cssi/SciSciGPT-SciSciNet huggingface dataset:
GOTCHA password hackers

We introduce GOTCHAs (Generating panOptic Turing Tests to Tell Computers and Humans Apart) as a way of preventing automated offline dictionary attacks against user selected passwords. A GOTCHA is a randomized puzzle generation protocol, which involves interaction between a computer and a human. Informally, a GOTCHA should satisfy two key properties: (1) The puzzles are easy for the human to solve. (2) The puzzles are hard for a computer to solve even if it has the random bits used by the computer to generate the final puzzle --- unlike a CAPTCHA [44]. Our main theorem demonstrates that GOTCHAs can be used to mitigate the threat of offline dictionary attacks against passwords by ensuring that a password cracker must receive constant feedback from a human being while mounting an attack. Finally, we provide a candidate construction of GOTCHAs based on Inkblot images. Our construction relies on the usability assumption that users can recognize the phrases that they originally used to describe each Inkblot image --- a much weaker usability assumption than previous password systems based on Inkblots which required users to recall their phrase exactly. We conduct a user study to evaluate the usability of our GOTCHA construction. We also generate a GOTCHA challenge where we encourage artificial intelligence and security researchers to try to crack several passwords protected with our scheme.
THE VELOCITY DISTRIBUTION FUNCTION OF GALAXY CLUSTERS AS A COSMOLOGICAL PROBE

We present a new approach for quantifying the abundance of galaxy clusters and constraining cosmological parameters using dynamical measurements. In the standard method, galaxy line-of-sight (LOS) velocities, $v$, or velocity dispersions are used to infer cluster masses, $M$, in order to quantify the halo mass function (HMF), $dn(M)/d\log(M)$, which is strongly affected by mass measurement errors. In our new method, the probability distribution of velocities for each cluster in the sample are summed to create a new statistic called the velocity distribution function (VDF), $dn(v)/dv$. The VDF can be measured more directly and precisely than the HMF and it can also be robustly predicted with cosmological simulations which capture the dynamics of subhalos or galaxies. We apply these two methods to mock cluster catalogs and forecast the bias and constraints on the matter density parameter $\Omega_m$ and the amplitude of matter fluctuations $\sigma_8$ in flat $\Lambda$CDM cosmologies. For an example observation of 200 massive clusters, the VDF with (without) velocity errors constrains the parameter combination $\sigma_8\Omega_m^{0.29\ (0.29)} = 0.587 \pm 0.011\ (0.583 \pm 0.011)$ and shows only minor bias. However, the HMF with dynamical mass errors is biased to low $\Omega_m$ and high $\sigma_8$ and the fiducial model lies well outside of the forecast constraints, prior to accounting for Eddington bias. When the VDF is combined with constraints from the cosmic microwave background (CMB), the degeneracy between cosmological parameters can be significantly reduced. Upcoming spectroscopic surveys that probe larger volumes and fainter magnitudes will provide a larger number of clusters for applying the VDF as a cosmological probe.
Chemical abundance constraints on white dwarfs as halo dark matter

We examine the chemical abundance constraints on a population of white dwarfs in the halo of our Galaxy. We are motivated by microlensing experiments that have reported evidence for massive compact halo objects (MACHOs) in the halo of our Galaxy, with an estimated mass of 0.1-1 M☉; the only conventional dark astrophysical candidates for objects in this mass range are white dwarfs. However, our work constrains white dwarfs in the halo regardless of what the MACHOs are. Further motivation for our work comes from the recent claimed possible detection of a large population of white dwarfs in the Hubble Deep Field. We focus on the composition of the material that would be ejected as the white dwarfs are formed. This material would bear the signatures of nucleosynthesis processing and contain abundance patterns that can be used to constrain white dwarf production scenarios. Using both analytical and numerical chemical evolution models, we confirm previous work that very strong constraints come from Galactic Population II and extragalactic carbon abundances. We also point out that in some cases, depending on the stellar model, significant nitrogen is produced rather than carbon. The combined constraints from carbon and nitrogen give ΩWDh 2 × 10-4 from comparison with the low abundances of these elements measured in the Lyα forest. We note, however, that these results are subject to uncertainties regarding the nucleosynthetic yields of low-metallicity stars. We thus investigate additional constraints from the light elements D and 4He, the nucleosynthesis of which is less uncertain. We find that these elements can be kept within observational limits only for ΩWD 0.003 and for a white dwarf progenitor initial mass function sharply peaked at low mass (2 M☉). Finally, we consider a Galactic wind, which is required to remove the ejecta accompanying white dwarf production from the galaxy. We show that such a wind can be driven by Type Ia supernovae arising from the white dwarfs themselves but find that these supernovae also lead to unacceptably large abundances of iron. The only ways we know of to avoid these constraints are that (1) the ejecta from low-metallicity MACHO progenitors are absent or completely unprocessed or (2) the processed ejecta remain as hot (0.3 keV) gas that is segregated from all observable neutral material to a precision of 99%. Aside from these loopholes, we conclude that abundance constraints exclude white dwarfs as MACHOs.
Efficient AUC Optimization for Information Ranking Applications

