Miracle-dz/UniFAR-FTUs huggingface dataset:
A New Capacity Scaling Law in Ultra-Dense Networks

We discover a new capacity scaling law in ultradense networks (UDNs) under practical system assumptions, such as a general multi-piece path loss model, a non-zero base station (BS) to user equipment (UE) antenna height difference, and a finite UE density. The intuition and implication of this new capacity scaling law are completely different from that found in year 2011. That law indicated that the increase of the interference power caused by a denser network would be exactly compensated by the increase of the signal power due to the reduced distance between transmitters and receivers, and thus network capacity should grow linearly with network densification. However, we find that both the signal and interference powers become bounded in practical UDNs, which leads to a constant capacity scaling law. As a result, network densification should be stopped at a certain level for a given UE density, because the network capacity will reach its limit due to (i) the bounded signal and interference powers, and (ii) a finite frequency reuse factor because of a finite UE density. Our new discovery on the constant capacity scaling law also resolves the recent concerns about network capacity collapsing in UDNs, e.g., the capacity crash due to a non-zero BS-to-UE antenna height difference, or a bounded path loss model of the near-field (NF) effect, etc.
Coupling the Conditional Moment Closure Model to a Fully Compressible Large Eddy Simulation Algorithm

The Conditional Moment Closure (CMC) model provides a means of closing the subgrid terms for the reaction rates through the assumption that departures of the mean filtered reaction rate (conditional on a mixture fraction or progress variable) are small. Turbulentchemistry interaction is incorporated through a conditional scalar dissipation. To date, all Large Eddy Simulation implementations of the CMC model are with incompressible solvers and for non-premixed flames. This paper presents a methodology of coupling the CMC model with a fully compressible solver, and resultant validation against DNS data. This methodology can be employed for both premixed and non-premixed flames. Several challenges associated with premixed modeling using this approach are outlined.
Multipressure Polytropes as Models for the Structure and Stability of Molecular Clouds. I. Theory

We present a theoretical formalism for determining the structure of molecular clouds and the precollapse conditions in star-forming regions. The model consists of a pressure-bounded, self-gravitating sphere of an ideal gas that is supported by several distinct pressures. Since each pressure component is assumed to obey a polytropic law Pi(r) ∝ ργpi, we refer to these models as "multipressure polytropes. " We treat the case without rotation. The time evolution of one of these polytropes depends additionally on the adiabatic index γi of each component, which is modified to account for the effects of any thermal coupling to the environment of the cloud. We derive structure equations as well as perturbation equations for performing a linear stability analysis. Special attention is given to representing properly the significant pressure components in molecular clouds: thermal motions, static magnetic fields, and turbulence. The fundamental approximation in our treatment is that the effects of turbulent motions in supporting a cloud against gravity can be approximated by a polytropic pressure component. In particular, we approximate the turbulent motions as a superposition of Alfvén waves. We generalize the standard treatment of the stability of polytropes to allow for the flow of entropy in response to a perturbation, as expected for the entropy associated with wave pressure. In contrast to the pressure components within stars, the pressure components within interstellar clouds are "soft," with polytropic indices γpi ≤ 4/3 and (except for Alfvén waves) adiabatic indices γi ≤ 4/3. This paper focuses on the characteristics of adiabatic polytropes with a single pressure component that are near the brink of gravitational instability as a function of γpi and γi for γpi ≤ 4/3. The properties of such polytropes are generally governed by the conditions at the surface. We obtain upper limits for the mass and size of polytropes in terms of the density and sound speed at the surface. The mean-to-surface density and pressure drops are limited to less than a factor 4 for γp ≤ 1, regardless of the value of γ. The central-to-surface density and pressure drops in isentropic clouds (γi = γpi) are also limited, but they can become quite large (as observed) in nonisentropic clouds, which have γi > γpi. We find that the motions associated with Alfvén waves are somewhat less effective in supporting clouds than are the kinetic motions in an isothermal gas.
Numerical modeling and experimental validation of dynamic fracture events along weak planes

The conceptual simplicity and the ability of cohesive finite el ment models to describe complex fracture phenomena makes them often the approach of oice to study dynamic fracture. These models have proven to reproduce some experi mental features, but to this point, no systematic study has validated their predictive a bility; the difficulty in producing a sufficiently complete experimental record, and the intens iv computational requirements needed to obtain converged simulations are possible causes . Here, we present a systematic integrated numerical-experimental approach to the verific at on and validation (V&V) of simulations of dynamic fracture along weak planes. We descr ibe the intertwined computational and the experimental sides of the work, present the V& V results, and extract general conclusions about this kind of integrative approach.
The Maze of Urban Housing Markets: Theory, Evidence, and Policy

