cssi/SciSciGPT-SciSciNet huggingface dataset:
STORE: sparse tensor response regression and neuroimaging analysis

Motivated by applications in neuroimaging analysis, we propose a new regression model, Sparse TensOr REsponse regression (STORE), with a tensor response and a vector predictor. STORE embeds two key sparse structures: element-wise sparsity and low-rankness. It can handle both a non-symmetric and a symmetric tensor response, and thus is applicable to both structural and functional neuroimaging data. We formulate the parameter estimation as a non-convex optimization problem, and develop an efficient alternating updating algorithm. We establish a non-asymptotic estimation error bound for the actual estimator obtained from the proposed algorithm. This error bound reveals an interesting interaction between the computational efficiency and the statistical rate of convergence. When the distribution of the error tensor is Gaussian, we further obtain a fast estimation error rate which allows the tensor dimension to grow exponentially with the sample size. We illustrate the efficacy of our model through intensive simulations and an analysis of the Autism spectrum disorder neuroimaging data.
A bimodule structure for the bounded cohomology of commutative local rings

Abstract Stable cohomology is a generalization of Tate cohomology to associative rings, first defined by Pierre Vogel. For a commutative local ring R with residue field k, stable cohomology modules Ext ˆ R n ( k , k ) , defined for n ∈ Z , have been studied by Avramov and Veliche. Stable cohomology carries a structure of Z -graded k-algebra. One of the main goals of this paper is to prove that, for a class of Gorenstein rings, this algebra is a trivial extension of absolute cohomology Ext R ( k , k ) and a shift of Hom k ( Ext R ( k , k ) , k ) . We use this information to characterize the rings R for which stable cohomology is graded-commutative. Stable cohomology is connected through an exact sequence to bounded cohomology. We use this connection to understand the algebra structure of Ext ˆ R ( k , k ) by investigating the structure of bounded cohomology Ext ‾ R ( k , k ) as a graded Ext R ( k , k ) -bimodule.
Scanning the Internet for ROS: A View of Security in Robotics Research

Security is particularly important in robotics, as robots can directly perceive and affect the physical world. We describe the results of a scan of the entire IPv4 address space of the Internet for instances of the Robot Operating System (ROS), a widely used robotics software platform. We identified a number of hosts supporting ROS that are exposed to the public Internet, thereby allowing anyone to access robotic sensors and actuators. As a proof of concept, and with the consent of the relevant researchers, we were able to read image sensor information from and actuate a physical robot present in a research lab in an American university. This paper gives an overview of our findings, including our methodology, the geographic distribution of publicly-accessible platforms, the sorts of sensor and actuator data that is available, and the different kinds of robots and sensors that our scan uncovered. Additionally, we offer recommendations on best practices to mitigate these security issues in the future.
Leakage Resilient Secret Sharing and Applications

A secret sharing scheme allows a dealer to share a secret among a set of n parties such that any authorized subset of the parties can recover the secret, while any unauthorized subset learns no information about the secret. A leakage-resilient secret sharing scheme (introduced in independent works by Goyal and Kumar, STOC ’18 and Benhamouda, Degwekar, Ishai and Rabin, CRYPTO ’18) additionally requires the secrecy to hold against every unauthorized set of parties even if they obtain some bounded leakage from every other share. The leakage is said to be local if it is computed independently for each share. So far, the only known constructions of local leakage resilient secret sharing schemes are for threshold access structures for very low (O(1)) or very high (\(n -o(\log n)\)) thresholds.
Attention Monitoring and Hazard Assessment with Bio-Sensing and Vision: Empirical Analysis Utilizing CNNs on the KITTI Dataset

Assessing the driver's attention and detecting various hazardous and non-hazardous events during a drive are critical for driver's safety. Attention monitoring in driving scenarios has mostly been carried out using vision (camera-based) modality by tracking the driver's gaze and facial expressions. It is only recently that bio-sensing modalities such as Electroencephalogram (EEG) are being explored. But, there is another open problem which has not been explored sufficiently yet in this paradigm. This is the detection of specific events, hazardous and non-hazardous, during driving that affects the driver's mental and physiological states. The other challenge in evaluating multi-modal sensory applications is the absence of very large scale EEG data because of the various limitations of using EEG in the real world. In this paper, we use both of the above sensor modalities and compare them against the two tasks of assessing the driver's attention and detecting hazardous vs. non-hazardous driving events. We collect user data on twelve subjects and show how in the absence of very large-scale datasets, we can still use pre-trained deep learning convolution networks to extract meaningful features from both of the above modalities. We used the publicly available KITTI dataset for evaluating our platform and to compare it with previous studies. Finally, we show that the results presented in this paper surpass the previous benchmark set up in the above driver awareness-related applications.
Load-Balanced Sparse MTTKRP on GPUs