Adequate evaluation of an information retrieval system to estimate future performance is a crucial task. Area under the ROC curve (AUC) is widely used to evaluate the generalization of a retrieval system. However, the objective function optimized in many retrieval systems is the error rate and not the AUC value. This paper provides an efficient and effective non-linear approach to optimize AUC using additive regression trees, with a special emphasis on the use of multi-class AUC (MAUC) because multiple relevance levels are widely used in many ranking applications. Compared to a conventional linear approach, the performance of the non-linear approach is comparable on binary-relevance benchmark datasets and is better on multi-relevance benchmark datasets.
Elementary wideband timing of radio pulsars

We present an algorithm for the simultaneous measurement of a pulse time-of-arrival (TOA) and dispersion measure (DM) from folded wideband pulsar data. We extend the prescription from Taylor's 1992 work to accommodate a general two-dimensional template "portrait," the alignment of which can be used to measure a pulse phase and DM. We show that there is a dedispersion reference frequency that removes the covariance between these two quantities and note that the recovered pulse profile scaling amplitudes can provide useful information. We experiment with pulse modeling by using a Gaussian-component scheme that allows for independent component evolution with frequency, a "fiducial component," and the inclusion of scattering. We showcase the algorithm using our publicly available code on three years of wideband data from the bright millisecond pulsar J1824–2452A (M28A) from the Green Bank Telescope, and a suite of Monte Carlo analyses validates the algorithm. By using a simple model portrait of M28A, we obtain DM trends comparable to those measured by more standard methods, with improved TOA and DM precisions by factors of a few. Measurements from our algorithm will yield precisions at least as good as those from traditional techniques, but is prone to fewer systematic effects and is without ad hoc parameters. A broad application of this new method for dispersion measure tracking with modern large-bandwidth observing systems should improve the timing residuals for pulsar timing array experiments, such as the North American Nanohertz Observatory for Gravitational Waves.
Dust Formation in AGN Winds

Infrared observations of active galactic nuclei (AGNs) reveal emission from the putative dusty circumnuclear "torus" invoked by AGN unification, which is heated up by radiation from the central accreting black hole (BH).The strong 9.7 and 18 m silicate features observed in the AGN spectra, in both emission and absorption, further indicate the presence of such dusty environments. We present detailed calculations of the chemistry of silicate dust formation in AGN accretion disk winds. The winds considered herein are magnetohydrodynamic winds driven off the entire accretion disk domain that extends from the BH vicinity to the radius of BH influence, of order ~1-100 pc depending on the mass of the resident BH. Our results indicate that these winds provide conditions conducive to the formation of significant amounts of dust, especially for objects accreting close to their Eddington limit, making AGNs a significant source of dust in the universe, especially for luminous quasars. Our models justify the importance of an r1 density law in the winds for efficient formation and survival of dust grains. The dust production rate scales linearly with the mass of the central BH and varies as a power law of index between 2 and 2.5 with the dimensionless mass accretion rate. The resultant distribution of the dense dusty gas resembles atoroidal shape, with high column density and optical depths along the equatorial viewing angles, in agreement with the AGN unification picture
Semantic Topic Analysis of Traffic Camera Images

Traffic cameras are commonly deployed monitoring components in road infrastructure networks, providing operators visual information about conditions at critical points in the network. However, human observers are often limited in their ability to process simultaneous information sources. Recent advancements in computer vision, driven by deep learning methods, have enabled general object recognition, unlocking opportunities for camera-based sensing beyond the existing human observer paradigm. In this paper, we present a Natural Language Pro-cessing-inspired approach, entitled Bag-of-Label-Words (BoLW), for analyzing image data sets using exclusively textual labels. The BoLW model represents the data in a conventional matrix form, enabling data compression and decomposition techniques, while preserving semantic interpretability. We apply the Latent Dirichlet Allocation topic model to decompose the label data into a small number of semantic topics. To illustrate our approach, we use freeway camera images collected from the Boston area between December 2017-January 2018. We analyze the cameras' sensitivity to weather events; identify temporal traffic patterns; and analyze the impact of infrequent events, such as the winter holidays and the “bomb cyclone” winter storm. This study demonstrates the flexibility of our approach, which allows us to analyze weather events and freeway traffic using only traffic camera image labels.
Reduced complexity belief propagation decoders for polar codes

