Miracle-dz/UniFAR-FTUs huggingface dataset:
Probing ultracold ytterbium in optical lattices with resonant light: from coherent control to dissipative dynamics

In this manuscript I present an experimental investigation of the dynamics of an ultracold gas of bosonic ytterbium loaded into optical lattices and exposed to resonant light. The interaction between atoms and light makes it possible to study the coherence properties of the gas. The resonant driving is performed on the relevant optical transitions featured by alkaline-earth-like atoms such as ytterbium. Specifically, I use the clock transition, which has no spontaneous emission, to coherently manipulate the internal degrees of freedom of the atoms. I also use the so-called intercombination transition, the spontaneous emission rate of which is tunable, to produce a controllable dissipation. On the one hand, I demonstrate the coherent driving of the internal state of the atoms on the clock transition, the excited state of which is metastable and cannot spontaneously decay, thus preserving the coherence of the gas. The temporal internal dynamics in a deep lattice allows me to measure the collisional properties at low temperature for both clock states. On the other hand, I use the spontaneously emitted photons of the intercombination transition excited level to induce a coupling to the external degrees of freedom of the atoms. I present the momentum diffusion of a superfluid excited on this transition. Strong interactions between atoms slow down the decoherence and lead to an anomalous sub-diffusive relaxation. A simple model comprising atomic motion, interactions and dissipation accounts for our observations. A theoretical study of the dissipative dynamics in optical lattices sheds light on complementary phenomena such as induced dipole-dipole interactions or collective effects in spontaneous emission.
Key subgroups and co-minimality in topological groups

We introduce a minimality property for subgroups of topological groups. A subgroup $H$ is a key subgroup of a topological group $G$ if there is no strictly coarser Hausdorff group topology on $G$ which induces on $H$ the original topology. In fact, this concept appears implicitly in some earlier publications [10,4,12]. Every co-minimal subgroup is a key subgroup while the converse is not true even for discrete groups. In this paper, we continue the study of minimality in topological matrix groups from [12]. Extending some results from [10,4] concerning the generalized Heisenberg group, we prove that the center of the upper unitriangular group $\operatorname{UT}(n,K)$, defined over a commutative topological unital ring $K$, is a key subgroup. If the multiplication map $m \colon K\times K\to K$ is strongly minimal, then this center is even co-minimal in $\operatorname{UT}(n,K)$. We show that this holds, for example, when $K$ is an archimedean absolute valued field or a local field.
Cognitive individual differences in the process and product of L2 writing

Abstract This paper examines how research in second language acquisition has approached the study of cognitive individual differences in the process and product of L2 writing from a theoretical and empirical perspective, paying special attention to the three empirical studies included in this special issue. The paper is divided into three sections. The first section examines the cognitive abilities that have been investigated in L2 writing research, among which working memory stands out as the most widely studied. The second section synthesizes the findings reported by the empirical studies in this issue in relation to the role of working memory in L2 writing behaviors and outcomes. The last section suggests future lines of research that can broaden the current scope of research on writing and cognitive individual differences, mostly centered on the components of the working memory system. This research has important theoretical implications, as little is known about how different cognitive individual differences are implicated in writing, as well as pedagogical implications, as the findings can inform about optimal performance and learning conditions for learners with diverse cognitive ability profiles.
Preoperative Diffusion Tensor Imaging in Supratentorial Intra-Axial Brain Tumors: Its Role in Predicting Tumor Histology and Prognosis as well in Surgical Planning and Resection

Abstract Objective There are a large number of prospective studies that use diffusion tensor imaging (DTI) to show the relationship between intracranial tumors and white matter (WM) fibers. We studied the role of DTI in supratentorial intra-axial (ST-IA) tumors of the brain in deciding the surgical approach with maximal resection and minimal or no deficit and in predicting the histological characterization of the tumor and the neurological outcome. Methods A total of 91 cases of ST-IA tumors were included in our study. The neurological status of the patients was assessed preoperatively, and the tumor volume and DTI pattern were noted radiologically. Surgical plan was decided by the senior consultants of the neurosurgery department taking into consideration the findings of tractography and magnetic resonance imaging. The neurological status and the extent of resection were evaluated postoperatively, and the correlation between histopathology with DTI was studied. Results Of the 91 patients, 25 had high-grade glioma (HGG), 60 had low-grade glioma (LGG), and 6 were metastatic lesions. Gross total excisions were done mostly in patients with DTI showing displaced fibers and subtotal/partial resections were done mostly in disrupted/infiltrated tracts, which was statistically significant. The correlation between histopathology and tractography revealed that intact/displaced tracts were seen mostly in LGG (79%), whereas 86% of HGG showed disrupted/infiltrated fibers; both were statistically significant. Conclusion Preoperative DTI in ST-IA brain tumors is an important tool for deciding the appropriate surgical approach for maximal safe resection, thus improving the post-op neurological outcome in patients. It also helps in predicting the tumor histology while also serving as an important prognostication indicator.
Nonlinear super-resolution signal processing allows intracellular tracking of calcium dynamics

