Miracle-dz/UniFAR-FTUs huggingface dataset:
Thermal equilibrium of collisional non-neutral plasma in a magnetic dipole trap

This paper discusses thermal equilibrium states of single-species plasmas, such as pure electron plasmas and pure positron plasmas, that are confined in a dipole trap. Thermal equilibrium states for such plasmas are routinely realized in the homogeneous magnetic field of Penning–Malmberg traps. We generalize the theory of these states to include inhomogeneous magnetic dipole fields. The approach to thermal equilibrium takes place in two stages with well separated time scales. On the collision time scale, thermal equilibrium is established along each magnetic field line. On the much longer transport time scale, heat conduction and viscosity bring the plasmas on different flux contours into thermal equilibrium, we call this a state of global thermal equilibrium. We present numerical results for local and global thermal equilibria. These results agree with the analytic predictions for charge collections that are large compared with the Debye length. There is, in principle, no limit to the confinement time of a single-species plasma in a global thermal equilibrium state. Experiments with hot electron–ion plasmas performed in the LDX and RT1 devices give us confidence that, in contrast to a Penning–Malmberg trap, a magnetic dipole field can also confine cold quasi-neutral electron–positron pair plasmas on the time scale of the phenomena of interest. Such pair plasmas are assumed to form in the magnetosphere of neutron stars but have so far not been realized in a laboratory. The creation of an electron–positron pair plasma is the main goal of the APEX collaboration.
COVID‐19 Impact on the Oil and Gas Industry NO2 Emissions: A Case Study of the Permian Basin

COVID‐19 caused a historic collapse in fossil fuel demand, a general decline in economic activity, and hydrocarbon price volatility. This resulted in an unprecedented scenario to evaluate the contribution of the O&G (Oil and Gas) industry NO2 (nitrogen dioxide) emissions in the Permian basin (United States), currently the second largest hydrocarbon‐bearing area on Earth. TROPOMI (Tropospheric Monitoring Instrument), on board the Sentinel‐5P satellite, has captured the impact of the oil and gas industry emissions during the COVID‐19 lockdown. A generalized drop (∼30%) of NO2 emissions derived using the divergence method in comparison with 2019 was observed following the decline in production and drilling (13% and 68% respectively) during the lockdown. NO2 tropospheric columns were less impacted with a smaller decrease (∼4%) across the basins. This study demonstrates that the impact of the COVID‐19 lockdown on NO2 emissions was not only present in urban areas but also in vast O&G production regions, which shows the potential of TROPOMI to assess future pollution mitigation strategies for this industry.
Source Apportionment of Cloud Nuclei at 260 m and Ground Level in Urban Beijing

Cloud condensation nuclei (CCN) is a critical parameter for simulating aerosol‐cloud interaction and aerosol indirect effects. Here we characterize the vertical differences of CCN number concentration (NCCN) between ground level and 260 m based on field measurements on a meteorological tower in Beijing. The observed ground aerosol number concentration was about 16% higher than 260 m, unexpectedly, the ground NCCN was on average 20% lower than 260 m within the atmospheric typical supersaturation (S) range of 0.1%–0.4%. Using positive matrix factorization analysis, we clarified that the higher NCCN at 260 m was primarily due to increased contributions from both regional transport and secondary processes. The aerosol particles from the regional transport contributed 20%–50% and 31%–57% of CCN respectively at ground and 260 m within typical S, with higher proportions at smaller S. The secondary processes contributed generally about the equal amount CCN between ground (42%–55%) and 260 m (37%–52%). The total contribution of regional transport and secondary processes, as the main sources, contributed 72%–94% CCN during the campaign. While, nucleation events and local primary sources accounted for the smallest proportion of NCCN at both heights. Under polluted conditions, the vertical difference was further enlarged. The results suggest that surface observations under clean conditions may well represent cloud nuclei aloft. However, the NCCN measured at ground may be unexpectedly much lower than the tower height in polluted conditions, likely leading to an underestimation on cloud nuclei aloft during the field campaign.
Poor subjective sleep reported by people living with HIV is associated with impaired working memory

