Miracle-dz/UniFAR-FTUs huggingface dataset:
Hibernate Container: A Deflated Container Mode for Fast Startup and High-density Deployment in Serverless Computing

Serverless computing is a popular cloud computing paradigm, which requires low response latency to handle on-demand user requests. There are two prominent techniques employed for reducing the response latency: keep fully initialized containers alive (Warm Container) or reduce the new container startup (cold start) latency. This paper presents the 3rd container startup mode: Hibernate Container, which starts faster than the cold start container mode and consumes less memory than the Warm Container mode. Hibernate Container is essentially a"deflated"Warm Container. Its application memory is swapped out to disk, the freed memory is reclaimed and file based mmap memory is cleaned-up. The Hibernate Container's deflated memory is inflated in response to user requests. As Hibernate Container's application is fully initialized, its response latency is less than the cold start mode; and as the application memory is deflated, its memory consumption is less than the Warm Container mode. Additionally, when a Hibernate Container is"woken up"to process a request, the Woken-up Container has similar response latency to Warm Container but less memory consumption because not all the deflated memory needs to be inflated. We implemented the Hibernate technique as part of the open source Quark secure container runtime project and our test demonstrated that Hibernate Container consumes about 7\% to 25\% of the Warm Container memory. All of this results in a higher deployment density, lower latency and appreciable improvements in the overall system performance.
A Subabdominal MRI Image Segmentation Algorithm Based on Multi-Scale Feature Pyramid Network and Dual Attention Mechanism

： This study aimed to solve the semantic gap and misalignment issue between encoding and decoding because of multiple convolutional and pooling operations in U ‐ Net when segmenting subabdominal MRI images during rectal cancer treatment. A MRI Image Segmentation is proposed based on a multi ‐ scale feature pyramid network and dual attention mechanism. Our innovation is the design of two modules: 1) a dilated convolution and multi ‐ scale feature pyramid network are used in the encoding to avoid the semantic gap. 2) a dual attention mechanism is designed to maintain spatial information of U ‐ Net and reduce misalignment. Experiments on a subabdominal MRI image dataset show the proposed method achieves better performance than others methods. In conclusion, a multi ‐ scale feature pyramid network can reduce the semantic gap, and the dual attention mechanism can make an alignment of features between encoding and decoding.
Learning the Visualness of Text Using Large Vision-Language Models

Visual text evokes an image in a person's mind, while non-visual text fails to do so. A method to automatically detect visualness in text will enable text-to-image retrieval and generation models to augment text with relevant images. This is particularly challenging with long-form text as text-to-image generation and retrieval models are often triggered for text that is designed to be explicitly visual in nature, whereas long-form text could contain many non-visual sentences. To this end, we curate a dataset of 3,620 English sentences and their visualness scores provided by multiple human annotators. We also propose a fine-tuning strategy that adapts large vision-language models like CLIP by modifying the model's contrastive learning objective to map text identified as non-visual to a common NULL image while matching visual text to their corresponding images in the document. We evaluate the proposed approach on its ability to (i) classify visual and non-visual text accurately, and (ii) attend over words that are identified as visual in psycholinguistic studies. Empirical evaluation indicates that our approach performs better than several heuristics and baseline models for the proposed task. Furthermore, to highlight the importance of modeling the visualness of text, we conduct qualitative analyses of text-to-image generation systems like DALL-E. Project webpage: https://gaurav22verma.github. io/text-visualness/
Accurate and Reliable Confidence Estimation Based on Non-Autoregressive End-to-End Speech Recognition System

Estimating confidence scores for recognition results is a classic task in ASR field and of vital importance for kinds of downstream tasks and training strategies. Previous end-to-end~(E2E) based confidence estimation models (CEM) predict score sequences of equal length with input transcriptions, leading to unreliable estimation when deletion and insertion errors occur. In this paper we proposed CIF-Aligned confidence estimation model (CA-CEM) to achieve accurate and reliable confidence estimation based on novel non-autoregressive E2E ASR model - Paraformer. CA-CEM utilizes the modeling character of continuous integrate-and-fire (CIF) mechanism to generate token-synchronous acoustic embedding, which solves the estimation failure issue above. We measure the quality of estimation with AUC and RMSE in token level and ECE-U - a proposed metrics in utterance level. CA-CEM gains 24% and 19% relative reduction on ECE-U and also better AUC and RMSE on two test sets. Furthermore, we conduct analysis to explore the potential of CEM for different ASR related usage.
Extreme ATM skew in a local volatility model with discontinuity: joint density approach