Sparse matricized tensor times Khatri-Rao product (MTTKRP) is one of the most computationally expensive kernels in sparse tensor computations. This work focuses on optimizing the MTTKRP operation on GPUs, addressing both performance and storage requirements. We begin by identifying the performance bottlenecks in directly extending the state-of-the-art CSF (compressed sparse fiber) format from CPUs to GPUs. A significant challenge with GPUs compared to multicore CPUs is that of utilizing the much greater degree of parallelism in a load-balanced fashion for irregular computations like sparse MTTKRP. To address this issue, we develop a new storage-efficient representation for tensors that enables high-performance, load-balanced execution of MTTKRP on GPUs. A GPU implementation of sparse MTTKRP using the new sparse tensor representation is shown to outperform all currently known parallel sparse CPU and GPU MTTKRP implementations.
Structural basis for transcription activation by Crl through tethering of σS and RNA polymerase

In bacteria, a primary σ-factor associates with the core RNA polymerase (RNAP) to control most transcription initiation, while alternative σ-factors are used to coordinate expression of additional regulons in response to environmental conditions. Many alternative σ-factors are negatively regulated by anti–σ-factors. In Escherichia coli, Salmonella enterica, and many other γ-proteobacteria, the transcription factor Crl positively regulates the alternative σS-regulon by promoting the association of σS with RNAP without interacting with promoter DNA. The molecular mechanism for Crl activity is unknown. Here, we determined a single-particle cryo-electron microscopy structure of Crl-σS-RNAP in an open promoter complex with a σS-regulon promoter. In addition to previously predicted interactions between Crl and domain 2 of σS (σS2), the structure, along with p-benzoylphenylalanine cross-linking, reveals that Crl interacts with a structural element of the RNAP β′-subunit that we call the β′-clamp-toe (β′CT). Deletion of the β′CT decreases activation by Crl without affecting basal transcription, highlighting the functional importance of the Crl-β′CT interaction. We conclude that Crl activates σS-dependent transcription in part through stabilizing σS-RNAP by tethering σS2 and the β′CT. We propose that Crl, and other transcription activators that may use similar mechanisms, be designated σ-activators.
Proof Without Words: The Pythagorean Theorem

Summary. We present a wordless proof of the Pythagorean theorem by rearranging pieces to form a single square.
US Water Pollution Regulation over the Last Half Century: Burning Waters to Crystal Springs?

Author(s): Shapiro, Joseph | Abstract: In the half century since the founding of the U.S. Environmental Protection Agency, public and private U.S. sources have spent nearly $5 trillion ($2017) to provide clean rivers, lakes, and drinking water, or annual spending of 0.8 percent of U.S. GDP in most years. Yet over half of rivers and substantial shares of drinking water systems violate standards, and polls for decades have listed water pollution as Americans’ number one environmental concern. We assess the history, effectiveness, and efficiency of the Clean Water Act and Safe Drinking Water Act, and obtain four main conclusions. First, water pollution has fallen since these laws, in part due to their interventions. Second, investments made under these laws could be more cost-effective. Third, most recent studies estimate benefits of cleaning up pollution in rivers and lakes which are less than their costs, though these studies may under-count several potentially important types of benefits. Analysis finds more positive net benefits of drinking water quality investments. Fourth, economic research and teaching on water pollution is relatively uncommon, as measured by samples of publications, conference presentations, and textbooks.
Lying, Reciprocity, and Free Speech – A Reply to Eight Critics

In this article, I reply to eight critics of my book Speech Matters: On Lying, Morality, and the Law. The topics include lying, promising, reciprocity, free speech, and the testimonial duties of institutions.
Unlimited FDIC Insurance and the Implications for Corporate Cash

This paper examines the cash management practices of firms during the period of increased FDIC insurance on noninterest-bearing accounts. While the Transaction Account Guarantee Program and Dodd–Fr...
On the wave turbulence theory for stratified flows in the ocean

After the pioneering work of Garrett and Munk, the statistics of oceanic internal gravity waves has become a central subject of research in oceanography. The time evolution of the spectral energy o...
Avoiding Implementation Pitfalls of “Matrix Capsules with EM Routing” by Hinton et al.