This powerful new theoretical approach to analyzing urban housing problems and the policies designed to rectify them will be a vital resource for urban planners, developers, policymakers, and economists. The search for the roots of serious urban housing problems such as homelessness, abandonment, rent burdens, slums, and gentrification has traditionally focused on the poorest sector of the housing market. The findings set forth in this volume show that the roots of such problems lie in the relationships among different parts of the market not solely within the lower-quality portion though that is where problems are most dramatically manifested and housing reforms are myopically focused. The authors propose a new understanding of the market structure characterized by a closely interrelated array of quality submarkets. Their comprehensive models ground a unified theory that accounts for demand by both renters and owner occupants, supply by owners of existing dwellings, changes in the stock of housing due to conversions and new construction, and interactions across submarkets. "
Psychophysics in insect hearing

Psychophysics has much to offer the study of insect hearing. Not only is there a rich set of experimental methods to apply, there is a large body of experimental work on vertebrate hearing that can suggest topics for investigation and provide material for cross‐species comparisons. We present an overview of the methods of psychophysics, followed by specific examples of their use in insects. Topics covered include intensity discrimination, frequency analysis and discrimination, temporal integration and acuity, and localization. We conclude by pointing out additional areas of research suggested by the reviewed work and areas in which a psychophysical approach would be useful. Microsc. Res. Tech. 63:375–387, 2004. © 2004 Wiley‐Liss, Inc.
Totally Corrective Boosting with Cardinality Penalization

We propose a totally corrective boosting algorithm with explicit cardinality regularization. The resulting combinatorial optimization problems are not known to be efficiently solvable with existing classical methods, but emerging quantum optimization technology gives hope for achieving sparser models in practice. In order to demonstrate the utility of our algorithm, we use a distributed classical heuristic optimizer as a stand-in for quantum hardware. Even though this evaluation methodology incurs large time and resource costs on classical computing machinery, it allows us to gauge the potential gains in generalization performance and sparsity of the resulting boosted ensembles. Our experimental results on public data sets commonly used for benchmarking of boosting algorithms decidedly demonstrate the existence of such advantages. If actual quantum optimization were to be used with this algorithm in the future, we would expect equivalent or superior results at much smaller time and energy costs during training. Moreover, studying cardinality-penalized boosting also sheds light on why unregularized boosting algorithms with early stopping often yield better results than their counterparts with explicit convex regularization: Early stopping performs suboptimal cardinality regularization. The results that we present here indicate it is beneficial to explicitly solve the combinatorial problem still left open at early termination.
Attitude-Based Strategic Negotiation for Conflict Management in Construction Projects

An innovative negotiation methodology for managing conflicts in construction projects is presented in this article where multiple decision makers are involved. The proposed negotiation methodology has a unique ability to consider the attitudes of the decision makers, which is an important psychological factor in the negotiations that take place in various stages of a construction project. The methodology is developed at the strategic level of decision making in which the graph model for conflict resolution (GMCR) is employed in assisting decision makers, such as project managers, to achieve the best strategic decision, given the competing interests and attitudes of the decision makers. A real-life case study is used to illustrate how the proposed methodology can be conveniently applied in practice and to demonstrate the importance and the benefits of incorporating the attitudes of multiple decision makers into the negotiation process in order to better identify the most feasible resolutions. The proposed negotiation methodology has been implemented in a negotiation decision support system that assists project managers in tackling real-world controversies, particularly in complex disputes that occur in construction projects.
Object Specific Deep Learning Feature and Its Application to Face Detection

We present a method for discovering and exploiting object specific deep learning features and use face detection as a case study. Motivated by the observation that certain convolutional channels of a Convolutional Neural Network (CNN) exhibit object specific responses, we seek to discover and exploit the convolutional channels of a CNN in which neurons are activated by the presence of specific objects in the input image. A method for explicitly fine-tuning a pre-trained CNN to induce an object specific channel (OSC) and systematically identifying it for the human face object has been developed. Based on the basic OSC features, we introduce a multi-resolution approach to constructing robust face heatmaps for fast face detection in unconstrained settings. We show that multi-resolution OSC can be used to develop state of the art face detectors which have the advantage of being simple and compact.
Ear recognition based on deep convolutional network

