Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparing Importance Sampling Based Methods for Mitigating the Effect of Class Imbalance

Most state-of-the-art computer vision models heavily depend on data. However, many datasets exhibit extreme class imbalance which has been shown to negatively impact model performance. Among the training-time and data-generation solutions that have been explored, one subset that leverages existing data is importance sampling. A good deal of this work focuses primarily on the CIFAR-10 and CIFAR-100 datasets which fail to be representative of the scale, composition, and complexity of current state-of-the-art datasets. In this work, we explore and compare three techniques that derive from importance sampling: loss reweighting, undersampling, and oversampling. Specifically, we compare the effect of these techniques on the performance of two encoders on an impactful satellite imagery dataset, Planet's Amazon Rainforest dataset, in preparation for another work. Furthermore, we perform supplemental experimentation on a scene classification dataset, ADE20K, to test on a contrasting domain and clarify our results. Across both types of encoders, we find that up-weighting the loss for and undersampling has a negigible effect on the performance on underrepresented classes. Additionally, our results suggest oversampling generally improves performance for the same underrepresented classes. Interestingly, our findings also indicate that there may exist some redundancy in data in the Planet dataset. Our work aims to provide a foundation for further work on the Planet dataset and similar domain-specific datasets. We open-source our code at https://github.com/RichardZhu123/514-class-imbalance for future work on other satellite imagery datasets as well.
Crecimiento y mortalidad de Farfantepenaeus notialis en la costa norte de la Península de Araya, Venezuela

Farfantepenaeus notialis, es una especie importante de camarón en la pesquería artesanal en la Península de Araya, estado Sucre, Venezuela. Debido al limitado conocimiento sobre su dinámica poblacional, es importante determinar su crecimiento y mortalidad como parámetros fundamentales para la evaluación y manejo del recurso. La estructura de tallas y peso se evaluó mensualmente de los desembarques, desde abril 2012 hasta marzo 2013. El ajuste de los parámetros de crecimiento al modelo de von Bertalanffy se realizó por análisis de superfi cie de respuesta mediante la reestructuración de los datos y su ajuste a una curva de crecimiento. Los parámetros de crecimiento estimados para hembras, machos y sexos agrupados, fueron L∞ = 237, 230, 200 mm y k = 1,4; 1,4 y 1,3 año-1, respectivamente. La estimación de la mortalidad natural (M) se utilizaron tres métodos, mientras que la mortalidad total fue estimada de la curva de captura linearizada y la mortalidad por pesca substrayendo el valor de (F). Los valores más elevados de M fueron de 2,4 año-1 en machos; mientras que la mortalidad por pesca (F) promedio fue 1,6. Los resultados indicaron que F. notialis entra a la pesquería artesanal a los 5 meses de edad, con longitudes superiores a 80 mm, hasta los 20 meses y una longitud de 230 mm. Los resultados de F se ubicaron encima del valor medio reportado para poblaciones altamente explotadas, lo cual pudiese sugerir que la pesca de camarón rosado ha alcanzado su punto más alto en términos de rentabilidad económica.
CoMeT: Count-Min-Sketch-based Row Tracking to Mitigate RowHammer at Low Cost

