Miracle-dz/UniFAR-FTUs huggingface dataset:
Aligning Large Language Models for Enhancing Psychiatric Interviews through Symptom Delineation and Summarization

Recent advancements in Large Language Models (LLMs) have accelerated their usage in various domains. Given the fact that psychiatric interviews are goal-oriented and structured dialogues between the professional interviewer and the interviewee, it is one of the most underexplored areas where LLMs can contribute substantial value. Here, we explore the use of LLMs for enhancing psychiatric interviews, by analyzing counseling data from North Korean defectors with traumatic events and mental health issues. Specifically, we investigate whether LLMs can (1) delineate the part of the conversation that suggests psychiatric symptoms and name the symptoms, and (2) summarize stressors and symptoms, based on the interview dialogue transcript. Here, the transcript data was labeled by mental health experts for training and evaluation of LLMs. Our experimental results show that appropriately prompted LLMs can achieve high performance on both the symptom delineation task and the summarization task. This research contributes to the nascent field of applying LLMs to psychiatric interview and demonstrates their potential effectiveness in aiding mental health practitioners.
The efficacy of the frontline platinum-based combination chemotherapy in malignant peritoneal mesothelioma.

OBJECTIVE Malignant peritoneal mesothelioma is a rare neoplasm that accounts for ∼1 per 1 million has limited data regarding its frontline therapy. We investigated the treatment outcomes in patients with malignant peritoneal mesothelioma receiving frontline cisplatin-based combination chemotherapy. METHODS We analyzed 14 patients with malignant peritoneal mesothelioma who had been treated by frontline cisplatin-based combination chemotherapy between January 2005 and March 2009. The chemotherapeutic agent added to platinum was gemcitabine in one patient, cyclophosphamide-doxorubicin in three patients and pemetrexed in 10 patients. RESULTS The confirmed overall response rate was 35.7% and the disease control rate was 71.4%. In all patients, two complete responses and three partial responses were observed (overall response rate, 35.7%). Stable disease was observed in five patients (35.7%). The median progression free survival was 4.4 months (95% CI, 0.6-9.0) and the median overall survival was 20.1 months (95% CI, 12.7-28.5). There was significant differences for progression free survival (P = 0.031) according to the different chemotherapeutic agents (pemetrexed versus non-pemetrexed agents) added to platinum. Grade 3 or 4 hematologic toxicities included leukopenia in one patient and anemia in three patients. There were no Grade 3 or 4 non-hematologic toxicities or treatment-related deaths. CONCLUSION The platinum-based combination chemotherapy showed moderate activity and a favorable toxicity profile as a frontline treatment for patients with malignant peritoneal mesothelioma. Pemetrexed in combination with platinum showed improved survival outcomes as compared with other combination regimens combined with platinum.
Corporate Governance, Accounting Conservatism, and Manipulation

We develop a model to analyze how board governance affects firms' financial reporting choices, and managers' incentives to manipulate accounting reports. In our setting, ceteris paribus, conservative accounting is desirable because it allows the board of directors to better oversee the firm's investment decisions. This feature of conservatism, however, causes the manager to manipulate the accounting system to mislead the board and distort its decisions. Effective reporting oversight curtails managers' ability to manipulate, which increases the benefits of conservative accounting and simultaneously reduces its costs. Our model predicts that stronger reporting oversight leads to greater accounting conservatism, manipulation, and investment efficiency.
Estimating the detection of antineutrinos in the future Brazilian neutron source

In line with the tradition of employing nuclear reactors for neutrino physics research, we present potential experiments that could be undertaken using the new Brazilian neutron source, the Brazilian Multipurpose Reactor (RMB). This upcoming facility has clearly defined objectives, including the production of radiopharmaceuticals for the healthcare system, material irradiation testing, and research using neutron beams to investigate the micro and mesostructures of various materials. However, we anticipate that the RMB will generate up to $15,500$ antineutrinos daily, potentially expanding the facility's scope to include neutrino physics experiments involving beta and inverse beta reactions. The modern operational conditions of the RMB and its location in the Iper\'o-Brazil region may also enable neutrino oscillation experiments and the determination of the $\theta_{13}$ parameter, as well as support safeguards activities by the International Atomic Energy Agency (IAEA), allowing for the monitoring of nuclear fuel burning cycles and isotope fractions. We discuss the potential utilization of the RMB as an antineutrino source, proposing experiments and addressing associated technical limitations.
The First-Line Investigation of Postmenopausal Bleeding: Transvaginal Ultrasound Scanning and Endometrial Biopsy May Be Enough