Fluorescence imaging of calcium dynamics has revolutionized cellular biology, and especially neuroscience as it allows the study of neural activity across spatially well defined populations. Quantification of fluorescence signals is commonly performed using ratiometric measures. These measures, such as ΔF/F, are easy to implement, but they depend on the definition of a baseline, which is often not trivial outside of the measurement of evoked responses, and needs to be defined for every cell (or other cellular compartment) separately. Here, we present a new quantitative measure of fluorescence by taking advantage of the time dimension of the signal. The new method, which we named ARES (Autoregressive RESiduals), is based on the quantification of residuals after linear autoregression and does not require arbitrary baseline assignment. We detail the basic characteristics of the ARES signal, compare it against ΔF/F, and quantify the improvement in spatial and temporal resolution of recorded calcium data. We further exemplify its utility in the study of intracellular calcium dynamics by describing the propagation of a calcium wave inside a dendrite. As a robust and accurate method for quantifying fluorescence signals, ARES is particularly well-suited for the study of spontaneous as well as evoked calcium dynamics, subcellular localization of calcium signals, and spatiotemporal tracking of calcium dynamics. Highlights A baseline-free quantitative measure of fluorescence is introduced The method, called ARES, is based on autoregression ARES improves the spatiotemporal resolution of calcium imaging recordings Its utility for the localization and spatiotemporal tracking of calcium is shown
Exploiting Subgraph Similarities for Efficient Auto-tuning of Tensor Programs

The requirement for deploying deep learning (DL) models efficiently has boosted the research of DL compilers. Especially, the difficulty of generating optimized tensor programs has driven DL compilers to commonly adopt the auto-tuning approaches. Consequently, there are increasing demands to improve the effectiveness of auto-tuning in terms of both search efficiency and search quality. However, existing auto-tuning approaches commonly treat subgraphs individually and overlook the similarities among them, and thus fail to generate better tensor programs under limited time budget. To address the above drawbacks, we propose FamilySeer, an auto-tuning framework that can generate better tensor programs by exploiting the subgraph similarities. Specifically, FamilySeer organizes similar subgraphs into subgraph families, where the cost models are built at family basis with improved accuracy for estimating high potential program candidates. To further leverage the similarity, FamilySeer uses the accurate cost model per family to reduce the number of program candidates for costly hardware measurements without degrading search quality. The experiment results on various DL models demonstrate that FamilySeer can achieve better search efficiency/quality on both CPU and GPU platforms compared to the state-of-the-art auto-tuning framework.
GPU Performance Acceleration via Intra-Group Sharing TLB

Unified virtual memory greatly simplifies GPU programming, but it introduces huge address translation overhead. To reduce this overhead, modern GPUs utilize the translation lookaside buffer (TLB) to accelerate the address translation process. However, the benefit of TLB is far from achieving optimal performance. In this work, we find that GPU performance deficiency mainly stems from the private property of L1 TLBs. First, there exist a lot of duplicate page table entries among L1 TLBs, which induces insufficient space utilization. Second, the miss rate of L2 TLB is high due to the massive number of requests from L1 TLB miss, which leads to a significant GPU performance degradation. To reduce L1 TLB miss and improve the address translation performance of GPU, we propose a hardware scheme by exploiting an Intra-Group Sharing approach, named IGS-TLB. In IGS-TLB, L1 TLBs are decoupled from the compute units and aggregated into groups. Specifically, there only exist shared L1 TLB entries inside TLB groups that are responsible for non-overlapping address ranges. This greatly eliminates duplicate page table entries in L1 TLBs and significantly reduces the request number of L1 TLB misses. Our evaluation on a wide set of GPU workloads shows that IGS-TLB can effectively reduce L1 TLB miss rate and the L2 TLB traffic, speeding up the GPU performance by 20.5% on average.
Long short-term memory model identifies ARDS and in-hospital mortality in both non-COVID-19 and COVID-19 cohort