Abstract Poor sleep can undermine health and may be especially disruptive to those with chronic conditions including HIV infection. Here, clinically well-described people living with HIV [PLWH] (74 men, 35 women) and healthy control (38 men, 35 women) participants were administered the Pittsburgh Sleep Quality Index (PSQI), a validated measure of subjective sleep with a global score ≥5 able to distinguish good from poor sleepers. In addition, participants completed a battery of neuropsychological tests. PLWH (6.8 ± 3.7) had higher global PSQI scores than healthy controls (4.1 ± 2.8): 39.7 % of uninfected controls and 68.8 % of PLWH had a PSQI≥5 indicative of poor sleep. There were no relations between the global PSQI score and any evaluated variables among uninfected individuals or with demographic or HIV-related variables in PLWH. Instead, a higher global PSQI score among PLWH was associated with worse “Quality of Life” scores [Global Assessment of Functioning (GAF, p=0.0007), Medical Outcomes Study survey (21-item short form, SF-21, p<0.0001), and Activities of Daily Living-Instrumental (ADL-I, p=0.0041)] and higher Beck Depression Index (BDI, p<0.0001) depressive symptoms. Further, in PLWH, higher global PSQI scores were associated with poor performance on a working memory task, the digit backward span (p=0.0036). In PLWH, the 5 variables together explained 32.3 % of the global PSQI score variance; only 3 variables – the SF-21, BDI, and digit backward scores – explained 30.6 % of the variance. To the extent that poor subjective sleep contributes to impaired working memory in HIV, we speculate that this impairment may be ameliorated by improved sleep health.
Long memory conditional random fields on regular lattices

This paper draws its motivation from applications in geophysics, agricultural, and environmental sciences where empirical evidence of slow decay of correlations have been found for data observed on a regular lattice. Spatial ARFIMA models represent a widely used class of spatial models for analyzing such data. Here, we consider their generalization to conditional autoregressive fractional integrated moving average (CARFIMA) models, a larger class of long memory models which allows a wider range of correlation behavior. For this class we provide detailed descriptions of important representative models, make the necessary comparison with some other existing models, and discuss some important inferential and computational issues on estimation, simulation and long memory process approximation. Results from model fit comparison and predictive performance of CARFIMA models are also discussed through a statistical analysis of satellite land surface temperature data.
Uncovering Gig Worker-Centered Design Opportunities in Food Delivery Work

The gig economy and digital labor platforms, such as food delivery, have become essential while also troubling the current socioeconomic landscape. Delivery platforms promise entry-level work, flexibility, and other benefits. However, researchers remain divided on if these platforms benefit workers and society at large. This study aims to shed light on the comprehensive challenges in food delivery work, uncovering gig worker-centered design opportunities to improve the lives of food couriers. Adopting an exploratory research process, we analyzed 19 ride-along food delivery videos and performed nine semi-structured interviews with food couriers in Portugal. Our findings illustrated the complexity and challenging nature of delivery work due to the entangled physical, digital, social, natural, and human factors. We captured and discussed gig worker-centered opportunities that surfaced from work challenges, echoing the needs of food couriers about supporting work, justice, inclusion, and work vision.
Awedyssey: Design Tensions in Eliciting Self-transcendent Emotions in Virtual Reality to Support Mental Well-being and Connection

