Miracle-dz/UniFAR-FTUs huggingface dataset:
Retrieval-augmented Generation to Improve Math Question-Answering: Trade-offs Between Groundedness and Human Preference

For middle-school math students, interactive question-answering (QA) with tutors is an effective way to learn. The flexibility and emergent capabilities of generative large language models (LLMs) has led to a surge of interest in automating portions of the tutoring process - including interactive QA to support conceptual discussion of mathematical concepts. However, LLM responses to math questions can be incorrect or mismatched to the educational context - such as being misaligned with a school's curriculum. One potential solution is retrieval-augmented generation (RAG), which involves incorporating a vetted external knowledge source in the LLM prompt to increase response quality. In this paper, we designed prompts that retrieve and use content from a high-quality open-source math textbook to generate responses to real student questions. We evaluate the efficacy of this RAG system for middle-school algebra and geometry QA by administering a multi-condition survey, finding that humans prefer responses generated using RAG, but not when responses are too grounded in the textbook content. We argue that while RAG is able to improve response quality, designers of math QA systems must consider trade-offs between generating responses preferred by students and responses closely matched to specific educational resources.
Index-Modulated Metasurface Transceiver Design Using Reconfigurable Intelligent Surfaces for 6G Wireless Networks

Higher spectral and energy efficiencies are the envisioned defining characteristics of high data-rate sixth-generation (6G) wireless networks. One of the enabling technologies to meet these requirements is index modulation (IM), which transmits information through permutations of indices of spatial, frequency, or temporal media. In this paper, we propose novel electromagnetics-compliant designs of reconfigurable intelligent surface (RIS) apertures for realizing IM in 6G transceivers. We consider RIS modeling and implementation of spatial and subcarrier IMs, including beam steering, spatial multiplexing, and phase modulation capabilities. Numerical experiments for our proposed implementations show that the bit error rates obtained via RIS-aided IM outperform traditional implementations. We further establish the programmability of these transceivers to vary the reflection phase and generate frequency harmonics for IM through full-wave electromagnetic analyses of a specific reflect-array metasurface implementation.
Divide, Conquer and Verify: Improving Symbolic Execution Performance

Symbolic Execution is a formal method that can be used to verify the behavior of computer programs and detect software vulnerabilities. Compared to other testing methods such as fuzzing, Symbolic Execution has the advantage of providing formal guarantees about the program. However, despite advances in performance in recent years, Symbolic Execution is too slow to be applied to real-world software. This is primarily caused by the \emph{path explosion problem} as well as by the computational complexity of SMT solving. In this paper, we present a divide-and-conquer approach for symbolic execution by executing individual slices and later combining the side effects. This way, the overall problem size is kept small, reducing the impact of computational complexity on large problems.
Classifying Whole Slide Images: What Matters?

Recently there have been many algorithms proposed for the classification of very high resolution whole slide images (WSIs). These new algorithms are mostly focused on finding novel ways to combine the information from small local patches extracted from the slide, with an emphasis on effectively aggregating more global information for the final predictor. In this paper we thoroughly explore different key design choices for WSI classification algorithms to investigate what matters most for achieving high accuracy. Surprisingly, we found that capturing global context information does not necessarily mean better performance. A model that captures the most global information consistently performs worse than a model that captures less global information. In addition, a very simple multi-instance learning method that captures no global information performs almost as well as models that capture a lot of global information. These results suggest that the most important features for effective WSI classification are captured at the local small patch level, where cell and tissue micro-environment detail is most pronounced. Another surprising finding was that unsupervised pre-training on a larger set of 33 cancers gives significantly worse performance compared to pre-training on a smaller dataset of 7 cancers (including the target cancer). We posit that pre-training on a smaller, more focused dataset allows the feature extractor to make better use of the limited feature space to better discriminate between subtle differences in the input patch.
LLM Based Multi-Document Summarization Exploiting Main-Event Biased Monotone Submodular Content Extraction

