cssi/SciSciGPT-SciSciNet huggingface dataset:
A California Statewide Three-Dimensional Seismic Velocity Model from Both Absolute and Differential Times

Abstract We obtain a seismic velocity model of the California crust and uppermost mantle using a regional-scale double-difference tomography algorithm. We begin by using absolute arrival-time picks to solve for a coarse three-dimensional (3D) P velocity ( V P ) model with a uniform 30 km horizontal node spacing, which we then use as the starting model for a finer-scale inversion using double-difference tomography applied to absolute and differential pick times. For computational reasons, we split the state into 5 subregions with a grid spacing of 10 to 20 km and assemble our final statewide V P model by stitching together these local models. We also solve for a statewide S -wave model using S picks from both the Southern California Seismic Network and USArray, assuming a starting model based on the V P results and a V P / V S ratio of 1.732. Our new model has improved areal coverage compared with previous models, extending 570 km in the SW–NE direction and 1320 km in the NW–SE direction. It also extends to greater depth due to the inclusion of substantial data at large epicentral distances. Our V P model generally agrees with previous separate regional models for northern and southern California, but we also observe some new features, such as high-velocity anomalies at shallow depths in the Klamath Mountains and Mount Shasta area, somewhat slow velocities in the northern Coast Ranges, and slow anomalies beneath the Sierra Nevada at midcrustal and greater depths. This model can be applied to a variety of regional-scale studies in California, such as developing a unified statewide earthquake location catalog and performing regional waveform modeling.
Deep spectral learning for label-free optical imaging oximetry with uncertainty quantification

Measurement of blood oxygen saturation (sO2) by optical imaging oximetry provides invaluable insight into local tissue functions and metabolism. Despite different embodiments and modalities, all label-free optical-imaging oximetry techniques utilize the same principle of sO2-dependent spectral contrast from haemoglobin. Traditional approaches for quantifying sO2 often rely on analytical models that are fitted by the spectral measurements. These approaches in practice suffer from uncertainties due to biological variability, tissue geometry, light scattering, systemic spectral bias, and variations in the experimental conditions. Here, we propose a new data-driven approach, termed deep spectral learning (DSL), to achieve oximetry that is highly robust to experimental variations and, more importantly, able to provide uncertainty quantification for each sO2 prediction. To demonstrate the robustness and generalizability of DSL, we analyse data from two visible light optical coherence tomography (vis-OCT) setups across two separate in vivo experiments on rat retinas. Predictions made by DSL are highly adaptive to experimental variabilities as well as the depth-dependent backscattering spectra. Two neural-network-based models are tested and compared with the traditional least-squares fitting (LSF) method. The DSL-predicted sO2 shows significantly lower mean-square errors than those of the LSF. For the first time, we have demonstrated en face maps of retinal oximetry along with a pixel-wise confidence assessment. Our DSL overcomes several limitations of traditional approaches and provides a more flexible, robust, and reliable deep learning approach for in vivo non-invasive label-free optical oximetry.
Noise-induced hearing loss and hearing protective devices

Longitudinal studies of hearing in workers in selected industries in Canada have shown that the effect of continuous high-level noise exposure is an increase in detection threshold of about 1 1/2 decibels per year. This is in contrast to 1/2 dB per year for non noise-exposed office workers in the same plants. Personal hearing protectors, in the form of ear plugs and muffs, have been chosen as an effective, inexpensive and easily implemented method of hearing conservation. However, behavioural experiments simulating the noise background in the industrial environment have shown that the use of these devices may give rise to difficulties in detecting warning signals and defects in the materials worked on, as well as in the perception of speech. This is particularly true in the case of individuals who have already sustained a moderate to severe sensorineural hearing loss, either through noise exposure or aging. To improve both hearing and comfort, many try to alter the device physically. This, of course, will result in a decrease in its sound attenuating capabilities. An experiment in progress is investigating the possibility of prescribing protectors on an individual basis, taking into account both hearing status and shape and size of the ear canal. Relatively little information is available on ear canal morphology. It appears that some types of widely used insert devices are less effective in reducing sound for women because of poor fit to their relatively smaller ear canals
Phase Transitions for the Information Bottleneck in Representation Learning

