Miracle-dz/UniFAR-FTUs huggingface dataset:
High Bandwidth Memory on FPGAs: A Data Analytics Perspective

FPGA-based data processing in datacenters is increasing in popularity due to the demands of modern workloads and the resulting need for specialization in hardware. Driven by this trend, vendors are rapidly adapting reconfigurable devices to suit data and compute intensive workloads. Inclusion of High Bandwidth Memory (HBM) in FPGA devices is a recent example. HBM promises overcoming the bandwidth bottleneck, often faced by FPGA-based accelerators due to their throughput oriented design. In this paper, we study the usage and benefits of HBM on FPGAs from a data analytics perspective. We consider three workloads that are often performed in analytics oriented databases and implement them on FPGA showing in which cases they benefit from HBM: range selection, hash join, and stochastic gradient descent for linear model training. We integrate our designs into a columnar database (MonetDB) and show the trade-offs arising from the integration related to data movement and partitioning. In certain cases, FPGA+HBM based solutions are able to surpass the highest performance provided by either a 2-socket POWER9 system or a 14-core XeonE5 by up to 1.8x (selection), 12.9x (join), and 3.2x (SGD).
Effect of In Situ High-Fidelity Simulation Training on the Emergency management of Pneumonia (INSTEP): a mixed-methods study

Background The patient safety agenda has propelled the rise of simulation education, but relatively few evaluations of simulation-based educational interventions have focused on patient outcomes. Objective To evaluate the impact of an in situ, high-fidelity simulation teaching intervention on the management of community-acquired pneumonia in the ambulatory care unit of a district general hospital. Methods This study used a mixed-methods approach to evaluate the impact of a programme of 10 in situ high-fidelity simulation education sessions delivered to a total of 10 junior doctors, nine nurses and seven healthcare assistants. Participants were tasked with managing a manikin simulating a patient with pneumonia in real time in a working clinical area. Subsequent structured debrief emphasised key themes from the national guidelines on pneumonia management. The intervention was evaluated through an immediate feedback form, follow-up semistructured interviews by independent qualitative researchers that underwent content analysis and triangulation with audit data on compliance with national pneumonia guidelines before and after the simulation intervention. Results The in situ simulation intervention was valued by participants both in immediate written feedback and in follow-up semistructured interviews. In these interviews, 17 of 18 participants were able to identify a self-reported change in practice following the simulation intervention. Furthermore, most participants reported observing a change in the clinical practice of their colleagues following the training. Collected audit data did not show a statistically significant change in compliance with the guidelines for the management of pneumonia. Conclusion This study found evidence of a change in both self-reported and observed clinical practice following a simulation intervention, supporting expert opinion that simulation education can impact clinician behaviours and patient outcomes in complex clinical scenarios. Furthermore, this feasibility study provides a transferrable method to evaluate the real-world impact of simulation education that merits further investigation through an appropriately powered study.
Constructions of difference sets in nonabelian 2-groups

Difference sets have been studied for more than 80 years. Techniques from algebraic number theory, group theory, finite geometry, and digital communications engineering have been used to establish constructive and nonexistence results. We provide a new theoretical approach which dramatically expands the class of $2$-groups known to contain a difference set, by refining the concept of covering extended building sets introduced by Davis and Jedwab in 1997. We then describe how product constructions and other methods can be used to construct difference sets in some of the remaining $2$-groups. We announce the completion of ten years of collaborative work to determine precisely which of the 56,092 nonisomorphic groups of order 256 contain a difference set. All groups of order 256 not excluded by the two classical nonexistence criteria are found to contain a difference set, in agreement with previous findings for groups of order 4, 16, and 64. We provide suggestions for how the existence question for difference sets in $2$-groups of all orders might be resolved.
Detailed Balance Modeling of Monochromatic Photovoltaic Power Conversion

I present detailed balance modeling of photonic power converters, which are photovoltaic devices designed to convert monochromatic light. The detailed balance formalism is standard in finding limiting efficiencies of photovoltaic devices. In this thesis, I discuss both existing theories and original work. I start by discussing optical processes in semiconductors. I reproduce a monochromatic detailed balance model by Martin A. Green, which has stated that monochromatic photovoltaic energy conversion is 100% efficient at infinite input intensity with input photon energy matched to the absorption bandgap. I discuss effects that are not included in this standard model and the existing theories to treat these effects: (1) non-radiative recombination loss, (2) luminescent coupling in multi-layer devices, and (3) light trapping. These background materials are followed by original work, which is divided into three chapters. In these chapters, I present extensions of Green’s theory to (1) a single-layer model that includes the effect of incomplete absorption, non-radiative recombination and input photon energy offset from absorption bandgap, and (2) a multi-layer model that includes luminescent coupling and light trapping effects, in addition to the effects already included in the single-layer extension. I show material quality and efficiency improvement predicted for a record-efficiency GaAs PPC. I also predict efficiency for a InAlGaAs device under development that targets conversion at telecommunication wavelength of 1310 nm.
Geometrical structures of the microscopic current and their macroscopic effects

