Miracle-dz/UniFAR-FTUs huggingface dataset:
Does the Nonlinear APT Outperform the Conditional CAPM

The conditional CAPM and the nonlinear APT are two important extensions of the Sharpe-Lintner constant beta CAPM. Bansal, Hsieh, and Viswanathan (1993), and Ghysels (1998) suggest that the nonlinear APT is empirically more successful than the conditional CAPM. Using a flexible nonparametric version of the conditional CAPM, we get the opposite result: the conditional CAPM does a substantially better job than the nonlinear APT in explaining the cross-section of time-varying expected returns on stocks. Moreover, we find simulation evidence that the econometric tools commonly recommended for nonlinear APT models are inadequate to detect misspecified models in finite samples. The Capital Asset Pricing Model (CAPM) of Sharpe (1964) and Lintner (1965) has been a focal point in the field of empirical asset pricing for more than thirty years now. Although the static version of the CAPM managed to withstand intense econometric investigation for decades, recent tests indicate that this static single-beta model is not quite enough for explaining expected returns on stocks.1 Financial economists are therefore searching for an empirically successful extension of the CAPM. Two classes of models, conditional linear factor models and nonlinear APT models, have received particular attention in the literature. Conditional asset pricing models are motivated by a couple of appealing developments. On one hand, recent research has documented mounting evidence of time-varying betas and time-varying risk premia. On the other hand, as Dybvig and Ross (1985) and Hansen and Richard (1987) show, the conditional version of the CAPM can hold perfectly even when the static version has serious pricing errors. In general, conditional models (with single factor or multiple factors) that allow for time-varying betas and risk premia can perform substantially better than unconditional models. Thus to explain the cross-section of asset returns, it is natural to consider time-varying beta models. In fact, numerous researchers such as Gibbons and Ferson (1985), Ferson, Kandel, and Stambaugh (1987), Bollerslev, Engle, and Wooldridge (1988), Harvey (1989), Shanken (1990), Carhart, Krail, Steven, and Welch (1995), Cochrane (1996), He, Kan, Ng, and Zhang (1996), Jagannathan and Wang (1996), Ferson and Siegel (1998), and Ferson and Harvey (1999) have studied various forms of conditional linear factor models. A different approach to extending the static CAPM is to consider unconditional nonlinear pricing models. A hypothesis of particular interest is that nonlinearity of the pricing kernel in the market return is important in asset valuation. Following Rubinstein (1973), for example, Kraus and Litzenberger (1976, 1983) derived a three-moment asset pricing model by assuming that investors have a preference for positive return skewness in their portfolios. In contrast to the CAPM which has a linear pricing kernel, the pricing kernel of this model 1For example, see Chan, Hamao, and Lakonishok (1991), and Fama and French (1992, 1993, 1996).
Test-Delivery Optimization in Manycore SOCs

We present two test-data delivery optimization algorithms for system-on-chip (SoC) designs with hundreds of cores, where a network-on-chip (NoC) is used as the interconnection fabric. We first present an effective algorithm based on a subset-sum formulation to solve the test-delivery problem in NOCs with arbitrary topology that use dedicated routing. We further propose an algorithm for the important class of NOCs with grid topology and XY routing. The proposed algorithm is the first to cooptimize the number of access points, access-point locations, pin distribution to access points, and assignment of cores to access points for optimal test resource utilization of such NOCs. Test-time minimization is modeled as an NoC partitioning problem and solved with dynamic programming in polynomial time. Both the proposed methods yield high-quality results and are scalable to large SOCs with many cores. We present results on synthetic grid topology NoC-based SOCs constructed using cores from the ITC'02 benchmark, and demonstrate the scalability of our approach for two SOCs of the future, one with nearly 1000 cores and the other with 1600 cores. Test scheduling under power constraints is also incorporated in the optimization framework.
Automated Methods for Analyzing Music Recordings in Sonata Form

