Miracle-dz/UniFAR-FTUs huggingface dataset:
Limitations of Linear Cross-Entropy as a Measure for Quantum Advantage

Demonstrating quantum advantage requires experimental implementation of a computational task that is hard to achieve using state-of-the-art classical systems. One approach is to perform sampling from a probability distribution associated with a class of highly entangled many-body wavefunctions. It has been suggested that this approach can be certified with the Linear Cross-Entropy Benchmark (XEB). We critically examine this notion. First, in a"benign"setting where an honest implementation of noisy quantum circuits is assumed, we characterize the conditions under which the XEB approximates the fidelity. Second, in an"adversarial"setting where all possible classical algorithms are considered for comparison, we show that achieving relatively high XEB values does not imply faithful simulation of quantum dynamics. We present an efficient classical algorithm that, with 1 GPU within 2s, yields high XEB values, namely 2-12% of those obtained in experiments. By identifying and exploiting several vulnerabilities of the XEB, we achieve high XEB values without full simulation of quantum circuits. Remarkably, our algorithm features better scaling with the system size than noisy quantum devices for commonly studied random circuit ensembles. To quantitatively explain the success of our algorithm and the limitations of the XEB, we use a theoretical framework in which the average XEB and fidelity are mapped to statistical models. We illustrate the relation between the XEB and the fidelity for quantum circuits in various architectures, with different gate choices, and in the presence of noise. Our results show that XEB's utility as a proxy for fidelity hinges on several conditions, which must be checked in the benign setting but cannot be assumed in the adversarial setting. Thus, the XEB alone has limited utility as a benchmark for quantum advantage. We discuss ways to overcome these limitations.
MaxRay: A Raytracing-based Integrated Sensing and Communication Framework

Integrated Sensing And Communication (ISAC) forms a symbiosis between the human need for communication and the need for increasing productivity, by extracting environmental information leveraging the communication network. As multiple sensory already create a perception of the environment, an investigation into the advantages of ISAC compare to such modalities is required. Therefore, we introduce MaxRay, an ISAC framework allowing to simulate communication, sensing, and additional sensory jointly. Emphasizing the challenges for creating such sensing networks, we introduce the required propagation properties for sensing and how they are leveraged. To compare the performance of the different sensing techniques, we analyze four commonly used metrics used in different fields and evaluate their advantages and disadvantages for sensing. We depict that a metric based on prominence is suitable to cover most algorithms. Further we highlight the requirement of clutter removal algorithms, using two standard clutter removal techniques to detect a target in a typical industrial scenario. In general a versatile framework, allowing to create automatically labeled datasets to investigate a large variety of tasks is demonstrated.
Quasilinear tropical compactifications

The prototypical examples of tropical compactifications are compactifications of complements of hyperplane arrangements, which posses a number of remarkable properties not satisfied by more general tropical compactifications of closed subvarieties of tori. We introduce a broader class of tropical compactifications, which we call quasilinear (tropical) compactifications, and which continue to satisfy the desirable properties of compactifications of complements of hyperplane arrangements. In particular, we show any quasilinear compactification is sch\"on, and its intersection theory is described entirely by the intersection theory of the corresponding tropical fan. As applications, we prove the quasilinearity of the moduli spaces of 6 lines in $\mathbb{P}^2$ and marked cubic surfaces, obtaining results on the geometry of the stable pair compactifications of these spaces.
Graph Neural Networks for Charged Particle Tracking on FPGAs

The determination of charged particle trajectories in collisions at the CERN Large Hadron Collider (LHC) is an important but challenging problem, especially in the high interaction density conditions expected during the future high-luminosity phase of the LHC (HL-LHC). Graph neural networks (GNNs) are a type of geometric deep learning algorithm that has successfully been applied to this task by embedding tracker data as a graph—nodes represent hits, while edges represent possible track segments—and classifying the edges as true or fake track segments. However, their study in hardware- or software-based trigger applications has been limited due to their large computational cost. In this paper, we introduce an automated translation workflow, integrated into a broader tool called hls4ml, for converting GNNs into firmware for field-programmable gate arrays (FPGAs). We use this translation tool to implement GNNs for charged particle tracking, trained using the TrackML challenge dataset, on FPGAs with designs targeting different graph sizes, task complexites, and latency/throughput requirements. This work could enable the inclusion of charged particle tracking GNNs at the trigger level for HL-LHC experiments.
User-click Modelling for Predicting Purchase Intent