Ear recognition is gaining on popularity in recent years. The human ear are neither affected by expressions like faces are nor do need closer touching like finger-prints do. In this paper, a novel algorithm was proposed to do ear recognition using deep convolutional neural network and provide a visualization of the learned network. We design a convolutional neural network with three convolutional layers, a fully-connected layer and a soft-max classifier. The experimental results on USTB ear database indict that our proposed method is easier botain high accuracy and outperforms the traditional method in dealing with partial occlusion.
Client based Object-Oriented Cohesion Metrics

For code quality control, there exists tens of object- oriented (OO) metrics on class cohesion and coupling. This abundant variety indicates that designing feasible metrics to differentiate good object-oriented programs from bad ones is not an easy task. Even worse, in practice OO cohesion metrics have a minor role in code reviews. In this paper we argue the proposed cohesion metrics, based on internal usage of class members, yield misleading (bad) values for several reasons. We propose a totally different kind of cohesion metric based on external class usage. Cohesion metrics typically define a bipartite usage graph between methods of a class and its variables. Providing different interpretations for methods, variables, usage relation, and calculation method have served as sources of variation. Classical interpretation is to measure internal usage of instance variables by the instance methods of a class to evaluate relatedness of the class properties. We argue that the relatedness also depends on the usage context, e.g. an application. Another problem is that objects often consist of property sets that are only slightly related internally, since an inheriting class often defines properties that are only slightly related to the inherited ones. Yet, the clients of such objects often make connections between the property sets by simultaneously advancing internally seemingly unrelated property sets. We propose forming cohesion graphs between the clients of a class and its member variables. Our approach complements the internal cohesion view with external views based on the external usage of class properties. We provide results for three popular open-source projects indicating our external cohesion measure to give much more meaningful results.
Enhancing Confidentiality and Privacy of Outsourced Spatial Data

The increase of spatial data has led organizations to upload their data onto third-party service providers. Cloud computing allows data owners to outsource their databases, eliminating the need for costly storage and computational resources. The main challenge is maintaining data confidentiality with respect to untrusted parties as well as providing efficient and accurate query results to the authenticated users. We propose a dual transformation scheme on the spatial database to overcome this problem, while the service provider executes queries and returns results to the users. First, our approach utilizes the space-filling Hilbert curve to map each spatial point in the multidimensional space to a one-dimensional space. This space transformation method is easy to compute and preserves the spatial proximity. Next, the order-preserving encryption algorithm is applied to the clustered data. The user issues spatial range queries to the service provider on the encrypted Hilbert index and then uses a secret key to decrypt the query response returned. This allows data protection and reduces the query communication cost between the user and service provider.
GRADIENT ANALYSIS OF LANDSCAPE PATTERN SPATIAL-TEMPORAL CHANGES IN BEIJING METROPOLITAN AREA , CHINA

The gradient based landscape metrics analysis is no w widely used to study the landscape pattern change in r spond to urbanization. In order to discover the Beijing metropolitan area l andscape pattern spatial and temporal change trends during the past 15 years, several landscape metrics are computed along a 96 k m long transect crossing Beijing metropolitan area f om west to east. Specially, the spatial extent issue was profound examined in t he gradient analysis. The temporal land use data se ts ar derived from three Landsat images got by year 1992, 1999 and 2006. The results show that the metrics varied smoothly and regularly along the land use transect when the window is large, while fluctuated irregularly when the window is small. The spatial and temporal characteristics of the metrics well match the hypothetical framework o f spatio-temporal urban sprawl in the form of alter nating processes of diffusion and coalescence. Some new trends of the urban spraw l style in Beijing metropolitan area are also detect ed by gradient landscape metrics analysis. * Corresponding author. E-mail address: yetao.yang@gmail.com. phone +86 027 67883257; fax +86 027 67883251.
Resilient decentralized consensus-based state estimation for smart grid in presence of false data

State estimation is an essential part of energy management system in smart grid as it is a basis for many of the associated management and control processes. In this paper, we present a decentralized state estimation approach, based on consensus optimization and the alternating direction method of multipliers, that is robust against certain harsh class of false data injection schemes. The proposed scheme provides a reliable estimate of the global system state in a distributed manner even if the system is regionally unobservable to some regional controllers, but globally observable across regions. The scheme also accommodates different communication network topologies for a given power network. We assess the performance of the presented schemes on IEEE 14 and 118 bus test systems.
A Brief History of Racism in Immigration Policies for Recruiting Domestics

by R h Cobm gan to allow immigration from countries other than Britain. Yet, as the L butnm trace I'histoire du raeimK au demographic composition of domesscin dcs politiques & l'immigration tic migrants changed from predomicanudiennc au vinp'hne s2cle cn ce nantly white women to primarily qui a trait au recrutement des women of colour, Canada's admistravailkusessdomestiq~es~ Son examen sion policies became more complex, eventually denying foreign dokstics citi&*As the hographic composition of domestic ship rights and somigrants changed fiom predominantly white cial entitlements
Research on Survivability of Networked Information System