In the Information Bottleneck (IB) (Tishby et al., 2000), when tuning the relative strength between compression and prediction terms, how do the two terms behave, and what’s their relationship with the dataset and the learned representation? In this paper, we set out to answer this question by studying multiple phase transitions in the IB objective: IBβ[p(z|x)] = I(X;Z) − βI(Y ;Z), where sudden jumps of dI(Y ;Z)/dβ and prediction accuracy are observed with increasing β. We introduce a definition for IB phase transitions as a qualitative change of the IB loss landscape, and show that the transitions correspond to the onset of learning new classes. Using second-order calculus of variations, we derive a formula that provides the condition for IB phase transitions, and draw its connection with the Fisher information matrix for parameterized models. We provide two perspectives to understand the formula, revealing that each IB phase transition is finding a component of maximum (nonlinear) correlation between X and Y orthogonal to the learned representation, in close analogy with canonical-correlation analysis (CCA) in linear settings. Based on the theory, we present an algorithm for discovering phase transition points. Finally, we verify that our theory and algorithm accurately predict phase transitions in categorical datasets, predict the onset of learning new classes and class difficulty in MNIST, and predict prominent phase transitions in CIFAR10 experiments.
Locality in the Everett Interpretation of Quantum Field Theory

Recently it has been shown that transformations of Heisenberg-picture operators are the causal mechanism which allows Bell-theorem-violating correlations at a distance to coexist with locality in the Everett interpretation of quantum mechanics. A calculation to first order in perturbation theory of the generation of EPRB entanglement in nonrelativistic fermionic field theory in the Heisenberg picture illustrates that the same mechanism leads to correlations without nonlocality in quantum field theory as well. An explicit transformation is given to a representation in which initial-condition information is transferred from the state vector to the field operators, making the locality of the theory manifest.
Engineering Music to Slow Breathing and Invite Relaxed Physiology

We engineered an interactive music system that influences a user's breathing rate to induce a relaxation response. This system generates ambient music containing periodic shifts in loudness that are determined by the user's own breathing patterns. We evaluated the efficacy of this music intervention for participants who were engaged in an attention-demanding task, and thus explicitly not focusing on their breathing or on listening to the music. We measured breathing patterns in addition to multiple peripheral and cortical indicators of physiological arousal while users experienced three different interaction designs: (1) a “Fixed Tempo” amplitude modulation rate at six beats per minute; (2) a “Personalized Tempo” modulation rate fixed at 75% of each individual's breathing rate baseline, and (3) a “Personalized Envelope” design in which the amplitude modulation matches each individual's breathing pattern in real-time. Our results revealed that each interactive music design slowed down breathing rates, with the “Personalized Tempo” design having the largest effect, one that was more significant than the non-personalized design. The physiological arousal indicators (electrodermal activity, heart rate, and slow cortical potentials measured in EEG) showed concomitant reductions, suggesting that slowing users' breathing rates shifted them towards a more calmed state. These results suggest that interactive music incorporating biometric data may have greater effects on physiology than traditional recorded music.
Network Semantic Segmentation with Application to GitHub

In this paper we introduce the concept of network semantic segmentation for social network analysis. We consider the GitHub social coding network which has been a center of attention for both researchers and software developers. Network semantic segmentation describes the process of associating each user with a class label such as a topic of interest. We augment node attributes with network significant connections and then employ machine learning approaches to cluster the users. We compare the results with a network segmentation performed using community detection algorithms and one executed by clustering with node attributes. Results are compared in terms of community diversity within the semantic segments along with topic coverage.
Topology of the nodal set of random equivariant spherical harmonics on $S^3$

We show that real and imaginary parts of equivariant spherical harmonics on $S^3$ have almost surely a single nodal component. Moreover, if the degree of the spherical harmonic is $N$ and the equivariance degree is $m$, then the expected genus is proportional to $m \left(\frac{N^2 - m^2}{2} + N\right) $. Hence if $\frac{m}{N}= c $ for fixed $0 < c < 1$, the genus has order $N^3$.
Profiling myelodysplastic syndromes by mass cytometry demonstrates abnormal progenitor cell phenotype and differentiation.

BACKGROUND We sought to enhance the cytometric analysis of myelodysplastic syndromes (MDS) by performing a pilot study of a single cell mass cytometry (MCM) assay to more comprehensively analyze patterns of surface marker expression in patients with MDS. METHODS Twenty-three MDS and five healthy donor bone marrow samples were studied using a 34-parameter mass cytometry panel utilizing barcoding and internal reference standards. The resulting data were analyzed by both traditional gating and high-dimensional clustering. RESULTS This high-dimensional assay provided three major benefits relative to traditional cytometry approaches: First, MCM enabled detection of aberrant surface maker at high resolution, detecting aberrancies in 27/31 surface markers, encompassing almost every previously reported MDS surface marker aberrancy. Additionally, three previously unrecognized aberrancies in MDS were detected in multiple samples at least one developmental stage: increased CD321 and CD99; and decreased CD47. Second, analysis of the stem and progenitor cell compartment (HSPCs), demonstrated aberrant expression in 21 of the 23 MDS samples, which were not detected in three samples from patients with idiopathic cytopenia of undetermined significance. These immunophenotypically abnormal HSPCs were also the single most significant distinguishing feature between clinical risk groups. Third, unsupervised clustering of high-parameter MCM data allowed identification of abnormal differentiation patterns associated with immunophenotypically aberrant myeloid cells similar to myeloid derived suppressor cells. CONCLUSIONS These results demonstrate that high-parameter cytometry methods that enable simultaneous analysis of all bone marrow cell types could enhance the diagnostic utility of immunophenotypic analysis in MDS.
Stability of Asai local factors for GL(2)

Let F be a non-archimedean local field of characteristic not equal to 2 and let E/F be a quadratic algebra. We prove the stability of local factors attached to irreducible admissible (complex) repr...
RNA-Seq Analysis Illuminates the Early Stages of Plasmodium Liver Infection.