First we discuss the definition of the instantaneous current in interacting particle systems, in particular in mass-energy systems and we point out its role in the derivation of the hydrodynamics. Later we present some geometrical structures of the instantaneous current when the rates of stochastic models satisfy a very common symmetry. This structures give some new ideas in non-gradient models and show new phenomenology in diffusive interacting particle systems. Specifically we introduce models with vorticity and present some new consideration for the Green- Kubo formula.
Getting jammed in all directions: Dynamic shear jamming around a cylinder towed through a dense suspension

Experimental results are presented from towing a cylinder through a dense suspension of cornstarch and sucrose-water. Focus is placed on the jamming fronts that exist in such systems. The literature has concentrated on the propagation of the jammed region under pushing, pulling or shearing conditions independently. How the different fronts interact and if the fronts are symmetric has remained unexplored. With the current setup, we are able to view a continuous, quasi-2D field around the cylinder. As such, a new way of generating jamming fronts is presented whereby pushing, pulling and shearing can be examined simultaneously. In agreement with previous studies, the front propagates roughly twice as fast in the longitudinal direction compared to the transverse direction, which is attributed to a single underlying onset strain, regardless of orientation from the cylinder. Although the jamming front shows nearly perfect transverse symmetry, there is small but repeatable longitudinal asymmetry. This is clearly evident in the velocity and strain field outside the jammed region, and also detectable in the front propagation and onset strain.
Toward Designer Modeling Through Design Style Clustering

We propose modeling designer style in mixed-initiative game content creation tools as archetypical design traces. These design traces are formulated as transitions between design styles; these design styles are in turn found through clustering all intermediate designs along the way to making a complete design. This method is implemented in the Evolutionary Dungeon Designer, a research platform for mixed-initiative systems to create adventure and dungeon crawler games. We present results both in the form of design styles for rooms, which can be analyzed to better understand the kind of rooms designed by users, and in the form of archetypical sequences between these rooms, i.e., designer personas.
A Deep Ensemble Multi-Agent Reinforcement Learning Approach for Air Traffic Control

Air traffic control is an example of a highly challenging operational problem that is readily amenable to human expertise augmentation via decision support technologies. In this paper, we propose a new intelligent decision making framework that leverages multi-agent reinforcement learning (MARL) to dynamically suggest adjustments of aircraft speeds in real-time. The goal of the system is to enhance the ability of an air traffic controller to provide effective guidance to aircraft to avoid air traffic congestion, near-miss situations, and to improve arrival timeliness. We develop a novel deep ensemble MARL method that can concisely capture the complexity of the air traffic control problem by learning to efficiently arbitrate between the decisions of a local kernel-based RL model and a wider-reaching deep MARL model. The proposed method is trained and evaluated on an open-source air traffic management simulator developed by Eurocontrol. Extensive empirical results on a real-world dataset including thousands of aircraft demonstrate the feasibility of using multi-agent RL for the problem of en-route air traffic control and show that our proposed deep ensemble MARL method significantly outperforms three state-of-the-art benchmark approaches.
PointGroup: Dual-Set Point Grouping for 3D Instance Segmentation

Instance segmentation is an important task for scene understanding. Compared to the fully-developed 2D, 3D instance segmentation for point clouds have much room to improve. In this paper, we present PointGroup, a new end-to-end bottom-up architecture, specifically focused on better grouping the points by exploring the void space between objects. We design a two-branch network to extract point features and predict semantic labels and offsets, for shifting each point towards its respective instance centroid. A clustering component is followed to utilize both the original and offset-shifted point coordinate sets, taking advantage of their complementary strength. Further, we formulate the ScoreNet to evaluate the candidate instances, followed by the Non-Maximum Suppression (NMS) to remove duplicates. We conduct extensive experiments on two challenging datasets, ScanNet v2 and S3DIS, on which our method achieves the highest performance, 63.6% and 64.0%, compared to 54.9% and 54.4% achieved by former best solutions in terms of mAP with IoU threshold 0.5.
Faster Gaussian Processes via Deep Embeddings