This thesis contributes a structured inquiry into the open actuarial mathematics problem of modelling user behaviour using machine learning methods, in order to predict purchase intent of non-life insurance products. It is valuable for a company to understand user interactions with their website as it provides rich and individualized insight into consumer behaviour. Most of existing research in user behaviour modelling aims to explain or predict clicks on a search engine result page or to estimate click-through rate in sponsored search. These models are based on concepts about users’ examination patterns of a web page and the web page’s representation of items. Investigating the problem of modelling user behaviour to predict purchase intent on a business website, we observe that a user’s intention yields high dependency on how the user navigates the website in terms of how many different web pages the user visited, what kind of web pages the user interacted with, and how much time the user spent on each web page. Inspired by these findings, we propose two different ways of representing features of a user session leading to two models for user click-based purchase prediction: one based on a Feed Forward Neural Network, and another based on a Recurrent Neural Network. We examine the discriminativeness of user-clicks for predicting purchase intent by comparing the above two models with a model using demographic features of the user. Our experimental results show that our click-based models significantly outperform the demographic model, in terms of standard classification evaluation metrics, and that a model based on a sequential representation of user clicks yields slightly greater performance than a model based on feature engineering of clicks.
Gesture Recognition with a Skeleton-Based Keyframe Selection Module

We propose a bidirectional consecutively connected two-pathway network (BCCN) for efficient gesture recognition. The BCCN consists of two pathways: (i) a keyframe pathway and (ii) a temporal-attention pathway. The keyframe pathway is configured using the skeleton-based keyframe selection module. Keyframes pass through the pathway to extract the spatial feature of itself, and the temporal-attention pathway extracts temporal semantics. Our model improved gesture recognition performance in videos and obtained better activation maps for spatial and temporal properties. Tests were performed on the Chalearn dataset, the ETRI-Activity 3D dataset, and the Toyota Smart Home dataset.
Detect Faces Efficiently: A Survey and Evaluations

Face detection is to search all the possible regions for faces in images and locate the faces if there are any. Many applications including face recognition, facial expression recognition, face tracking and head-pose estimation assume that both the location and the size of faces are known in the image. In recent decades, researchers have created many typical and efficient face detectors from the Viola-Jones face detector to current CNN-based ones. However, with the tremendous increase in images and videos with variations in face scale, appearance, expression, occlusion and pose, traditional face detectors are challenged to detect various “in the wild” faces. The emergence of deep learning techniques brought remarkable breakthroughs to face detection along with the price of a considerable increase in computation. This paper introduces representative deep learning-based methods and presents a deep and thorough analysis in terms of accuracy and efficiency. We further compare and discuss the popular and challenging datasets and their evaluation metrics. A comprehensive comparison of several successful deep learning-based face detectors is conducted to uncover their efficiency using two metrics: FLOPs and latency. The paper can guide to choose appropriate face detectors for different applications and also to develop more efficient and accurate detectors.
Near-optimal estimation of smooth transport maps with kernel sums-of-squares

It was recently shown that under smoothness conditions, the squared Wasserstein distance between two distributions could be efficiently computed with appealing statistical error upper bounds. However, rather than the distance itself, the object of interest for applications such as generative modeling is the underlying optimal transport map. Hence, computational and statistical guarantees need to be obtained for the estimated maps themselves. In this paper, we propose the first tractable algorithm for which the statistical $L^2$ error on the maps nearly matches the existing minimax lower-bounds for smooth map estimation. Our method is based on solving the semi-dual formulation of optimal transport with an infinite-dimensional sum-of-squares reformulation, and leads to an algorithm which has dimension-free polynomial rates in the number of samples, with potentially exponentially dimension-dependent constants.
Towards Intrinsic Interactive Reinforcement Learning: A Survey

Reinforcement learning (RL) and brain-computer interfaces (BCI) are two fields that have been growing over the past decade. Until recently, these fields have operated independently of one another. With the rising interest in human-in-the-loop (HITL) applications, RL algorithms have been adapted to account for human guidance giving rise to the sub-field of interactive reinforcement learning (IRL). Adjacently, BCI applications have been long interested in extracting intrinsic feedback from neural activity during human-computer interactions. These two ideas have set RL and BCI on a collision course for one another through the integration of BCI into the IRL framework where intrinsic feedback can be utilized to help train an agent. This intersection has been denoted as intrinsic IRL. To further help facilitate deeper ingratiation of BCI and IRL, we provide a review of intrinsic IRL with an emphasis on its parent field of feedback-driven IRL while also providing discussions concerning the validity, challenges, and future research directions. CCS Concepts: • General and reference → Surveys and overviews ; • Computing methodologies → Reinforcement learning ; • Human-centered computing → Interaction design theory, concepts and paradigms .
Black Liquor Gasification-Based Biorefineries – Determining Factors for Economic Performance and CO2 Emission Balances