ABSTRACT The apicomplexan parasites Plasmodium spp. are the causative agents of malaria, a disease that poses a significant global health burden. Plasmodium spp. initiate infection of the human host by transforming and replicating within hepatocytes. This liver stage (LS) is poorly understood compared to other Plasmodium life stages, which has hindered our ability to target these parasites for disease prevention. We conducted an extensive transcriptome sequencing (RNA-Seq) analysis throughout the Plasmodium berghei LS, covering as early as 2 h postinfection (hpi) and extending to 48 hpi. Our data revealed that hundreds of genes are differentially expressed at 2 hpi and that multiple genes shown to be important for later infection are upregulated as early as 12 hpi. Using hierarchical clustering along with coexpression analysis, we identified clusters functionally enriched for important liver-stage processes such as interactions with the host cell and redox homeostasis. Furthermore, some of these clusters were highly correlated to the expression of ApiAP2 transcription factors, while showing enrichment of mostly uncharacterized DNA binding motifs. This finding indicates potential LS targets for these transcription factors, while also hinting at alternative uncharacterized DNA binding motifs and transcription factors during this stage. Our work presents a window into the previously undescribed transcriptome of Plasmodium upon host hepatocyte infection to enable a comprehensive view of the parasite’s LS. These findings also provide a blueprint for future studies that extend hypotheses concerning LS gene function in P. berghei to human-infective Plasmodium parasites. IMPORTANCE The LS of Plasmodium infection is an asymptomatic yet necessary stage for producing blood-infective parasites, the causative agents of malaria. Blocking the liver stage of the life cycle can prevent clinical malaria, but relatively less is known about the parasite’s biology at this stage. Using the rodent model P. berghei, we investigated whole-transcriptome changes occurring as early as 2 hpi of hepatocytes. The transcriptional profiles of early time points (2, 4, 12, and 18 hpi) have not been accessible before due to the technical challenges associated with liver-stage infections. Our data now provide insights into these early parasite fluxes that may facilitate establishment of infection, transformation, and replication in the liver.
High school science fair: Positive and negative outcomes

The goal of our ongoing research is to identify strengths and weaknesses of high school level science fair and improvements that can help science educators make science fair a more effective, inclusive and equitable learning experience. In this paper, we confirm and extend our previous findings in several important ways. We added new questions to our anonymous and voluntary surveys to learn the extent to which students had an interest in science or engineering careers and if science fair participation increased their interest in science or engineering. And we surveyed a national rather than regional high school student group by incorporating our survey into the Scienteer online portal now used by Texas and some other states for science fair registration, parental consent, and project management. We learned that about ~60% of the more than 300 students in the national cohorts completing surveys in 2017 and 2018 said that they were interested in a career in science or engineering, and ~60% said that participating in science fair increased their interest in science or engineering. About two-thirds of the students were required to participate in science fair, and that requirement reduced the frequency of students who said that science fair increased their interest. In the worst case, ~10% of the students who said that they were not interested in a career in science or engineering and who were required to participate in science fair engaged in research misconduct (i.e., plagiarism and making up their results). Students' positive comments about competition in science fair focused on the competition incentive, whereas their positive comments about science fair that was non-competitive focused on learning about the scientific process and learning in general. We discuss the findings in the context of National Science Teaching Association guidance about voluntary science fair participation and begin to identify features of science fair practice consistent with increased student interest in the sciences or engineering.
Complete padovan sequences in finite fields

Given a prime $p\ge 5$, and given $1<\kappa<p-1$, we call a sequence $(a_n)_{n}$ in $\mathbb{F}_p$ a $\Phi_{\kappa}$-sequence if it is periodic with period $p-1$, and if it satisfies the linear recurrence $a_n+a_{n+1}=a_{n+\kappa}$ with $a_0=1$. Such a sequence is said to be a complete $\Phi_{\kappa}$-sequence if in addition $\{a_0,a_1,...,a_{p-2}\}=\{1,...,p-1\}$. For instance, every primitive root $b$ mod $p$ generates a complete $\Phi_{\kappa}$-sequence $a_n=b^n$ for some (unique) $\kappa$. A natural question is whether every complete $\Phi_{\kappa}$-sequence is necessarily defined by a primitive root. For $\kappa=2$ the answer is known to be positive. In this paper we reexamine that case and investigate the case $\kappa=3$ together with the associated cases $\kappa=p-2$ and $\kappa=p-3$.
Selective enrichment of A-to-I edited transcripts from cellular RNA using Endonuclease V