Gaussian processes provide a probabilistic framework for quantifying uncertainty of prediction and have been adopted in many applications in Statistics and Bayesian optimization. Unfortunately, they are hard to scale to large datasets as they necessitate inverting matrices whose size is linear in the number of observations. Moreover, they necessitate an a priori chosen functional form for their kernels with predetermined features. Our contribution is a framework that addresses both challenges. We use deep neural networks for automatic feature extraction, combined with explicit functional forms for the eigenspectrum of Gaussian processes with Gaussian kernels, to derive a Gaussian process inference and prediction framework whose complexity scales linearly in the number of observations and which accommodates automatic feature extraction. On a series of datasets, our method outperforms state of the art scalable Gaussian process approximations.
Credible, Truthful, and Bounded-Round Mechanisms via Cryptographic Commitments

We consider the sale of a single item to multiple buyers by a revenue-maximizing seller. Recent work of Akbarpour and Li formalizes credibility as an auction desideratum, and prove that the only optimal, credible, strategyproof auction is the ascending price auction [AL19]. In contrast, when buyers’ valuations are MHR, we show that the mild additional assumption of a cryp-tographically secure commitment scheme suﬃces for a simple two-round auction which is optimal, credible, and strategyproof. Weextendouranalysis to the case when buyer valuations are α -strongly regular for any α > 0, up to arbitrary ε in credibility. Interestingly, we also prove that this construction cannot be extended to regular distributions, nor can the ε be removed with multiple bidders.
Measurement-Level Fusion for OTHR Network Using Message Passing

Tracking an unknown number of targets based on multipath measurements provided by an over-the-horizon radar (OTHR) network with a statistical ionospheric model is complicated, which requires solving four subproblems: target detection, target tracking, multipath data association and ionospheric height identification. A joint solution is desired since the four subproblems are highly correlated, but suffering from the intractable inference problem of high-dimensional latent variables. In this paper, a unified message passing approach, combining belief propagation (BP) and mean-field (MF) approximation, is developed for simplifying the intractable inference. Based upon the factor graph corresponding to a factorization of the joint probability distribution function (PDF) of the latent variables and a choice for a separation of this factorization into BP region and MF region, the posterior PDFs of continuous latent variables including target kinematic state, target visibility state, and ionospheric height, are approximated by MF due to its simple MP update rules for conjugate-exponential models. With regard to discrete multipath data association which contains one-to-one frame (hard) constraints, its PDF is approximated by loopy BP. Finally, the approximated posterior PDFs are updated iteratively in a closed-loop manner, which is effective for dealing with the coupling issue among target detection, target tracking, multipath data association, and ionospheric height identification. Meanwhile, the proposed approach has the measurement-level fusion architecture due to the direct processing of the raw multipath measurements from an OTHR network, which is benefit to improving target tracking performance. Its performance is demonstrated on a simulated OTHR network multitarget tracking scenario.
Cell Segmentation by Combining Marker-Controlled Watershed and Deep Learning

We propose a cell segmentation method for analyzing images of densely clustered cells. The method combines the strengths of marker-controlled watershed transformation and a convolutional neural network (CNN). We demonstrate the method universality and high performance on three Cell Tracking Challenge (CTC) datasets of clustered cells captured by different acquisition techniques. For all tested datasets, our method reached the top performance in both cell detection and segmentation. Based on a series of experiments, we observed: (1) Predicting both watershed marker function and segmentation function significantly improves the accuracy of the segmentation. (2) Both functions can be learned independently. (3) Training data augmentation by scaling and rigid geometric transformations is superior to augmentation that involves elastic transformations. Our method is simple to use, and it generalizes well for various data with state-of-the-art performance.
Interferon-alpha and survival in metastatic renal carcinoma: early results of a randomised controlled trial

Background Metastatic renal carcinoma has a 2-year survival of around 20% and is largely resistant to chemotherapy. The use of interferons in the treatment of metastatic renal carcinoma remains controversial. Although non-randomised studies suggest that biological therapy with interferons produces a small number of tumour responses, most clinicians judge such treatment to be ineffective. We have investigated the effect of treatment with interferon-alpha on survival inpatients with metastatic renal carcinoma.Methods In a multicentre, randomised trial, patients with metastatic renal carcinoma were randomly assigned subcutaneous interferon-alpha (three doses-5 MU, 5 MU, 10 MU-for the first week, then 10 MU three times per week for a further 11 weeks; n=174) or oral medroxyprogesterone acetate (MPA; 300 mg once daily for 12 weeks; n=176); The primary endpoint was overall survival. Analysis was by intention to treat. The trial used a triangular sequential design for early termination as soon as results were conclusive. The trial was stopped in November, 1997, when data were available for 335 patients (167 interferon-alpha, 168 MPA). Findings A total of 111 patients have died in the interferon-a group, and 125 patients have died in the MPA group. There was a 28% reduction in the risk of death in the interferon-alpha group (hazard ratio 0.72 [95% CI 0.55-0.94], p=0.017). Interferon-ol gave an improvement in 1-year survival of 12% (MPA 31% survival, interferon-alpha 43%), and an improvement in median survival of 2.5 months (MPA 6 months, interferon-alpha 8.5 months). Interpretation The benefit of treatment with interferon-oc should be weighed against the drug's toxic effects. Combination regimens of biological therapy and chemotherapy should now be compared with interferon-ce monotherapy in randomised controlled trials.
Can Machine Learning Be Used to Recognize and Diagnose Coughs?