Multi-document summarization is a challenging task due to its inherent subjective bias, highlighted by the low inter-annotator ROUGE-1 score of 0.4 among DUC-2004 reference summaries. In this work, we aim to enhance the objectivity of news summarization by focusing on the main event of a group of related news documents and presenting it coherently with sufficient context. Our primary objective is to succinctly report the main event, ensuring that the summary remains objective and informative. To achieve this, we employ an extract-rewrite approach that incorporates a main-event biased monotone-submodular function for content selection. This enables us to extract the most crucial information related to the main event from the document cluster. To ensure coherence, we utilize a fine-tuned Language Model (LLM) for rewriting the extracted content into a coherent text. The evaluation using objective metrics and human evaluators confirms the effectiveness of our approach, as it surpasses potential baselines, demonstrating excellence in both content coverage, coherence, and informativeness.
Organochlorine pesticide residues in bed sediments of the San Joaquin River, California.

: Bed sediments of the San Joaquin River and its tributaries were sampled during October 7–11, 1985, and analyzed for organochiorine pesticide residues in order to determine their areal distribution and to evaluate and prioritize needs for further study. Residues of DDD, DDE, DDT, and dieldrin are widespread in the fine-grained bed sediments of the San Joaquin River and its tributaries despite little or no use of these pesticides for more than 15 years. The San Joaquin River has among the highest bed-sediment concentrations of DDD, DDE, DDT, and dieldrin residues of major rivers in the United States. Concentrations of all four pesticides were correlated with each other and with the amount of organic carbon and fine-grained particles in the bed sediments. The highest concentrations occurred in bed sediments of westside tributary streams. Potential tributary loads of DDD, DDE, DDT, and dieldrin to the San Joaquin River were computed from bed-sediment concentrations and data on streamfiow and suspended-sediment concentration in order to identify the general magnitude of differences between streams and to determine study priorities. The estimated loads indicate that the most important sources of residues during the study period were Salt Slough because of a high load of fine sediment, and Newman Wasteway, Orestimba Creek, and Hospital Creek because of high bed-sediment concentrations. Generally, the highest estimated loads of DDD, DDE, DDT, and dieldrin were in Orestimba and Hospital Creeks.
Constraint-Conditioned Policy Optimization for Versatile Safe Reinforcement Learning

Safe reinforcement learning (RL) focuses on training reward-maximizing agents subject to pre-defined safety constraints. Yet, learning versatile safe policies that can adapt to varying safety constraint requirements during deployment without retraining remains a largely unexplored and challenging area. In this work, we formulate the versatile safe RL problem and consider two primary requirements: training efficiency and zero-shot adaptation capability. To address them, we introduce the Conditioned Constrained Policy Optimization (CCPO) framework, consisting of two key modules: (1) Versatile Value Estimation (VVE) for approximating value functions under unseen threshold conditions, and (2) Conditioned Variational Inference (CVI) for encoding arbitrary constraint thresholds during policy optimization. Our extensive experiments demonstrate that CCPO outperforms the baselines in terms of safety and task performance while preserving zero-shot adaptation capabilities to different constraint thresholds data-efficiently. This makes our approach suitable for real-world dynamic applications.
Culture, Behaviour, and the 8200 cal BP Cold Event : Organisational Change and Culture Environment Dynamics in Late Mesolithic Northern Fennoscandia

