Miracle-dz/UniFAR-FTUs huggingface dataset:
CLEP: Contrastive Learning for Epileptic Seizure Prediction Using a Spatio-Temporal-Spectral Network

Seizure prediction of epileptic preictal period through electroencephalogram (EEG) signals is important for clinical epilepsy diagnosis. However, recent deep learning-based methods commonly employ intra-subject training strategy and need sufficient data, which are laborious and time-consuming for a practical system and pose a great challenge for seizure predicting. Besides, multi-domain characterizations, including spatio-temporal-spectral dependencies in an epileptic brain are generally neglected or not considered simultaneously in current approaches, and this insufficiency commonly leads to suboptimal seizure prediction performance. To tackle the above issues, in this paper, we propose Contrastive Learning for Epileptic seizure Prediction (CLEP) using a Spatio-Temporal-Spectral Network (STS-Net). Specifically, the CLEP learns intrinsic epileptic EEG patterns across subjects by contrastive learning. The STS-Net extracts multi-scale temporal and spectral representations under different rhythms from raw EEG signals. Then, a novel triple attention layer (TAL) is employed to construct inter-dimensional interaction among multi-domain features. Moreover, a spatio dynamic graph convolution network (sdGCN) is proposed to dynamically model the spatial relationships between electrodes and aggregate spatial information. The proposed CLEP-STS-Net achieves a sensitivity of 96.7% and a false prediction rate of 0.072/h on the CHB-MIT scalp EEG database. We also validate the proposed method on clinical intracranial EEG (iEEG) database from our Xuanwu Hospital of Capital Medical University, and the predicting system yielded a sensitivity of 95%, a false prediction rate of 0.087/h. The experimental results outperform the state-of-the-art studies which validate the efficacy of our method. Our code is available at https://github.com/LianghuiGuo/CLEP-STS-Net.
Generating complex explanations from machine learning models using class-contrastive reasoning

Objective. One of the major limitations of most black-box machine learning models is the lack of explainability. In healthcare, explainability is important. Furthermore, most healthcare professionals do not have technical knowledge of machine learning. Consequently, it is necessary to translate the predictions of the machine learning model into an explainable narrative. Our research focuses on the healthcare domain. The goal of this study is to generate complex explanations from a black-box machine learning model applied to heaalthcare. Results. Class-contrastive techniques can be used to generate explanations. In this method, classcontrastive counterfactual reasoning is applied to a machine learning model on tabular data (in healthcare). The model predictions are explained by observing the changes in prediction by altering the inputs. This is visualized using heatmaps (class-contrastive heatmaps). This approach displays prediction results as visualizations (heatmaps). Our contribution is to extend class-contrastive analysis of black-box machine learning models to numeric features. Our work also allows machine learning scientists to visually inspect class-contrastive heatmaps and generate complex explanations for models. The resulting explanations (visual and text) are easier for non-technical people to follow. We show how machine learning scientists can extract complex explanations from machine learning models which can be interpreted by nontechnical audiences. Our work may be broadly applicable in domains where explainability is important.
Characteristics, treatment and outcomes of myelodysplastic syndrome in two Finnish hospital districts

Abstract Introduction: Myelodysplastic syndromes (MDS) are hematologic malignancies characterized by changes in haematopoiesis and a high risk for progressing into acute myeloid leukemia (AML). In this retrospective registry based real-world study, from two Finnish hospital data lakes we characterized specialised health care treated MDS patients, their treatment landscape, outcomes, and healthcare resource utilization. Methods: This study consisted of adult patients with MDS diagnosed in either of two hospital districts in Finland: hospital district of Southwest Finland (HDSF) and Pirkanmaa hospital district (PHD). Two different time windows were used depending on data availability: 1.1. 2010-31.12. 2019 (HDSF) and 1.1. 2012-31.12. 2019 (PHD). Electronic health record data, including demographics, diagnoses, and medications was accessed via the respective hospital data lakes and dates and causes of death data was collected from Statistics Finland. Results: We identified 565 adult MDS patients, of whom 424 received active life-prolonging treatment at specialized healthcare and 141 were treated with watchful observation or supportive care at primary care. 72 patients were treated with azacitidine and 26 patients received allogeneic hematologic stem cell transplant. Median overall survival for the specialty healthcare treated patients was 27,5 months (95 confidence interval [CI] 24,1-35,2) and costs per patient year were 17 563e. Conclusion: This hospital data lake-based analysis identified patient groups with differing disease severity and need for treatment. High-risk, azacitidine treated patients have suboptimal outcomes and high costs, highlighting the need for new therapeutic approaches to prevent disease progression and reduce disease burden.
Structural development and petrofabrics of eclogite facies shear zones, Bergen Arcs, western Norway: implications for deep crustal deformational processes