Postmenopausal bleeding is a common problem in clinical practice with significant implications; however, the diagnostic strategies often vary among different centers. In the recent years, there was a trend to adopt less invasive procedures without compromising efficacy and safety. There is continuous debate about the different available modalities for investigation, and a large number of studies were conducted to define their roles. Some authors recommended that hysteroscopy should be the standard investigation procedure for women with postmenopausal bleeding. Nonetheless, there is a strong evidence to suggest that transvaginal ultrasound scanning, with or without endometrial biopsy, is a safe and more cost-effective initial tool in investigating these women. This commentary highlights this important issue and discusses the role of these different modalities.
Transmission line parameter identification using PMU measurements

Accurate knowledge of transmission line (TL) impedance parameters helps to improve accuracy in relay settings and power flow modeling. To improve TL parameter estimates, various algorithms have been proposed in the past to identify TL parameters based on measurements from Phasor Measurement Units (PMUs). These methods are based on the positive sequence TL models and can generate accurate positive sequence impedance parameters for a fully transposed TL when measurement noise is absent; however, these methods may generate erroneous parameters when the TLs are not fully transposed or when measurement noise is present. PMU field-measure data are often corrupted with noise and this noise is problematic for all parameter identification algorithms, particularly so when applied to short TLs. This paper analyzes the limitations of the positive sequence TL model when used for parameter estimation of TLs that are untransposed and proposes a novel method using linear estimation theory to identify TL parameters more reliably. This method can be used for the most general case: short/long lines that are fully transposed or untransposed and have balanced/unbalance loads. Besides the positive/negative sequence impedance parameters, the proposed method can also be used to estimate the zero sequence parameters and the mutual impedances between different sequences. This paper also examines the influence of noise in the PMU data on the calculation of TL parameters. Several case studies are conducted based on simulated data from ATP to validate the effectiveness of the new method. Through comparison of the results generated by this novel method and several other methods, the effectiveness of the proposed approach is demonstrated. Copyright © 2010 John Wiley & Sons, Ltd.
Primary thyroid lymphoma: a review of recent developments in diagnosis and histology-driven treatment

Purpose of review Primary thyroid lymphoma (PTL) is a rare but clinically important malignancy of the thyroid. This article reviews the diagnosis, histologic subtypes, pathogenesis, and treatment of PTL, with the objective of optimizing diagnosis and management of the disease. Recent findings Recent studies have shed light on the clinicopathologic features of the histologic subtypes of PTL. Analysis of the pathogenesis of PTL indicates that both antigenic stimulation in the setting of Hashimoto's thyroiditis and aberrant somatic hypermutation may play a role. The first large, population-based study of PTL indicated that age, stage, histologic subtype, and treatment modality have prognostic implications. The U.S. Food and Drug Administration approval of rituximab for non-Hodgkin's lymphoma has improved standard chemotherapeutic options, as data on efficacy in nodal and other extranodal lymphomas have been extrapolated to PTL. Summary Advances in the understanding of the histologic subtypes of PTL have led to more accurate diagnosis and tailored treatments. The introduction of a new chemotherapeutic agent has expanded the treatment paradigm for PTL. Important prognostic indicators of survival for patients with PTL have been confirmed in the first population-based study. This article emphasizes current diagnostic and treatment approaches and discusses the potential for future developments.
CatmullRom Splines-Based Regression for Image Forgery Localization

IFL (Image Forgery Location) helps secure digital media forensics. However, many methods suffer from false detections (i.e., FPs) and inaccurate boundaries. In this paper, we proposed the CatmullRom Splines-based Regression Network (CSR-Net), which first rethinks the IFL task from the perspective of regression to deal with this problem. Specifically speaking, we propose an adaptive CutmullRom splines fitting scheme for coarse localization of the tampered regions. Then, for false positive cases, we first develop a novel re-scoring mechanism, which aims to filter out samples that cannot have responses on both the classification branch and the instance branch. Later on, to further restrict the boundaries, we design a learnable texture extraction module, which refines and enhances the contour representation by decoupling the horizontal and vertical forgery features to extract a more robust contour representation, thus suppressing FPs. Compared to segmentation-based methods, our method is simple but effective due to the unnecessity of post-processing. Extensive experiments show the superiority of CSR-Net to existing state-of-the-art methods, not only on standard natural image datasets but also on social media datasets.
The impact of designing and evaluating molecular animations on how well middle school students understand the particulate nature of matter

In this study, we investigated whether the understanding of the particulate nature of matter by students was improved by allowing them to design and evaluate molecular animations of chemical phenomena. We developed Chemation, a learner-centered animation tool, to allow seventh-grade students to construct flipbook-like simple animations to show molecular models and dynamic processes. Eight classes comprising 271 students were randomly assigned to three treatments in which students used Chemation to (1) design, interpret, and evaluate animations, (2) only design and interpret animations, or (3) only view and interpret teacher-made animations. We employed 2-factor analysis of covariance and calculated effect sizes to examine the impact of the three treatments on student posttest performances and on student-generated animations and interpretations during class. We used the pretest data as a covariate to reduce a potential bias related to students' prior knowledge on their learning outcomes. The results indicate that designing animations coupled with peer evaluation is effective at improving student learning with instructional animation. On the other hand, the efficacy of allowing students to only design animations without peer evaluation is questionable compared with allowing students to view animations. © 2009 Wiley Periodicals, Inc. Sci Ed94:73–94, 2010
Solving Poisson’s equation with interior conditions