We propose a new RowHammer mitigation mechanism, CoMeT, that prevents RowHammer bitflips with low area, performance, and energy costs in DRAM-based systems at very low RowHammer thresholds. The key idea of CoMeT is to use low-cost and scalable hash-based counters to track DRAM row activations. CoMeT uses the Count-Min Sketch technique that maps each DRAM row to a group of counters, as uniquely as possible, using multiple hash functions. When a DRAM row is activated, CoMeT increments the counters mapped to that DRAM row. Because the mapping from DRAM rows to counters is not completely unique, activating one row can increment one or more counters mapped to another row. Thus, CoMeT may overestimate, but never underestimates, a DRAM row's activation count. This property of CoMeT allows it to securely prevent RowHammer bitflips while properly configuring its hash functions reduces overestimations. As a result, CoMeT 1) implements substantially fewer counters than the number of DRAM rows in a DRAM bank and 2) does not significantly overestimate a DRAM row's activation count. Our comprehensive evaluations show that CoMeT prevents RowHammer bitflips with an average performance overhead of only 4.01% across 61 benign single-core workloads for a very low RowHammer threshold of 125, normalized to a system with no RowHammer mitigation. CoMeT achieves a good trade-off between performance, energy, and area overheads. Compared to the best-performing state-of-the-art mitigation, CoMeT requires 74.2x less area overhead at the RowHammer threshold 125 and incurs a small performance overhead on average for all RowHammer thresholds. Compared to the best-performing low-area-cost mechanism, at a very low RowHammer threshold of 125, CoMeT improves performance by up to 39.1% while incurring a similar area overhead. CoMeT is openly and freely available at https://github.com/CMU-SAFARI/CoMeT.
Stochastic contextual bandits with graph feedback: from independence number to MAS number

We consider contextual bandits with graph feedback, a class of interactive learning problems with richer structures than vanilla contextual bandits, where taking an action reveals the rewards for all neighboring actions in the feedback graph under all contexts. Unlike the multi-armed bandits setting where a growing literature has painted a near-complete understanding of graph feedback, much remains unexplored in the contextual bandits counterpart. In this paper, we make inroads into this inquiry by establishing a regret lower bound $\Omega(\sqrt{\beta_M(G) T})$, where $M$ is the number of contexts, $G$ is the feedback graph, and $\beta_M(G)$ is our proposed graph-theoretical quantity that characterizes the fundamental learning limit for this class of problems. Interestingly, $\beta_M(G)$ interpolates between $\alpha(G)$ (the independence number of the graph) and $\mathsf{m}(G)$ (the maximum acyclic subgraph (MAS) number of the graph) as the number of contexts $M$ varies. We also provide algorithms that achieve near-optimal regrets for important classes of context sequences and/or feedback graphs, such as transitively closed graphs that find applications in auctions and inventory control. In particular, with many contexts, our results show that the MAS number completely characterizes the statistical complexity for contextual bandits, as opposed to the independence number in multi-armed bandits.
Enhancing key rates of QKD protocol by Coincidence Detection

In theory, quantum key distribution (QKD) provides unconditional security; however, its practical implementations are susceptible to exploitable vulnerabilities. This investigation tackles the constraints in practical QKD implementations using weak coherent pulses. We improve on the conventional approach of using decoy pulses by integrating it with the coincidence detection (CD) protocol. Additionally, we introduce an easy-to-implement algorithm to compute asymptotic key rates for the protocol. Furthermore, we have carried out an experimental implementation of the protocol, where we demonstrate that monitoring coincidences in the decoy state protocol leads to enhanced key rates under realistic experimental conditions.
The VOROS: Lifting ROC curves to 3D

The area under the ROC curve is a common measure that is often used to rank the relative performance of different binary classifiers. However, as has been also previously noted, it can be a measure that ill-captures the benefits of different classifiers when either the true class values or misclassification costs are highly unbalanced between the two classes. We introduce a third dimension to capture these costs, and lift the ROC curve to a ROC surface in a natural way. We study both this surface and introduce the VOROS, the volume over this ROC surface, as a 3D generalization of the 2D area under the ROC curve. For problems where there are only bounds on the expected costs or class imbalances, we restrict consideration to the volume of the appropriate subregion of the ROC surface. We show how the VOROS can better capture the costs of different classifiers on both a classical and a modern example dataset.
Identifying Assumptions and Research Dynamics

A representative researcher pursuing a question has repeated opportunities for empirical research. To process findings, she must impose an identifying assumption, which ensures that repeated observation would provide a definitive answer to her question. Research designs vary in quality and are implemented only when the assumption is plausible enough according to a KL-divergence-based criterion, and then beliefs are Bayes-updated as if the assumption were perfectly valid. We study the dynamics of this learning process and its induced long-run beliefs. The rate of research cannot uniformly accelerate over time. We characterize environments in which it is stationary. Long-run beliefs can exhibit history-dependence. We apply the model to stylized examples of empirical methodologies: experiments, causal-inference techniques, and (in an extension) ``structural'' identification methods such as ``calibration'' and ``Heckman selection. ''
Angle Parametrization of Teichm\"uller space and hyperelliptic surfaces