Polar codes are newly discovered capacity-achieving codes, which have attracted lots of research efforts. Polar codes can be efficiently decoded by the low-complexity successive cancelation (SC) algorithm and the SC list (SCL) decoding algorithm. The belief propagation (BP) decoding algorithm not only is an alternative to the SC and SCL decoders, but also provides soft outputs that are necessary for joint detection and decoding. Both the BP decoder and the soft cancelation (SCAN) decoder were proposed for polar codes to output soft information about the coded bits. In this paper, first a belief propagation decoding algorithm, called reduced complexity soft cancelation (RCSC) decoding algorithm, is proposed. Let N denote the block length. Our RCSC decoding algorithm needs to store only 5N − 3 log-likelihood ratios (LLRs), significantly less than 4N − 2+ N log2N/2 and N(log2 N +1) LLRs needed by the BP and SCAN decoders, respectively, when N ≥ 64. Besides, compared to the SCAN decoding algorithm, our RCSC decoding algorithm eliminates unnecessary additions over the real field. Then the simplified SC (SSC) principle is applied to our RCSC decoding algorithm, and the resulting SSC-aided RCSC (S-RCSC) decoding algorithm further reduces the computational complexity. Finally, based on the S-RCSC decoding algorithm, we propose a corresponding memory efficient decoder architecture, which has better error performance than existing architectures. Besides, our decoder architecture consumes less energy on updating LLRs.
Characterization of a Serializer ASIC Chip for the Upgrade of the ATLAS Muon Detector

We report on the design of a serializer ASIC to be used in the ATLAS forward muon detector for trigger data transmission. We discuss the performance of a prototype chip covering power dissipation, latency and stable operating line rate. Tests show that the serializer is capable of running at least at 5.76 Gbps with a bit error ratio below $1 \times 10^{- 15}$ , and a power consumption of 200 mW running at 4.8 Gbps. The latency between the start of loading 30 bits into the serializer to the transmission of the first bit from the serializer is measured to be about 6 ns.
Beurling-Selberg extremization and modular bootstrap at high energies

We consider previously derived upper and lower bounds on the number of operators in a window of scaling dimensions $[\Delta - \delta,\Delta + \delta]$ at asymptotically large $\Delta$ in 2d unitary modular invariant CFTs. These bounds depend on a choice of functions that majorize and minorize the characteristic function of the interval $[\Delta - \delta,\Delta + \delta]$ and have Fourier transforms of finite support. The optimization of the bounds over this choice turns out to be exactly the Beurling-Selberg extremization problem, widely known in analytic number theory. We review solutions of this problem and present the corresponding bounds on the number of operators for any $\delta \geq 0$. When $2\delta \in \mathbb Z_{\geq 0}$ the bounds are saturated by known partition functions with integer-spaced spectra. Similar results apply to operators of fixed spin and Virasoro primaries in $c>1$ theories.
Saddle tangencies and the distance of Heegaard splittings

We give another proof of a theorem of Scharlemann and Tomova and of a theorem of Hartshorn. The two theorems together say the following. Let M be a compact orientable irreducible 3–manifold and P a Heegaard surface of M . Suppose Q is either an incompressible surface or a strongly irreducible Heegaard surface in M . Then either the Hempel distance d.P / 2genus.Q/ or P is isotopic to Q . This theorem can be naturally extended to bicompressible but weakly incompressible surfaces.
Lightlike Wilson loops from AdS/CFT

We investigate the lightlike limit of stationary spacelike string configurations on a large class of five-dimensional asymptotically AdS backgrounds. Specific examples include gravity duals which incorporate finite 't Hooft coupling, curvature-squared corrections, and chemical potentials. A universal feature of these AdS/CFT models is that the string solution with minimum action yields a lightlike Wilson loop whose leading behavior is exponentially linear, rather than quadratic, in the width of the loop. Unless there is a compelling reason for discarding the leading saddlepoint contribution to the Wilson loop, following the proposal of Liu et. al. [2] leads to zero jet-quenching parameter for all of these models.
The Unified Astronomy Thesaurus: Semantic Metadata for Astronomy and Astrophysics

Several different controlled vocabularies have been developed and used by the astronomical community, each designed to serve a specific need and a specific group. The Unified Astronomy Thesaurus (UAT) attempts to provide a highly structured controlled vocabulary that will be relevant and useful across the entire discipline, regardless of content or platform. As two major use cases for the UAT include classifying articles and data, we examine the UAT in comparison with the Astronomical Subject Keywords used by major publications and the JWST Science Keywords used by STScI's Astronomer's Proposal Tool.
Coherence and Optical Emission from Bilayer Exciton Condensates

Experiments aimed at demonstrating Bose-Einstein condensation of excitons in two types of experiments with bilayer structures (coupled quantum wells) are reviewed, with an emphasis on the basic effects. Bose-Einstein condensation implies the existence of a macroscopic coherence, also known as off-diagonal long-range order, and proposed tests and past claims for coherence in these excitonic systems are discussed.
Leading multi-soft limits from scattering equations

A Cachazo-He-Yuan (CHY) type formula is derived for the leading gluon, bi-adjoint scalar ϕ 3, Yang-Mills-scalar and non-linear sigma model m-soft factors S m in arbitrary dimension. The general formula is used to evaluate explicit examples for up to three soft legs analytically and up to four soft legs numerically via comparison with amplitude ratios under soft kinematics. A structural pattern for gluon m-soft factor is inferred and a simpler formula for its calculation is conjectured. In four dimensions, a Cachazo-Svrcek-Witten (CSW) recursive procedure producing the leading m-soft gluon factor in spinor helicity formalism is developed as an alternative, and Britto-Cachazo-Feng-Witten (BCFW) recursion is used to obtain the leading four-soft gluon factor for all analytically distinct helicity configurations.
Sub-system quantum dynamics using coupled cluster downfolding techniques