We consider the problem of extending the solution of a particular two-dimensional Poisson equation to a larger domain. This problem is related to the problem of putting a non-neutral plasma into equilibrium by applying a suitable wall potential, and to similar problems in two-dimensional fluid dynamics. While one cannot always find an exact solution, one can always find an approximate solution if the plasma has no holes.
of Nebraska - Lincoln DigitalCommons@University of Nebraska - Lincoln How to develop an effective obstetric checklist How to develop an effective obstetric checklist

Checklists to guide critical procedures are becoming an increasingly important part of medical practice. These tools have proved effective in improving outcome in a variety of medical settings, including obstetrics. In this review, we outline essential principles of successful checklist creation and implementation and review our experience with checklist development in a worldwide, multiinstitutional health care delivery system.
Apportioning Development Effort in a Probabilistic LR Parsing System Through Evaluation

We describe an implemented system for robust domain-independent syntactic parsing of English, using a unification-based grammar of part-ofspeech and punctuation labels coupled with a probabilistic LR parser. We present evaluations of the system’s performance along several different dimensions; these enable us to assess the contribution that each individual part is making to the success of the system as a whole, and thus prioritise the effort to be devoted to its further enhancement. Currently, the system is able to parse around 80% of sentences in a substantial corpus of general text containing a number of distinct genres. On a random sample of 250 such sentences the system has a mean crossing bracket rate of 0.71 and recall and precision of 83% and 84% respectively when evaluated against manually-disambiguated analyses.
Globalization and supply chain networks: the auto industry in Brazil and India

The automotive industries of emerging markets were transformed in the 1990s by trade liberalization and large investments by global assemblers. The impact of these investments was decisively influenced by government policies aimed at promoting local production and assembly of vehicles and by changing assembler—supplier relationships in the global auto industry. The cases of Brazil and India show how the assemblers created new linkages between operations in emerging markets and their global operations through the adoption of follow design and follow sourcing policies. These policies were hard to implement in practice, but they decisively transformed the components industries in both countries.
Intra- and Inter-group Optimal Transport for User-Oriented Fairness in Recommender Systems

Recommender systems are typically biased toward a small group of users, leading to severe unfairness in recommendation performance, i.e., User-Oriented Fairness (UOF) issue. Existing research on UOF exhibits notable limitations in two phases of recommendation models. In the training phase, current methods fail to tackle the root cause of the UOF issue, which lies in the unfair training process between advantaged and disadvantaged users. In the evaluation phase, the current UOF metric lacks the ability to comprehensively evaluate varying cases of unfairness. In this paper, we aim to address the aforementioned limitations and ensure recommendation models treat user groups of varying activity levels equally. In the training phase, we propose a novel Intra- and Inter-GrOup Optimal Transport framework (II-GOOT) to alleviate the data sparsity problem for disadvantaged users and narrow the training gap between advantaged and disadvantaged users. In the evaluation phase, we introduce a novel metric called ? -UOF, which enables the identification and assessment of various cases of UOF. This helps prevent recommendation models from leading to unfavorable fairness outcomes, where both advantaged and disadvantaged users experience subpar recommendation performance. We conduct extensive experiments on three real-world datasets based on four backbone recommendation models to prove the effectiveness of ? -UOF and the efficiency of our proposed II-GOOT.
Exception Handling for Repair in Service-Based Processes

This paper proposes a self-healing approach to handle exceptions in service-based processes and to repair the faulty activities with a model-based approach. In particular, a set of repair actions is defined in the process model, and repairability of the process is assessed by analyzing the process structure and the available repair actions. During execution, when an exception arises, repair plans are generated by taking into account constraints posed by the process structure, dependencies among data, and available repair actions. The paper also describes the main features of the prototype developed to validate the proposed repair approach for composed Web services; the self-healing architecture for repair handling and the experimental results are illustrated.
A New Mechanism for Eliminating Implicit Conflict in Graph Contrastive Learning