The sonata form has been one of the most important large-scale musical structures used since the early Classical period. Typically, the first movements of symphonies and sonatas follow the sonata form, which (in its most basic form) starts with an exposition and a repetition thereof, continues with a development, and closes with a recapitulation. The recapitulation can be regarded as an altered repeat of the exposition, where certain substructures (first and second subject groups) appear in musically modified forms. In this paper, we introduce automated methods for analyzing music recordings in sonata form, where we proceed in two steps. In the first step, we derive the coarse structure by exploiting that the recapitulation is a kind of repetition of the exposition. This requires audio structure analysis tools that are invariant under local modulations. In the second step, we identify finer substructures by capturing relative modulations between the subject groups in exposition and recapitulation. We evaluate and discuss our results by means of the Beethoven piano sonatas. In particular, we introduce a novel visualization that not only indicates the benefits and limitations of our methods, but also yields some interesting musical insights into the data.
ESI, a New Keck Observatory Echellette Spectrograph and Imager

The Echellette Spectrograph and Imager (ESI) is a multipurpose instrument that has been delivered by the Instrument Development Laboratory of Lick Observatory for use at the Cassegrain focus of the Keck II telescope. ESI saw first light on 1999 August 29. ESI is a multimode instrument that enables the observer to seamlessly switch between three modes during an observation. The three modes of ESI are an R = 13,000 echellette mode, a low‐dispersion prismatic mode, and a direct‐imaging mode. ESI contains a unique flexure compensation system that reduces the small instrument flexure to negligible proportions. Long‐exposure images on the sky show FWHM spot diameters of 34 μm (0.″34) averaged over the entire field of view. These are the best non–adaptive optics images taken in the visible at Keck Observatory to date. Maximum efficiencies are measured to be 28% for the echellette mode and greater than 41% for low‐dispersion prismatic mode including atmospheric, telescope, and detector losses. In this paper, we describe the instrument and its development. We also discuss the performance testing and some observational results.
Scaling Out to a Single-Node 80Gbps Memcached Server with 40Terabytes of Memory

Current web infrastructure relies increasingly on distributed in-memory key-value stores such as memcached whereby typical x86-based implementations of TCP/IP compliant memcached yield limited performance scalability. FPGA-based data-flow architectures overcome and exceed every other published and fully compliant implementation in regards to throughput and provide scalability to 80Gbps, while offering much higher power efficiency and lower latency. However, value store capacity remains limited given the DRAM support in today's devices. In this paper, we present and quantify novel hybrid memory systems that combine conventional DRAMs and serial-attached flash to increase value store capacity to 40Terabytes with up to 200 million entries while providing access at 80Gbps. This is achieved by an object distribution based on size using different storage devices over DRAM and flash and data-flow based architectures using customized memory controllers that compensate for large variations in access latencies and bandwidths. We present measured experimental proofpoints, mathematically validate these concepts for published value size distributions from Facebook, Wikipedia, Twitter and Flickr and compare to existing solutions.
PowerWalk: Scalable Personalized PageRank via Random Walks with Vertex-Centric Decomposition

Most methods for Personalized PageRank (PPR) precompute and store all accurate PPR vectors, and at query time, return the ones of interest directly. However, the storage and computation of all accurate PPR vectors can be prohibitive for large graphs, especially in caching them in memory for real-time online querying. In this paper, we propose a distributed framework that strikes a better balance between offline indexing and online querying. The offline indexing attains a fingerprint of the PPR vector of each vertex by performing billions of ``short'' random walks in parallel across a cluster of machines. We prove that our indexing method has an exponential convergence, achieving the same precision with previous methods using a much smaller number of random walks. At query time, the new PPR vector is composed by a linear combination of related fingerprints, in a highly efficient vertex-centric decomposition manner. Interestingly, the resulting PPR vector is much more accurate than its offline counterpart because it actually uses more random walks in its estimation. More importantly, we show that such decomposition for a batch of queries can be very efficiently processed using a shared decomposition. Our implementation, PowerWalk, takes advantage of advanced distributed graph engines and it outperforms the state-of-the-art algorithms by orders of magnitude. Particularly, it responses to tens of thousands of queries on graphs with billions of edges in just a few seconds.
Dynamical formation of an extra‐tropical tropopause inversion layer in a relatively simple general circulation model

