Miracle-dz/UniFAR-FTUs huggingface dataset:
Measurements andCharacterization forMillimeter-WaveMassive MIMO Channel in High-Speed Railway Station Environment at 28GHz

*e millimeter-wave (mmWave) and massive multiple-input multiple-output (MIMO) wireless communication technologies provide vital means to resolve many technical challenges of the fifth-generation (5G) or beyond 5G (B5G) network. Analyzing the measured datasets extracted from the channel measurements can provide insight into the characteristics of radio channels in different scenarios. *erefore, mmWave massive MIMO channel measurements, simulation, and modeling are carried out in the high-speed railway waiting hall environments at 28GHz. *e multipath components (MPCs) parameters are estimated for lineof-sight (LOS) and non-line-of-sight (NLOS) scenarios based on the space-alternating generalized expectation-maximization (SAGE) algorithm. Delay spread (DS), azimuth angle of arrival (AAoA), and elevation angle of arrival (EAoA) are analyzed. And they are processed by using the K-mean algorithm. In addition, propagation characteristics are simulated based on the improved ray tracing method of shooting and bouncing ray tracing/image (SBR/IM). *e correctness of the improved ray tracing method is verified by comparing the measured results with the simulated results. *e large-scale path loss (PL) is characterized based on close-in (CI) free-space reference distance model and the floating-intercept (FI) path loss model. Furthermore, statistical distributions for root-mean-square delay spread (RMS DS) are investigated. *e Gaussian distribution best fits the measured data of RMS delay spread. Finally, multipath clustering is identified using themultipath component distance (MCD). *e analysis of these results frommmWave massive MIMO channel measurements and simulation may be instructive for the deployment of the 5G or B5G wireless communications systems at 28GHz.
Alert-Driven Attack Graph Generation Using S-PDFA

Ideal cyber threat intelligence (CTI) includes insights into attacker strategies that are specific to a network under observation. Such CTI currently requires extensive expert input for obtaining, assessing, and correlating system vulnerabilities into a graphical representation, often referred to as an attack graph (AG). Instead of deriving AGs based on system vulnerabilities, this work advocates the direct use of intrusion alerts. We propose SAGE, an explainable sequence learning pipeline that automatically constructs AGs from intrusion alerts without a priori expert knowledge. SAGE exploits the temporal and probabilistic dependence between alerts in a suffix-based probabilistic deterministic finite automaton (S-PDFA) — a model that brings infrequent severe alerts into the spotlight and summarizes paths leading to them. Attack graphs are extracted from the model on a per-victim, per-objective basis. SAGE is thoroughly evaluated on three open-source intrusion alert datasets collected through security testing competitions in order to analyze distributed multi-stage attacks. SAGE compresses over 330k alerts into 93 AGs that show how specific attacks transpired. The AGs are succinct, interpretable, and provide directly relevant insights into strategic differences and fingerprintable paths. They even show that attackers tend to follow shorter paths after they have discovered a longer one in 84.5% of the cases.
The Response of Creative Class Members to Regions Vying to Attract Them With Subsidies

There are no theoretical studies in regional science that examine which region to locate in from the standpoint of a creative class member, given that the pertinent regional authorities (RAs) are competing among themselves to attract the creative class using subsidies. This gap provides the motivation for our paper. This paper’s contribution is that it is the first to theoretically study the regional location choice of creative class members when the RAs of the locations in which they might locate are using subsidies to attract them. Specifically, a knowledge good producing creative class member must decide which of two regions (A or B) to locate his plant in. This good is produced using a Cobb–Douglas function with creative and physical capital. We analyze plant location in four cases. In the benchmark case, we show that the representative creative class member ought to locate his plant in the less expensive region B. Next, we show that a small subsidy to creative capital by region A switches the plant location decision from region B to A. Finally, when both regions grant identical subsidies to creative capital, the representative creative class member is indifferent between locating in regions A and B. So, for identical subsidies to affect the plant location decision, they are better targeted to physical and not to creative capital. JEL Codes: R11, R58
Potential effectiveness of seat belt interlocks