Graph contrastive learning (GCL) has attracted considerable attention because it can self-supervisedly extract low-dimensional representation of graph data. InfoNCE-based loss function is widely used in graph contrastive learning, which pulls the representations of positive pairs close to each other and pulls the representations of negative pairs away from each other. Recent works mainly focus on designing new augmentation methods or sampling strategies. However, we argue that the widely used InfoNCE-based methods may contain an implicit conflict which seriously confuses models when learning from negative pairs. This conflict is engendered by the encoder's message-passing mechanism and the InfoNCE loss function. As a result, the learned representations between negative samples cannot be far away from each other, compromising the model performance. To our best knowledge, this is the first time to report and analysis this conflict of GCL. To address this problem, we propose a simple but effective method called Partial ignored Graph Contrastive Learning (PiGCL). Specifically, PiGCL first dynamically captures the conflicts during training by detecting the gradient of representation similarities. It then enables the loss function to ignore the conflict, allowing the encoder to adaptively learn the ignored information without self-supervised samples. Extensive experiments demonstrate the effectiveness of our method.
Learning Small Decision Trees with Few Outliers: A Parameterized Perspective

Decision trees is a fundamental tool in machine learning for representing, classifying, and generalizing data. It is desirable to construct ``small'' decision trees, by minimizing either the size (s) or the depth (d) of the decision tree (DT). Recently, the parameterized complexity of Decision Tree Learning has attracted a lot of attention. We consider a generalization of Decision Tree Learning where given a classification instance E and an integer t, the task is to find a ``small'' DT that disagrees with E in at most t examples. We consider two problems: DTSO and DTDO, where the goal is to construct a DT minimizing s and d, respectively. We first establish that both DTSO and DTDO are W[1]-hard when parameterized by s+y and d+y, respectively, where y is the maximum number of features in which two differently labeled examples can differ. We complement this result by showing that these problems become FPT if we include the parameter t. We also consider the kernelization complexity of these problems and establish several positive and negative results for both DTSO and DTDO.
Convex Functions: Constructions, Characterizations and Counterexamples

Preface 1. Why convex? 2. Convex functions on Euclidean spaces 3. Finer structure of Euclidean spaces 4. Convex functions on Banach spaces 5. Duality between smoothness and strict convexity 6. Further analytic topics 7. Barriers and Legendre functions 8. Convex functions and classifications of Banach spaces 9. Monotone operators and the Fitzpatrick function 10. Further remarks and notes References Index.
[Early erythropoietin deficiency in diabetic kidney lesion].

AIM To evaluate the renal production of erythropoietin (EPO) in relation to filtration function in patients with diabetic kidney lesion. SUBJECTS AND METHODS The investigation enrolled 183 patients with types 1 and 2 diabetes mellitus (DM), of whom 128 were diagnosed as having diabetic kidney lesion. Serum EPO levels were measured by enzyme immunoassay. Patients who had a glomerular filtration rate (GFR) of below 15 ml/min/1.73 m2 and received erythropoiesis-stimulating agents were excluded from the investigation. RESULTS The mean serum EPO levels in the patients with diabetic kidney lesion did not vary with the presence or absence of anemia, the degree of albuminuria, or GFR. A physiological inverse relationship was found between the level of EPO and that of hemoglobin in the blood of the patients with DM without kidney disease and in those with renal lesion and GFR > or = 60 ml/min/1.73 m2. The magnitude of the association of the values increased as GFR was higher. The level of EPO was found to be unassociated with hemoglobin in patients with GFR < 60 ml/min/1.73 m2. CONCLUSION In the patients with diabetic kidney lesion, serum EPO concentrations did not depend on the stage of chronic kidney disease and the degree of albuminuria in spite of more severe anemia as renal failure progressed. These patients showed inadequate EPO production just in early diminished renal filtration function.
Nanomedicine strategy for optimizing delivery to outer hair cells by surface-modified poly(lactic/glycolic acid) nanoparticles with hydrophilic molecules

Targeted drug delivery to outer hair cells (OHCs) in the cochlea by nanomedicine strategies forms an effective therapeutic approach for treating hearing loss. Surface chemistry plays a deciding role in nanoparticle (NP) biodistribution, but its influence on such distribution in the cochlea remains largely unknown. Herein, we report the first systematic comparison of poly(lactic/glycolic acid) nanoparticles (PLGA NPs) with or without surface modification of hydrophilic molecules for optimizing the delivery to OHCs both in vitro and in vivo. NPs that were surface modified with poloxamer 407 (P407), chitosan, or methoxy poly(ethylene glycol) and the unmodified NPs were highly biocompatible with L929 and House Ear Institute-organ of Corti 1 cells as well as cochlear tissues. Interestingly, among all the examined NPs, P407-PLGA NPs showed the greatest cellular uptake and prominent fluorescence in cochlear imaging. More importantly, we provide novel evidence that the surface-modified NPs reached the organ of Corti and were transported into the OHCs at a higher level. Together, these observations suggest that surface modification with hydrophilic molecules will allow future clinical applications of PLGA NPs, especially P407-PLGA NPs, in efficient hearing loss therapy.