Miracle-dz/UniFAR-FTUs huggingface dataset:
Symbol rate estimation of PSK signals based on cyclic correntropy in impulsive noise

: In order to solve the problem that existing algorithms for the symbol rate estimation of phase shift keying (PSK) signals will undergo performance degradation or even become invalid in the Alpha stable noise environment, a novel cyclic correntropy based method under the cyclostationary framework for symbol rate estimation of PSK signals is proposed. The paper presents the cyclic correntropy function, and deduces the cyclic correntropy function expression of binary phase shift keying (BPSK) signals theoretically. This method is easy to be realized owing to the adoption of fast fourier transform (FFT) in calculating the one-dimensional slices of the cyclic correntropy function, and can estimate the symbol rate by detecting the discrete spectral lines of the PSK signal cyclic correntropy function. Moreover, it does not require the prior information of the impulsive noise. The simulation results show that the proposed method can suppress the Alpha stable noise eﬃciently, and has excellent symbol rate estimation performance of PSK signals, especially in the strong impulsive noise environment.
Freshwater Mussel Assemblages at the Lotic-Lentic Interface along Lake Erie

Abstract: Where a stream enters a large lake, the mouth represents a transitional environment that is neither truly lotic nor lentic in nature and therefore is likely to affect the fauna present. Theory on habitat area and stream size predicts that species richness will increase farther downstream as watershed size increases, but as stream gradient and thus flow rate declines, stream mouths present a different and understudied habitat. Freshwater mussels in the family Unionidae are in decline, and therefore understanding how they respond across diverse habitats is also critical. We sampled mussel assemblages from 2010 to 2012 in the lower reaches of twelve small tributaries and two large embayments of the western and central basins of Lake Erie, where watershed size ranged from 10 to 4000 km2. These watersheds were assessed for land use by remote sensing and for basic water chemistry and the composition of their benthos by standard protocols. Evidence of native unionid mussels occurred in all watersheds, with 14 species found alive, which make up 75% of mussel species still present throughout Lake Erie. A species-area relationship occurred, although the effect was weaker than that present for flowing streams in the region. Additionally, the habitat and corresponding assemblages were characterized as depositional in nature, which logically follow high agricultural land use and corresponding high levels of turbidity and the proportion of silt and clay. Therefore, the lake environment influenced mussel assemblages, yet these conditions appear to limit dreissenid mussels in river mouths.
Epithelial and Stromal Expression of Syndecan-1 (CD138) in Cutaneous Basal Cell Carcinoma

Syndecan-1 (Sdc-1) is an important regulator of cell–cell and cell–extracellular matrix interactions. It is significantly involved in the carcinogenesis and tumor progression in many human malignancies. We immunohistochemically investigated Sdc-1 expression in cutaneous basal cell carcinoma (BCC) in order to clarify, whether there is difference in the expression pattern related to tumor growth characteristics. The study group consisted of 46 BCCs cathegorized into non-infiltrative and infiltrative growth variants. Specific antibody against Sdc-1 (clone MI15) was used for staining. There were 4 cases (8.7%) with preserved and 40 cases (87%) with reduced epithelial Sdc-1 expression, of which 20 cases were slightly and 20 cases severely reduced. Two BCCs (4.3%) showed a completely negative staining. Further, there were 13 BCCs (28.3%) with focal immunoreactivity in adjacent peritumorous stroma, a ll of which manifested infiltrative growth feature. We did not confirm a significant association between the expression of Sdc-1 in tumor tissue and both, non-infiltrative and infiltrative BCC variants. However, there was a significant association between the stromal immunoreactivity for Sdc-1 and infiltrative growth pattern of tumor. Sdc-1 expression was commonly reduced in neoplastic epithelial BCC tissue regardless of histologic subtypes. It did not clearly appear to be associated with tumor invasiveness. However, immunoreactivity in adjacent peritumorous stroma was found only in BCCs with infiltrative growth pattern, most of which showed apparent loss of Sdc-1 in epithelial cancer tissue. This immunostaining pattern could be a sign of more aggressive biologic behaviour of BCC.
Dependence of the IRX-β Dust Attenuation Relation on Metallicity and Environment