Seat belt interlocks are a vehicle safety technology that aims to increase seat belt usage by restricting the vehicle from being driven until occupants have fastened their seat belts. The aim of this study was to estimate the potential effectiveness of mandatory seat belt interlocks on new vehicles. Three data sets were used to obtain vehicle age profiles for unrestrained drivers: an observational study from 2009 (belt use 98 per cent), hospital admission data from 2008-2010 (belt use 89 per cent), and Coronerrs report data from 2008 (belt use 66 per cent). A hypothetical scenario was considered in which seat belt interlocks were made mandatory in all new vehicle models from 2015 onwards. Under this scenario, the vehicle age profiles from each dataset were used to examine the time it would take for seat belt interlocks to be a feature in vehicles driven by those who would otherwise not be wearing a seat belt. These results were used to calculate a lbest caser estimate of the potential effectiveness of seat belt interlocks: by 2030 there would be a potential 2 per cent reduction in injuries requiring hospital admission, and a 7 per cent reduction in fatalities. By 2050 these values would approach 5 per cent and 16 per cent respectively. These reductions would apply on top of any casualty savings already made through enhanced vehicle technologies, infrastructure and regulations. Despite the relatively long time required for interlocks to reach maximum effectiveness, their introduction would have low marginal costs.
Systematic Review of Cerebral Phenotypes Associated With Monogenic Cerebral Small‐Vessel Disease

Background: Cerebral small vessel disease (cSVD) is an important cause of stroke and vascular dementia. Most cases are multifactorial, but an emerging minority have a monogenic cause. While NOTCH3 is the best-known gene, several others have been reported. We aimed to summarise the cerebral phenotypes associated with these more recent cSVD genes. Methods: We performed a systematic review (PROSPERO: CRD42020196720), searching Medline/Embase (conception to July 2020) for any language publications describing COL4A1/2, TREX1, HTRA1, ADA2, or CTSA pathogenic variant carriers. We extracted data about individuals characteristics, clinical and vascular radiological cerebral phenotypes. We summarised phenotype frequencies per gene, comparing patterns across genes. Results: We screened 6,485 publications including 402, and extracted data on 390 COL4A1, 123 TREX1, 44 HTRA1 homozygous, 41 COL4A2, 346 ADA2, 82 HTRA1 heterozygous, and 14 CTSA individuals. Mean age ranged from 15 (ADA2) to 59 years (HTRA1 heterozygotes). Clinical phenotype frequencies varied widely: stroke 9% (TREX1) to 52% (HTRA1 heterozygotes), cognitive features 0% (ADA2) to 64% (HTRA1 homozygotes), psychiatric features 0% (COL4A2; ADA2) to 57% (CTSA). Among individuals with neuroimaging, vascular radiological phenotypes appeared common, ranging from 62% (ADA2) to 100% (HTRA1 homozygotes; CTSA). White matter lesions were the most common pathology, except in ADA2 and COL4A2 cases, where ischaemic and haemorrhagic lesions dominated, respectively. Conclusions: There appear to be differences in cerebral manifestations across cSVD genes. Vascular radiological changes were more common than clinical neurological phenotypes, and present in the majority of individuals with reported neuroimaging. However, these results may be affected by age and biases inherent to case reports. In the future, better characterisation of associated phenotypes, as well as insights from population-based studies, should improve our understanding of monogenic cSVD to inform genetic testing, guide clinical management, and help unravel underlying disease mechanisms.
Post-stroke executive function impairments in relation to white matter damage due to stroke lesions versus leukoaraiosis