The recent progress on capsule networks by Hinton et al. has generated considerable excitement in the machine learning community. The idea behind a capsule is inspired by a cortical minicolumn in the brain, whereby a vertically organised group of around 100 neurons receive common inputs, have common outputs, are interconnected, and may well constitute a fundamental computation unit of the cerebral cortex. However, Hinton’s paper on “Matrix Capsule with EM Routing” was unfortunately not accompanied by a release of source code, which left interested researchers attempting to implement the architecture and reproduce the benchmarks on their own. This has certainly slowed the progress of research building on this work. While writing our own implementation, we noticed several common mistakes in other open source implementations that we came across. In this paper we share some of these learnings, specifically focusing on three implementation pitfalls and how to avoid them: (1) parent capsules with only one child; (2) normalising the amount of data assigned to parent capsules; (3) parent capsules at different positions compete for child capsules. While our implementation is a considerable improvement over currently available implementations, it still falls slightly short of the performance reported by Hinton et al. (2018). The source code for this implementation is available on GitHub at the following URL: https://github.com/IBM/matrix-capsules-with-em-routing.
Multitiered blood supply chain network competition: Linking blood service organizations, hospitals, and payers

Abstract In this paper, we present a multitiered competitive supply chain network model for the blood banking industry, with a focus on the United States, that captures the economic interactions between three tiers of stakeholders; namely, the blood service organizations, the hospitals or medical centers, which transfuse blood to patients, and the payer groups that patients belong to. In addition, the supply chain framework for this life-saving product includes the competition among blood service organizations and their various supply chain activities. We model the behavior of each category of stakeholder and use the theory of variational inequalities to derive the equilibrium conditions for the entire supply chain. Illustrative examples are provided, along with qualitative properties, followed by an algorithm, accompanied by convergence results, that is used to solve simulated numerical examples. Results from these examples demonstrate that such a model can be effectively used to determine the prices and blood pathways from blood service organizations to hospitals to payers.
Vanishing theorems for parabolic Higgs bundles

This is a sequel to "Kodaira-Saito vanishing via Higgs bundles in positive characteristic" (arXiv:1611.09880). However, unlike the previous paper, all the arguments here are in characteristic zero. The main result is a Kodaira vanishing theorem for semistable parabolic Higgs bundles with trivial parabolic Chern classes. This implies a general semipositivity theorem. This also implies a Kodaira-Saito vanishing theorem for complex variations of Hodge structure.
Reversed sonority in Pashto initial clusters

Abstract: Initial consonant clusters with a falling slope of sonority, or reversed clusters, are of interest because they violate the overwhelming preference of languages for initial clusters with a rising sonority slope. Some acoustic characteristics of three reversed clusters of Pashto, lm-, wl-, and wr- are investigated. It is concluded that these are true clusters phonetically, and that they do not differ as a class from comparable preferred clusters in their voicing, intensity, and duration structures. The significance of the results for sonority theories of syllable structure is discussed.
Symmetries of the quaternionic Ginibre ensemble

We establish a few properties of eigenvalues and eigenvectors of the quaternionic Ginibre ensemble (QGE), analogous to what is known in the complex Ginibre case (see [7, 11, 14]). We first recover ...
Evaluating State and Local Business Tax Incentives

This essay describes and evaluates state and local business tax incentives in the United States. In 2014, states spent between $5 and $216 per capita on incentives for firms in the form of firm-specific subsidies and general tax credits, which mostly target investment, job creation, and research and development. Collectively, these incentives amounted to nearly 40% of state corporate tax revenues for the typical state, but some states' incentive spending exceeded their corporate tax revenues. States with higher per capita incentives tend to have higher state corporate tax rates. Recipients of firm-specific incentives are usually large establishments in manufacturing, technology, and high-skilled service industries, and the average discretionary subsidy is $178M for 1,500 promised jobs. Firms tend to accept subsidy deals from places that are richer, larger, and more urban than the average county, and poor places provide larger incentives and spend more per job. Comparing “winning” and runner-up locations for each deal in a bigger and more recent sample than in prior work, we find that average employment within the 3-digit industry of the deal increases by roughly 1,500 jobs. While we find some evidence of direct employment gains from attracting a firm, we do not find strong evidence that firm-specific tax incentives increase broader economic growth at the state and local level. Although these incentives are often intended to attract and retain high-spillover firms, the evidence on spillovers and productivity effects of incentives appears mixed. As subsidy-giving has become more prevalent, subsidies are no longer as closely tied to firm investment. If subsidy deals do not lead to high spillovers, justifying these incentives requires substantial equity gains, which are also unclear empirically.
The weak form of Malle’s conjecture and solvable groups

For a fixed finite solvable group G and number field K, we prove an upper bound for the number of G-extensions L / K with restricted local behavior (at infinitely many places) and $$\mathrm{inv}(L/K)<X$$ for a general invariant “$$\mathrm{inv}$$”. When the invariant is given by the discriminant for a transitive embedding of a nilpotent group $$G\subset S_n$$, this realizes the upper bound given in the weak form of Malle’s conjecture. For other solvable groups, the upper bound depends on the size of torsion of the class group of number fields with fixed degree. In particular, the bounds we prove realize the upper bound given in the weak form of Malle’s conjecture for the transitive embedding of a solvable group $$G\subset S_n$$ if we assume that for each finite abelian group A the average size of class group torsion $$|\mathrm{Hom}(\mathrm{Cl}(L),A)|$$ is smaller than $$X^{\epsilon }$$ as L / K varies over certain families of extensions with $$\mathrm{inv}(L/K)<X$$.
Analytic Calculation of Covariance between Cosmological Parameters from Correlated Data Sets, with an Application to SPTpol