In this paper, we discuss extending the sub-system embedding sub-algebra coupled cluster formalism and the double unitary coupled cluster (DUCC) ansatz to the time domain. An important part of the analysis is associated with proving the exactness of the DUCC ansatz based on the general many-body form of anti-Hermitian cluster operators defining external and internal excitations. Using these formalisms, it is possible to calculate the energy of the entire system as an eigenvalue of downfolded/effective Hamiltonian in the active space, which is identifiable with the sub-system of the composite system. It can also be shown that downfolded Hamiltonians integrate out Fermionic degrees of freedom that do not correspond to the physics encapsulated by the active space. In this paper, we extend these results to the time-dependent Schrodinger equation, showing that a similar construct is possible to partition a system into a sub-system that varies slowly in time and a remaining sub-system that corresponds to fast oscillations. This time-dependent formalism allows coupled cluster quantum dynamics to be extended to larger systems and for the formulation of novel quantum algorithms based on the quantum Lanczos approach, which has recently been considered in the literature.
The General twisted open WZW string

We recently studied two large but disjoint classes of twisted open WZW strings: the open-string sectors of the WZW orientation orbifolds and the so-called basic class of twisted open WZW strings. In this paper, we discuss all T-dualizations of the basic class to construct the general twisted open WZW string — which includes the disjoint classes above as special cases. For the general case, we give the branes and twisted noncommutative geometry at the classical level and the twisted open-string KZ equations at the operator level. Many examples of the general construction are discussed, including in particular the simple case of twisted free-bosonic open strings. We also revisit the open-string sectors of the general WZW orientation orbifold in further detail. For completeness, we finally review the general twisted boundary state equation which provides a complementary description of the general twisted open WZW string.
A Constraint on the Distance Scale to Cosmological Gamma--Ray Bursts

If γ-ray bursts are cosmological in origin, the sources are expected to trace the large-scale structure of luminous matter in the universe. I use a new likelihood method that compares the counts-in-cells distribution of γ-ray bursts in the BATSE 3B catalog with that expected from the known large-scale structure of the universe, in order to place a constraint on the distance scale to cosmological bursts. I find, at the 95% confidence level, that the comoving distance to the "edge" of the burst distribution is greater than 630 h-1 Mpc (z > 0.25), and that the nearest burst is farther than 40 h-1 Mpc. The median distance to the nearest burst is 170 h-1 Mpc, implying that the total energy released in γ-rays during a burst event is of order 3 × 1051 h-2 ergs. None of the bursts that have been observed by BATSE are in nearby galaxies, nor is a signature from the Coma Cluster or the "Great Wall" likely to be seen in the data at present.
Fermi’s golden rule, the origin and breakdown of Markovian master equations, and the relationship between oscillator baths and the random matrix model

Fermi's golden rule applies to a situation in which a single quantum state $|\psi\rangle$ is coupled to a near-continuum. This "quasi-continuum coupling" structure results in a rate equation for the population of $|\psi\rangle$. Here we show that the coupling of a quantum system to the standard model of a thermal environment, a bath of harmonic oscillators, can be decomposed into a "cascade" made up of the quasi-continuum coupling structures of Fermi's golden rule. This clarifies the connection between the physics of the golden rule and that of a thermal bath, and provides a non-rigorous but physically intuitive derivation of the Markovian master equation directly from the former. The exact solution to the Hamiltonian of the golden rule, known as the Bixon-Jortner model, generalized for an asymmetric spectrum, provides a window on how the evolution induced by the bath deviates from the master equation as one moves outside the Markovian regime. Our analysis also reveals the relationship between the oscillator bath and the "random matrix model" (RMT) of a thermal bath. We show that the cascade structure is the one essential difference between the two models, and the lack of it prevents the RMT from generating transition rates that are independent of the initial state of the system. We suggest that the cascade structure is one of the generic elements of thermalizing many-body systems.
OCR Post Correction for Endangered Language Texts

There is little to no data available to build natural language processing models for most endangered languages. However, textual data in these languages often exists in formats that are not machine-readable, such as paper books and scanned images. In this work, we address the task of extracting text from these resources. We create a benchmark dataset of transcriptions for scanned books in three critically endangered languages and present a systematic analysis of how general-purpose OCR tools are not robust to the data-scarce setting of endangered languages. We develop an OCR post-correction method tailored to ease training in this data-scarce setting, reducing the recognition error rate by 34% on average across the three languages.
Towards a sharp converse of Wall’s theorem on arithmetic progressions