This paper concerns a local volatility model in which volatility takes two possible values, and the specific value depends on whether the underlying price is above or below a given threshold value. The model is known, and a number of results have been obtained for it. In particular, a power law behaviour of the implied volatility skew has been established in the case when the threshold is taken at the money. This result as well as some others have been obtained by techniques based on the Laplace transform. The purpose of this paper is to demonstrate how to obtain similar results by another method. The proposed alternative approach is based on the natural relationship of the model with Skew Brownian motion and consists of the systematic use of the joint distribution of this stochastic process and some of its functionals.
The Effect of Environment on the Properties of the Most Massive Galaxies at 0.5 < z < 2.5 in the COSMOS-DASH Field

How the environment influences the most massive galaxies is still unclear. To explore the effects of the environment on morphology and star formation in the most massive galaxies at high redshift, we select galaxies with stellar mass log(M⋆/M⊙)>11 at 0.5 < z < 2.5 in the COSMOS-DASH field, which is the largest field with near-infrared photometric observations using HST/WFC3 to date. Combining these with the newly published COSMOS2020 catalog, we estimate the localized galaxy overdensity using a density estimator within the Bayesian probability framework. With the overdensity map, no significant environmental dependence is found in the distributions of Sérsic index and effective radius. When we consider the star formation state, galaxies at a lower density are found to have a higher median specific star formation rate (sSFR) at 0.5 < z < 1.5. But for star-forming galaxies only, sSFR is independent of the environment over the whole redshift range, indicating that the primary effect of the environment might be to control the quiescent fraction. Based on these observations, the possible environmental quenching process for these massive galaxies might be mergers.
Metal nanodumbbells for nanomanipulations and tribological experiments

Nanomanipulation experiments were carried out on nanodumbbells (NDs) to study their kinetic behavior and tribological properties. Ag, Au and Cu NDs were produced by laser-induced melting of corresponding nanowires (NWs). NDs were characterized by transmission electron microscopy (TEM) and scanning electron microscopy (SEM). Manipulation experiments were performed first with atomic force microscope (AFM) at ambient conditions, and then inside SEM at high vacuum conditions. Different regimes of motion were observed. In-plane and out-of-substrate-plane rotation were identified as the most preferred motion types of NDs.
Moduli spaces of stable objects in Enriques categories

We study moduli spaces of stable objects in Enriques categories by exploiting their relation to moduli spaces of stable objects in associated K3 categories. In particular, we settle the nonemptiness problem for moduli spaces of stable objects in the Kuznetsov components of several interesting classes of Fano varieties, and deduce the nonemptiness of fixed loci of certain antisymplectic involutions on modular hyperk\"{a}hler varieties.
Latent Space Planning for Multiobject Manipulation With Environment-Aware Relational Classifiers

Objects rarely sit in isolation in everyday human environments. If we want robots to operate and perform tasks in our human environments, they must understand how the objects they manipulate will interact with structural elements of the environment for all but the simplest of tasks. As such, we would like our robots to reason about how multiple objects and environmental elements relate to one another and how those relations may change as the robot interacts with the world. We examine the problem of predicting interobject and object–environment relations between previously unseen objects and novel environments purely from partial-view point clouds. Our approach enables robots to plan and execute sequences to complete multiobject manipulation tasks defined from logical relations. This removes the burden of providing explicit, continuous object states as goals to the robot. We explore several different neural network architectures for this task. We find the best performing model to be a novel transformer-based neural network that both predicts object–environment relations and learns a latent-space dynamics function. We achieve reliable sim-to-real transfer without any fine-tuning. Our experiments show that our model understands how changes in observed environmental geometry relate to semantic relations between objects.
Fair Allocation of Indivisible Chores: Beyond Additive Costs

We study the maximin share (MMS) fair allocation of $m$ indivisible chores to $n$ agents who have costs for completing the assigned chores. It is known that exact MMS fairness cannot be guaranteed, and so far the best-known approximation for additive cost functions is $\frac{13}{11}$ by Huang and Segal-Halevi [EC, 2023]; however, beyond additivity, very little is known. In this work, we first prove that no algorithm can ensure better than $\min\{n,\frac{\log m}{\log \log m}\}$-approximation if the cost functions are submodular. This result also shows a sharp contrast with the allocation of goods where constant approximations exist as shown by Barman and Krishnamurthy [TEAC, 2020] and Ghodsi et al. [AIJ, 2022]. We then prove that for subadditive costs, there always exists an allocation that is $\min\{n,\lceil\log m\rceil\}$-approximation, and thus the approximation ratio is asymptotically tight. Besides multiplicative approximation, we also consider the ordinal relaxation, 1-out-of-$d$ MMS, which was recently proposed by Hosseini et al. [JAIR and AAMAS, 2022]. Our impossibility result implies that for any $d\ge 2$, a 1-out-of-$d$ MMS allocation may not exist. Due to these hardness results for general subadditive costs, we turn to studying two specific subadditive costs, namely, bin packing and job scheduling. For both settings, we show that constant approximate allocations exist for both multiplicative and ordinal relaxations of MMS.
Lepto-hadronic Interpretation of 2021 RS Ophiuchi Nova Outburst