Consistency checks of cosmological data sets are an important tool because they may suggest systematic errors or the type of modifications to ΛCDM necessary to resolve current tensions. In this work, we derive an analytic method for calculating the level of correlations between model parameters from two correlated cosmological data sets, which complements more computationally expensive simulations. This method is an extension of the Fisher analysis that assumes a Gaussian likelihood and a known data covariance matrix. We apply this method to the South Pole Telescope Polarimeter (SPTpol) temperature and polarization cosmic microwave background (CMB) spectra (TE and EE). We find weak correlations between ΛCDM parameters with a 9% correlation between the TE-only and EE-only constraints on H 0 and a 25% and 32% correlation for log(A s ) and n s respectively. The TE-EE parameter differences are consistent with zero, with a probability to exceed of 0.53. Using simulations we show that this test is independent of the consistency of the SPTpol TE and EE band powers with the best-fit ΛCDM model spectra. Despite the negative correlations between the TE and EE power spectra, the correlations between TE-only and EE-only ΛCDM parameters are positive. Ignoring correlations in the TT-TE and TE-EE comparisons biases the χ 2 low, artificially making parameters look more consistent. Therefore, we conclude that these correlations need to be accounted for when performing internal consistency checks of the TT versus TE versus EE power spectra for future CMB analyses.
Defense-in-depth: A recipe for logic locking to prevail

Abstract Logic locking/obfuscation has emerged as an auspicious solution for protecting the semiconductor intellectual property (IP) from the untrusted entities in the design and fabrication process. Logic locking disguises the implementation and functionality of the IP by implanting additional key-gates in the circuit. The right output of the locked chip is produced, once the correct key value is available at the input of the key-gates. The confidentiality of the key is imperative for the security of the locked IP as it stands as the lone barrier against IP infringement. Therefore, the logic locking is considered as a broken scheme once the key value is exposed. The logic locking techniques have shown vulnerability to different classes of attacks, such as Oracle-guided and physical attacks. Although the research community has proposed a number of countermeasures against such attacks, none of them is simultaneously unbreakable against Oracle-guided, Oracle-less, and physical attacks. Under such circumstances, a defense-in-depth mechanism can be considered as a feasible approach in addressing the vulnerabilities of logic locking. Defense-in-depth is a multilayer defense strategy where several independent countermeasures are implemented in the device to provide aggregated protection against different attack vectors. Introducing such a multilayer shielding model in logic locking is the major contribution of this paper. With regard to this, we first identify the core components of logic locking schemes, which need to be protected. Afterwards, we categorize the vulnerabilities of core components according to potential threats for the locking key in logic locking schemes. Furthermore, we propose several defense layers and countermeasures to protect the device from those vulnerabilities. In conclusion, we believe that a logic locking technique with a layered defense mechanism can be a possible solution against IP piracy.
Emergency Medicine Career Outcomes and Scholarly Pursuits: The Impact of Transitioning From a Three-year to a Four-year Niche-based Residency Curriculum.

Objectives In 2008, our emergency medicine (EM) residency program transitioned from a 3-year to a 4-year format. We analyzed the effect that this change had on the scholarly productivity and career choice of graduates, hypothesizing that it would lead residents to be more scholarly productive and graduates to more frequently obtain academic appointments and leadership roles in their first postresidency positions. Methods This was a retrospective analysis of graduates (N = 95) from a single residency program that underwent a curriculum change from a 3-year to a 4-year format. Three cohorts prior to (n = 36) and five cohorts after (n = 59) this transition were included. The primary outcome of interest was the setting of graduates' first postresidency position. Secondary outcomes included completion of scholarly activity during training and attaining a leadership role in the first postresidency position. Results Of the 4-year program graduates, 44% obtained an academic position compared to 28% of 3-year program graduates. After confounders were controlled for, this difference was statistically discernible only if fellowships were excluded (including fellowship, odds ratio [OR] = 2.25, 95% CI = 0.87 to 5.78; excluding fellowship, OR = 3.53, 95% CI = 1.13 to 11.05). Four-year graduates were more likely to obtain a leadership position immediately after graduation (OR = 13.72, 95% CI = 2.45 to 76.99). Compared to residents in the 3-year program, residents in the 4-year format had a similar likelihood of producing any scholarly work by graduation (OR = 1.69, 95% CI = 0.49 to 5.80) but were more likely to publish peer-reviewed manuscripts (OR = 3.92, 95% CI = 2.25 to 6.83). Conclusions Compared to 3-year residency graduates, graduates of our 4-year curriculum were more likely to obtain nonfellowship academic appointments and leadership positions immediately after graduation and to publish their scholarly work during residency. This study suggests that residency applicants seeking to be academically productive during residency and leaders in the field of EM should consider training in a 4-year program with similar goals.
Empirical Measures of Park Use in American Cities, and the Demographic Biases of Spatial Models