Caledonian eclogite facies shear zones developed from Grenvillian garnet granulite facies anorthosites and gabbros in the Bergen Arcs of western Norway allow direct investigation of the relations between macroscopic structures and crystallographic preferred orientation (CPO) in lower continental crust. Field relations on the island of Holsnoy show that the eclogites formed locally from granulite facies rocks by progressive development of: (1) eclogite adjacent to fractures; (2) eclogite in discrete shear zones (> 2 m thick); (3) eclogite breccia consisting of >80% well-foliated eclogite that wraps around rotated granulite blocks; and (4) anastomosing, subparallel, eclogite facies shear zones 30–100 m thick continuous over distances > 1 km within the granulite terrane. These shear zones deformed under eclogite facies conditions at an estimated temperature of 670 ± 50°C and a minimum pressure of 1460 MPa, which corresponds to depths of >55 km in the continental crust. Detailed investigation of the major shear zones shows the development of a strong foliation defined by the shape preferred orientation of omphacite and by alternating segregations of omphacite/garnet-rich and kyanite/zoisite-rich layers. A consistent lineation throughout the shear zones is defined by elongate aggregates of garnet and omphacite. The CPO of omphacite, determined from five-axis universal stage measurements, shows a strong b-axis maximum normal to foliation, and a c-axis girdle within the foliation plane with weak maxima parallel to the lineation direction. These patterns are consistent with deformation of omphacite by slip parallel to [001] and suggest glide along (010). The lineation and CPO data reveal a consistent sense of shear zone movement, although the displacement was small. Localized faulting of high-grade rocks accompanied by fluid infiltration can be an important mode of failure in the lower continental crust. Field relations show that granulite facies rocks can exist in a metastable state under eclogite facies conditions and imply that the lower crust can host differing metamorphic facies at the same depth. Deformation of granulite and partial conversion to eclogite, such as is exposed on Holsnoy Island, may be an orogenic-scale process in the lowermost crust of collisional orogens.
Offshore ambient-noise surface-wave tomography above 0.1 Hz and its applications

By demonstrating offshore ambient-noise surface-wave tomography (ANSWT) at reservoir scale, we add a method to the commercially usable geophysical methods. Analysis of ambient-noise records at 126 locations above a hydrocarbon reservoir offshore Norway proves that the marine environment provides good conditions for 3D estimation of shear-wave velocities at frequencies above 0.1 Hz. The presented results are used to discuss potential application areas.
Analysis of forest health monitoring surveys on the Allegheny National Forest (1998-2001)

Describes forest vegetation and health conditions on the Allegheny National Forest (ANF). During the past 20 years, the ANF has experienced four severe droughts, several outbreaks of exotic and native insect defoliators, and the effects of other disturbance agents. An increase in tree mortality has raised concerns about forest health. Historical aerial surveys (1984-98), Forest Inventory and Analysis plot data collected in 1989, and FHM plot data collected 1998-2001 were analyzed to compare disturbed and undisturbed areas. Tree mortality and crown dieback levels were compared between undefoliated areas and areas defoliated by cherry scallopshell moth, elm spanworm, and gypsy moth. American beech mortality was compared inside and outside the beech bark disease killing front. This study illustrates the value of an intensified grid of P3 plots and demonstrates the integration of aerial survey and plot data.
Diurnal variations of surface ocean pCO2 and sea‐air CO2 flux evaluated using remotely sensed data