Wall's theorem on arithmetic progressions says that if $0.a_1a_2a_3\dots$ is normal, then for any $k,\ell\in \mathbb{N}$, $0.a_ka_{k+\ell}a_{k+2\ell}\dots$ is also normal. We examine a converse statement and show that if $0.a_{n_1}a_{n_2}a_{n_3}\dots$ is normal for periodic increasing sequences $n_1<n_2<n_3<\dots$ of asymptotic density arbitrarily close to $1$, then $0.a_1a_2a_3\dots$ is normal. We show this is close to sharp in the sense that there are numbers $0.a_1a_2a_3\dots$ that are not normal, but for which $0.a_{n_1}a_{n_2}a_{n_3}\dots$ is normal along a large collection of sequences whose density is bounded a little away from $1$.
Photometric Redshifts with the LSST: Evaluating Survey Observing Strategies

In this paper we present and characterize a nearest-neighbors color-matching photometric redshift estimator that features a direct relationship between the precision and accuracy of the input magnitudes and the output photometric redshifts. This aspect makes our estimator an ideal tool for evaluating the impact of changes to LSST survey parameters that affect the measurement errors of the photometry, which is the main motivation of our work (i.e., it is not intended to provide the "best" photometric redshifts for LSST data). We show how the photometric redshifts will improve with time over the 10 year LSST survey and confirm that the nominal distribution of visits per filter provides the most accurate photo-z results. The LSST survey strategy naturally produces observations over a range of airmass, which offers the opportunity of using an SED- and z-dependent atmospheric affect on the observed photometry as a color-independent redshift indicator. We show that measuring this airmass effect and including it as a prior has the potential to improve the photometric redshifts and can ameliorate extreme outliers, but that it will only be adequately measured for the brightest galaxies, which limits its overall impact on LSST photometric redshifts. We furthermore demonstrate how this airmass effect can induce a bias in the photo-z results, and caution against survey strategies that prioritize high-airmass observations for the purpose of improving this prior. Ultimately, we intend for this work to serve as a guide for the expectations and preparations of the LSST science community with regard to the minimum quality of photo-z as the survey progresses.
An irregular narrow complex tachycardia.

Xiaodong Zhang1, MD, PhD, Ruike Yang1 2, MD, Luigi Di Biase1, MD, PhD, FHRS1 Division of Cardiology, Department of Medicine, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, New York2 Division of Cardiology, Department of Medicine, Henan Provincial People’s Hospital, Zhengzhou, ChinaCorrespondence : Dr Luigi Di Biase, Montefiore Medical Center, Albert Einstein College of Medicine, 111 East 210th St, Bronx, NY 10467. E-mail address: dibbia@gmail.com .Keywords : narrow complex tachycardia, dual atrioventricular nodal non-re-entrant tachycardia, dual atrioventricular nodal re-entrant tachycardia, autonomic nervous system
Designing high-fidelity multi-qubit gates for semiconductor quantum dots through deep reinforcement learning

In this paper, we present a machine learning framework to design high-fidelity multi-qubit gates for quantum processors based on quantum dots in silicon, with qubits encoded in the spin of single electrons. In this hardware architecture, the control landscape is vast and complex, so we use the deep reinforcement learning method to design optimal control pulses to achieve high fidelity multi-qubit gates. In our learning model, a simulator models the physical system of quantum dots and performs the time evolution of the system, and a deep neural network serves as the function approximator to learn the control policy. We evolve the Hamiltonian in the full state-space of the system, and enforce realistic constraints to ensure experimental feasibility.
Fault intensity map analysis with neural network key distinguisher

Physical cryptographic implementations are vulnerable to side-channel attacks, including fault attacks, which can be used to recover a secret key. Using a deep neural network (NN) with fault intensity map analysis (FIMA), we present a new highly efficient statistical fault injection analysis (FIA) technique called FIMA-NN. This technique employs a convolutional neural network to rank the key candidates based on multiple features in data distribution under fault with varying intensities and generalizes most existing statistical techniques including fault sensitivity analysis, differential fault intensity analysis, statistical ineffective fault analysis, and FIMA. As FIMA-NN does not rely on a single feature of data distribution, it is successful even in the presence of a wide variety of countermeasures against FIA. We introduce a generic statistical model for timing failure attacks using dynamic timing analysis of an AES S-box implemented in TSMC 65 nm technology with standard ASIC design flow. Using the simulated fault mechanism, we demonstrate that, in terms of required amount of collected ciphertexts, FIMA-NN is 12.6 times more efficient than statistical techniques using bias alone, when faulty and fault-free values are not filtered. Further, in the presence of error detection and infective countermeasures, FIMA-NN is 4.8 and 5 times more efficient than bias-alone techniques, respectively.
Testing compilers for programmable switches through switch hardware simulation

Programmable switches have emerged as powerful and flexible alternatives to fixed-function forwarding devices. But because of the unique hardware constraints of network switches, the design and implementation of compilers targeting these devices is tedious and error-prone. Despite the important role that compilers play in software development, there is a dearth of tools for testing compilers for programmable network devices. We present Druzhba, a programmable switch simulator used for testing compilers targeting programmable packet-processing substrates. We show that we can model the low-level behavior of a switch's programmable hardware. We further show how compiler developers can target Druzhba as a compiler backend. Generated machine code programs are fed into Druzhba and tested using a fuzzing-based approach that allows compiler developers to test the correctness of their compilers. Using a program-synthesis-based compiler as a case study, we demonstrate how Druzhba has been successful in testing compiler-generated machine code for our simulated switch pipeline instruction set.
Long-Lived LoRa: Prolonging the Lifetime of a LoRa Network