City planners have a professional and ethical responsibility to provide public goods equitably. Parks improve mental and physical health by nurturing social cohesion and enabling physical activity. So who gets parks? Park access has traditionally been evaluated using constructed variables of potential access: distance buffers or gravity models. These models have major limitations: they ignore commutes and other more intricate mobility behaviors. To address these issues, I propose a nationally scalable,empirical measure of realized use. Using a dataset of smartphone locations, I identify visits to parks in the twenty largest American cities. I use these data to calibrate existing models, and then contrast the models with realized use. The traditional models are not simply imprecise; they systematically over-estimate realized access by minority populations. In other words, they understate inequity. On the other hand,the new data come with substantial challenges. They are a convenience sample, biased towards wealthier,whiter populations. While these biases appear to be moderate, continued work with these and similar data will require continued attention to the sample frame.
Harmonic means of Wishart random matrices

We use free probability to compute the limiting spectral properties of the harmonic mean of n i.i.d. Wishart random matrices Wi whose limiting aspect ratio is γ ∈ (0, 1) when &#x1d53c;[Wi] = I. We demonstr...
DataLair: Efficient Block Storage with Plausible Deniability against Multi-Snapshot Adversaries

Sensitive information is present on our phones, disks, watches and computers. Its protection is essential. Plausible deniability of stored data allows individuals to deny that their device contains a piece of sensitive information. This constitutes a key tool in the fight against oppressive governments and censorship. Unfortunately, existing solutions, such as the now defunct TrueCrypt, can defend only against an adversary that can access a users device at most once (single-snapshot adversary). Recent solutions have traded significant performance overheads for the ability to handle more powerful adversaries, that are able to access the device at multiple points in time (multi-snapshot adversary). In this paper we show that this sacrifice is not necessary. We introduce and build DataLair, a practical plausible deniability mechanism. When compared with existing approaches, DataLair is two orders of magnitude faster for public data accesses, and 5 times faster for hidden data accesses. An important component in DataLair is a new write-only ORAM construction which improves on the complexity of the state of the art write-only ORAM by a factor of O(logN ), where N denotes the underlying storage disk size.
RLC Circuits-Based Distributed Mirror Descent Method

We consider distributed optimization with smooth convex objective functions defined on an undirected connected graph. Inspired by mirror descent mehod and RLC circuits, we propose a novel distributed mirror descent method. Compared with mirror-prox method, our algorithm achieves the same $\mathcal {O}$ ( $1/k$ ) iteration complexity with only half the computation cost per iteration. We further extend our results to cases where a) gradients are corrupted by stochastic noise, and b) objective function is composed of both smooth and non-smooth terms. We demonstrate our theoretical results via numerical experiments.
A Universal Score for Deconvolution of Intact Protein and Native Electrospray Mass Spectra.

The growing use of intact protein mass analysis, top-down proteomics, and native mass spectrometry have created a need for improved data analysis pipelines for deconvolution of electrospray (ESI) m...
Physical Layer Secret Key Generation in Static Environments

Two legitimate parties, referred to as Alice and Bob, wish to generate secret keys from the wireless channel in the presence of an eavesdropper, referred to as Eve, in order to use such keys for encryption and decryption. In general, the secret key rate highly depends on the coherence time of the channel. In particular, a straightforward method of generating secret keys in static environments results in ultra-low rates. In order to resolve this problem, we introduce a low-complexity method called induced randomness . In this method, Alice and Bob independently generate local randomness to be used together with the uniqueness of the wireless channel coefficients in order to enable high-rate secret key generation. In this work, two scenarios are considered: first, when Alice and Bob share a direct communication channel, and second, when Alice and Bob do not have a direct link and communicate through an untrusted relay. After exchanging the induced randomness, post-processing is done by Alice and Bob to generate highly-correlated samples that are used for the key generation. Such samples are then converted into bits, disparities between the sequences generated by Alice and Bob are mitigated, and the resulting sequences are then hashed to compensate for the information leakage to the eavesdropper and to allow consistency checking of the generated key bit sequences. We utilize semantic security measures and information-theoretic inequalities to upper bound the probability of successful eavesdropping attack in terms of the mutual information measures that can be numerically computed. Given certain reasonable system parameters this bound is numerically evaluated to be $2^{-31}$ and $2^{-10.57}$ in the first and the second scenario, respectively.
Diffusion tensor tractography of brainstem fibers and its application in pain