Let $S_g$ be a closed orientable surface of genus $g \geq 2$, and let $\mathcal{T}_g$ be the Teichm\"uller space of $S_g$. Let $\mathcal{H}_g$ denotes the space of all hyperelliptic surfaces of genus $g$. For $g\geq 3$, we have proved that $\mathcal{T}_g$ can be parametrized by $6g-5$ angle parameters. We also prove that for $g\geq 2$, $\mathcal{H}_g$ can be parametrized by $4g-2$ angle parameters.
Percept, Chat, and then Adapt: Multimodal Knowledge Transfer of Foundation Models for Open-World Video Recognition

Open-world video recognition is challenging since traditional networks are not generalized well on complex environment variations. Alternatively, foundation models with rich knowledge have recently shown their generalization power. However, how to apply such knowledge has not been fully explored for open-world video recognition. To this end, we propose a generic knowledge transfer pipeline, which progressively exploits and integrates external multimodal knowledge from foundation models to boost open-world video recognition. We name it PCA, based on three stages of Percept, Chat, and Adapt. First, we perform Percept process to reduce the video domain gap and obtain external visual knowledge. Second, we generate rich linguistic semantics as external textual knowledge in Chat stage. Finally, we blend external multimodal knowledge in Adapt stage, by inserting multimodal knowledge adaptation modules into networks. We conduct extensive experiments on three challenging open-world video benchmarks, i.e., TinyVIRAT, ARID, and QV-Pipe. Our approach achieves state-of-the-art performance on all three datasets.
Locally approximable CR functions, a sharp maximum modulus principle and holomorphic extension

We introduce a notion of locally approximable continuous CR functions on locally closed subsets of reduced complex spaces, generalizing both holomorphic functions and CR functions on CR submanifolds. Under additional assumptions of set-theoretical weak pseudoconcavity we prove optimal maximum modulus principles for these functions. Restricting to real submanifolds (possibly with CR singularities) of complexmanifolds, we generalize results on holomorphic extension known for CR submanifolds.
Survey in Characterization of Semantic Change

Live languages continuously evolve to integrate the cultural change of human societies. This evolution manifests through neologisms (new words) or \textbf{semantic changes} of words (new meaning to existing words). Understanding the meaning of words is vital for interpreting texts coming from different cultures (regionalism or slang), domains (e.g., technical terms), or periods. In computer science, these words are relevant to computational linguistics algorithms such as translation, information retrieval, question answering, etc. Semantic changes can potentially impact the quality of the outcomes of these algorithms. Therefore, it is important to understand and characterize these changes formally. The study of this impact is a recent problem that has attracted the attention of the computational linguistics community. Several approaches propose methods to detect semantic changes with good precision, but more effort is needed to characterize how the meaning of words changes and to reason about how to reduce the impact of semantic change. This survey provides an understandable overview of existing approaches to the \textit{characterization of semantic changes} and also formally defines three classes of characterizations: if the meaning of a word becomes more general or narrow (change in dimension) if the word is used in a more pejorative or positive/ameliorated sense (change in orientation), and if there is a trend to use the word in a, for instance, metaphoric or metonymic context (change in relation). We summarized the main aspects of the selected publications in a table and discussed the needs and trends in the research activities on semantic change characterization.
Summary Cycles: Exploring the Impact of Prompt Engineering on Large Language Models’ Interaction with Interaction Log Information