Creating accurate maps of A-to-I RNA editing activity is vital to improving our understanding of the biological role of this process and harnessing it as a signal for disease diagnosis. Current RNA sequencing techniques are susceptible to random sampling limitations due to the complexity of the transcriptome and require large amounts of RNA material, specialized instrumentation, and high read counts to accurately interrogate A-to-I editing sites. To address these challenges, we show that Escherichia coli Endonuclease V (eEndoV), an inosine-cleaving enzyme, can be repurposed to bind and isolate A-to-I edited transcripts from cellular RNA. While Mg2+ enables eEndoV to catalyze RNA cleavage, we show that similar levels of Ca2+ instead promote binding of inosine without cleavage and thus enable high affinity capture of inosine in RNA. We leverage this capability to demonstrate EndoVIPER-seq (Endonuclease V inosine precipitation enrichment sequencing) as a facile and effective method to enrich A-to-I edited transcripts prior to RNA-seq, producing significant increases in the coverage and detection of identified editing sites. We envision the use of this approach as a straightforward and cost-effective strategy to improve the epitranscriptomic informational density of RNA samples, facilitating a deeper understanding of the functional roles of A-to-I editing.
Lessons and recommendations from three decades as an NSF REU site: A call for systems‐based assessment

For more than 30 years, the US National Science Foundation's Research Experiences for Undergraduates (REU) program has supported thousands of undergraduate researchers annually and provides many students with their first research experiences in field ecology or evolution. REUs embed students in scientific communities where they apprentice with experienced researchers, build networks with their peers, and help students understand research cultures and how to work within them. REUs are thought to provide formative experiences for developing researchers that differ from experiences in a college classrooms, laboratories, or field trips. REU assessments have improved through time but they are largely ungrounded in educational theory. Thus, evaluation of long-term impacts of REUs remains limited and best practices for using REUs to enhance student learning are repeatedly re-invented. We describe how one sociocultural learning framework, cultural-historical activity theory (CHAT), could be used to guide data collection to characterize the effects of REU programs on participant's learning in an educationally meaningful context. CHAT embodies a systems approach to assessment that accounts for social and cultural factors that influence learning. We illustrate how CHAT has guided assessment of the Harvard Forest Summer Research Program in Ecology (HF-SRPE), one of the longest-running REU sites in the United States. Characterizing HF-SRPE using CHAT helped formalize thoughts and language for the program evaluation, reflect on potential barriers to success, identify assessment priorities, and revealed important oversights in data collection.
Nonparametric System identification of Stochastic Switched Linear Systems

We address the problem of learning the parameters of a mean square stable switched linear systems(SLS) with unknown latent space dimension, or order, from its noisy input–output data. In particular, we focus on learning a good lower order approximation of the underlying model allowed by finite data. This is achieved by constructing Hankel-like matrices from data and obtaining suitable approximations via SVD truncation where the threshold for SVD truncation is purely data dependent. By exploiting tools from theory of model reduction for SLS, we find that the system parameter estimates are close to a balanced truncated realization of the underlying system with high probability.
DEVELOPMENT OF MICRO-DIESEL INJECTOR NOZZLES VIA MEMS TECHNOLOGY AND EFFECTS ON SPRAY CHARACTERISTICS

Micro-machined diesel injector nozzles have been designed, fabricated and used with commercially produced diesel injection systems in the study of spray dynamics. Such a system, properly designed, may improve spray behavior in DI diesel engines due to improved atomization and fuel-air mixing. In this work, fourteen micro-planar orifice nozzles were fabricated using the MEMS (Micro-Electro-Mechanical-Systems) technique. Circular orifice diameters were varied from 40 to 260 microns and the number of orifices was varied from one to 169. Three plates with noncircular orifices were also fabricated to examine the effect of orifice shape on spray characteristics. These nozzles were then attached to commercial injectors and the associated injection systems were used for the spray experiments. Given these novel injection systems, jet spray characteristics of micro-planar orifice nozzles were investigated experimentally using optical diagnostic techniques in a pressurized constant volume cylindrical chamber. Local drop sizes were measured by the laser diffraction technique, and average drop sizes of the whole sprays were measured by the light extinction * Corresponding author : Address : 295 Roger Adams Laboratory, Box C-3, 600 South Mathews Avenue, Urbana, IL 61801, USA Tel: +1-217-333-5233 E-mail address: sbaik@scs.uiuc.edu 1 technique. Current test results show expected qualitative trends in spray kinematics and drop sizes, but quantitative magnitudes of the behavior are less dependent on orifice geometry than first anticipated. Droplet coalescence among adjacent sprays were apparent for the multiple orifice nozzles because all the orifices were aligned in the same direction with limited spacing. Non-planar configurations are under development and may show improved performance.
Perceived Inequality and Cross-National Homicide Rates