BACKGROUND There is a high prevalence of executive function impairments among stroke survivors. However, the underlying aetiology remains unclear. In particular, we address whether focal, stroke-specific white matter damage or diffuse comorbid white matter damage (leukoaraiosis) is more associated with executive function impairments. METHODS This project is a retrospective analysis of data collected within the Oxford Cognitive Screening programme. Patients were recruited in the acute stage of stroke if they had a confirmed diagnosis of stroke, were at least 18 years of age, were able to remain alert for 20 minutes, and were able to provide informed consent. Patients in the present analysis completed follow up neuropsychological assessment at six-months with the Oxford Cognitive Screen-Plus to assess executive function. Stroke lesions were manually delineated on acute clinical brain scans allowing us to quantify focal stroke-specific white matter damage using the HCP-842 tractography atlas. Leukoaraiosis was visually rated on clinical scans using the Age-Related White Matter Changes scale. RESULTS This study included data from 90 stroke patients (mean age = 73.76 years; 47.78% female). Multiple linear regression analyses showed that the presence of leukoaraiosis predicted poorer executive functioning six-months after stroke (B = -0.33, p = .031, 95% CI [-0.64 -0.03]). However, post-stroke executive functioning was not predicted by stroke-specific damage to white matter tracts. CONCLUSIONS Overall, these results provide novel insight into the neural substrates underlying post-stroke executive dysfunction and highlight the prognostic utility of using routine clinical CT scans to assess leukoaraiosis.
Bringing Events into Video Deblurring with Non-consecutively Blurry Frames

Recently, video deblurring has attracted considerable research attention, and several works suggest that events at high time rate can benefit deblurring. Existing video deblurring methods assume consecutively blurry frames, while neglecting the fact that sharp frames usually appear nearby blurry frame. In this paper, we develop a principled framework D2Nets for video deblurring to exploit non-consecutively blurry frames, and propose a flexible event fusion module (EFM) to bridge the gap between event-driven and video deblurring. In D2Nets, we propose to first detect nearest sharp frames (NSFs) using a bidirectional LST-M detector, and then perform deblurring guided by NSFs. Furthermore, the proposed EFM is flexible to be incorporated into D2Nets, in which events can be leveraged to notably boost the deblurring performance. EFM can also be easily incorporated into existing deblurring networks, making event-driven deblurring task benefit from state-of-the-art deblurring methods. On synthetic and real-world blurry datasets, our methods achieve better results than competing methods, and EFM not only benefits D2Nets but also significantly improves the competing deblurring networks.
Body-Face Joint Detection via Embedding and Head Hook

Detecting pedestrians and their associated faces jointly is a challenging task. On one hand, body or face could be absent because of occlusion or non-frontal human pose. On the other hand, the association becomes difficult or even miss-leading in crowded scenes due to the lack of strong correlational evidence. This paper proposes Body-Face Joint (BFJ) detector, a novel framework for detecting bodies and their faces with accurate correspondance. We follow the classical multi-class detector design by detecting body and face in parallel but with two key contributions. First, we propose an Embedding Matching Loss (EML) to learn an associative embedding for matching body and face of the same person. Second, we introduce a novel concept, "head hook", to bridge the gap of matching body and faces spatially. With the new semantical and geometrical sources of information, BFJ greatly reduces the difficulty of detecting body and face in pairs. Since the problem is unexplored yet, we design a new metric named log-average miss matching rate (mMR−2) to evaluate the association performance and extend the CrowdHuman and CityPersons benchmarks by annotating each face box. Experiments show that our BFJ detector can maintain state-of-the-art performance in pedestrian detection on both one-stage and two-stage structures while greatly outperform various body-face association strategies. Code will be available at: https://github.com/AibeeDetect/BFJDet.
I censimenti industriali in Italia (1911-1991). Problemi di raccordo ed alcune evidenze empiriche a livello territoriale

The aim of this paper is twofold. First of all, we intend to examine the empirical problems associated with the standardisation of the industrial census conducted in Italy between 1911 and 1991. The main problems concern the link among the different industrial classification, the reconstruction of some geographical units and, finally, the estimate of the employment data for the 1911 census. Secondly, we attempt to provide a first interpretation of this evidence, focussing manly on the processes of structural change and production specialization experienced by the Italian manufacturing industry during the last century.
Auditory Long Latency Responses in Children with Learning Disabilities