Access to nature and feeling connection is critical for mental health and well-being. These benefits may be mediated by self-transcendent emotions evoked by nature. We need to explore the capacity of technology to offer such experiences when we lack access to the restorative power of nature. Despite growing research validating this capacity, there is a lack of articulated design knowledge of how such experiences can be created. Here, we explore a technologically-mediated support for mental well-being through an agile design approach informed by self-transcendent emotion theory and transformative experience design. We developed Awedyssey as a sensory deprivation countermeasure experience in virtual reality (VR) and as a part of a public exhibition. We gathered qualitative feedback through interviews, diaries, and surveys. From this data we identified design tensions for designing complex emotional experiences when considering the diversity of individuals and dual effects of designing for realism, autonomy, vastness, and comfort.
PosterTalk: Expanding Participatory Agency in Public Survey Platforms via Middle-Out Gatekeeping

Public surveys intend to integrate civic values into official decision-making. Because such surveys are typically infrastructured from the top-down, citizens’ participatory agency is often reduced to reacting to concerns that may not align with their bottom-up needs. This paper presents PosterTalk, a physically situated and digitally facilitated middle-out survey platform that explicitly involved bottom-up citizens in its gatekeeping activities. Over a four months in-the-wild deployment, PosterTalk accumulated 41 inquiries and 420 responses spanning over 13 civic themes. Its emerging survey content directly influenced the local policy agenda of the facilitating neighbourhood committee, and nudged them to reassess their civic representativeness. By critically analysing the different survey content and stakeholder engagements, this paper contributes an encompassing middle-out framework to guide and benchmark participatory agency in future public survey deployments and offers four guidelines for diversifying bottom-up civic agendas, readjusting top-down representation, avoiding bottom-up misperceptions of power, and promoting top-down introspection.
“I'm a little less inclined to do it”: How Afterschool Programs’ Culture Impact Co-Design Processes and Outcomes

The importance of considering local context and partnering with target users is well established in co-design. Less common is an examination of the adaptations needed when deploying the same co-design program across heterogenous settings to maximize program efficacy and equity. We report on our experience co-designing educational games with six culturally and socioeconomically diverse afterschool sites over two years, and insights from interviewing ten program administrators across all sites. We found that even within the same afterschool program network, site differences in organizational culture and resources impacted the effectiveness of co-design programs, the co-design output, and expectations for student engagement. We characterize our afterschool partners into different archetypes – Safe Havens, Recreation Centers, Homework Helpers, and STEM Enrichment Centers. We provide recommendations for conducting co-design at each archetype and reflect on strategies for increasing equitable partnerships between researchers and afterschool centers.
Designing Voice-First Ambient Interfaces to Support Aging in Place

We focus on the stories of five older adults who became voice assistant users through our study, and with whom we speculated about future interfaces through two design probes, one for health data reporting and one for positive reminiscing. We delivered a voice-first ambient interface (VFAI) to each participant, and closely observed participants’ journeys through periodic themed interviews (16 hours, 21 minutes of transcribed recordings), usage log reviews (4,657 entries), and phone and text support. Participants’ lived experiences impacted their perceptions and interactions with their VFAI, fueling rich insights about how to design for diverse needs. For example, while one participant saw increased potential in the VFAI after interacting with the design probe for health data reporting, another was skeptical of using it to communicate with her doctor. We contribute an in-depth exploration of VFAIs to support aging in place, implications for design, and areas for future work for tailoring VFAIs towards enabling continuity of care in people’s homes.
Sensing Care Through Design: A Speculative Role-play Approach to "Living with" Sensor-supported Care Networks

Sensor networks are increasingly commonplace in visions of smart cities and future healthcare systems, promising greater efficiency and increased wellbeing. However, the design of these technologies remains focused on specific users and fragmented by context, overlooking the diversity of needs, wants and values present when technologies, people, and lived realities interact within instrumented spaces. In this paper we present a workshop method – Sensing Care – that can help researchers, interdisciplinary design and development teams, and potentially affected users, to explore what it takes to design for living with sensor technologies that intersect and interact across private and public spaces, through speculative scenarios and role play. Drawing from three deployments of the workshop, we discuss how this approach supports the design of future care-oriented sensor networks, and helps designers understand what it means to live with complex technologies as people traverse diverse contexts.
Validity of a Portable APDM Inertial Sensor System for Stride Time and Stride Length during Treadmill Walking