We use a sample of star-forming field and protocluster galaxies at z = 2.0–2.5 with Keck/MOSFIRE K-band spectra, a wealth of rest-frame ultraviolet (UV) photometry, and Spitzer/MIPS and Herschel/PACS observations, to dissect the relation between the ratio of infrared (IR) to UV luminosity (IRX) versus UV slope (β) as a function of gas-phase metallicity ( ∼ 8.2–8.7). We find no significant dependence of the IRX-β trend on environment. However, we find that at a given β, IRX is highly correlated with metallicity, and less correlated with mass, age, and specific star formation rate (sSFR). We conclude that, of the physical properties tested here, metallicity is the primary physical cause of the IRX-β scatter, and the IRX correlation with mass is presumably due to the mass dependence on metallicity. Our results indicate that the UV attenuation curve steepens with decreasing metallicity, and spans the full range of slope possibilities from a shallow Calzetti-type curve for galaxies with the highest metallicity in our sample ( ∼ 8.6) to a steep Small Magellanic Cloud (SMC)-like curve for those with ∼ 8.3. Using a Calzetti (SMC) curve for the low (high) metallicity galaxies can lead to up to a factor of 3 overestimation (underestimation) of the UV attenuation and obscured star formation rate. We speculate that this change is due to different properties of dust grains present in the interstellar medium of low- and high-metallicity galaxies.
DuoRAT: Towards Simpler Text-to-SQL Models

Recent neural text-to-SQL models can effectively translate natural language questions to corresponding SQL queries on unseen databases. Working mostly on the Spider dataset, researchers have proposed increasingly sophisticated solutions to the problem. Contrary to this trend, in this paper we focus on simplifications. We begin by building DuoRAT, a re-implementation of the state-of-the-art RAT-SQL model that unlike RAT-SQL is using only relation-aware or vanilla transformers as the building blocks. We perform several ablation experiments using DuoRAT as the baseline model. Our experiments confirm the usefulness of some techniques and point out the redundancy of others, including structural SQL features and features that link the question with the schema.
Anomaly Detection in a Large-Scale Cloud Platform

Cloud computing is ubiquitous: more and more companies are moving the workloads into the Cloud. However, this rise in popularity challenges Cloud service providers, as they need to monitor the quality of their ever-growing offerings effectively. To address the challenge, we designed and implemented an automated monitoring system for the IBM Cloud Platform. This monitoring system utilizes deep learning neural networks to detect anomalies in near-real-time in multiple Platform components simultaneously. After running the system for a year, we observed that the proposed solution frees the DevOps team's time and human resources from manually monitoring thousands of Cloud components. Moreover, it increases customer satisfaction by reducing the risk of Cloud outages. In this paper, we share our solutions' architecture, implementation notes, and best practices that emerged while evolving the monitoring system. They can be leveraged by other researchers and practitioners to build anomaly detectors for complex systems.
A Large-Scale Analysis of IoT Firmware Version Distribution in the Wild

This paper examines the up-to-dateness of installed firmware versions of Internet of Things devices accessible via public Internet. It takes a novel approach to identify versions based on the source code of their web interfaces. It analyzes data sets of 1.06m devices collected using the IoT search engine Censys and then maps the results against the latest version each manufacturer offers. A fully scalable and adaptive approach is developed by applying the SEMMA data mining process. This approach relies on three data artifacts: raw data from Censys, a mapping table with firmware versions, and a keyword search list. The results confirm the heterogeneity of connected IoT devices and show that only 2.45 percent of the IoT devices “in the wild” run the latest available firmware. Installed versions are 19.2 months old on average. This real-world evidence suggests that the updating processes and methods used by engineers so far are not sufficient to keep IoT devices up-to-date. This paper identifies and quantifies influencing factors and captures the global and diverse distribution of IoT devices. It finds manufacturer and device type influence the up-to-dateness of firmware, whereas the country in which the device is deployed is less significant.
Dark-state impact on the exciton recombination of 
WS2
 monolayers as revealed by multi-timescale pump-probe spectroscopy

The luminescence yield of transition metal dichalcogenide monolayers frequently suffers from the formation of long-lived dark states, which include excitons with intervalley charge carriers, spin-forbidden transitions, and a large center-of-mass momentum located outside the light cone of dispersion relations. Efficient relaxation from bright exciton states to dark states suppresses the quantum yield of photon emission. In addition, the radiative recombination of excitons is heavily influenced by Auger-type exciton-exciton scattering, which yields another nonradiative relaxation channel at room temperature. Here, we show that Auger-type scattering is promoted not only between (bright) excitons but also between excitons and long-lived dark states. We studied the luminescence dynamics of monolayer WS2 capped with hexagonal BN over broad time ranges of picoseconds to milliseconds using carefully designed pump-and-probe techniques. We observed that luminescence quenching associated with Auger-type scattering occurs on 1-100 microsecond time scales, which thus correspond to the lifetimes of the relevant dark states. The broad distribution of the measured lifetimes implies the impact of various types of long-lived states on the exciton annihilation process.
STN4DST: A Scalable Dialogue State Tracking based on Slot Tagging Navigation