Objective of the study: To measure and compare the latencies of P1, N1 & P2 of ALLR in children with learning disability. Methodology: For 20 children diagnosed with learning disability (LD), Pure Tone Audiometry, Speech Audiometry, Impedance Audiometry, and ALLR were obtained, and the results were compared to a control group. Important Findings: Between children with LD and the normal control group, there were highly statistically significant variations in the amplitudes of the waves P1, N1, and P2. The latencies of the ALLR waves P1, N1, P2, and N2 did show differences, though the results did not reach statistical significance. These results indicate that children with LD may have problems in auditory sensory processing. Conclusions: Auditory Long Latency Responses can be used for the early detection of children with learning disabilities and provides the opportunity to plan the treatment for improvement of auditory skills.
Multimode fibre broadband access and self-referencing sensor networks

Future Internet Access technologies are supposed to bring us a very performing connection to the main door of our homes. At the same time, new services and devices and their increase use will require data transfers at speeds exceeding 1Gbps inside the building or home at the horizon 2012. Both drivers lead to the deployment of a high-quality, futureproof network inside buildings and homes. This environment may end up taking advantage of optical cabling solutions as an alternative to more traditional copper or pure wireless approaches. Related to this latter fact, the objectives of this work are: • The achievement of a full convergence scenario between optical networks from the telecommunication services providers to the end users underscores the necessity of accurate and realistic fibre models in assessing the performance of broadband access networks with the premises of high-capacity and total compatibility. Silicabased MMFs and PF GIPOFs are the most promising candidates for such a convergence within the in-building/home scenario. Contributions to a better understanding of the possibilities of signal transmission outside the baseband of such fibres are investigated, in order to extend their capabilities, together with the evaluation of current fibre frequency response theoretical models by means of an extensive set of measurements. • The achievement of a full convergence scenario between optical networks from the telecommunication services providers to the end users is also contingent on research and development in the field of optical fibre sensors, mainly driven by the growing demand of fully building/home and industry automation, leading to a reliable integration of the optical networks. Related to this, development of multiplexing and measurement techniques for fibre-optic intensity-based sensors are analyzed and experimentally investigated. In the sensor network topology proposed, by replacing the fibre delay line with an electronic delay in the reception stage, it is possible to avoid long fibre delay coils in the remote sensing points and achieving a compact, flexible and re-configurable self-referencing technique. Applications in both scenarios can be considered, on the one hand the in-building/home network and on the other hand the WDM-PON access network topology through which operators provision the different services. -----------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Understanding key concepts of electric circuits: Students' use of mental models

This study presents an action research project on the teaching and learning of fundamental ideas about electric circuits, gathering data from two cohorts. Students’ ideas were probed using diagnostic test questions asked in pre-tests, post-tests and delayed post-tests. Semi-structured interviews were used with students of different abilities to indicate the mental models that students appeared to be using. Additional teaching activities were introduced with Cohort 1. The effect of these activities was reflected upon, guiding further additions to teaching activities used with Cohort 2. These activities addressed specific points that seemed to pose particular difficulties for students with the aim of improving students’ qualitative understanding through guided reflection and discussion. The performance of Cohort 2 was significantly weaker at the pre-test stage but Cohort 2 made better overall progress through the course of study when compared with Cohort 1. Both cohorts made noticeable improvement in their understanding of current conservation. However, problems with parallel circuits and with distinguishing between potential difference (p.d.) and current remained. While p.d. was described by the high ability students in terms of forces between negative charges and the battery terminals, no student referred to the electric field which exists between battery terminals even in open circuit. In attempting to understand the behaviour of electric circuits, students appear first to construct a mental model of electric current. The data collected suggest that students start to understand p.d. when they ‘see’ it as some kind of difference between points. The data also suggest that the scientific model of p.d. is more difficult to visualise and use, putting p.d. at a higher level than current, in a logical hierarchy of ideas. This study proposes a unified learning model for electric circuits, in terms of a possible sequence of intermediate mental models of current, resistance and p.d. leading towards the scientific view. This learning model can help both students and teachers. Students can use it to gauge their level of understanding of circuits and to reflect on what still needs to be understood. Teachers may use the learning model as a tool helping in understanding the difficulties students experience and guiding in what next to teach to improve students’ understanding of electric circuits.
Sensitivity analysis of Wasserstein distributionally robust optimization problems