Survivability is still an unsolved problem in the field of computer science. This paper surveys and discusses research results in recent years, including survivability definition, requirement and development of survivability system, survivability analysis, survivability enhancement and architecture. At the end, this paper points out the key problems of survivability research from view of theory and practice and our future works.
Survey Expectations of Returns and Asset Pricing Puzzles

Survey expectations of returns negatively predict future returns both in the cross section of countries and in the time series in three major asset classes: global equities, currencies, and global fixed income. While past returns and survey expectations of economic growth explain some of the variation in survey expectations of returns, the residual variation in survey expectations of returns also predicts future returns in all three asset classes. We find that the variation in expected returns that is related to survey expectations of returns is positively correlated with the amount of excess volatility across international equity markets.
Biology of the mononuclear phagocyte system of the central nervous system and HIV infection

Infection of the central nervous system by HIV‐1 results in widespread neuropathology and neurological symptoms. The cells infected in the brain belong to the mononuclear phagocyte lineage. We review aspects of the biology of the macrophage populations associated with the central nervous system and consider their distribution, phenotype, and kinetics in relation to HIV pathology. J. Leukoc. Biol. 56: 399–406; 1994.
Development of Acoustic Space in 3 to 5 Years Old Hindi Speaking Children

Many studies have described the acoustics of speech focusing on the speech of adults, but only a few have analyzed children's speech. Studies on development of language in children often include their articulatory speech patterns but the process of speech development is only partially understood. This research focuses on the development of Acoustic/Vowel space in Hindi speaking children. The study assumes that the acoustic space is continuously being redefined and modified in order to achieve and maintain a certain perceptual contrast and attempts to explore how acoustic space develops in children from three to five years of age.An acoustic study of seven peripheral vowels of Hindi, this study uses the first two formants of the vowels to arrive at a graphic representation of the acoustic space for peripheral vowels as articulated by the subjects under study. The area of the vowel/ acoustic space is then calculated using Irregular Polygon Area Calculator. The development of acoustic space in six Hindi speaking children from 3 to 5 years of age shows interesting results which are presented in this paper.
Functional Neuroanatomy for Posture and Gait Control

Here we argue functional neuroanatomy for posture-gait control. Multi-sensory information such as somatosensory, visual and vestibular sensation act on various areas of the brain so that adaptable posture-gait control can be achieved. Automatic process of gait, which is steady-state stepping movements associating with postural reflexes including headeye coordination accompanied by appropriate alignment of body segments and optimal level of postural muscle tone, is mediated by the descending pathways from the brainstem to the spinal cord. Particularly, reticulospinal pathways arising from the lateral part of the mesopontine tegmentum and spinal locomotor network contribute to this process. On the other hand, walking in unfamiliar circumstance requires cognitive process of postural control, which depends on knowledges of self-body, such as body schema and body motion in space. The cognitive information is produced at the temporoparietal association cortex, and is fundamental to sustention of vertical posture and construction of motor programs. The programs in the motor cortical areas run to execute anticipatory postural adjustment that is optimal for achievement of goal-directed movements. The basal ganglia and cerebellum may affect both the automatic and cognitive processes of posturegait control through reciprocal connections with the brainstem and cerebral cortex, respectively. Consequently, impairments in cognitive function by damages in the cerebral cortex, basal ganglia and cerebellum may disturb posture-gait control, resulting in falling.
Homotopical dynamics: suspension and duality

Flow-type suspension and homotopy suspension agree for attractor–repellor homotopy data. The connection maps associated in Conley index theory to an attractor–repellor decomposition with respect to the direct flow and its inverse are Spanier–Whitehead duals in the stably parallelizable context and are duals modulo a certain Thom construction in general.
Survivability in optical networks

Survivability, the ability of a network to withstand and recover from failures, is one of the most important requirements of networks. Its importance is magnified in fiber optic networks with throughputs on the order of gigabits and terabits per second. This article takes a look at the techniques used to achieve survivability in traditional optical networks, and how those techniques are evolving to make next-generation WDM networks survivable.