With the aim of improving work efficiency, we examine how Large Language Models (LLMs) can better support the handoff of information by summarizing user interactions in collaborative intelligence analysis communication. We experiment with interaction logs, or a record of user interactions with a system. Inspired by chain-of-thought prompting, we describe a technique to avoid API token limits with recursive summarization requests. We then apply ChatGPT over multiple iterations to extract named entities, topics, and summaries, combined with interaction sequence sentences, to generate summaries of critical events and results of analysis sessions. We quantitatively evaluate the generated summaries against human-generated ones using common accuracy metrics (e.g., ROUGE-L, BLEU, BLEURT, and TER). We also report qualitative trends and the factuality of the output. We find that manipulating the audience feature or providing single-shot examples minimally influences the model’s accuracy. While our methodology successfully summarizes interaction logs, the lack of significant results raises questions about prompt engineering and summarization effectiveness generally. We call on explainable artificial intelligence research to better understand how terms and their placement may change LLM outputs, striving for more consistent prompt engineering guidelines.
HIT-MI&T Lab’s Submission to Eval4NLP 2023 Shared Task

Recently, Large Language Models (LLMs) have boosted the research in natural language processing and shown impressive capabilities across numerous domains, including machine translation evaluation. This paper presents our methods developed for the machine translation evaluation sub-task of the Eval4NLP 2023 Shared Task. Based on the provided LLMs, we propose a generation-based method as well as a probability-based method to perform evaluation, explore different strategies when selecting the demonstrations for in-context learning, and try different ensemble methods to further improve the evaluation accuracy. The experiment results on the development set and test set demonstrate the effectiveness of our proposed method.
Delving into Evaluation Metrics for Generation: A Thorough Assessment of How Metrics Generalize to Rephrasing Across Languages

Language generation has been an important task in natural language processing (NLP) with increasing variety of applications especially in the recent years. The evaluation of generative language models typically rely on automatic heuristics which search for overlaps over word or phrase level patterns in generated outputs and traditionally some hand-crafted reference sentences in the given language ranging in the forms from sentences to entire documents. Language, on the other hand, is productive by nature, which means the same concept can be expressed potentially in many different lexical or phrasal forms, making the assessment of generated outputs a very difficult one. Many studies have indicated potential hazards related to the prominent choice of heuristics matching generated language to selected references and the limitations raised by this setting in developing robust generative models. This paper undertakes an in-depth analysis of evaluation metrics used for generative models, specifically investigating their responsiveness to various syntactic structures, and how these characteristics vary across languages with different morphosyntactic typologies. Preliminary findings indicate that while certain metrics exhibit robustness in particular linguistic contexts, a discernible variance emerges in their performance across distinct syntactic forms. Through this exploration, we highlight the imperative need for more nuanced and encompassing evaluation strategies in generative models, advocating for metrics that are sensitive to the multifaceted nature of languages.
An MDE modeling framework for measurable goal-oriented requirements

It is a proved fact that the appropriate management of requirements is one of the most influential factors in the success of software development projects. With the advent of the model-driven engineering (MDE) development paradigm, the need for formal gathering mechanisms, which provide the necessary degree of nonambiguity and detail, has led to the proposal of a myriad of requirements metamodels. However, a great disparity exists, both in the concepts-relationships and in the underlying semantics involved in each metamodel. Moreover, most existing proposals lack backward (e.g., alignment with business goals) or forward (e.g., connection with validation methods) traceability. In view of this situation, this article proposes a “measurable requirements metamodel” that offers support to the elicitation of measurable requirements. This support is based on the explicit connection of goals, requirements, and measures, thus fostering a goal-driven measurable requirements engineering (RE) perspective. Additionally, since it is well known that metamodels only reflect the abstract syntax of the modeling language, the proposed metamodel also includes a notation (concrete syntax) which, for reasons of understandability, is based on the goal-oriented requirements language (GRL) notation. This notation is supported by a unified modeling language (UML) profile that facilitates its adoption by RE analysts in the context of any UML-based software engineering process. To support this proposal, an Eclipse tool has been developed. This tool permits the integration of measurable requirements as a driving force in the context of a given MDE development process. © 2010 Wiley Periodicals, Inc.
ClNO2 Yields From Aircraft Measurements During the 2015 WINTER Campaign and Critical Evaluation of the Current Parameterization