Biorefineries constitute an attractive future development option for the pulp and paper industry, allowing mills to produce not only pulp or paper but also other value-added products. Black liquor gasification (BLG) is currently being developed as an alternative technology for energy and chemical recovery in kraft pulp and paper mills. The technology enables the mill to increase the internal electricity generation or produce chemicals such as motor fuels. This thesis investigates the influence of different factors, including choice of product, type of mill, alternative investments, opportunities for carbon capture and storage (CCS) and future energy market conditions, on the economic performance and CO2 emission balances for BLG-based biorefinery concepts. Implementation of biorefinery concepts such as BLG with electricity production in future market pulp mills can be achieved without making the mill dependent on external wood fuel. However, implementation in integrated pulp and paper mills requires external wood fuel and reduces the amount of wood fuel available for other applications, thereby increasing the CO2 emissions from those applications. The results show that BLG with motor fuel production could be profitable for both market and integrated mills, whereas BLG with electricity generation is primarily an attractive option for market mills. For mills that operate with conventional recovery boiler technology, potentially profitable biorefinery concepts include lignin extraction or motor fuel production from gasified wood fuel. Few of the biorefinery concepts investigated in this work achieve a significant reduction of CO2 emissions, especially for integrated mills. However, if commercially available, CCS could contribute to significant CO2 emissions reduction and enhanced profitability for future energy market conditions characterized by a high CO2 emissions charge, for both combustion- and gasification-based biorefinery concepts. Steam-saving measures could also significantly improve the economic performance as well as the CO2 emission balances, especially for biorefinery concepts that use external wood fuel. The results also show that even if the recovery boiler has not reached the end of its technical lifetime, it could nevertheless be attractive for mills to consider investment in a smaller BLG plant.
Microstrip filter with multiple transmission zeros using shorted stub loaded resonators

A novel microstrip bandpass filter with multiple transmission zeros is proposed, where two half-wavelength resonators loaded by shortcircuited stubs are implemented into its design. A section of coupled lines and a grounded via-hole are introduced to serve as a capacitor and an inductor, respectively. Then, four controllable transmission zeros can be successfully obtained, and the locations of two outer transmission zeros are controlled by the diameter of the via-hole. A secondorder filter sample with better frequency selectivity and wide stopband was fabricated using standard printed circuit board (PCB) technology, and its measured S-parameters agree well with the simulated ones.
Structural basis of transport and inhibition of the Plasmodium falciparum transporter PfFNT

We recently reported the first structures of the Plasmodium falciparum transporter PfFNT, both in the absence and presence of the inhibitor MMV007839 (Lyu et al, 2021). These structures indicated that PfFNT assembles as a pentamer. The bound MMV007839 was found in the middle of the elongated channel formed by each PfFNT protomer, adjacent to residue G107. MMV007839 exists in two tautomeric forms and can adopt either a cyclic hemiketal-like structure or a linear vinylogous acid conformation (Fig 3A). Unfortunately, these two tautomeric forms could not be clearly distinguished based on the existing cryo-EM data at 2.78 A resolution. The bound MMV007839 inhibitor was reported as the cyclic hemiketal-like form in the structure in Figs 3A and F, and 4C, Appendix Figs S10A and B, and S13 and in the online synopsis image. Eric Beitz alerted us to the findings reported by his group that the linear vinylogous acid tautomer of MMV007839 constitutes the binding and inhibitory entity of PfFNT (Golldack et al, 2017).
Wideband protection filter (WPF) integrated within optical systems

Laser radiation may lead to permanent damage of the human eye when it is exposed to high power irradiation, especially when using magnifying optics such as binoculars, sights or periscopes. Into such an optical system we integrated a novel passive solid-state threshold-triggered Wideband Protection Filter (WPF) that blocks the transmission only if the power exceeds a certain threshold. At input powers below threshold, the filter has high transmission over the whole spectral band. However, when the input power exceeds the threshold power, transmission is decreased dramatically. We demonstrate the WPF integration within a typical optical system and the influence of system parameters on the protection capability of the filter.
Improving Diagnosis and Prognosis in Acute Severe Brain Injury: A Multimodal Imaging Protocol

Multi-modal neuroimaging techniques have the potential to dramatically improve the diagnosis of the level consciousness and prognostication of neurological outcome for patients with severe brain injury in the intensive care unit (ICU). This protocol describes a study that will utilize functional Magnetic Resonance Imaging (fMRI), electroencephalography (EEG), and functional Near Infrared Spectroscopy (fNIRS) to measure and map the brain activity of acute critically ill patients. Our goal is to investigate whether these modalities can provide objective and quantifiable indicators of good neurological outcome and reliably detect conscious awareness. To this end, we will conduct a prospective longitudinal cohort study to validate the prognostic and diagnostic utility of neuroimaging techniques in the ICU. We will recruit 350 individuals from two ICUs over the course of 7 years. Participants will undergo fMRI, EEG, and fNIRS testing several times over the first 10 days of care to assess for residual cognitive function and evidence of covert awareness. Patients who regain behavioral awareness will be asked to complete web-based neurocognitive tests for 1 year, as well as return for follow up neuroimaging to determine which acute imaging features are most predictive of cognitive and functional recovery. Ultimately, multi-modal neuroimaging techniques may improve the clinical assessments of patients' level of consciousness, aid in the prediction of outcome, and facilitate efforts to find interventional methods that improve recovery and quality of life.
Functional and Binding H1N1pdm09-Specific Antibody Responses in Occasionally and Repeatedly Vaccinated Healthcare Workers: A Five-Year Study (2009-2014)