The key factors contributing to the formation and maintenance of the recently discovered extra‐tropical tropopause inversion layer are presently unclear. In this study, it is shown that such a layer can form as a consequence of the turbulent dynamics of synoptic‐scale baroclinic eddies alone, in the absence of explicitly parameterized, small‐scale, radiative‐convective processes. A simple general circulation model, initialized from a state of rest, and driven with idealized forcings, is found to spontaneously develop an inversion layer above the tropopause under a wide variety of parameter choices and model resolutions. Furthermore, such a model is able to capture, qualitatively, both the latitudinal and (in part) the seasonal dependence of the observed tropopause inversion layer. However, the inability of our simple model to capture some detailed quantitative features strongly suggests that other physical processes, beyond balanced synoptic‐scale dynamics, are likely to play an important role.
Estimating the magnitude of method bias on account of text similarity using a natural language processing-based technique

A number of potential biases have been identified that may contribute a spurious component to the observed correlation between variables. One such potential bias is the manner in which items are worded. This paper presents a technique and a program of research to estimate the magnitude of method bias arising from item wording. We hypothesize that the greater the level of text similarity between items employed to capture predictor and criterion variables, the greater the magnitude of the observed effect size between them. Two samples will be employed; one investigating the perceived usefulness-use correlations reported in the TAM literature and the other investigating the attitude behavior (physical activity) correlation reported literature drawing upon Theory of Planned Behavior (TPB). An NLP-based technique is developed to rate predictorcriterion pairs on similarity. The hypothesis will be tested by meta-regressing the predictor-criterion correlations against their respective similarity scores. Implications for research and practice are discussed.
Experimental investigation of the fluid driven carbonation of serpentinites and spinel-peridotites: implications for the carbon and trace element cycle in the forearc region of the mantle wedge

vi likely occurs via an interface-coupled dissolution-precipitation mechanisms. Carbonation occurs as soon as the COH-fluid is in contact with primary silicates. Carbonation is fast and completed within <1 h of the experiments using powdered serpentinite and within <96 h using serpentinite-cores. The carbonation rate of peridotite-cores approximates the rate of serpentinite-cores. The extent of C sequestering is enhanced when reducing fluids cause additionally graphite precipitation. Fluid mobile elements (e.g. Sr, Ba, Pb) are compatible in carbonates and suitable for monitoring the carbonation process. Carbonation of the partially hydrated forearc mantle is relevant to the cycle of C and trace elements in subduction zones. Over time, the forearc mantle may become a significant reservoir for C contributing to explain the imbalance between carbon released from the slab and carbon output in arc magmatism. Thus, carbonation within the forearc mantle should be considered when estimating carbon fluxes within the Earth’s deep C cycle. Further, a carbonated forearc mantle may host a crustal trace element and isotope signature imposed by metasomatizing fluids liberated from the subducting oceanic crust.
Incorporating psychological theory into the model of diffusion of innovations in healthcare