Objective: The purpose of this study was to compare the accuracy of stride time and stride length provided by a commercial APDM inertial sensor system (APDM) with the results of three dimensional motion capture system (3D motion) during treadmill walking. Method: Five healthy men participated in this experiment. All subjects walked on the treadmill for 3 minutes at their preferred walking speed. The 3D motion and the APDM were simultaneously used for extracting gait variables such as stride time and stride length. Mean difference and root mean squared (RMS) difference were used to compare the measured gait variables from the two measurement devices. The regression equation derived from the range of motion of the lower limb was also applied to correct the error of stride length. Results: The stride time extracted from the APDM was almost the same as that from the 3D motion (the mean difference and RMS difference were less than 0.0001 sec and 0.0085 sec, respectively). For stride length, mean difference and RMS difference were less than 0.1141 m and 0.1254 m, respectively. However, after correction of the stride length error using the derived regression equation, the mean difference and the RMS difference decreased to 0.0134 m and 0.0556 m or less, respectively. Conclusion: In this study, we confirmed the possibility of using the temporal variables provided from the APDM during treadmill walking. By applying the regression equation derived only from the range of motion provided by the APDM, the error of the spatial variable could be reduced. Although further studies are needed with additional subjects and various walking speeds, these results may provide the basic data necessary for using APDM in treadmill walking.
A Neural Network-based Low-cost Soft Sensor for Touch Recognition and Deformation Capture

We propose a novel, cost-effective soft sensor capable of detecting contact force, multiple touch points, and reflecting sensor interaction in real-time with a 3D virtual surface representation. Our fabrication process has been optimized for cost efficiency through careful material selection, utilization of automated machinery, and low-cost hardware. The sensor can be easily replicated without the need for complex laboratory equipment. The sensor employs trained neural network models for real-time signal translation into localization, force measurement, and deformation mapping. We have also developed an efficient data collection system that captures accurate 2D localization, force measurement, and 3D surface data to generate a high-quality pre-validated data set. This data set is filtered using prior knowledge before being fed to two neural network models. Our interactive prototype demonstrates the stability and accuracy of the low-cost soft sensor, delivering reliable results in both single-point and multi-point contact scenarios.
Your Text Is Hard to Read: Facilitating Readability Awareness to Support Writing Proficiency in Text Production

Allowing users of interactive systems to reflect on their task proficiency is often incidental. This is unfortunate, as communicating meaningful task-related proficiency feedback could improve users’ awareness of their abilities and their willingness to improve. To highlight the feasibility of this concept, we evaluated how different methods of readability feedback impacted users during a text production task. In general, our results showed that having access to readability feedback allowed participants to reflect on their task solving approach, facilitating the users’ understanding of their proficiency. Revision-based methods are less distracting for the user than continuous feedback methods, while still offering high efficacy. Further, feedback should be paired with a subtle form of gamification elements. We envision this reflection-oriented design to user proficiency to be applicable to a variety of interactive systems, allowing for an improved and engaging user experience.
SRI-B’s Systems for IWSLT 2023 Dialectal and Low-resource Track: Marathi-Hindi Speech Translation

This paper describes the speech translation systems SRI-B developed for the IWSLT 2023 Evaluation Campaign Dialectal and Low-resource track: Marathi-Hindi Speech Translation. We propose systems for both the constrained (systems are trained only on the datasets provided by the organizers) and the unconstrained conditions (systems can be trained with any resource). For both the conditions, we build end-to-end speech translation networks comprising of a conformer encoder and a transformer decoder. Under both the conditions, we leverage Marathi Automatic Speech Recognition (ASR) data to pre-train the encoder and subsequently train the entire model on the speech translation data. Our results demonstrate that pre-training the encoder with ASR data is a key step in significantly improving the speech translation performance. We also show that conformer encoders are inherently superior to its transformer counterparts for speech translation tasks. Our primary submissions achieved a BLEU% score of 31.2 on the constrained condition and 32.4 on the unconstrained condition. We secured the top position in the constrained condition and second position in the unconstrained condition.
DeTexD: A Benchmark Dataset for Delicate Text Detection