Objective To identify the risk of acute respiratory distress syndrome (ARDS) and in-hospital mortality using long short-term memory (LSTM) framework in a mechanically ventilated (MV) non-COVID-19 cohort and a COVID-19 cohort. Methods We included MV ICU patients between 2017 and 2018 and reviewed patient records for ARDS and death. Using active learning, we enriched this cohort with MV patients from 2016 to 2019 (MV non-COVID-19, n=3905). We collected a second validation cohort of hospitalised patients with COVID-19 in 2020 (COVID+, n=5672). We trained an LSTM model using 132 structured features on the MV non-COVID-19 training cohort and validated on the MV non-COVID-19 validation and COVID-19 cohorts. Results Applying LSTM (model score 0.9) on the MV non-COVID-19 validation cohort had a sensitivity of 86% and specificity of 57%. The model identified the risk of ARDS 10 hours before ARDS and 9.4 days before death. The sensitivity (70%) and specificity (84%) of the model on the COVID-19 cohort are lower than MV non-COVID-19 cohort. For the COVID-19 + cohort and MV COVID-19 + patients, the model identified the risk of in-hospital mortality 2.4 days and 1.54 days before death, respectively. Discussion Our LSTM algorithm accurately and timely identified the risk of ARDS or death in MV non-COVID-19 and COVID+ patients. By alerting the risk of ARDS or death, we can improve the implementation of evidence-based ARDS management and facilitate goals-of-care discussions in high-risk patients. Conclusion Using the LSTM algorithm in hospitalised patients identifies the risk of ARDS or death.
Investigating Troublesome Knowledge in Middle-Year Computer Science Courses to Support Retention

Computing Education Research (CER) has spent a large effort focusing on the difficulties faced by first year students, particularly in the first programming course students take (commonly referred to as CS1), as well as final-year students as they transition from their undergraduate studies to professional positions. There exists little if any research into middle-year students and fails to consider what difficulties students face and potential retention issues that exist within the middle-years. Recent data has suggested that retention is not a problem limited to first-year students as has been previously assumed, indicating that there is an even greater need for research into supporting middle-years students. This work attempts to fill this gap in our understanding of middle-year student difficulties framed around the retention issues they face. Retention has been shown to be influenced by three factors: students’ sense of belonging, their identity, and their self-efficacy. These three retention factors are impacted negatively when students run into troublesome learning experiences while not feeling as though they have the support needed to overcome them. This work looks to identify what these troublesome learning experiences consist of in terms of content knowledge and develop and pilot an intervention to support their experiences with learning, retention factors, and retention. The proposed work will contribute to CER by providing a framework for retention, a concept inventory of middle-year troublesome knowledge, and potential interventions to support one or more of the concepts.
My Eyes Speak: Improving Perceived Sociability of Autonomous Vehicles in Shared Spaces Through Emotional Robotic Eyes

The ability of autonomous vehicles (AVs) to interact socially with pedestrians poses a significant impact on their integration with urban traffic. This is particularly important for vehicle-pedestrian shared spaces due to increased social requirements in comparison to vehicular roads. Current pedestrian experience in shared spaces suffers from negative attitudes towards AVs and the consequently low acceptability of AVs in these spaces. HRI work shows that the acceptability of robots in public spaces can be positively impacted by their perceived sociability (i.e., possessing social skills), which can be enhanced by their ability to express emotions. Inspired by this approach, we follow a systematic process to design emotional expressions for AVs using the headlight ("eye'') area and investigate their impact on perceived sociability of AVs in shared spaces, by conducting expert focus groups (N=12) and an online video-based user study (N=106). Our findings confirm that the perceived sociability of AVs can be enhanced by emotional expressions indicated through emotional eyes. We further discuss implications of our findings for improving pedestrian experience and attitude in shared spaces and highlight opportunities to use AVs' emotional expressions as a new external communication strategy for future research.
Deep learning detection of diabetic retinopathy in Scotland’s diabetic eye screening programme

Background/Aims Support vector machine-based automated grading (known as iGradingM) has been shown to be safe, cost-effective and robust in the diabetic retinopathy (DR) screening (DES) programme in Scotland. It triages screening episodes as gradable with no DR versus manual grading required. The study aim was to develop a deep learning-based autograder using images and gradings from DES and to compare its performance with that of iGradingM. Methods Retinal images, quality assurance (QA) data and routine DR grades were obtained from national datasets in 179 944 patients for years 2006–2016. QA grades were available for 744 images. We developed a deep learning-based algorithm to detect whether either eye contained ungradable images or any DR. The sensitivity and specificity were evaluated against consensus QA grades and routine grades. Results Images used in QA which were ungradable or with DR were detected by deep learning with better specificity compared with manual graders (p<0.001) and with iGradingM (p<0.001) at the same sensitivities. Any DR according to the DES final grade was detected with 89.19% (270 392/303 154) sensitivity and 77.41% (500 945/647 158) specificity. Observable disease and referable disease were detected with sensitivities of 96.58% (16 613/17 201) and 98.48% (22 600/22 948), respectively. Overall, 43.84% of screening episodes would require manual grading. Conclusion A deep learning-based system for DR grading was evaluated in QA data and images from 11 years in 50% of people attending a national DR screening programme. The system could reduce the manual grading workload at the same sensitivity compared with the current automated grading system.
General-purpose Asynchronous Periodic Checkpointing in Hybrid Memory