Evidence-based medicine calls for the use of current best evidence (together with individual clinical expertise). Guidelines aim to distil such evidence, yet clinical practice often fails to follow guidelines, for multiple reasons that are still not well understood. One under-researched aspect of the gap between guidelines and practice is psychology. My literature review found that the application of psychology to implementation research has been limited, and such research is not well integrated into wider implementation research. In this study, I sought to a) systematically apply psychological theory to understand the different psychological processes in the stages of adoption described in the diffusion of innovations model; b) collect and analyse data to explore and test this new, psychologically-enhanced model of guideline adoption; and c) improve and extend my model in the light of my empirical data. Having populated my proposed framework with potentially relevant psychological theories based on my literature review, I undertook a first assessment of the validity and added value of this proposed theoretical framework through a case study of the implementation of guidance on universal offering of HIV testing in hospitals serving populations meeting the criteria for high HIV prevalence in the catchment population, interviewing 20 healthcare professionals across two sites. My findings broadly supported my proposed theoretical approach, and illustrated relevant psychological theories for different stages of adoption. My findings support two provisional conclusions. First, that there is potential to improve the effectiveness of efforts to implement guidelines by augmenting the widely-used innovation adoption model with specific psychological theories. Second, that policymakers would do well to shift from viewing the health system as a complicated entity that policy can control and direct is misconceived; I recommend shifting to a perspective of the health system as a complex system, and rethinking the role of policy from that perspective.
Extended Report

Background The Ankylosing Spondylitis Disease Activity Score (ASDAS) is a new composite index to assess disease activity in ankylosing spondylitis (AS). It fulfi ls important aspects of truth, feasibility and discrimination. Criteria for disease activity states and improvement scores are important for use in clinical practice, observational studies and clinical trials and so far have not been developed for the ASDAS. Objective To determine clinically relevant cutoff values for disease activity states and improvement scores using the ASDAS. Methods For the selection of cutoffs data from the Norwegian disease modifying antirheumatic drug (NOR-DMARD) registry, a cohort of patients with AS starting conventional or biological DMARDs, were used. Receiver operating characteristic analysis against several external criteria was performed and several approaches to determine the optimal cutoffs used. The fi nal choice was made on clinical and statistical grounds, after debate and voting by Assessment of SpondyloArthritis international Society members. Crossvalidation was performed in NOR-DMARD and in Ankylosing Spondylitis Study for the Evaluation of Recombinant Infl iximab Therapy, a database of patients with AS participating in a randomised placebo-controlled trial with a tumour necrosis factor blocker. Results Four disease activity states were chosen by consensus: inactive disease, moderate, high and very high disease activity. The three cutoffs selected to separate these states were: 1.3, 2.1 and 3.5 units. Selected cutoffs for improvement were: change ≥1.1 units for clinically important improvement and change ≥2.0 units for major improvement. Results of the crossvalidation strongly supported the cutoffs. Conclusions Cutoff values for disease activity states and improvement using the ASDAS have been developed. They proved to have external validity and a good performance compared to existing criteria.
History-Based Dynamic Test Case Prioritization for Requirement Properties in Regression Testing

Regression testing is an important but extremely costly and time-consuming process. Because of limited resources in practice, test case prioritization focuses on the improvement of testing efficiency. However, traditional test case prioritization techniques emphasize only one-time testing without considering huge historical data generated in regression testing. This paper proposes an approach to prioritizing test cases based on historical data. Requirements are a significant factor in the testing process, the priorities of test cases are initialized based on requirement priorities in our history-based approach, and then are calculated dynamically according to historical data in regression testing. To evaluate our approach, an empirical study on an industrial system is conducted. Experimental results show an improved performance for our proposed method using measurements of Average Percentage of Faults Detected and Fault Detection Rate.
Sharing the cost of backbone networks: cui bono?

