecise Local Boost). Numerical experiments with real data show that the proposed models outperform the standard AdaBoost algorithm for several well-known data sets. [Copyright &y& Elsevier]

ABSTRACT The application of geophysical methods to archaeological sites is limited by physical, environmental, economic and time considerations. The presence of numerous kinds of noise means that in many cases the archaeological targets and surrounding media are best approached as probabilistic objects, such that the amount of information potentially available from different geophysical methods can be estimated by probabilistic and statistical methods, including the risks associated with this decision-making. Here it is shown that simple informational and probabilistic criteria can be applied to formalize the information that can be obtained by applying different geophysical methods. To assess their relative value, geophysical methods, geophysical information and cost and time factors are convoluted in order to generate integrated parameters. This theoretical presentation of the information parameters is illustrated by the calculation of actual results. The solution to this 'four colour' mathematical problem shows that two independent geophysical methods are sufficient to characterize the archaeological potential of a site. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The B phase of superfluid 3He is a three-dimensional time-reversal invariant topological superfluid, predicted to support gapless Majorana surface states. We confine superfluid 3He into a thin nanofluidic slab geometry. In the presence of a weak symmetry-breaking magnetic field, we have observed two possible states of the confined 3He-S phase manifold, through the small tipping angle NMR response. Large tipping angle nonlinear NMR has allowed the identification of the order parameter of these states and enabled a measurement of the surface-induced gap distortion. The results for two different quasiparticle surface scattering boundary conditions are compared with the predictions of weak-coupling quasiclassical theory. We identify a textural domain wall between the two B phase states, the edge of which at the cavity surface is predicted to host gapless states, protected in the magnetic field. [ABSTRACT FROM AUTHOR]

A new approach for ensemble construction based on restricting a set of weights of examples in training data to avoid overfitting is proposed in the paper. The algorithm called EPIBoost (Extreme Points Imprecise Boost) applies imprecise statistical models to restrict the set of weights. The updating of the weights within the restricted set is carried out by using its extreme points. The approach allows us to construct various algorithms by applying different imprecise statistical models for producing the restricted set. It is shown by various numerical experiments with real data sets that the EPIBoost algorithm may outperform the standard AdaBoost for some parameters of imprecise statistical models. [ABSTRACT FROM AUTHOR]

Abstract: We start from reviewing different epistemological constructions used for explaining morphogenesis. Among them, we explore the explanatory power of a law-centered approach which includes top-down causation and the basic concepts of a self-organization theory. Within such a framework, we discuss the morphomechanical models based upon the presumption of feedbacks between mechanical stresses imposed onto a given embryo part from outside and those generated within the latter as a kind of active response. A number of elementary morphogenetic events demonstrating that these feedbacks are directed towards hyper-restoration (restoration with an overshoot) of the initial state of mechanical stresses are described. Moreover, we show that these reactions are bound together into the larger scale feedbacks. That permits to suggest a reconstruction of morphogenetic successions in early Metazoan development concentrated around two main archetypes distinguished by the blastopores geometry. The perspectives of applying the same approach to cell differentiation are outlined. By discussing the problem of positional information we suggest that the developmental pathway of a given embryo part depends upon its preceded deformations and the corresponding mechanical stresses rather than upon its static position at any moment of development. [Copyright &y& Elsevier]

Marine protected areas (MPA) become part of modern fishery management to safeguard marine life and sustain ecosystem processes. Based on a classical Ricker's model, the deterministic nonlinear sink-source model of MPA is presented. Sufficient conditions for the existence of positive bounded steady-states are obtained. The present value of net revenue is maximized subject to population dynamics in the fishing zone and in the protected area. The analysis has shown that there is an optimal equilibrium solution, and the best harvesting policy to attain this equilibrium position is a bang-bang control effort. It was demonstrated numerically by comparing the optimal harvesting rate against a constant harvesting rate, and the fast convergence to the optimal equilibrium guarantees greater profits under the optimal harvesting strategy. [ABSTRACT FROM AUTHOR]

Abstract: Magnetic prospecting is one of the most widely used methods for investigating archaeological sites in the world. It is often applied before and during various types of industrial development and in agricultural areas. In Israel, most potential archaeological targets are located in urban settings, which substantially complicate their geophysical signatures. Noise from natural factors such as the inclined magnetization (about 44°) complex geological structure of the sites, and uneven terrain relief as well as artificial sources such as modern iron-containing objects, power lines and underground communications can confound the interpretation of magnetic anomalies. For the quantitative analysis of magnetic anomalies from ancient targets in Israel nonconventional procedures (; ) were applied. In this paper the effects of power lines on the quantitative analysis of magnetic anomalies indicative of archaeological objects are investigated. The method was tested on two typical models of physical–archaeological ancient remains by using different distances to the power line. [Copyright &y& Elsevier]