Non-volatile memory (NVM) is attractive because it enables us to make in-memory data structures persistent without serialization overhead. To implement persistent data structures durable against crashes, periodic checkpointing in NVM has been well studied. A remarkable technique that takes advantage of both DRAM and NVM (i.e., hybrid memory) for periodic checkpointing is mirroring with epoch-based write-address tracking. Its straightforward adoption, however, results in user thread blocking to checkpoint data structures mirrored in DRAM into NVM, from which applications suffer in throughput and responsiveness. To resolve this problem, we incorporate epoch-based versioning into this mirroring technique. The proposed method enables us to delegate checkpointing of data structures mirrored in DRAM into NVM to dedicated background threads that do not block user threads. We develop a system based on our method and evaluate it through experiments with memcached. Our system achieved +13% better throughput than an existing synchronous counterpart and such responsiveness that more than 50% of the performance of the original memcached kept for any time window of 0.5 ms in 99.83% of the entire execution.
A Mixed-Method Exploration into the Mobile Phone Rabbit Hole

Smartphones provide various functions supporting users in their daily lives. However, the temptation of getting distracted and tuning out is high leading to so-called rabbit holes. To quantify rabbit hole behavior, we developed an Android tracking application that collects smartphone usage enriched with experience sampling questionnaires. We analyzed 14,395 smartphone use sessions from 21 participants, collected over two weeks, showing that rabbit hole sessions are significantly longer and contain more user interaction, revealing a certain level of restlessness in use. The context of rabbit hole sessions and subjective results revealed different triggers for spending more time on the phone. Next, we conduct an expert focus group (N=6) to put the gained insights into perspective and formulate a definition of the mobile phone rabbit hole. Our results form the foundation for predicting and communicating the mobile phone rabbit hole, especially when prolonged smartphone use results in regret.
A First Exploration on the Use of Head-Mounted Augmented Reality in the Context of the Portuguese Military

In this paper, we present the design and implementation of a first iteration of an augmented reality (AR) system for dismounted soldiers in the Portuguese military. We started the work via a survey of 86 members of the military to better understand their experience, needs, and preferences with current Command & Control (C2) systems. We then assessed the effects of our prototype on the performance, situational awareness, and perceived usability and workload of 13 participants from a local Commando Regiment. We compared our results to a representative baseline using a paper map and radio in a hostage extraction simulation and found that our first AR iteration, despite a short practice session, increased the quality of the information available and decreased the complexity, temporal demands, and effort required to complete the study tasks; leading to an overall decrease in perceived workload. Overall, participants described the AR experience as more user-friendly. We conclude our case study with research ideas for further iterations of our prototype.
The Open-Source DeLiBA2 Hardware/Software Framework for Distributed Storage Accelerators

With the trend towards ever larger “big data” applications, many of the gains achievable by using specialized compute accelerators become diminished due to the growing I/O overheads. While there have been several research efforts into computational storage and FPGA implementations of the NVMe interface, to our knowledge there have been only very limited efforts to move larger parts of the Linux block I/O stack into FPGA-based hardware accelerators. Our hardware/software framework DeLiBA initially addressed this deficiency by allowing high-productivity development of software components of the I/O stack in user instead of kernel space and leverages a proven FPGA SoC framework to quickly compose and deploy the actual FPGA-based I/O accelerators. In its initial form, it achieves 10% higher throughput and up to 2.3× the I/Os per second (IOPS) for a proof-of-concept Ceph accelerator running in a real multi-node Ceph cluster. In DeLiBA2, we have extended the framework further to better support distributed storage systems, specifically by directly integrating the block I/O accelerators with a hardware-accelerated network stack, as well as by accelerating more storage functions. With these improvements, performance grows significantly: The cluster-level speed-ups now reach up to 2.8× for both throughput and IOPS relative to Ceph in software in synthetic benchmarks, and achieve end-to-end wall clock speed-ups of 20% for the real workload of building a large software package.
Interrogating Implicit Power in Secondary Computer Science Education

Abstract Beyond broadening access to computer science (CS), we want to provide just and excellent CS instruction to all of our students. We have not yet reached that goal. In addition to explicit access barriers to CS, we need to know what implicit barriers are preventing students from engaging in deep CS learning? To surface some of these barriers, I used qualitative methods to examine a secondary CS elective summer classroom and the counternarratives students created, and what tensions they faced. I conducted semi-structured interviews with candidate CS teachers as they decide whether or not to enroll into a certification program to better understand what factors enabled and prevented them from becoming CS teachers. I conducted semi-structured interviews with secondary CS teachers, post secondary instructors, and students to understand what they perceived rigorous learning to be and learned what prevented rigorous learning.