This paper evaluates the effect of diurnal variations of sea surface temperature (SST) and wind speed on the surface ocean CO2 partial pressure (pCO2sw) and sea‐air CO2 flux. This is carried out using a combination of climatological and remote sensing data. The calculations show that the diurnal heating cycle can drive a diurnal pCO2sw variability which may cause the global ocean uptake of CO2 to be more than twice as large during night than during day. The effect of diurnal wind speed variations on the sea‐air CO2 flux is restricted to the tropics. The concurrent variations of SST and wind speed on diurnal time scales bring around covariance terms that may contribute to the monthly mean flux. These were estimated and found to be negligible. Thus, this study validates the use of diurnally averaged fields for computation of sea‐air CO2 fluxes.
Endoscopic closure of a refractory urethroanal fistula using an innovative wound closure device

Gastrointestinal (GI) fistula is a rare chronic disease that affects the quality of life of patients and represents a real therapeutic challenge, with frequent recurrences [1]. Endoscopic management of GI fistulas combines both endoscopic submucosal dissection (ESD) and mechanical closure of the orifice [2]. We recently reported that the strategy of fistula endoscopic submucosal dissection with clip closure (FESDC) is effective and safe for permanent closure of GI fistulas [3], including in the exceptional cases where an aortoesophageal fistula has occurred [4]. In the case of anal fistulas, mechanical closure remains the greatest challenge because of the proximity of the anal sphincter, which does not allow effective closure using standard or overthe-scope clips. We herein report the case of a 29-yearold man referred for a refractory urethroanal fistula with an existing intermediate imperforate anus requiring multiple surgical and endoscopic procedures. We first performed the endoscopic examination without anesthetic. The fistula was visualized on the posterior wall of the anal verge during micturition by the patient, the bladder having first been filled with blue dye using a urinary catheter (▶Fig. 1). After ESD of the internal orifice of the fistula, we decided to use the new Sutuart flexible needle holder (Olympus, Tokyo, Japan) [5] with a barbed suture (Medtronic, USA) to suture together the edges of the dissected fistula tract (▶Fig. 2, ▶Video 1). This novel device allowed us to suture under endoscopic control in a tight area using barbed suture (▶Fig. 3). Technical success was achieved, defined by tight sealing of the orifice confirmed by opacification at the end of the procedure without any urine leakage (▶Fig. 4). No adverse event was E-Videos
Investigation of the resolution limit of Talbot lithography with compact EUV exposure tools

This paper focuses on the design and fabrication of phase-shifting transmission masks tailored for high-resolution nanopatterning using a compact EUV exposure tool. The authors analyze various factors that influence the achievable resolution, aiming to push the boundaries towards the sub-10 nm range, approaching the theoretical resolution limit. The demand for high-resolution nanoscale patterns spans across diverse applications, driving the need for compact exposure tools and lithographic concepts. The developed EUV exposure tool can be operated at either 10.9 nm or 13.5 nm exposure wavelengths depending on the specific use case. This capability allows for large area nanopatterning with enhanced throughput as well as industrial resist qualification with focus on highest resolution. The utilized discharge-produced plasma (DPP) EUV source offers partially coherent radiation. For this radiation type, the (achromatic) Talbot lithography has proven to be the most effective with resolution in the sub-30 nm range and a theoretical resolution limit of less than 10 nm. To optimize the intensity distribution in the wafer plane, the authors use rigorous coupled-wave analysis (RCWA) simulations to fine-tune the material composition and geometry of the masks. Various factors influencing the achievable resolution are identified and presented. In addition to simulative optimization, the fabrication of dense periodic nanopatterns poses increasing challenges for smaller periods. In this work, the mask fabrication process is optimized to produce stable and high-resolution periodic mask patterns, leading to record resolutions for both line and contact hole periodic nanopatterns with the presented setup.
Development of an e-beam/i-line stepper intra-level mix and match approach with the photoresist mr-EBL 6000.5 for PIC related structures such as waveguides, ring resonators and coupling structures