Scalability for handling unknown slot values is a important problem in dialogue state tracking (DST). As far as we know, previous scalable DST approaches generally rely on either the candidate generation from slot tagging output or the span extraction in dialogue context. However, the candidate generation based DST often suffers from error propagation due to its pipelined two-stage process; meanwhile span extraction based DST has the risk of generating invalid spans in the lack of semantic constraints between start and end position pointers. To tackle the above drawbacks, in this paper, we propose a novel scalable dialogue state tracking method based on slot tagging navigation, which implements an end-to-end single-step pointer to locate and extract slot value quickly and accurately by the joint learning of slot tagging and slot value position prediction in the dialogue context, especially for unknown slot values. Extensive experiments over several benchmark datasets show that the proposed model performs better than state-of-the-art baselines greatly.
Adaptive Frequency-Regulation Demand Response Using Substation Solar Irradiance Measurement in High-PV Power Systems

This letter proposes a distributed and adaptive demand response approach for primary frequency regulation in high-PV power systems. Using solar irradiance measurement at substations, the proposed approach allows accurate estimation of real-time system inertia of high-PV systems, thus facilitating the estimation of power imbalance after a contingency. The test results from the U.S. Electric Reliability Council of Texas (ERCOT) high-PV penetration models validated its effectiveness.
Dual Effects of Ram Pressure on Star Formation in Multiphase Disk Galaxies with Strong Stellar Feedback

We investigate the impact of ram pressure stripping due to the intracluster medium (ICM) on star-forming disk galaxies with a multiphase interstellar medium maintained by strong stellar feedback. We carry out radiation-hydrodynamic simulations of an isolated disk galaxy embedded in a 1011 M⊙ dark matter halo with various ICM winds mimicking the cluster outskirts (moderate) and the central environment (strong). We find that both star formation quenching and triggering occur in ram pressure–stripped galaxies, depending on the strength of the winds. H i and H2 in the outer galactic disk are significantly stripped in the presence of moderate winds, whereas turbulent pressure provides support against ram pressure in the central region, where star formation is active. Moderate ICM winds facilitate gas collapse, increasing the total star formation rates by ∼40% when the wind is oriented face-on or by ∼80% when it is edge-on. In contrast, strong winds rapidly blow away neutral and molecular hydrogen gas from the galaxy, suppressing star formation by a factor of 2 within ∼200 Myr. Dense gas clumps with nH ≳ 10 M⊙ pc−2 are easily identified in extraplanar regions, but no significant young stellar populations are found in such clumps. In our attempts to enhance radiative cooling by adopting a colder ICM of T = 106 K, only a few additional stars are formed in the tail region, even if the amount of newly cooled gas increases by an order of magnitude.
Optimal Recovery of Block Models with $q$ Communities

This paper is motivated by the reconstruction problem on the sparse stochastic block model. The paper "Belief Propagation, robust reconstruction and optimal recovery of block models" by Mossel, Neeman, and Sly provided and proved a reconstruction algorithm that recovers an optimal fraction of the communities in the 2 community case. The main step in their proof was to show that when the signal to noise ratio is sufficiently large, in particular $\theta^2d > C$, the reconstruction accuracy on a regular tree with or without noise on the leaves is the same. This paper will generalize their results, including the main step, to any number of communities, providing an algorithm related to Belief Propagation that recovers a provably optimal fraction of community labels.
Solving row-sparse principal component analysis via convex integer programs

Row-sparse principal component analysis (rsPCA), also known as principal component analysis (PCA) with global support, is the problem of finding the top-$r$ leading principal components such that all these principal components are linear combination of a subset of $k$ variables. rsPCA is a popular dimension reduction tool in statistics that enhances interpretability compared to regular principal component analysis (PCA). Popular methods for solving rsPCA mentioned in literature are either greedy heuristics (in the special case of $r = 1$) where guarantees on the quality of solution found can be verified under restrictive statistical-models, or algorithms with stationary point convergence guarantee for some regularized reformulation of rsPCA. There are no known good heuristics when $r >1$, and more importantly none of the existing computational methods can efficiently verify the quality of the solutions via comparing objective values of feasible solutions with dual bounds, especially in a statistical-model-free setting. We propose: (a) a convex integer programming relaxation of rsPCA that gives upper (dual) bounds for rsPCA, and; (b) a new local search algorithm for finding primal feasible solutions for rsPCA in the general case where $r >1$. We also show that, in the worst-case, the dual bounds provided by the convex IP is within an affine function of the global optimal value. Numerical results are reported to demonstrate the advantages of our method.
NV-Fogstore : Device-aware hybrid caching in fog computing environments

Edge caching via the placement of distributed storages throughout the network is a promising solution to reduce latency and network costs of content delivery. With the advent of the upcoming 5G future, billions of F-RAN (Fog-Radio Access Network) nodes will created and used for for the purpose of Edge Caching. Hence, the total amount of memory deployed at the edge is expected to increase 100 times. Currently, used DRAM-based caches in CDN (Content Delivery Networks) are extremely power-hungry and costly. Our purpose is to reduce the cost of ownership and recurring costs (of power consumption) in an F-RAN node while maintaining Quality of Service. For our purpose, we propose NV-FogStore, a scalable hybrid key-value storage architecture for the utilization of Non-Volatile Memories (such as RRAM, MRAM, Intel Optane) in Edge Cache. We further describe in detail a novel, hierarchical, write-damage, size and frequency aware content caching policy H-GREEDY for our architecture. We show that our policy can be tuned as per performance objectives, to lower the power, energy consumption and total cost over an only DRAM-based system for only a relatively smaller trade-off in the average access latency.
DuetSGX: Differential Privacy with Secure Hardware