This dissertation focuses on Late Mesolithic (ca. 8450–6850 cal BP) lithic technological changes in the northernmost parts of Finland, Norway, and Sweden and on the relationship between these changes and the 8.2 ka climate event that was caused by a disruption in the North Atlantic Thermohaline circulation. The study uses a framework derived from Darwinian evolutionary theory and acknowledges the effects of both environmental constraints and socially transmitted information, i.e. , culture, in the way lithic technology was organised in the studied region. The study discusses whether climatic cooling and its effects on the biotic environment could explain the way lithic technology and settlement patterns were reorganised during the Late Mesolithic. The dissertation takes an organisational approach to the study of past cultural change and seeks to understand changes in prehistoric material culture by studying lithic technology and settlement configuration using lithic technological, statistical, and spatial analyses. The results suggest that Late Mesolithic coastal communities were affected by a marked decrease in marine productivity that resulted from the cooling caused by the 8.2 ka event and a subsequent cold episode at ca. 7700 cal BP. It is concluded that the technological changes that occurred during the marine cooling were a result of developments that led to increased use of terrestrial resources and an accompanying long-distance coast/inland residential mobility pattern. The study contributes to a wider field of research into past climate change as a factor in prehistoric ecological, cultural, and behavioural change and provides reference material for studies on the impacts of future climate change on human communities. The results suggest that in northernmost Fennoscandia, the marine ecosystem is particularly sensitive to disturbances in the North Atlantic oceanographic system. In addition, the study provides new knowledge concerning the relationships between raw material availability, lithic technology, and culture. This new knowledge is widely applicable in research on the way lithic technology was organised in relation to other behavioural and organisational dimensions in past human adaptations.
A chlorophyll c synthase widely co-opted by phytoplankton

Marine and terrestrial photosynthesis exhibit a schism in the accessory chlorophyll (Chl) that complements the function of Chl a: Chl b for green plants versus Chl c for most eukaryotic phytoplankton. The enzymes that mediate Chl c biosynthesis have long remained elusive. In this work, we identified the CHLC dioxygenase (Phatr3_J43737) from the marine diatom Phaeodactylum tricornutum as the Chl c synthase. The chlc mutants lacked Chl c, instead accumulating its precursors, and exhibited growth defects. In vitro, recombinant CHLC protein converted these precursors into Chl c, thereby confirming its identity. Phylogenetic evidence demonstrates conserved use of CHLC across phyla but also the existence of distinct Chl c synthases in different algal groups. Our study addresses a long-outstanding question with implications for both contemporary and ancient marine photosynthesis. Description Editor’s summary Chlorophylls, the key photosynthetic pigments in plants, algae, and cyanobacteria, come in a few different types depending on the identity, habitat, and needs of the organism. Most algae use chlorophyll c as an accessory pigment, but the biosynthetic origin and physiological importance of this pigment is not well understood. Jiang et al. used recently developed genetic tools to identify a gene coding for a dioxygenase that is involved in producing chlorophyll c. They confirmed the activity of this enzyme with in vitro assays and found that algae with mutation in the gene had disrupted pigment levels and impaired photosynthetic activity. —Michael A. Funk Genetics uncover an enzyme that makes the dominant accessory chlorophyll in eukaryotic phytoplankton.
Resolving Actor Coreferences in Hindi Narrative Text

An important aspect of understanding narrative text is identiﬁcation of actors, its mentions and coreferences among them. Coreference Resolution in Hindi is a relatively under-explored area. In this paper, we focus on the task of resolving corefer-ences of actor mentions in Hindi narrative text. We propose a linguistically grounded approach for the task using Markov Logic Networks (MLN). Our approach outperforms two strong baselines on a publicly available dataset and 4 other manually created datasets.
Summarization of Table Citations from Text

This paper reports our attempt to design a corpus for table content summarization, abstractive and extractive, where a table is cited in a scientiﬁc document. We have utilized 200 scientiﬁc publications in the computer science ﬁeld covering 10 different domains like machine learning, automatic summarization etc. to construct the corpus. The dataset preparation for this work has been extremely daunting due to the nature of the data. The prepared dataset has been used for training, testing and evaluation. Manual annotators have been employed to validate the gold standard data in corpus. Moreover, we have also proposed two systems based on TF-IDF approach and Transition point approach to generate an extractive summarization system. The similarity score between the system generated summaries and gold standard data is calculated us-ing standard metrics to evaluate the quality of the generated extractive summary. Finally, we have documented our observation have presented an error analysis of the system using standard metrics viz. BLEU and ROGUE.
Does Curriculum Learning help Deep Learning for Natural Language Generation?