Prolonging the network lifetime is a major consideration in many Internet of Things applications. In this paper, we study maximizing the network lifetime of an energy-harvesting LoRa network. Such a network is characterized by heterogeneous recharging capabilities across the nodes that is not taken into account in existing work. We propose a link-layer protocol to achieve a long-lived LoRa network which dynamically enables the nodes with depleting batteries to exploit the superfluous energy of the neighboring nodes with affluent batteries by letting a depleting node offload its packets to an affluent node. By exploiting the LoRa’s capability of adjusting multiple transmission parameters, we enable low-cost offloading by depleting nodes instead of high-cost direct forwarding. Such offloading requires synchronization of wake-up times as well as transmission parameters between the two nodes which also need to be selected dynamically. The proposed protocol addresses these challenges and prolongs the lifetime of a LoRa network through three novel techniques. (1) We propose a lightweight medium access control protocol for peer-to-peer communication to enable packet offloading which circumvents the synchronization overhead between the two nodes. (2) We propose an intuitive heuristic method for effective parameter selections for different modes (conventional vs. offloading). (3) We analyze the energy overhead of offloading and, based on it, the protocol dynamically selects affluent and depleting nodes while ensuring that an affluent node is not overwhelmed by the depleting ones. Simulations in NS-3 as well as real experiments show that our protocol can increase the network lifetime up to 4 times while maintaining the same throughput compared to traditional LoRa network.
Preregistration Is Neither Sufficient nor Necessary for Good Science

To address widespread perceptions of a reproducibility crisis in the social sciences, a growing number of scholars recommend the systematic preregistration of empirical studies. The purpose of this essay is to contribute to an epistemological dialogue on the value of preregistration in consumer research by identifying the limitations, drawbacks, and potential adverse effects of a preregistration system. After a brief review of some of the implementation challenges that commonly arise with preregistration, we raise three levels of issues with a system of preregistration. First, we identify its limitations as a means of advancing consumer knowledge, thus questioning the sufficiency of preregistration in promoting good consumer science. Second, we elaborate on why consumer science can progress even in the absence of preregistration, thereby also questioning the necessity of preregistration in promoting good consumer science. Third, we discuss serious potential adverse effects of preregistration, both at the individual researcher level and at the level of the field as a whole. We conclude by offering a broader perspective on the narrower role that preregistration can play within the general pursuit of building robust and useful knowledge about consumers.
Measuring the Effectiveness of Privacy Policies for Voice Assistant Applications

Voice Assistants (VA) such as Amazon Alexa and Google Assistant are quickly and seamlessly integrating into people’s daily lives. The increased reliance on VA services raises privacy concerns such as the leakage of private conversations and sensitive information. Privacy policies play an important role in addressing users’ privacy concerns and informing them about the data collection, storage, and sharing practices. VA platforms (both Amazon Alexa and Google Assistant) allow third-party developers to build new voice-apps and publish them to app stores. Voice-app developers are required to provide privacy policies to disclose their apps’ data practices. However, little is known whether these privacy policies are informative and trustworthy or not on emerging VA platforms. On the other hand, many users invoke voice-apps through voice and thus there exists a usability challenge for users to access these privacy policies. In this paper, we conduct the first large-scale data analytics to systematically measure the effectiveness of privacy policies provided by voice-app developers on two mainstream VA platforms. We seek to understand the quality and usability issues of privacy policies provided by developers in the current app stores. We analyzed 64,720 Amazon Alexa skills and 16,002 Google Assistant actions. Our work also includes a user study to understand users’ perspectives on privacy policies of voice-apps. Our findings reveal a worrisome reality of privacy policies in two mainstream voice-app stores. For the 17,952 skills and 9,955 actions that have privacy policies, there are many voice-apps with incorrect privacy policy URLs or broken links. We found that 1,755 Alexa skills and 192 Google actions provide a broken privacy policy URL. Amazon Alexa has more than 56% of skills with duplicate privacy policy URLs. While the Google Assistant platform has 9.0% of actions with duplicate privacy policy URLs. There are also skills/actions with inconsistency between the privacy policy and description. 6,047 Google actions do not have a privacy policy although they are required to provide one. Google and Amazon even have official voice-apps violating their own requirements regarding the privacy policy. We have reported our findings to both Amazon Alexa and Google Assistant teams, and received acknowledgments from both vendors.
Epitaxial stabilization of (111)-oriented frustrated quantum pyrochlore thin films