Economic inequality is a persistent structural covariate of cross-national homicide rates. The most common criminological explanation is that perceived inequality creates frustration among individu...
Human EMF Exposure in Wearable Networks for Internet of Battlefield Things

Numerous antenna design approaches for wearable applications have been investigated in the literature. As on-body wearable communications become more ingrained in our daily activities, the necessity to investigate the impacts of these networks burgeons as a major requirement. In this study, we investigate the human electromagnetic field (EMF) exposure effect from on-body wearable devices at 2.4 GHz and 60 GHz, and compare the results to illustrate how the technology evolution to higher frequencies from wearable communications can impact our health. Our results suggest the average specific absorption rate (SAR) at 60 GHz can exceed the regulatory guidelines within a certain separation distance between a wearable device and the human skin surface. To the best of authors' knowledge, this is the first work that explicitly compares the human EMF exposure at different operating frequencies for on-body wearable communications, which provides a direct roadmap in design of wearable devices to be deployed in the Internet of Battlefield Things (IoBT).
Adsorption of Chiral Modifiers from Solution onto Supported Platinum Catalysts: The Effect of the Solvent, Other Coadsorbates, and the Support

The adsorption from a solution of s-1-(1-naphthyl)ethylamine (s-NEA), cinchonidine, and cinchonine on Pt/SiO2, Pt/Al2O3, and a Pt disk was probed in situ by infrared absorption spectroscopy. Adsorp...
Local Well-Posedness of Vlasov–Poisson–Boltzmann Equation with Generalized Diffuse Boundary Condition

The Vlasov–Poisson–Boltzmann equation is a classical equation governing the dynamics of charged particles with the electric force being self-imposed. We consider the system in a convex domain with the Cercignani–Lampis boundary condition. We construct a uniqueness local-in-time solution based on an $$L^\infty $$-estimate and $$W^{1,p}$$-estimate. In particular, we develop a new iteration scheme along the characteristic with the Cercignani–Lampis boundary for the $$L^\infty $$-estimate, and an intrinsic decomposition of boundary integral for $$W^{1,p}$$-estimate.
What Happens to Efficiency and Equity? The Cost Implications of Developmental Education Reform

Florida passed legislation in 2013 that exempted many students from developmental education, and required colleges to implement new instructional modalities for optional developmental education courses. This paper uses an economic model of remediation to examine how changes in college course taking before and after the reform altered costs to completing introductory college-level courses in math and English for students and institutions, and whether cost savings differ by racial/ethnic subgroup. We also take into account changes in short-term costs relative to course completion rates to examine whether the reform is cost effective overall. We find that the total cost per completer (including costs to both students and institutions) decreased after the reform by $894 in English and $1851 in math. This resulted in efficiency gains of 34% in English and 30% in math. The results from Florida provide promising evidence that statewide developmental education reforms may be an effective mechanism for reducing costs to students and institutions. Reform efforts may also help to reduce racial/ethnic gaps in the costs to gateway completion in both subject areas, which has important implications for equity.
Alignment of the ATLAS Inner Detector tracking system

These proceedings present the track-based method of aligning tracking systems such as the ATLAS Inner Detector at the CERN Large Hadron Collider. This alignment is crucial for commissioning and operating the spectrometer. We discuss one of the most difficult challenges of the method, alignment weak modes. Initial results from cosmic-ray data collected with the ATLAS Inner Detector are presented.
CHAPTER 12 – Default Reasoning

Commonsense reasoning requires default reasoning. As one engage in commonsense reasoning, one makes certain assumptions so that he can proceed. This chapter discusses default reasoning in general, temporal default reasoning, and then temporal default reasoning. Further, it also describes a general method for default reasoning about time based on the circumscription of abnormality predicates and how this method can be used to address the qualification problem. Then, explains the representation of default events and properties. The event calculus assumes by default that unexpected events do not occur and events have no unexpected effects. In addition, default reasoning can be used to address the qualification problem. Default reasoning provides a more elaboration-tolerant way of specifying qualifications that prevent events from having their intended effects.
Evaluating new copy-prevention techniques for audio CDs