Natural Language Generation (NLG) is a challenging problem in the field of Artificial Intelligence. The difficulty stems from the natural language’s flexibility to convey the same message in different ways. Psycholinguists have always believed that learning simpler sentences early can lead to complex sentence creation using the same knowledge.This is also the intuition behind curriculum learning. Thus, in this paper, we investigate the use of curriculum learning for natural language generation. We show that curriculum learning is a promising training methodology for deep learning systems for NLG. We show this by reporting improvements obtained using i) a particular curriculum strategy and ii) augmenting data using curriculum logic. We use TGen, a deep learning based NLG system, for experimentation. We use 5 metrics for NLG evaluation and 8 metrics for readability evaluation. Our quantitative and qualitative evaluation shows that the system trained using curriculum methodology produces better quality text as compared to the sys-tem trained on normal data.
Khasi Shallow Parser

A shallow parser is constructed for Khasi an Austro-Asiatic language, where noun phrases and verb phrases are identified. Formulation of what constitutes a Khasi noun phrase or verb phrase is carried out manually and marked in a corpus consisting of words already tagged with their corresponding part-of-speech tags. The parser is the first of its kind for the language, where the training corpus comprises of 24,194 chunks of noun and verb chunks out of a total of 3,997 sentences and 86,087 tokens. The approach in developing the parser is taken as a tagging problem using the Hidden Markov Model (HMM) and the results obtained have shown that a shallow parser is an appropriate first step when there is lack of information with regards to other phrases and the possible existence of lexical or syntactic mistakes in the training corpus.
Wellbeing Wednesdays: a pilot trial of acceptance and commitment therapy embedded in a freshman seminar

Abstract University students face vast mental health challenges, and both attitudinal and structural barriers to seeking care. Embedding interventions in college courses is one solution. Acceptance and commitment therapy (ACT) is an ideal candidate intervention given its emphasis on values, context, and skill building from a transdiagnostic perspective. This study embedded a brief ACT intervention in a required freshman seminar that was delivered by trained but unlicensed graduate students. In two class sessions of the freshman seminar taught by the same instructor, one session was randomly assigned to receive the course as usual, and one session received the ACT intervention. ACT content was delivered to all students in the intervention course on five consecutive weekly class periods. Students in both classes who chose to participate in the study completed assessments before and after the intervention and at follow-up. There were no significant changes with tests that were run, including non-parametric tests given the small sample sizes. Descriptively, the intervention group had slight improvements in wellbeing and mindfulness and decreases in distress, and the control group had worsened wellbeing, mindfulness and distress. A moderate portion of intervention group students enjoyed the intervention and indicated use of ACT skills, particularly mindfulness. Results suggest that this classroom-based intervention was feasible and acceptable, but further study should occur given small sample sizes. Future work should continue course-based ACT interventions, and should also explore potential applications of student training to deliver interventions given the shortage of mental health providers on college campuses. Key learning aims (1) Can acceptance and commitment therapy content and skills be integrated into an existing freshman seminar curriculum? (2) Can acceptance and commitment therapy improve wellbeing and decrease distress amongst college students? (3) How will students engage with and practise acceptance and commitment therapy skills outside of the context of session delivery?
Derivation and external validation of a prediction model for pneumococcal urinary antigen test positivity in patients with community-acquired pneumonia