Evaluation of brainstem pathways with diffusion tensor imaging (DTI) and tractography may provide insights into pathophysiologies associated with dysfunction of key brainstem circuits. However, identification of these tracts has been elusive, with relatively few in vivo human studies to date. In this paper we proposed an automated approach for reconstructing nine brainstem fiber trajectories of pathways that might be involved in pain modulation. We first performed native-space manual tractography of these fiber tracts in a small normative cohort of participants and confirmed the anatomical precision of the results using existing anatomical literature. Second, region-of-interest pairs were manually defined at each extracted fiber's termini and nonlinearly warped to a standard anatomical brain template to create an atlas of the region-of-interest pairs. The resulting atlas was then transformed non-linearly into the native space of 17 veteran patients' brains for automated brainstem tractography. Lastly, we assessed the relationships between the integrity levels of the obtained fiber bundles and pain severity levels. Fractional anisotropy (FA) measures derived using automated tractography reflected the respective tracts' FA levels obtained via manual tractography. A significant inverse relationship between FA and pain levels was detected within the automatically derived dorsal and medial longitudinal fasciculi of the brainstem. This study demonstrates the feasibility of DTI in exploring brainstem circuitries involved in pain processing. In this context, the described automated approach is a viable alternative to the time-consuming manual tractography. The physiological and functional relevance of the measures derived from automated tractography is evidenced by their relationships with individual pain severities.
Valid p-values and expectations of p-values revisited

We focus on valid definitions of p-values. A valid p-value (VpV) statistic can be used to make a prefixed level- $$ \alpha $$ decision. In this context, Kolmogorov–Smirnov goodness-of-fit tests and the normal two-sample problem are considered. We examine an issue regarding the goodness-of-fit testability based on a single observation. We exemplify constructions of new test procedures, advocating practical reasons to implement VpV mechanisms. The VpV framework induces an extension of the conventional expected p-value (EPV) tool for measuring the performance of a test. Associating the EPV concept with the receiver operating characteristic (ROC) curve methodology, a well-established biostatistical approach, we propose a Youden’s index-based optimality to derive critical values of tests. In these terms, the significance level $$ \alpha = 0.05 $$ is suggested. We introduce partial EPV’s to characterize properties of tests including their unbiasedness. We provide the intrinsic relationship between the Bayes Factor (BF) test statistic and the BF of test statistics.
Toward a homogeneous set of transiting planet parameters

With 40 or more transiting exoplanets now known, the time is ripe to seek patterns and correlations among their observed properties, which may give important insights into planet formation, structure, and evolution. This task is made difficult by the widely different methodologies that have been applied to measure their properties in individual cases. Furthermore, in many systems our knowledge of the planet properties is limited by the knowledge of the properties of the parent stars. To address these difficulties we have undertaken the first comprehensive analysis of the data for 23 transiting planets using a uniform methodology. We revisit several of the recently proposed correlations, and find new ones involving the metallicity of the parent stars.
Floors are Flat: Leveraging Semantics for Real-Time Surface Normal Prediction

We propose 4 insights that help to significantly improve the performance of deep learning models that predict surface normals and semantic labels from a single RGB image. These insights are: (1) denoise the "ground truth" surface normals in the training set to ensure consistency with the semantic labels; (2) concurrently train on a mix of real and synthetic data, instead of pretraining on synthetic and finetuning on real; (3) jointly predict normals and semantics using a shared model, but only backpropagate errors on pixels that have valid training labels; (4) slim down the model and use grayscale instead of color inputs. Despite the simplicity of these steps, we demonstrate consistently improved state of the art results on several datasets, using a model that runs at 12 fps on a standard mobile phone.
Reinforcement Learning-Enabled Reliable Wireless Sensor Networks in Dynamic Underground Environments

Wireless underground sensor networks play an important role in underground sensing such as climate-smart agriculture and underground infrastructure monitoring. Existing works consider a static underground environment, which is not practical since the dielectric parameters of soil change frequently due to precipitation and harsh weather. This challenge cannot be ignored in real implementation due to the drastic change of wireless underground channel. In this paper, we study the effect of dynamic underground environment on wireless communications for sensor networks. We use the real data collected by in-situ sensors to train a Hidden Markov Model. Then, by using reinforcement learning, we derive the optimal transmission policies for underground sensors to efficiently use their energy and reduce the number of dropped and unsuccessfully transmitted packets. Through simulations using real data, we find that the developed algorithm can reduce the packet loss and transmit the sensed data in a timely manner.
Validation of the Interfacial Area Transport Equation Coupled with the Void Transport Equation for Prediction of Flashing Flows

The static correlations from RELAP5 and TRACE as well as the interfacial area transport equation (IATE) are benchmarked for flashing flow with selected cases from a recently published experimental ...
Mask-Dependent Phase Estimation for Monaural Speaker Separation