We study the problem of how to share the cost of a backbone network among its customers. A variety of empirical cost-sharing policies are used in practice by backbone network operators but very little ever reaches the research literature about their properties. Motivated by this, we present a systematic study of such policies focusing on the discrepancies between their cost allocations. We aim at quantifying how the selection of a particular policy biases an operator's understanding of cost generation. We identify F-discrepancies due to the specific function used to map traffic into cost (e.g., volume vs. peak rate vs. 95-percentile) and M-discrepancies, which have to do with where traffic is metered (per device vs. ingress metering). We also identify L-discrepancies relating to the liability of individual customers for triggered upgrades and consequent costs (full vs. proportional), and finally, TCO-discrepancies emanating from the fact that the cost of carrying a bit is not uniform across the network (old vs. new equipment, high vs. low energy or real estate costs, etc.). Using extensive traffic, routing, and cost data from a tier-1 network we show that F-discrepancies are large when looking at individual links but cancel out when considering network-wide cost-sharing. Metering at ingress points is convenient but leads to large M-discrepancies, while TCO-discrepancies are huge. Finally, L-discrepancies are intriguing and esoteric but understanding them is central to determining the cost a customer inflicts on the network.
Relationship between various supersymmetric lattice models

We comment on the relationships between several supersymmetric lattice models; the ``orbifold lattice theory'' by Cohen-Kaplan-Katz-Unsal (CKKU), lattice regularization of the topological field theory by Sugino and the ``geometrical approach'' by Catterall. We point out that these three models have close relationships; the = (2, 2) model by Catterall [1] and the two-dimensional = (2, 2) lattice theory being similar to Sugino's construction [2] can be derived by appropriate truncation of fields in the two-dimensional = (4, 4) orbifold lattice theory by CKKU [3]. Catterall's = (2, 2) description possesses extra degrees of freedom compared to the target = (2, 2) theory. If we remove those extra degrees of freedom in a way keeping supersymmetry on the lattice, Catterall's description reduces to a model of the Sugino type.
Representing Objects in a Logic Programming Langueage with Scoping Constructs

We present a logic programming language that uses implications and universal quantifiers in goals and in the bodies of clauses to provide a simple scoping mechanism for program clauses and constants. Within this language it is possible to define a simple notion of parametric module and local constant. Given this ability to structure programs, we explore how object-oriented programming, where objects are viewed as abstractions with behaviors, state, and inheritance, might be accommodated. To capture the notion of mutable state, we depart from the pure logic setting by adding a declaration that certain local predicates are deterministic (they succeed at most once). This declaration, along with a goal-continuation passing style of programming is adequate to model the state of objects. We also examine a few aspects of how having objects embedded in logic programming can be used to enrich the notion of object: for example, objects may be partial (that is, may contain free variables) and non-deterministic, and it is possible not only to search for objects with certain properties but also to do hypothetical reasoning about them. Comments University of Pennsylvania Department of Computer and Information Science Technical Report No. MSCIS-90-31. This technical report is available at ScholarlyCommons: https://repository.upenn. edu/cis_reports/545 Representing Object In A Logic Programming Language With Scoping Constructs MS-CIS-90-31 LINC LAB 172 Joshua S. Hodas Dale Miller Department of Computer and Information Science School of Engineering and Applied Science University of Pennsylvania Philadelphia, PA 19104
The Ras Superfamily of Small GTPases in Non-neoplastic Cerebral Diseases

The small GTPases from the Ras superfamily play crucial roles in basic cellular processes during practically the entire process of neurodevelopment, including neurogenesis, differentiation, gene expression, membrane and protein traffic, vesicular trafficking, and synaptic plasticity. Small GTPases are key signal transducing enzymes that link extracellular cues to the neuronal responses required for the construction of neuronal networks, as well as for synaptic function and plasticity. Different subfamilies of small GTPases have been linked to a number of non-neoplastic cerebral diseases such as Alzheimer’s disease (AD), Parkinson’s disease (PD), intellectual disability, epilepsy, drug addiction, Huntington’s disease (HD), amyotrophic lateral sclerosis (ALS) and a large number of idiopathic cerebral diseases. Here, we attempted to make a clearer illustration of the relationship between Ras superfamily GTPases and non-neoplastic cerebral diseases, as well as their roles in the neural system. In future studies, potential treatments for non-neoplastic cerebral diseases which are based on small GTPase related signaling pathways should be explored further. In this paper, we review all the available literature in support of this possibility.