Background In 2009, a novel influenza A/H1N1pdm09 emerged and caused a pandemic. This strain continued to circulate and was therefore included in the seasonal vaccines up to the 2016/2017-season. This provided a unique opportunity to study the long-term antibody responses to H1N1pdm09 in healthcare workers (HCW) with a different vaccination history. Methods HCW at Haukeland University Hospital, Bergen, Norway were immunized with the AS03-adjuvanted H1N1pdm09 vaccine in 2009 (N=55) and divided into groups according to their vaccination history; one vaccination (N=10), two vaccinations (N=15), three vaccinations (N=5), four vaccinations (N=15) and five vaccinations (N=10). HCW are recommended for influenza vaccination to protect both themselves and their patients, but it is voluntary in Norway. Blood samples were collected pre- and at 21 days, 3, 6, and 12 months after each vaccination, or annually from 2010 HCW without vaccination. ELISA, haemagglutination inhibition (HI) and microneutralization (MN) assays were used to determine the antibody response. Results Pandemic vaccination induced a significant increase in the H1N1-specific antibodies measured by ELISA, HI and MN. Seasonal vaccination boosted the antibody response, both in HCW with only the current vaccination and those with prior and current vaccination during 2010/11-2013/14. We observed a trend of increased antibody responses in HCW with only the current vaccination in 2013/14. A two- and three-year gap before vaccination in 2013/14 provided a more potent antibody response compared to annually vaccinated HCW. Conclusions Our long term follow up study elucidates the antibody response in HCW with different vaccination histories. Our findings contribute to our understanding of the impact of repeated vaccination upon antibody responses.
Dynamic Reliability Evaluation of Diesel Generator System of One Chinese 1000MWe NPP Considering Temporal Failure Effects

Loss of power supply from the diesel generator system (DGS) after loss of offsite power (LOOP) will pose great threat to the safety of GEN-II pressurized water reactors (PWR). Therefore, it is very desirable to evaluate the DGS’s reliability. The traditional analyzing tools are limited to static approaches neglecting the dynamic sequence failure behaviors, such as reliability block diagram (RBD), static fault tree (SFT). Static reliability modeling techniques are not capable of capturing the dynamic sequence-dependent failure behaviors typically existing in NPP safety systems such as DGS, and thus often overestimate the unreliability of systems. In this paper, motivated to study the effects of sequence failure behaviors, dynamic fault tree (DFT) is applied to evaluate the reliability of the DGS of one Chinese 1000MWe Nuclear Power Plant (NPP), and an integrated two-phased Markov Chain model is also developed, which can be considered as a contribution of this article. Comparative study of DGS reliability between DFT and SFT is carried out. The results indicate that compared with the result derived from the DFT model, the unreliability of DGS calculated by SFT is greatly overestimated by about one to two orders of magnitude. Therefore, DFT has a potential to improve the economy of NPP by relaxing the overestimated unreliability of nuclear power systems.
Isolation and Fractionation of the Tobacco Stalk Lignin for Customized Value-Added Utilization

The value-added utilization of tobacco stalk lignin is the key to the development of tobacco stalk resources. However, the serious heterogeneity is the bottleneck for making full use of tobacco stalk lignin. Based on this, lignin was separated from tobacco stalk through hydrothermal assisted dilute alkali pretreatment. Subsequently, the tobacco stalk alkaline lignin was fractionated into five uniform lignin components by sequential solvent fractionation. Advanced spectral technologies (FT-IR, NMR, and GPC) were used to reveal the effects of hydrothermal assisted dilute alkali pretreatment and solvent fractionation on the structural features of tobacco stalk lignin. The lignin fractions extracted with n-butanol and ethanol had low molecular weight and high phenolic hydroxyl content, thus exhibiting superior chemical reactivity and antioxidant capacity. By contrast, the lignin fraction extracted with dioxane had high molecular weight and low reactivity, nevertheless, the high residual carbon rate made it suitable as a precursor for preparing carbon materials. In general, hydrothermal assisted dilute alkali pretreatment was proved to be an efficient method to separate lignin from tobacco stalk, and the application of sequential solvent fractionation to prepare lignin fractions with homogeneous structural features has specific application prospect.