Several major record labels are adopting a new family of copy-prevention techniques intended to limit casual copying by compact disc owners using their personal computers. These employ deliberate data errors introduced into discs during manufacturing to cause incompatibility with PCs without affecting ordinary CD players. We examine three such recordings: A Tribute to Jim Reeves by Charley Pride, A New Day Has Come by Celine Dion, and More Music from The Fast and the Furious by various artists. In tests with different CD-ROM drives, operating systems, and playback software, we find these discs are unreadable in several widely-used applications as of July 2002. We analyze the specific technical differences between the modified recordings and standard audio CDs, and we consider repairs to hardware and software that would restore compatibility. We conclude that these schemes are harmful to legitimate CD owners and will not reduce illegal copying in the long term, so the music industry should reconsider their deployment.
Chapter 4 – Systems

This is an introduction to systems science as it pertains to biotechnology and environmental science. Various connotations and perspectives of systems are explored, especially those of thermodynamics and the biological sciences, including medicine and ecology. Tools for describing and evaluating the condition of environmental systems are discussed, including computational approaches, e.g., omics, indexes (especially indexes of biological integrity (IBI) and resilience), and models (e.g., fugacity-based models). These are considered at several scales, from cellular to planetary.
Helmholtz Stereopsis: Exploiting Reciprocity for Surface Reconstruction

We present a method - termed Helmholtz stereopsis - for reconstructing the geometry of objects from a collection of images. Unlike most existing methods for surface reconstruction (e.g., stereo vision, structure from motion, photometric stereo), Helmholtz stereopsis makes no assumptions about the nature of the bidirectional reflectance distribution functions (BRDFs) of objects. This new method of multinocular stereopsis exploits Helmholtz reciprocity by choosing pairs of light source and camera positions that guarantee that the ratio of the emitted radiance to the incident irradiance is the same for corresponding points in the two images. The method provides direct estimates of both depth and field of surface normals, and consequently weds the advantages of both conventional and photometric stereopsis. Results from our implementations lend empirical support to our technique.
Understanding policy in the great recession: Some unpleasant fiscal arithmetic

I use the valuation equation of government debt to understand fiscal and monetary policy in and following the great recession of 2008–2009. I also examine policy alternatives to avoid deflation, and how fiscal pressures might lead to inflation. I conclude that the central bank may be almost powerless to avoid deflation or inflation; that an eventual fiscal inflation can come well before large deficits or monetization are realized, and that it is likely to come with stagnation rather than a boom.
Dark side of the neutron

Author(s): Fornal, Bartosz; Grinstein, Benjamin | Abstract: We discuss our recently proposed interpretation of the discrepancy between the bottle and beam neutron lifetime experiments as a sign of a dark sector. The difference between the outcomes of the two types of measurements is explained by the existence of a neutron dark decay channel with a branching fraction 1%. Phenomenologically consistent particle physics models for the neutron dark decay can be constructed and they involve a strongly self-interacting dark sector. We elaborate on the theoretical developments around this idea and describe the efforts undertaken to verify it experimentally.
Financing Innovation: Legal Development of Intellectual Property as Security in Financing, 1845-2014

There is a need for both traditional and online lenders to appreciate the intellectual property assets held by small businesses. The intellectual property assets should be included in the analytics in assessing the overall health of a business seeking a loan or a line of credit for its new innovative product, idea, or vision. The Article ends with a brief conclusion that in order to maintain the United States’ innovative edge, attention to the access to financing by small businesses must be at the center of the discussion, and intellectual property must be recognized as part of that center.
Large-cell Monte Carlo renormalization of irreversible growth processes.

Monte Carlo sampling is applied to a recently formulated direct-cell renormalization method for irreversible, disorderly growth processes. Large-cell Monte Carlo renormalization is carried out for various nonequilibrium problems based on the formulation dealing with relative probabilities. Specifically, the method is demonstrated by application to the 'true' self-avoiding walk and the Eden model of growing animals for d = 2, 3, and 4 and to the invasion percolation problem for d = 2 and 3. The results are asymptotically in agreement with expectations; however, unexpected complications arise, suggesting the possibility of crossovers, and in any case, demonstrating the danger of using small cells alone, because of the very slow convergence as the cell size b is extrapolated to infinity. The difficulty of applying the present method to the diffusion-limited-aggregation model, is commented on.
Dynamic Assortment Personalization in High Dimensions

Learning and Earning in High Dimensions: Assortment Personalization Using Low-Rank Preferences
Free Energy Rate Density and Self-organization in Complex Systems

One of the most important tasks in science is to understand the self-organization’s arrow of time. To attempt this we utilize the connection between self-organization and non-equilibrium thermodynamics. Eric Chaisson calculated an exponential increase of Free Energy Rate Density (FERD) in Cosmic Evolution, from the Big Bang until now, paralleling the increase of systems’ structure. We term these studies “Devology”. We connect the exponential growth of FERD to the principle of least action for complex systems leading to exponential increase of action efficiency. We study CPUs as a specific system in which the organization, the total amount of action and FERD are connected in a positive feedback loop, providing exponential growth of all three and power law relations between them. This is a deep connection, reaching to the first principles of physics: the least action principle and the second law of thermodynamics. We propose size-density and complexity-density rules in addition to the established size-complexity one.
Hypothesis Testing Approach to Detecting Collusion in Competitive Environments