We consider sensitivity of a generic stochastic optimization problem to model uncertainty. We take a non-parametric approach and capture model uncertainty using Wasserstein balls around the postulated model. We provide explicit formulae for the first-order correction to both the value function and the optimizer and further extend our results to optimization under linear constraints. We present applications to statistics, machine learning, mathematical finance and uncertainty quantification. In particular, we provide an explicit first-order approximation for square-root LASSO regression coefficients and deduce coefficient shrinkage compared to the ordinary least-squares regression. We consider robustness of call option pricing and deduce a new Black–Scholes sensitivity, a non-parametric version of the so-called Vega. We also compute sensitivities of optimized certainty equivalents in finance and propose measures to quantify robustness of neural networks to adversarial examples.
Characterizing and Improving the Robustness of Self-Supervised Learning through Background Augmentations

Recent progress in self-supervised learning has demonstrated promising results in multiple visual tasks. An important ingredient in high-performing self-supervised methods is the use of data augmentation by training models to place different augmented views of the same image nearby in embedding space. However, commonly used augmentation pipelines treat images holistically, ignoring the semantic relevance of parts of an image—e.g. a subject vs. a background—which can lead to the learning of spurious correlations. Our work addresses this problem by investigating a class of simple, yet highly effective “background augmentations”, which encourage models to focus on semantically-relevant content by discouraging them from focusing on image backgrounds. Through a systematic investigation, we show that background augmentations lead to substantial improvements in performance across a spectrum of state-of-the-art self-supervised methods (MoCo-v2, BYOL, SwAV) on a variety of tasks, e.g. ∼ +1-2% gains on ImageNet, enabling performance on par with the supervised baseline. Further, we find the improvement in limited-labels settings is even larger (up to 4.2%). Background augmentations also improve robustness to a number of distribution shifts, including natural adversarial examples, ImageNet-9, adversarial attacks, ImageNet-Renditions. We also make progress in completely unsupervised saliency detection, in the process of generating saliency masks used for background augmentations.
The Jodrell Bank Glitch Catalogue: 106 new rotational glitches in 70 pulsars

Pulsar glitches are rapid spin-up events that occur in the rotation of neutron stars, providing a valuable probe into the physics of the interiors of these objects. Long-term monitoring of a large number of pulsars facilitates the detection of glitches and the robust measurements of their parameters. The Jodrell Bank pulsar timing programme regularly monitors more than 800 radio pulsars and has accrued, in some cases, over 50 years of timing history on individual objects. In this paper we present 106 new glitches in 70 radio pulsars as observed up to the end of 2018. For 70% of these pulsars, the event we report is its only known glitch. For each new glitch we provide measurements of its epoch, amplitude and any detected changes to the spin-down rate of the star. Combining these new glitches with those listed in the Jodrell Bank glitch catalogue we analyse a total sample of 543 glitches in 178 pulsars. We model the distribution of glitch amplitudes and spin-down rate changes using a mixture of two Gaussian components. We corroborate the known dependence of glitch rate and activity on pulsar spin-down rates and characteristic ages, and show that younger pulsars tend to exhibit larger glitches. Pulsars whose spin-down rates between $10^{-14}$ Hz s$^{-1}$ and $10^{-10.5}$ Hz s$^{-1}$ show a mean reversal of 1.8% of their spin-down as a consequence of glitches. Our results are qualitatively consistent with the superfluid vortex unpinning models of pulsar glitches.