Frustrated rare-earth pyrochlore titanates, Yb 2Ti 2O 7 and Tb 2Ti 2O 7, have been proposed as promising candidates to realize quantum spin ice (QSI). Multiple exotic quantum phases, including Coulombic ferromagnet, quantum valence bond solid, and quadrupolar ordering, have been predicted to emerge in the QSI state upon the application of a (111)-oriented external magnetic field. Here, we report on the successful layer-by-layer growth of thin films of the frustrated quantum pyrochlores, R 2Ti 2O 7 ( R = Er, Yb, and Tb), along the (111) direction. We confirm their high crystallinity and proper chemical composition by a combination of methods, including in situ RHEED, x-ray diffraction, reciprocal space mapping, and x-ray photoelectron spectroscopy. The availability of large area (111)-oriented QSI structures with planar geometry offers a new complementary to the bulk platform to explore the strain and the magnetic field-dependent properties in the quasi-2D limit.
Approximations of the Reproducing Kernel Hilbert Space (RKHS) Embedding Method over Manifolds

The reproducing kernel Hilbert space (RKHS) embedding method is a recently introduced estimation approach that seeks to identify the unknown or uncertain function in the governing equations of a nonlinear set of ordinary differential equations (ODEs). While the original state estimate evolves in Euclidean space, the function estimate is constructed in an infinite dimensional RKHS and must be approximated in practice. When a finite dimensional approximation is constructed using a basis defined in terms of shifted kernel functions centered at the observations along a trajectory, the RKHS embedding method can be understood as a data-driven approach. This paper derives sufficient conditions that ensure that approximations of the unknown function converge in a Sobolev norm over a submanifold that supports the dynamics. Moreover, the rate of convergence for the finite dimensional approximations is derived in terms of the fill distance of the samples in the embedded manifold. A numerical simulation of an example problem is carried out to illustrate the qualitative nature of convergence results derived in the paper.
Fixed-Time Nash Equilibrium Seeking in Non-Cooperative Games

We introduce a novel class of Nash equilibrium seeking dynamics for non-cooperative games with a finite number of players, where the convergence to the Nash equilibrium is bounded by a $\mathcal{K}\mathcal{L}$ function with a settling time that can be upper bounded by a positive constant that is independent of the initial conditions of the players, and which can be prescribed a priori by the system designer. The dynamics are model-free, in the sense that the mathematical forms of the cost functions of the players are unknown. Instead, in order to update its own action, each player needs to have access only to real-time evaluations of its own cost, as well as to auxiliary states of neighboring players characterized by a communication graph. Stability and convergence properties are established for both potential games and strongly monotone games. Numerical examples are presented to illustrate our theoretical results.
Hybridized quadrupolar excitations in the spin-anisotropic frustrated magnet FeI2

Magnetic order is usually associated with well-defined magnon excitations. Exotic magnetic fluctuations with fractional, topological or multipolar character have been proposed for unconventional forms of magnetic matter such as spin liquids1. As a result, considerable effort has been expended to search for, and uncover, low-spin materials with suppressed dipolar order at low temperatures2,3. However, long-range order of magnetic dipoles is much more common. Here we report neutron-scattering experiments and quantitative theoretical modelling of a spin-1 system—the uniaxial triangular magnet FeI2 (ref. 4)—where a dispersive band of mixed dipolar–quadrupolar fluctuations with large spectral weight emerges just above a dipolar ordered ground state. This excitation arises from anisotropic exchange interactions that hybridize overlapping modes carrying fundamentally different quantum numbers. A generalization of spin–wave theory to local SU(3) degrees of freedom5 accounts for all details of the low-energy dynamical response of FeI2, without going beyond quadratic order. Our work highlights that quantum excitations without classical counterparts can be realized, even in the presence of fully developed magnetic order. Neutron-scattering measurements on the magnetically ordered triangular-lattice compound FeI2 reveal a dispersive band of mixed dipolar–quadrupolar fluctuations just above its ground state—a quantum excitation without a classical counterpart.
Excitation Conditions for Uniform Exponential Stability of the Cooperative Gradient Algorithm Over Weakly Connected Digraphs

In this letter, we study the problem of robust adaptive parameter estimation over networks with persistently exciting (PE) nodes and cooperative estimation dynamics. For this problem, it is well known that for networks characterized by undirected connected graphs, the property of uniform exponential stability (UES) can be established under a cooperative PE condition that relaxes the standard individual PE assumptions traditionally used in adaptive control. However, it is an open question whether similar cooperative PE conditions can also be used in general directed graphs . We provide an answer to this question by characterizing a generalized cooperative PE condition that is proved to be necessary and sufficient for UES in cooperative gradient dynamics evolving over arbitrary weakly connected digraphs. We also derive a similar generalized cooperative data-based condition for distributed learning dynamics that use recorded data instead of persistently exciting signals. We further present numerical experiments that study the rates of convergence of the dynamics.
The permissive power of the ban on war