Abstract Objective: Derive and externally validate a prediction model for pneumococcal urinary antigen test (pUAT) positivity. Methods: Retrospective cohort study of adults admitted with community-acquired pneumonia (CAP) to 177 U.S. hospitals in the Premier Database (derivation and internal validation samples) or 12 Cleveland Clinic hospitals (external validation sample). We utilized multivariable logistic regression to predict pUAT positivity in the derivation dataset, followed by model performance evaluation in both validation datasets. Potential predictors included demographics, comorbidities, clinical findings, and markers of disease severity. Results: Of 198,130 Premier patients admitted with CAP, 27,970 (14.1%) underwent pUAT; 1962 (7.0%) tested positive. The strongest predictors of pUAT positivity were history of pneumococcal infection in the previous year (OR 6.99, 95% CI 4.27–11.46), severe CAP on admission (OR 1.76, 95% CI 1.56–1.98), substance abuse (OR 1.57, 95% CI 1.27–1.93), smoking (OR 1.23, 95% CI 1.09–1.39), and hyponatremia (OR 1.35, 95% CI 1.17–1.55). Negative predictors included IV antibiotic use in past year (OR 0.65, 95% CI 0.52–0.82), congestive heart failure (OR 0.72, 95% CI 0.63–0.83), obesity (OR 0.71, 95% CI 0.60–0.85), and admission from skilled nursing facility (OR 0.60, 95% CI 0.45–0.78). Model c-statistics were 0.60 and 0.67 in the internal and external validation cohorts, respectively. Compared to guideline-recommended testing of severe CAP patients, our model would have detected 23% more cases with 5% fewer tests. Conclusion: Readily available data can identify patients most likely to have a positive pUAT. Our model could be incorporated into automated clinical decision support to improve test efficiency and antimicrobial stewardship.
Structural-semantics Guided Program Simplification for Understanding Neural Code Intelligence Models

Neural code intelligence models are cutting-edge automated code understanding technologies that have achieved remarkable performance in various software engineering tasks. However, the lack of deep learning models’ interpretability hinders the application of deep learning based code intelligence models in real-world scenarios, particularly in security-critical domains. Previous studies use program simplification to understand neural code intelligence models, but they have overlooked the fact that the most significant difference between source code and natural language is the code’s structural semantics. In this paper, we first conduct an empirical study to identify the critical code structural semantic features valued by neural code intelligence models, and then we propose a novel program simplification method called SSGPS (Structural-Semantics Guided Program Simplification). Results on three code summarization models show that SSGPS can reduce training and testing time by 20-40% while controlling the decrease in model performance by less than 4%, demonstrating that our method can retain the critical code structural semantics for understanding neural code intelligence models.
Conflict-free Replicated Priority Queue: Design, Verification and Evaluation

Internet-scale distributed systems often rely on replication to achieve fault-tolerance and load distribution. To provide low latency and high availability, the systems are often required to accept updates on one replica immediately and then propagate the updates among replicas asynchronously. Conflict-free Replicated Data Type (CRDT) is a principled approach to addressing the challenge for these systems to resolve conflicts among concurrent updates. Although many CRDTs have been studied, little research has been done on Conflict-free Replicated Priority Queue (CRPQ), which is a collection of elements that focuses on maintaining element orderings based on their priority values, and can be used in many applications scenarios such as task scheduling and network routing. In this work, we discuss the design rationales of CRPQs and introduce two CRPQ designs: Add-Win CRPQ and Remove-Win CRPQ. The correctness of the designs is formally verified using TLA+. We also demonstrate the effectiveness of our designs by implementing them over Redis. Our evaluation shows that both CRPQs perform well in terms of data consistency and memory overhead.
Distributed Scrum: A Case Meta-analysis

Distributed Scrum adapts the Scrum project management framework for geographically distributed software teams. Experimentally evaluating the effectiveness of Distributed Scrum is impractical, but many case studies and experience reports describe teams and projects that used Distributed Scrum. This article synthesizes the results of these cases using case meta-analysis, a technique for quantitatively analyzing qualitative case reports. On balance, the evidence suggests that Distributed Scrum has no impact, positive or negative, on overall project success. Consequently, claims by agile consultants who present Distributed Scrum as a recipe for project success should be treated with great caution, while researchers should investigate more varied perspectives to identify the real drivers of success in distributed and global software development.