Microgravity investigations are widely applied at present for solving various environmental and geological problems. Unfortunately, microgravity survey is comparatively rarely used for searching for hidden ancient targets. It is caused mainly by small geometric size of the desired archaeological objects and various types of noise complicating the observed useful signal. At the same time, development of modern generation of field gravimetric equipment allows to register promptly and digitally microGal (10-8 m/s2) anomalies that offer a new challenge in this direction. An advanced methodology of gravity anomalies analysis and modern 3D modeling, intended for ancient targets delineation, is briefly presented. It is supposed to apply in archaeological microgravity the developed original methods for the surrounding terrain relief computing. Calculating second and third derivatives of gravity potential are useful for revealing some closed peculiarities of the different Physical-Archaeological Models (PAMs). It is underlined that physical measurement of vertical gravity derivatives in archaeological studying has a significant importance and cannot be replaced by any transformation methods. Archaeological targets in Israel have been ranged by their density/geometrical characteristics in several groups. The performed model computations indicate that microgravity investigations might be successfully applied at least in 20-25% of archaeological sites in Israel. [ABSTRACT FROM AUTHOR]

1An integrated pest management (IPM) system incorporating the introduction and field release of Diadegma semiclausum (Hellén), a parasitoid of diamondback moth (DBM) Plutella xylostella (L.), comprising the worst insect pest of the cabbage family, has been developed in Kenya to replace the pesticides-only approach. 2Mathematical modelling using differential equations has been used in theoretical studies of host–parasitoid systems. Although, this method helps in gaining an understanding of the system's dynamics, it is generally less accurate when used for prediction. The artificial neural network (ANN) approach was therefore chosen to aid prediction. 3The ANN methodology was applied to predict the population density of the DBM and D. semiclausum, its larval parasitoid. Two data sets, each from different release areas in the Kenya highlands, and both collected during a 3-year period after the release of the parasitoid, were used in the present study. Two ANN models were developed using these data. 4The ANN approach gave satisfactory results for DBM and for D. semiclausum. Sensitivity analysis suggested that pest populations may be naturally controlled by rainfall. 5The ANN provides a powerful tool for predicting host–parasitoid population densities and made few assumptions on the field data. The approach allowed the use of data collected at any appropriate scale of the system, bypassing the assumptions and uncertainties that could have occurred when parameters are imported from other systems. The methodology can be explored with respect to the development of tools for monitoring and forecasting the population densities of a pest and its natural enemies. In addition, the model can be used to evaluate the relative effectiveness of the natural enemies and to investigate augmentative biological control strategies. [ABSTRACT FROM AUTHOR]

The performance of discrete mathematical models to describe the population dynamics of diamondback moth (DBM) (Plutella xylostella L.) and its parasitoid Diadegma semiclausum was investigated. The parameter values for several well-known models (Nicholson¿Bailey, Hassell and Varley, Beddington, Free and Lawton, May, Holling type 2, 3 and Getz and Mills functional responses) were estimated. The models were tested on 20 consecutive sets of time series data collected at 14 days interval for pest and parasitoid populations obtained from a highland cabbage growing area in eastern Kenya. Model parameters were estimated from minimized squared difference between the numerical solution of the model equations and the empirical data using Powell's method. Maximum calculated DBM growth rates varied between 0.02 and 0.07. The carrying capacity determined at 16.5 DBM/plant by the Beddington et al. model was within the range of field data. However, all the estimated parameter values relating to the parasitoid, including the instantaneous searching rate (0.07¿0.28), per capita searching efficiency (0.20¿0.27), search time (5.20¿5.33), handling time (0.77¿0.90), and parasitism aggregation index (0.33), were well outside the range encountered empirically. All models evaluated for DBM under Durbin¿Watson criteria, except the May model, were not autocorrelated with respect to residuals. In contrast, the criteria applied to the parasitoid residuals showed strong autocorrelations. Thus, these models failed to estimate parasitoid dynamics. We conclude that the interactions of the DBM with its parasitoid cannot be explained by any of the models tested. Two factors may be associated with this failure. First, the parasitoid in this integrated biological control system may not be playing a major role in regulating DBM population. Second, and perhaps more likely, poor correlations reflect gross inadequacies in the theoretical assumptions that underlie the existing models. [Copyright &y& Elsevier]