The ban on inter-state war in the UN Charter is widely identified as central to the modern international order–Michael Byers calls it ‘one of the twentieth century’s greatest achievements’. Even if it is only imperfectly observed, it is often seen as a constraint on state autonomy and an improvement on the pre-legal, unregulated world before 1945. In response to this conventional view, this article shows that the laws on war in the Charter are better seen as permissive rather than constraining. I make two points. First, by creating a legal category around ‘self-defence’, the laws on war authorise, and thus legitimate, wars that are motivated by the security needs of the state, while forbidding other motives for wars. Second, state practice since 1945 has expanded the scope of this authorisation, extending it in both time and space beyond the black-letter text of the Charter. The permissive effect of law on war has therefore been getting larger. These two effects suggest that international law is a resource that increases state power, at least for powerful states, and this relation between international law and power politics is missed by both realists and liberal internationalists.
Taking Cues: Inferring Legality from Others' Conduct

A lawyer told me this story about what he saw one day on his travels in northern Europe. A farmer was driving a tractor down the road. As the farmer reached a crossroad, the traffic light turned red. The farmer stopped the tractor. No one was waiting to cross the road and there were no other vehicles coming from any direction as far as the eye could see. The farmer could tell it was entirely safe to proceed and no one would have cared if he had done so. But the farmer waited until the light turned green. “And that,” the lawyer concluded, “is the rule of law.” “And that,” H.L.A. Hart would likewise have concluded, “is the internal point of view.”1 As Hart explained, people viewing the traffic light from an “internal point of view” treat it not “merely as a natural sign that people will behave in certain ways,” but “as a signal for them to stop, and so a reason for stopping in conformity to rules which make stopping when the light is red a standard of behavior and an obligation.”2 The farmer was apparently indifferent to how the traffic law would be enforced—or, more precisely, to the fact that there would be no financial, reputational, or other penalty if he broke the law. He obeyed the law not because he felt any moral obligation independent of the law to stop his tractor in the road, and not because the traffic law’s purpose of preventing accidents was thereby served. Rather, he felt a moral compulsion to abide by the law, regardless not only of enforcement implications, but also of the law’s utility in the particular context. He obeyed the law simply because it is the law.3
Who Needs Bands? Exchange Rate Policy before EMU

Two issues are discussed. The first is which countries might benefit from entry into EMU before the millennium. Germany and her immediate neighbors appear the most likely to benefit; however, our knowledge is too uncertain to say whether all, some, or no countries would reap economic benefits. The second issue is how to avoid exchange rate instability in the transition to EMU. Experience from earlier exchange rate regimes suggests that an early announcement the parities at which different currencies would enter EMU could reduce such instability if governments were willing to accept the required limitations on domestic policies.
The macroeconomic effects of uncertainty shocks: The role of the financial channel

Abstract This paper studies the macroeconomic effects of uncertainty shocks with an emphasis on the interaction between elevated uncertainty and credit market conditions when the economy is in different regimes (recessions vs. non-recessions). We use a smooth-transition factor-augmented vector autoregression (ST-FAVAR) and a large monthly panel of U.S. macroeconomic and financial indicators in our estimation. Our findings are twofold. First, while an unanticipated increase in uncertainty has adverse effects on the real economy and financial markets, the effects are quantitatively larger during recessions. Second, the financial channel is important in the transmission of uncertainty shocks, with a greater role during recessions and in the short run.
Differential and Distributional Effects of Energy Efficiency Surveys: Evidence from Electricity Consumption

Our research investigates the effects of residential energy efficiency audit programs on subsequent household electricity consumption. Here there is a one-time interaction between households, which participate voluntarily, and the surveyors. Our research objective is to determine whether and to what extent the surveys lead to behavioral changes. We then examine how persistent the intervention is over time and whether the effects decay or intensify. The main evaluation problem here is survey participants’ self-selection, which we address econometrically via several non-parametric estimators involving kernel-based propensity-score matching. In the first method we use difference-in-differences (DID) estimation. Our second estimator is quantile DID, which produces estimates on distributions. The comparison group consists of households who were not yet participating in the survey but participated later. Our evidence is that the customers who participated in the survey reduced their electricity consumption by about 7%, on average compared to customers who had not yet participated in the survey. Considering the total number of high-usage households participating in the survey in 2009, we estimate that electricity consumption was reduced by an aggregate of 2 million kWh per year, which is approximately equal to the monthly consumption of 3500 typical households in California with an estimated 1527 metric tons less of carbon dioxide emissions. Because the energy audit program is inexpensive (20 per household) a key issue is that while the program is cost-effective, is it regressive? We find that as the quantiles of the outcome distribution increase, high-use households save proportionally less electricity than do low-use customers. Overall, our results imply that program designers can better target low-use and low-income households, because they are more likely to benefit from the programs through energy savings.
The Risk Premium on Italian Government Debt, 1976-88

The behavior of the yield differential between government and nongovernment bonds in Italy between 1976 and 1988 is considered. The trend increase of the differential in this period was significantly influenced by the deterioration of public finances, as reflected both by an increase in the supply of government paper relative to nongovernment paper and by a worsening of selected default-risk indicators. The effect of relative supply factors, in turn, was found to be statistically more robust and quantitatively more important than that of risk indicators in explaining movements in yield differentials.