Are Large Language Models Robust Coreference Resolvers?

Recent work on extending coreference resolution across domains and languages relies on annotated data in both the target domain and language. At the same time, pre-trained large language models (LMs) have been reported to exhibit strong zero- and few-shot learning abilities across a wide range of NLP tasks. However, prior work mostly studied this ability using artificial sentence-level datasets such as the Winograd Schema Challenge. In this paper, we assess the feasibility of prompt-based coreference resolution by evaluating instruction-tuned language models on difficult, linguistically-complex coreference benchmarks (e.g., CoNLL-2012). We show that prompting for coreference can outperform current unsupervised coreference systems, although this approach appears to be reliant on high-quality mention detectors. Further investigations reveal that instruction-tuned LMs generalize surprisingly well across domains, languages, and time periods; yet continued fine-tuning of neural models should still be preferred if small amounts of annotated examples are available

Aligning assessment with the needs of work-integrated learning: the challenges of authentic assessment in a complex context

Work-integrated learning (WIL) is a feature of university courses, both in professional areas, where it is commonplace, but also across many different disciplines. Assessment of WIL can be complex as it involves parties and settings external to the university, and it can be problematic because of difficulties in aligning learning activities during placements with what is or can be assessed by the university. This paper explores the relationship between students’ placement experiences and accompanying assessments in contexts where activities are tightly coupled with the curriculum, and in those where it is not. It draws on a qualitative analysis of student interviews and drawings by the interviewees of their WIL experiences, supplemented with analysis of unit guides. Our findings highlight that students’ perceptions of authenticity of assessment were undermined by misalignments between the student, university and industry. Assessment authenticity was perceived by students as based on alignment between their current and future selves in the assessment process, involvement of industry supervisors and relevance of placement activities to assessment activities. The paper discusses the complexity of coordination of educational activities with external partners, especially when one party drives assessment. It then suggests a reframing of WIL assessment to promote alignment and authenticity

Application of AHP algorithm on power distribution of load shedding in island microgrid

This paper proposes a method of load shedding in a microgrid system operated in an Island Mode, which is disconnected with the main power grid and balanced loss of the electrical power. This proposed method calculates the minimum value of the shed power with reference to renewable energy sources such as wind power generator, solar energy and the ability to control the frequency of the generator to restore the frequency to the allowable range and reduce the amount of load that needs to be shed. Computing the load importance factor (LIF) using AHP algorithm supports to determine the order of which load to be shed. The damaged outcome of load shedding, thus, will be noticeably reduced. The experimental results of this proposed method is demonstrated by simulating on IEEE 16-Bus microgrid system with six power sources

Federated Deep Reinforcement Learning-based Bitrate Adaptation for Dynamic Adaptive Streaming over HTTP

In video streaming over HTTP, the bitrate adaptation selects the quality of video chunks depending on the current network condition. Some previous works have applied deep reinforcement learning (DRL) algorithms to determine the chunk's bitrate from the observed states to maximize the quality-of-experience (QoE). However, to build an intelligent model that can predict in various environments, such as 3G, 4G, Wifi, \textit{etc.}, the states observed from these environments must be sent to a server for training centrally. In this work, we integrate federated learning (FL) to DRL-based rate adaptation to train a model appropriate for different environments. The clients in the proposed framework train their model locally and only update the weights to the server. The simulations show that our federated DRL-based rate adaptations, called FDRLABR with different DRL algorithms, such as deep Q-learning, advantage actor-critic, and proximal policy optimization, yield better performance than the traditional bitrate adaptation methods in various environments.Comment: 13 pages, 1 colum

Combination Antifungal Therapy for Cryptococcal Meningitis

Background Combination antifungal therapy (amphotericin B deoxycholate and flucytosine) is the recommended treatment for cryptococcal meningitis but has not been shown to reduce mortality, as compared with amphotericin B alone. We performed a randomized, controlled trial to determine whether combining flucytosine or high-dose fluconazole with high-dose amphotericin B improved survival at 14 and 70 days. Methods We conducted a randomized, three-group, open-label trial of induction therapy for cryptococcal meningitis in patients with human immunodeficiency virus infection. All patients received amphotericin B at a dose of 1 mg per kilogram of body weight per day; patients in group 1 were treated for 4 weeks, and those in groups 2 and 3 for 2 weeks. Patients in group 2 concurrently received flucytosine at a dose of 100 mg per kilogram per day for 2 weeks, and those in group 3 concurrently received fluconazole at a dose of 400 mg twice daily for 2 weeks. Results A total of 299 patients were enrolled. Fewer deaths occurred by days 14 and 70 among patients receiving amphotericin B and flucytosine than among those receiving amphotericin B alone (15 vs. 25 deaths by day 14; hazard ratio, 0.57; 95% confidence interval [CI], 0.30 to 1.08; unadjusted P=0.08; and 30 vs. 44 deaths by day 70; hazard ratio, 0.61; 95% CI, 0.39 to 0.97; unadjusted P=0.04). Combination therapy with fluconazole had no significant effect on survival, as compared with monotherapy (hazard ratio for death by 14 days, 0.78; 95% CI, 0.44 to 1.41; P=0.42; hazard ratio for death by 70 days, 0.71; 95% CI, 0.45 to 1.11; P=0.13). Amphotericin B plus flucytosine was associated with significantly increased rates of yeast clearance from cerebrospinal fluid (−0.42 log10 colony-forming units [CFU] per milliliter per day vs. −0.31 and −0.32 log10 CFU per milliliter per day in groups 1 and 3, respectively; P<0.001 for both comparisons). Rates of adverse events were similar in all groups, although neutropenia was more frequent in patients receiving a combination therapy. Conclusions Amphotericin B plus flucytosine, as compared with amphotericin B alone, is associated with improved survival among patients with cryptococcal meningitis. A survival benefit of amphotericin B plus fluconazole was not found