Speaker separation refers to isolating speech of interest in a multi-talker environment. Most methods apply real-valued Time-Frequency (T-F) masks to the mixture Short-Time Fourier Transform (STFT) to reconstruct the clean speech. Hence there is an unavoidable mismatch between the phase of the reconstruction and the original phase of the clean speech. In this paper, we propose a simple yet effective phase estimation network that predicts the phase of the clean speech based on a T-F mask predicted by a chimera++ network. To overcome the label-permutation problem for both the T-F mask and the phase, we propose a mask-dependent permutation invariant training (PIT) criterion to select the phase signal based on the loss from the T-F mask prediction. We also propose an Inverse Mask Weighted Loss Function for phase prediction to focus the model on the T-F regions in which the phase is more difficult to predict. Results on the WSJ0-2mix dataset show that the phase estimation network achieves comparable performance to models that use iterative phase reconstruction or end-to-end time-domain loss functions, but in a more straightforward manner.
Quantifying thermal transport in amorphous silicon using mean free path spectroscopy

The wide application of amorphous materials in solar cells, memristors, and optical sensors has stimulated interest in understanding heat conduction in amorphous systems owing to their thermal management issues. Thermal transport in amorphous materials fundamentally differs from their crystalline counterparts due to the lack of long-range order. Despite great progress in understanding the thermal transport in crystalline materials over the past few decades from both first-principles computations and thermal transport characterizations, details of heat conduction in amorphous systems remain largely unknown. Here, we quantify different types of heat carriers in amorphous silicon using mean free path spectroscopy, with characteristic sizes down to 50 nm. We show that despite its disordered nature, more than half of thermal conductivity is contributed by propagating vibrational waves, which have mean free paths mostly above 100 nm. This provides direct evidence supporting the diversity of heat carriers in amorphous systems; some modes transport heat as propagating waves, while others do not. Our results suggest mean free path spectroscopy is a versatile tool for understanding thermal transport in disordered systems.
Cotorsion pairs and a K-theory localization theorem

Abstract We show that a complete hereditary cotorsion pair ( C , C ⊥ ) in an exact category E , together with a subcategory Z ⊆ E containing C ⊥ , determines a Waldhausen category structure on the exact category C , in which Z is the class of acyclic objects. This allows us to prove a new version of Quillen's Localization Theorem, relating the K-theory of exact categories A ⊆ B to that of a cofiber. The novel idea in our approach is that, instead of looking for an exact quotient category that serves as the cofiber, we produce a Waldhausen category, constructed through a cotorsion pair. Notably, we do not require A to be a Serre subcategory, which produces new examples. Due to the algebraic nature of our Waldhausen categories, we are able to recover a version of Quillen's Resolution Theorem, now in a more homotopical setting that allows for weak equivalences.
Noise sources in a low-Reynolds-number turbulent jet at Mach 0.9

The mechanisms of sound generation in a Mach 0.9, Reynolds number 3600 turbulent jet are investigated by direct numerical simulation. Details of the numerical method are briefly outlined and results are validated against an experiment at the same flow conditions (Stromberg, McLaughlin & Troutt 1980). Lighthill's theory is used to define a nominal acoustic source in the jet, and a numerical solution of Lighthill's equation is compared to the simulation to verify the computational procedures. The acoustic source is Fourier transformed in the axial coordinate and time and then filtered in order to identify and separate components capable of radiating to the far field. This procedure indicates that the peak radiating component of the source is coincident with neither the peak of the full unfiltered source nor that of the turbulent kinetic energy. The phase velocities of significant components range from approximately 5% to 50% of the ambient sound speed which calls into question the commonly made assumption that the noise sources convect at a single velocity. Space–time correlations demonstrate that the sources are not acoustically compact in the streamwise direction and that the portion of the source that radiates at angles greater than 45° is stationary. Filtering non-radiating wavenumber components of the source at single frequencies reveals that a simple modulated wave forms for the source, as might be predicted by linear stability analysis. At small angles from the jet axis the noise from these modes is highly directional, better described by an exponential than a standard Doppler factor.
Aluminum Oxynitride (ALON) Spinel

Publisher Summary This chapter describes aluminum oxynitride (ALON) as a crystal made of various amounts of aluminum oxide and aluminum nitride, with the aluminum–nitride content between 27 and 40 mole percent. The resulting material—a nitrogen-stabilized cubic aluminum oxide—is face-centered cubic with spinel structure. ALON is termed a nonstoichiometric spinel because some cation sites are occupied by vacancies. It can be produced by a variety of methods, including reaction sintering of alumina and aluminum nitride, simultaneous reduction and nitridation of alumina, combustion-like reaction of aluminum metal with oxygen and nitrogen, and reaction of gases. Commercial optical ALON is made by reacting the raw materials, pressing them into a near-finished shape, and firing at a high temperature to produce a fully dense, transparent ceramic. The resulting high-temperature, high-strength ceramic is used for windows and optical elements where hardness and resistance to harsh environments is important. This chapter provides an overview of the optical elements of ALON and reviews property data for this material given in the literature. The chapter also presents transverse and longitudinal optical frequencies of ALON as determined by reflectance measurements. The transverse optical frequencies are the mode locations, and the maximum longitudinal frequency is an important parameter in the multiphonon model as it designates the maximum phonon frequency.
The Effect of Changes in Reserve Requirements During the 1930s: The Evidence from Nonmember Banks