Emerging wireless technologies, such as 5G and beyond, are bringing new use cases to the forefront, one of the most prominent being machine learning empowered health care. One of the notable modern medical concerns that impose an immense worldwide health burden are respiratory infections. Since cough is an essential symptom of many respiratory infections, an automated system to screen for respiratory diseases based on raw cough data would have a multitude of beneficial research and medical applications. In literature, machine learning has already been successfully used to detect cough events in controlled environments. In this paper, we present a low complexity, automated recognition and diagnostic tool for screening respiratory infections that utilizes Convolutional Neural Networks (CNNs) to detect cough within environment audio and diagnose three potential illnesses (i.e., bronchitis, bronchiolitis and pertussis) based on their unique cough audio features. Both proposed detection and diagnosis models achieve an accuracy of over 89%, while also remaining computationally efficient. Results show that the proposed system is successfully able to detect and separate cough events from background noise. Moreover, the proposed single diagnosis model is capable of distinguishing between different illnesses without the need of separate models.
Infrared colours and spectral energy distributions of hard X-ray selected obscured and Compton-thick active galactic nuclei

We investigate the infrared colours and spectral energy distributions (SEDs) of 338 X-ray selected active galactic nuclei (AGNs) from the Swift/Burst Alert Telescope (BAT) 105-month survey catalogue, which have been detected using AKARI, in order to find new selection criteria for Compton-thick AGNs. By combining data from the Galaxy Evolution Explorer (GALEX), the Sloan Digital Sky Survey (SDSS) Data Release 14 (DR14), the Two-Micron All Sky Survey (2MASS), the Wide-field Infrared Survey Explorer (WISE), AKARI and Herschel for the first time, we perform ultraviolet (UV) to far-infrared (FIR) SEDs, fitting 158 Swift/BAT AGNs using cigale and constraining the AGN model parameters of obscured and Compton-thick AGNs. The comparison of average SEDs shows that while the mid-infrared (MIR) SEDs are similar for the three AGN populations, the optical/UV and FIR regions have differences. We measure the dust luminosity, the pure AGN luminosity and the total infrared luminosity. We examine the relationships between the measured infrared luminosities and the hard X-ray luminosity in the 14–195 keV band. We show that the average covering factor of Compton-thick AGNs is higher compared with the obscured and unobscured AGNs. We present new infrared selection criteria for Compton-thick AGNs based on MIR and FIR colours ([9–22 μm] > 3.0 and [22–90 μm] < 2.7) from WISE and AKARI. We find two known Compton-thick AGNs that are not included in the Swift/BAT sample. We conclude that MIR colours covering 9.7-μm silicate absorption and the MIR continuum could be promising new tools to identify Compton-thick AGNs.
Hot-carrier separation in heterostructure nanowires observed by electron-beam induced current

The separation of hot carriers in semiconductors is of interest for applications such as thermovoltaic photodetection and third-generation photovoltaics. Semiconductor nanowires offer several potential advantages for effective hot-carrier separation such as: a high degree of control and flexibility in heterostructure-based band engineering, increased hot-carrier temperatures compared to bulk, and a geometry well suited for local control of light absorption. Indeed, InAs nanowires with a short InP energy barrier have been observed to produce electric power under global illumination, with an open-circuit voltage exceeding the Shockley-Queisser limit. To understand this behaviour in more detail, it is necessary to establish control over the precise location of electron-hole pair-generation in the nanowire. In this work we perform electron-beam induced current measurements with high spatial resolution, and demonstrate the role of the InP barrier in extracting energetic electrons.We interprete the results in terms of hot-carrier separation, and extract estimates of the hot carriers’ mean free path.
English Proficiency and Test Scores of Immigrant Children in the US

Immigrant children in the US tend to perform worse in reading, mathematics, and science compared to native children. This paper explores how much of such differences in achievement can be accounted for by a lack of English proficiency. To identify the causal effect of English proficiency on cognitive test scores, I use the fact that language proficiency is closely linked to age at arrival, and that migrant children arrive at different ages from different countries of origin. In particular, I instrument English proficiency by comparing children from English-speaking countries to children from non-English-speaking countries who migrated to the US at different ages. Using data from the New Immigrant Survey, I find that speaking English very badly or badly can explain 27–33% of the achievement gap between native and immigrant children in standardized language-related tests. However, I find no significant language effects for applied maths problems or calculations.