Nitryl chloride (ClNO2) plays an important role in the budget and distribution of tropospheric oxidants, halogens, and reactive nitrogen species. ClNO2 is formed from the heterogeneous uptake and reaction of dinitrogen pentoxide (N2O5) on chloride‐containing aerosol, with a production yield, ϕ(ClNO2), defined as the moles of ClNO2 produced relative to N2O5 lost. The ϕ(ClNO2) has been increasingly incorporated into 3‐D chemical models where it is parameterized based on laboratory‐derived kinetics and currently accepted aqueous‐phase formation mechanism. This parameterization models ϕ(ClNO2) as a function of the aerosol chloride to water molar ratio. Box model simulations of night flights during the 2015 Wintertime INvestigation of Transport, Emissions, and Reactivity (WINTER) aircraft campaign derived 3,425 individual ϕ(ClNO2) values with a median of 0.138 and range of 0.003 to 1. Comparison of the box model median to those predicted by two other field‐based ϕ(ClNO2) derivation methods agreed within a factor of 1.3, within the uncertainties of each method. In contrast, the box model median was 75–84% lower than predictions from the laboratory‐based parameterization (i.e., [parameterization − box model]/parameterization). An evaluation of factors influencing this difference reveals a positive dependence of ϕ(ClNO2) on aerosol water, opposite to the currently parameterized trend. Additional factors may include aqueous‐phase competition reactions for the nitronium ion intermediate and/or direct ClNO2 loss mechanisms. Further laboratory studies of ClNO2 formation and the impacts of aerosol water, sulfate, organics, and ClNO2 aqueous‐phase reactions are required to elucidate and quantify these processes on ambient aerosol, critical for the development of a robust ϕ(ClNO2) parameterization.
The 5-HT7 receptor antagonist SB 269970 ameliorates maternal fluoxetine exposure-induced impairment of synaptic plasticity in the prefrontal cortex of the offspring female mice

Background Although the use of a selective serotonin reuptake inhibitor fluoxetine in depression during pregnancy is considered safe, it might increase the risk of affective disorders and cognitive symptoms in progeny. In animal models, maternal exposure to fluoxetine throughout gestation and lactation negatively affects the behavior of the offspring. Little is known about the effects of maternal fluoxetine on synaptic transmission and plasticity in the offspring cerebral cortex. Methods During pregnancy and lactation C57BL/6J mouse dams received fluoxetine (7.5 mg/kg/day) with drinking water. Female offspring mice received intraperitoneal injections of the selective 5-HT7 receptor antagonist SB 269970 (2.5 mg/kg) for 7 days. Whole-cell and field potential electrophysiological recordings were performed in the medial prefrontal cortex (mPFC) ex vivo brain slices. Results Perinatal exposure to fluoxetine resulted in decreased field potentials and impaired long-term potentiation (LTP) in layer II/III of the mPFC of female young adult offspring. Neither the intrinsic excitability nor spontaneous excitatory postsynaptic currents were altered in layer II/III mPFC pyramidal neurons. In mPFC slices obtained from fluoxetine-treated mice that were administered SB 269970 both field potentials and LTP magnitude were restored and did not differ from controls. Conclusions Treatment of fluoxetine-exposed mice with a selective 5-HT7 receptor antagonist, SB269970, normalizes synaptic transmission and restores the potential for plasticity in the mPFC of mice exposed in utero and postnatally to fluoxetine.
A new look at functional beta diversity

The variability in species composition among a set of sampling sites, or beta diversity, is considered a key signature of the ecological processes that shape the spatial structure of species assemblages. In this paper, we propose to decompose this variability into three additive components: i) the standard compositional similarity between individuals of the same species among sites, ii) the degree of functional dissimilarity between individuals of distinct species among sites, and iii) the degree of functional similarity between individuals of distinct species among sites. These three components can be used to portray the functional resemblance among sites on a ternary diagram. The potential of this method is shown with real data on the functional turnover of Alpine species along a primary succession on glacial deposits in northern Italy. Synthesis. With the resulting ternary diagram of ‘functional resemblance’ we can relate various aspects of taxonomic and functional variability among sites to community assembly processes more completely than just looking at individual components.