Despite the widespread acceptance of Friedman and Schwartz's interpretation of the 1936/37 increase in member bank reserve requirements as the major cause of the 1937/38 recession there is surprisingly little straightforward evidence on this issue, perhaps because data limitations and structural instability preclude econometric modeling. We exploit a simple alternative, comparing member banks with nonmember banks not subject to changes in reserve requirements. The results support the hypothesis that the increase in reserve requirements reduced the availability of bank credit and contributed to the recession.
6 – TAKING THE NATION'S ECONOMIC PULSE

Publisher Summary This chapter discusses the strengths and weaknesses of the tools that have been developed to assist in the measurement of economic performance. The chapter presents the concept of gross national product (GNP). It is a measure of the market value of goods and services that were produced during a specific time period. GNP is a flow concept and is typically measured in terms of an annual rate. GNP is a device designed to measure the market value of production that flows through the economy's factories and shops each year. As GNP measures only current production, it cannot be arrived at merely by summing the totals on all of the nation's cash registers. Many transactions have to be excluded and only final goods are counted. A final good is a good in the hands of its ultimate user. Goods go through many stages of production. However, GNP counts only the dollar market value of all final goods and services produced during a year.
A neuronal signature for monogamous reunion

Pair-bond formation depends vitally on neuromodulatory signaling within the nucleus accumbens, but the neuronal dynamics underlying this behavior remain unclear. Using 1-photon in vivo Ca2+ imaging in monogamous prairie voles, we found that pair bonding does not elicit differences in overall nucleus accumbens Ca2+ activity. Instead, we identified distinct ensembles of neurons in this region that are recruited during approach to either a partner or a novel vole. The partner-approach neuronal ensemble increased in size following bond formation, and differences in the size of approach ensembles for partner and novel voles predict bond strength. In contrast, neurons comprising departure ensembles do not change over time and are not correlated with bond strength, indicating that ensemble plasticity is specific to partner approach. Furthermore, the neurons comprising partner and novel-approach ensembles are nonoverlapping while departure ensembles are more overlapping than chance, which may reflect another key feature of approach ensembles. We posit that the features of the partner-approach ensemble and its expansion upon bond formation potentially make it a key neuronal substrate associated with bond formation and maturation.
Multimodal Image Outpainting with Regularized Normalized Diversification

In this paper, we study the problem of generating a set of realistic and diverse backgrounds when given only a small foreground region. We refer to this task as image outpainting. The technical challenge of this task is to synthesize not only plausible but also diverse image outputs. Traditional generative adversarial networks suffer from mode collapse. While recent approaches [32], [28] propose to maximize or preserve the pairwise distance between generated samples with respect to their latent distance, they do not explicitly prevent the diverse samples of different conditional inputs from collapsing. Therefore, we propose a new regularization method to encourage diverse sampling in conditional synthesis. In addition, we propose a feature pyramid discriminator to improve the image quality. Our experimental results show that our model can produce more diverse images without sacrificing visual quality compared to state-of-the-arts approaches in both the CelebA face dataset [29] and the Cityscape scene dataset [2]. Code is available at: https://github.com/owenzlz/DiverseOutpaint
Essential self-adjointness of the wave operator and the limiting absorption principle on Lorentzian scattering spaces

We discuss the essential self-adjointness of wave operators, as well as the limiting absorption principle, in generalizations of asymptotically Minkowski settings. This is obtained via using a Fredholm framework for inverting the spectral family first, and then refining its conclusions to show its dense range in L^2 when acting on an appropriate subdomain.
On the Regularization Properties of Structured Dropout

Dropout and its extensions (e.g. DropBlock and DropConnect) are popular heuristics for training neural networks, which have been shown to improve generalization performance in practice. However, a theoretical understanding of their optimization and regularization properties remains elusive. Recent work shows that in the case of single hidden-layer linear networks, Dropout is a stochastic gradient descent method for minimizing a regularized loss, and that the regularizer induces solutions that are low-rank and balanced. In this work we show that for single hidden-layer linear networks, DropBlock induces spectral k-support norm regularization, and promotes solutions that are low-rank and have factors with equal norm. We also show that the global minimizer for DropBlock can be computed in closed form, and that DropConnect is equivalent to Dropout. We then show that some of these results can be extended to a general class of Dropout-strategies, and, with some assumptions, to deep non-linear networks when Dropout is applied to the last layer. We verify our theoretical claims and assumptions experimentally with commonly used network architectures.