In this paper the development of an intra-level mix and match (ILM&M) process, an expression for the exposure of one resist layer with at least two different exposure technologies, for the negative tone resist mr-EBL 6000.5 (micro resist technology, Germany) is demonstrated. Process development is conducted on a layout with photonic integrated circuit (PIC) related waveguides (WG), ring resonators and coupling structures on 150 mm silicon wafers with a 1000 nm SiO2 layer and a 450 nm low pressure (LP) Si3N4 layer on top. In order to match the intended structure dimensions perfectly, the ideal exposure dose has to be determined with an i-line wafer stepper and in parallel with an e-beam lithography (EBL) system. In addition, different post exposure bake (PEB) processes and their influence on resulting structures, which are investigated by means of CD-SEM and profilometer measurements are investigated. It is shown, that regarding pattern fidelity, coupling structures exposed by EBL match the layout design better than those exposed by the i-line stepper. For the purpose of further optimizing the matching of generated coupling structures to the targeted design, different proximity effect correction (PEC) parameter sets are applied. CD-SEM measurements reveal the PEC parameter set which is most suitable for generating the targeted coupling structures. By combining the measurement results of structures exposed with different exposure doses and selecting the best PEC parameter set regarding structure dimensions and pattern fidelity, a processing recipe for an e-beam/i-line stepper ILM&M with the negative tone resist mr-EBL 6000.5 is successfully established.
Video Games to Study and Improve Collaboration Skills

This research project aims to design and develop video games for studying and improving collaboration and collaboration skills. To this end, we reviewed the scientific literature across many disciplines, including psychology, computer science, and game theory. This literature review led to key insights and an attempt to develop a framework grounded in Reinforcement Learning Theory with the ambition of integrating a diversity of disparate scientific results but also to provide a lens through which to study existing multiplayer games as well as drive the development of research video games for studying and improving collaboration and collaboration skills. We will review commercial multiplayer games to identify key concepts and mechanics that game designers use for engaging collaborative gameplay. We will then develop game prototypes based on both our theoretical framework and our video game reviews and conduct small-scale user tests to help us identify the most promising ideas and to iteratively update the framework and game design for future large-scale experimental studies. Here we present key ideas behind this project; it is still in its early stages and could greatly benefit from discussions with the CHI-Play community.
The Cost of Reward: A Critical Reflection on the `What', `How', and `Why' of Gamification for Motivation in Sports

Sports is greatly valued both for its internal benefits (e.g., joy and fulfilment) and its external benefits (e.g., physical health). Still, many people struggle to find or uphold the motivation to practice sports. To ameliorate this issue, researchers in the field of SportsHCI have been actively exploring various gamification strategies. In this contribution, we critically reflect on the ‘what’, ‘how’, and ‘why’ of gamification in sports. We argue against the use of gamification for ‘quick wins’, instead we argue that gamification can only be truly successful if it supports the spontaneous, self-sustained, and autotelic propensity in people to play sports.
DHH People in Co-located Collaborative Multiplayer AR Environments

Deaf and hard of hearing (DHH) people often require assistive environments or technologies while communicating, and there have been several technologies and studies to serve this purpose, namely augmented reality (AR). Although technical aspects and users’ behavior with these technologies have been covered in plenty of prior literature, very few studies have paid attention to how DHH people communicate, collaborate, and coordinate in co-located collaborative AR environments. After reviewing the literature, I present a primary agenda consisting of an iterative research plan that uses qualitative and quantitative methodologies to address the knowledge gap. Furthermore, I discuss the initial findings from my previous research, followed by suggestions for future work.
Trends in extreme temperature over Nigeria from percentile‐based threshold indices