There is growing concern about tacit collusion using algorithmic pricing, and regulators need tools to help detect the possibility of such collusion. This paper studies how to design a hypothesis testing framework in order to decide whether agents are behaving competitively or not. In our setting, agents are utility-maximizing and compete over prices of items. A regulator, with no knowledge of the agent's utility function, has access only to the agents' strategies (i.e., pricing decisions) and external shock values in order to decide if agents are behaving in competition according to some equilibrium problem. We leverage the formulation of such a problem as an inverse variational inequality and design a hypothesis test under a minimal set of assumptions. We demonstrate our method with computational experiments of the Bertrand competition game (with and without collusion) and show how our method performs.
Quantifying epigenetic stability with minimum action paths

Chromatin can adopt multiple stable, heritable states with distinct histone modifications and varying levels of gene expression. Insight on the stability and maintenance of such epigenetic states can be gained by mathematical modeling of stochastic reaction networks for histone modifications. Analytical results for the kinetic networks are particularly valuable. Compared to computationally demanding numerical simulations, they often are more convenient at evaluating the robustness of conclusions with respect to model parameters. In this communication, we developed a second-quantization-based approach that can be used to analyze discrete stochastic models with a fixed, finite number of particles using a representation of the SU(2) algebra. We applied the approach to a kinetic model of chromatin states that captures the feedback between nucleosomes and the enzymes conferring histone modifications. Using a path-integral expression for the transition probability, we computed the epigenetic landscape that helps to identify the emergence of bistability and the most probable path connecting the two steady states. We anticipate the generalizability of the approach will make it useful for studying more complicated models that couple epigenetic modifications with transcription factors and chromatin structure.
GANs May Have No Nash Equilibria

Generative adversarial networks (GANs) represent a zero-sum game between two machine players, a generator and a discriminator, designed to learn the distribution of data. While GANs have achieved state-of-the-art performance in several benchmark learning tasks, GAN minimax optimization still poses great theoretical and empirical challenges. GANs trained using first-order optimization methods commonly fail to converge to a stable solution where the players cannot improve their objective, i.e., the Nash equilibrium of the underlying game. Such issues raise the question of the existence of Nash equilibrium solutions in the GAN zero-sum game. In this work, we show through several theoretical and numerical results that indeed GAN zero-sum games may not have any local Nash equilibria. To characterize an equilibrium notion applicable to GANs, we consider the equilibrium of a new zero-sum game with an objective function given by a proximal operator applied to the original objective, a solution we call the proximal equilibrium. Unlike the Nash equilibrium, the proximal equilibrium captures the sequential nature of GANs, in which the generator moves first followed by the discriminator. We prove that the optimal generative model in Wasserstein GAN problems provides a proximal equilibrium. Inspired by these results, we propose a new approach, which we call proximal training, for solving GAN problems. We discuss several numerical experiments demonstrating the existence of proximal equilibrium solutions in GAN minimax problems.
Quadrupedal Locomotion via Event-Based Predictive Control and QP-Based Virtual Constraints

This letter aims to develop a hierarchical nonlinear control algorithm, based on model predictive control (MPC), quadratic programming (QP), and virtual constraints, to generate and stabilize locomotion patterns in a real-time manner for dynamical models of quadrupedal robots. The higher level of the proposed control scheme is developed based on an event-based MPC that computes the optimal center of mass (COM) trajectories for a reduced-order linear inverted pendulum (LIP) model subject to the feasibility of the net ground reaction force (GRF). The asymptotic stability of a desired target point for the reduced-order model under the event-based MPC approach is investigated. It is shown that the event-based nature of the proposed MPC approach can significantly reduce the computational burden associated with the real-time implementation of MPC techniques. To bridge the gap between reduced- and full-order models, QP-based virtual constraint controllers are developed at the lower level of the proposed control scheme to impose the full-order dynamics to track the optimal trajectories while having all individual GRFs in the friction cone. The analytical results of the letter are numerically verified to demonstrate stable and robust locomotion of a 22 degree of freedom quadrupedal robot, in the presence of payloads, external disturbances, ground height variations, and uncertainty in contact models.
Optimal Robust Learning of Discrete Distributions from Batches