Over the past few years, much research has been conducted to identify and regulate toxic language. However, few studies have addressed a broader range of sensitive texts that are not necessarily overtly toxic. In this paper, we introduce and define a new category of sensitive text called “delicate text. ” We provide the taxonomy of delicate text and present a detailed annotation scheme. We annotate DeTexD, the first benchmark dataset for delicate text detection. The significance of the difference in the definitions is highlighted by the relative performance deltas between models trained each definitions and corpora and evaluated on the other. We make publicly available the DeTexD Benchmark dataset, annotation guidelines, and baseline model for delicate text detection.
NetEase.AI at SemEval-2023 Task 2: Enhancing Complex Named Entities Recognition in Noisy Scenarios via Text Error Correction and External Knowledge

Complex named entities (NE), like the titles of creative works, are not simple nouns and pose challenges for NER systems. In the SemEval 2023, Task 2: MultiCoNER II was proposed, whose goal is to recognize complex entities against out of knowledge-base entities and noisy scenarios. To address the challenges posed by MultiCoNER II, our team NetEase.AI proposed an entity recognition system that integrates text error correction system and external knowledge, which can recognize entities in scenes that contain entities out of knowledge base and text with noise. Upon receiving an input sentence, our systems will correct the sentence, extract the entities in the sentence as candidate set using the entity recognition model that incorporates the gazetteer information, and then use the external knowledge to classify the candidate entities to obtain entity type features. Finally, our system fused the multi-dimensional features of the candidate entities into a stacking model, which was used to select the correct entities from the candidate set as the final output. Our system exhibited good noise resistance and excellent entity recognition performance, resulting in our team’s first place victory in the Chinese track of MultiCoNER II.
Annotators-in-the-loop: Testing a Novel Annotation Procedure on Italian Case Law

The availability of annotated legal corpora is crucial for a number of tasks, such as legal search, legal information retrieval, and predictive justice. Annotation is mostly assumed to be a straightforward task: as long as the annotation scheme is well defined and the guidelines are clear, annotators are expected to agree on the labels. This is not always the case, especially in legal annotation, which can be extremely difficult even for expert annotators. We propose a legal annotation procedure that takes into account annotator certainty and improves it through negotiation. We also collect annotator feedback and show that our approach contributes to a positive annotation environment. Our work invites reflection on often neglected ethical concerns regarding legal annotation.
A Weakly-Supervised Learning Approach to the Identification of “Alternative Lexicalizations” in Shallow Discourse Parsing

Recently, the identification of free connective phrases as signals for discourse relations has received new attention with the introduction of statistical models for their automatic extraction. The limited amount of annotations makes it still challenging to develop well-performing models. In our work, we want to overcome this limitation with semi-supervised learning from unlabeled news texts. We implement a self-supervised sequence labeling approach and filter its predictions by a second model trained to disambiguate signal candidates. With our novel model design, we report state-of-the-art results and in addition, achieve an average improvement of about 5% for both exactly and partially matched alternativelylexicalized discourse signals due to weak supervision.
CSIRO Data61 Team at BioLaySumm Task 1: Lay Summarisation of Biomedical Research Articles Using Generative Models

Lay summarisation aims at generating a summary for non-expert audience which allows them to keep updated with latest research in a specific field. Despite the significant advancements made in the field of text summarisation, lay summarisation remains relatively under-explored. We present a comprehensive set of experiments and analysis to investigate the effectiveness of existing pre-trained language models in generating lay summaries. When evaluate our models using a BioNLP Shared Task, BioLaySumm, our submission ranked second for the relevance criteria and third overall among 21 competing teams.