This study analyses spatial and temporal trends in extreme temperature indices over Nigeria. The percentile indices were calculated from newly homogenized daily minimum and maximum temperature data for the period 1971–2012 for 21 stations in Nigeria. Indices describing the characteristics of hot extremes and cold extremes are calculated with the RClimDex software. The annual and seasonal trends in these indices are obtained using the ordinary least square fit and the statistical significance tested using the R‐based modified Mann–Kendall test. We examine characteristics of these indices for the entire country and separately for the three geographical zones in Nigeria: Guinea, Savanna, and Sahel. The spatial and temporal patterns of trends in the indices indicate that Nigeria has experienced statistically significant increase in the frequency of hot extreme and decrease in cold extreme events. Although majority of the stations have significant trends in warm days and warm nights, the annual trend is greatest in warm nights. In addition, the rate of warming in minimum temperature (‘warm nights’) is stronger in June, July, August (JJA) and September, October, November (SON) compare with December, January, February (DJF) and March, April, May (MAM). As for the trends in cold days and cold nights, the trends in cold nights are larger than for cold days at both the annual and seasonal scales. The regional analysis indicates that trends in warm nights and cold nights are most pronounced in Guinea and Sahel regions.
Multiple modes of data sharing can facilitate secondary use of sensitive health data for research

Evidence-based healthcare relies on health data from diverse sources to inform decision-making across different domains, including disease prevention, aetiology, diagnostics, therapeutics and prognosis. Increasing volumes of highly granular data provide opportunities to leverage the evidence base, with growing recognition that health data are highly sensitive and onward research use may create privacy issues for individuals providing data. Concerns are heightened for data without explicit informed consent for secondary research use. Additionally, researchers—especially from under-resourced environments and the global South—may wish to participate in onward analysis of resources they collected or retain oversight of onward use to ensure ethical constraints are respected. Different data-sharing approaches may be adopted according to data sensitivity and secondary use restrictions, moving beyond the traditional Open Access model of unidirectional data transfer from generator to secondary user. We describe collaborative data sharing, facilitating research by combining datasets and undertaking meta-analysis involving collaborating partners; federated data analysis, where partners undertake synchronous, harmonised analyses on their independent datasets and then combine their results in a coauthored report, and trusted research environments where data are analysed in a controlled environment and only aggregate results are exported. We review how deidentification and anonymisation methods, including data perturbation, can reduce risks specifically associated with health data secondary use. In addition, we present an innovative modularised approach for building data sharing agreements incorporating a more nuanced approach to data sharing to protect privacy, and provide a framework for building the agreements for each of these data-sharing scenarios.
Quasi-experimental study finding no localised gun crime or call reduction after gun buybacks in Philadelphia

Introduction Gun buyback programmes have been popular in the USA since the 1970s. Studies show that they have no effect on citywide gun crime rates, but more microlevel examinations around gun buyback locations have not been conducted. This study tests for local effects of 34 Philadelphia, PA buyback events at 30 locations between 2019 and 2021. Methods We analysed all gun-related crime events and gun-related calls for service attended by the police from 2019 to 2021. Multilevel models with an autoregressive residual structure were estimated on weekly gun crime and call event intensity (inverse distance weighted) totals across a range of distances (4000–8000 feet). Impacts of a gun buyback event were estimated for 1–4 weeks postevent. Results Statistically significant weekly increases in gun event intensity are associated with seasonality and after the murder of George Floyd. Gun event intensity was not significantly affected by gun buybacks. Across 20 sensitivity tests of different distances and time periods (4000–8000 feet and between 1 and 4 weeks), gun buybacks were not statistically associated with any localised reduction in the intensity of gun crimes and calls. Conclusions Extant research has failed to uncover any effect of gun buybacks on citywide gun crime rates. The current results now contribute a lack of evidence at the local level to this literature. While gun buybacks remain popular with politicians and the public, this study adds to the ongoing question of whether buyback funds could be better spent more effectively.