Differential privacy offers a formal privacy guarantee for individuals, but many deployments of differentially private systems require a trusted third party (the data curator). We propose DuetSGX, a system that uses secure hardware (Intel's SGX) to eliminate the need for a trusted data curator. Data owners submit encrypted data that can be decrypted only within a secure enclave running the DuetSGX system, ensuring that sensitive data is never available to the data curator. Analysts submit queries written in the Duet language, which is specifically designed for verifying that programs satisfy differential privacy; DuetSGX uses the Duet typechecker to verify that each query satisfies differential privacy before running it. DuetSGX therefore provides the benefits of local differential privacy and central differential privacy simultaneously: noise is only added to final results, and there is no trusted third party. We have implemented a proof-of-concept implementation of DuetSGX and we release it as open-source.
Adaptive Structured Sparse Network for Efficient CNNs with Feature Regularization

Neural networks have made great progress in pixel to pixel image processing tasks, e.g. super resolution, style transfer and image denoising. However, recent algorithms have a tendency to be too structurally complex to deploy on embedded systems. Traditional accelerating methods fix the options for pruning network weights to produce unstructured or structured sparsity. Many of them lack flexibility for different inputs. In this paper, we propose a Feature Regularization method that can generate input-dependent structured sparsity for hidden features. Our method can improve sparsity level in intermediate features by 60% to over 95% through pruning along the channel dimension for each pixel, thus relieving the computational and memory burden. On BSD100 dataset, the multiply-accumulate operations can be reduced by over 80% for super resolution tasks. In addition, we propose a method to quantitatively control the level of sparsity and design a way to train one model that supports multi-sparsity. We identify the effectiveness of our method for pixel to pixel tasks by qualitative theoretical analysis and experiments.
Polymer Electrolytes in Strong External Electric Fields: Modification of Structure and Dynamics

We present the results from an extensive atomistic molecular dynamics simulation study of poly(ethylene oxide) (PEO) doped with various amounts of lithium-bis(trifluoromethane)sulfonimide (LiTFSI) salt under the influence of external electric field strengths up to $1\,$V/nm. The motivation stems from recent experimental reports on the nonlinear response of mobilities to the application of an electric field in such electrolyte systems and arising speculations on field-induced alignment of the polymer chains, creating channel-like structures that facilitate ion passage. Hence, we systematically examine the electric field impact on the lithium coordination environment, polymer structure as well as ionic transport properties and further present a procedure to quantify the susceptibility of both structural and dynamical observables to the external field. Our investigation reveals indeed a coiled-to-stretched transformation of the PEO strands along with a concurrent nonlinear behavior of the dynamic properties. However, from studying the temporal response of the unperturbed electrolyte system to field application we are able to exclude a structurally conditioned enhancement of ion transport and surprisingly observe a slowing down. A microscopic understanding is supplied.
A PDE--based Power Tracking Control of Heterogeneous TCL Populations

This chapter presents the development and the analysis of a scheme for aggregate power tracking control of heterogeneous populations of thermostatically controlled loads (TCLs) based on partial differential equations (PDEs) control theory and techniques. By employing a thermostat--based deadband control with forced switching in the operation of individual TCLs, the aggregated dynamics of TCL populations are governed by a pair of Fokker--Planck equations coupled via the actions located both on the boundaries and in the domain. The technique of input-output feedback linearization is used for the design of aggregate power tracking control, which results in a nonlinear system in a closed loop. As the considered setting is a problem with time-varying boundaries, well-posedness assessment and stability analysis are carried out to confirm the validity of the developed control scheme. A simulation study is contacted to evaluate the effectiveness and the performance of the proposed approach.
Photovoltaic System equipped Interleaved Boost Converter for Maximum Power Tracking Technique

Due to depletion of natural resources, the demand for new and clean energy sources is increasing. And what can be better than solar power as an alternative power source which is free and available abundant. The only hurdle in this energy source is that it is not efficient due to obvious reasons that maximum power cannot be extracted and other switching & transmission losses. This paper deals with the solution to extract maximum power output from the solar PV module using P&O methodology and transform this power with implementation of interleaved boost converters and hence to meet the requirement of generation and effectively transmit power up to its utility in more efficient manner. Use of interleaved boost converter and its design and integration with P&O algorithm is something new and challenging for the application.