Many applications, including natural language processing, sensor networks, collaborative filtering, and federated learning, call for estimating discrete distributions from data collected in batches, some of which may be untrustworthy, erroneous, faulty, or even adversarial. Previous estimators for this setting ran in exponential time, and for some regimes required a suboptimal number of batches. We provide the first polynomial-time estimator that is optimal in the number of batches and achieves essentially the best possible estimation accuracy.
Understanding the Curse of Horizon in Off-Policy Evaluation via Conditional Importance Sampling

Off-policy policy estimators that use importance sampling (IS) can suffer from high variance in long-horizon domains, and there has been particular excitement over new IS methods that leverage the structure of Markov decision processes. We analyze the variance of the most popular approaches through the viewpoint of conditional Monte Carlo. Surprisingly, we find that in finite horizon MDPs there is no strict variance reduction of per-decision importance sampling or stationary importance sampling, comparing with vanilla importance sampling. We then provide sufficient conditions under which the per-decision or stationary estimators will provably reduce the variance over importance sampling with finite horizons. For the asymptotic (in terms of horizon $T$) case, we develop upper and lower bounds on the variance of those estimators which yields sufficient conditions under which there exists an exponential v.s. polynomial gap between the variance of importance sampling and that of the per-decision or stationary estimators. These results help advance our understanding of if and when new types of IS estimators will improve the accuracy of off-policy estimation.
The Use of Customer value changing trends in business analysis

With the development of competition in business and also today’s rapid changes in competitive market, understanding these changes are the key factor of being more efficient in markets. CRM which is known as basic structure for describing customer’s needs for efficiently understand customer’s behavior and finely get the maximum of market share and profit. There are major differences between B2B and B2C businesses such as long term purchase cycle, purchase interests and the amount of the transactions. These differences needs more interactive strategies. The knowledge that gets from CRM is extremely related to market changes. In recent years data mining increasingly help organizations to get and understand customer’s behavior. But with the rapid changes in market these procedures must be change too. Change mining as the higher order of data mining tries to get knowledge by analysis patterns instead of data. In this paper we attempt to calculate customer’s value by using RFM model and K-MEANS clustering method and then analysis changes in deferent time periods. We tries to find out cluster transitions and most frequent customer value changing trend for proactive decision making. For this purpose we use customer purchase transactions in insurance industry which are gathered in 3 years.
Analysis and design of trussed structures made of elastic/stiffening materials

An extremum problem formulation for the equilibrium analysis of general structures made of stiffening material is applied to the analysis of trussed structures. The nonlinear material is modelled in a way to simulate an arbitrary, polygonal stress-strain relation; material properties may vary over the truss. The form of this convex nonlinear programming problem statement is convenient for the prediction of the evolution (over the truss system) of local member stiffening under increasing proportional load. Computational solutions are obtained directly on the basis of the extremum problem statement, using commercial minimizer software. The formulation for analysis is extended to model a design problem for the prediction of the optimal modification of certain material properties, namely the bounds that reflect the onset of stiffening in the material. Computational results for this optimal material design problem are also provided.
Quasi-Experimental Designs for Assessing Response on Social Media to Policy Changes

Regulation of tobacco products is rapidly evolving. Understanding public sentiment in response to changes is very important as authorities assess how to effectively protect population health. Social media systems are widely recognized to be useful for collecting data about human preferences and perceptions. However, how social media data may be used, in rapid policy change settings, given challenges of narrow time periods and specific locations and non-representative the population using social media is an open question. In this paper we apply quasi-experimental designs, which have been used previously in observational data such as social media, to control for time and location confounders on social media, and then use content analysis of Twitter and Reddit posts to illustrate the content of reactions to tobacco flavor bans and the effect of taxation on e-cigarettes. Conclusions distill the potential role of social media in settings of rapidly changing regulation, in complement to what is learned by traditional denominator-based representative surveys.
A Simple Derivation of AMP and its State Evolution via First-Order Cancellation

We consider the linear regression problem, where the goal is to recover the vector $\boldsymbol{x}\in \mathbb {R}^n$ from measurements $\boldsymbol{y}=\boldsymbol{A}\boldsymbol{x}+\boldsymbol{w}\in \mathbb {R}^m$ under known matrix $\boldsymbol{A}$ and unknown noise $\boldsymbol{w}$ . For large i.i.d. sub-Gaussian $\boldsymbol{A}$ , the approximate message passing (AMP) algorithm is precisely analyzable through a state-evolution (SE) formalism, which furthermore shows that AMP is Bayes optimal in certain regimes. The rigorous SE proof, however, is long and complicated. And, although the AMP algorithm can be derived as an approximation of loop belief propagation (LBP), this viewpoint provides little insight into why large i.i.d. $\boldsymbol{A}$ matrices are important for AMP, and why AMP has a state evolution. In this work, we provide a heuristic derivation of AMP and its state evolution, based on the idea of “first-order cancellation,” that provides insights missing from the LBP derivation while being much shorter than the rigorous SE proof.