Very-high-energy (VHE; 100 GeV < E ≤ 100 TeV) and high-energy (HE; 100 MeV < E ≤ 100 GeV) gamma rays were observed from the symbiotic recurrent nova RS Ophiuchi (RS Oph) during its outburst in 2021 August by various observatories, such as the High Energy Stereoscopic System (HESS), Major Atmospheric Gamma Imaging Cherenkov (MAGIC), and Fermi-Large Area Telescope (LAT). The models that have been explored so far tend to favor a hadronic scenario of particle acceleration over an alternative leptonic scenario. This paper explores a time-dependent lepto-hadronic scenario to explain the emission from the RS Oph source region. We have used simultaneous low-frequency radio data observed by various observatories along with the data provided by HESS, MAGIC, and Fermi-LAT to explain the multiwavelength spectral energy distributions corresponding to 4 days after the outburst. Our results show that a lepto-hadronic interpretation of the source not only explains the observed HE-VHE gamma-ray data but the corresponding model synchrotron component is also consistent with the first 4 days of low-radio-frequency data, indicating the presence of nonthermal radio emission at the initial stage of the nova outburst. We have also calculated the expected neutrino flux from the source region and discuss the possibility of detecting neutrinos.
Thermal transfer enhancement by hydrodynamic plasmons in electron bilayers

We develop a theory of heat transfer induced by thermal charge fluctuations in two-dimensional electron double layers. We consider pristine systems comprised of identical layers, and focus on the regime of sufficiently high temperatures and interlayer distances $d$, where the relevant charge fluctuations may be described using the hydrodynamic approach. In this limit heat transfer is dominated by the plasmon resonances. For systems with Galilean-invariant electron dispersion the interlayer thermal conductance $\varkappa$ is proportional to the kinematic viscosity of the electron liquid, and decreases as $1/d^4$. In the absence of Galilean invariance $\varkappa \propto \sigma/d^3$, where $\sigma$ is the intrinsic conductivity of the liquid. This strong enhancement can be traced to a drastically different broadening of plasmon resonances in systems with and without Galilean invariance.
EventNet-ITA: Italian Frame Parsing for Events

This paper introduces EventNet-ITA, a large, multi-domain corpus annotated full-text with event frames for Italian. Moreover, we present and thoroughly evaluate an efficient multi-label sequence labeling approach for Frame Parsing. Covering a wide range of individual, social and historical phenomena, with more than 53,000 annotated sentences and over 200 modeled frames, EventNet-ITA constitutes the first systematic attempt to provide the Italian language with a publicly available resource for Frame Parsing of events, useful for a broad spectrum of research and application tasks. Our approach achieves a promising 0.9 strict F1-score for frame classification and 0.72 for frame element classification, on top of minimizing computational requirements. The annotated corpus and the frame parsing model are released under open license.
Segment Any Anomaly without Training via Hybrid Prompt Regularization

We present a novel framework, i.e., Segment Any Anomaly + (SAA+), for zero-shot anomaly segmentation with hybrid prompt regularization to improve the adaptability of modern foundation models. Existing anomaly segmentation models typically rely on domain-specific fine-tuning, limiting their generalization across countless anomaly patterns. In this work, inspired by the great zero-shot generalization ability of foundation models like Segment Anything, we first explore their assembly to leverage diverse multi-modal prior knowledge for anomaly localization. For non-parameter foundation model adaptation to anomaly segmentation, we further introduce hybrid prompts derived from domain expert knowledge and target image context as regularization. Our proposed SAA+ model achieves state-of-the-art performance on several anomaly segmentation benchmarks, including VisA, MVTec-AD, MTD, and KSDD2, in the zero-shot setting. We will release the code at \href{https://github.com/caoyunkang/Segment-Any-Anomaly}{https://github.com/caoyunkang/Segment-Any-Anomaly}.
Quantum linear polynomial evaluation based on XOR oblivious transfer compatible with classical partially homomorphic encryption

XOR oblivious transfer is a universal cryptographic primitive that can be related to linear polynomial evaluation. We firstly introduce some bipartite quantum protocols for XOR oblivious transfer, which are not secure if one party cheats, and some of them can be combined with a classical XOR homomorphic encryption scheme for evaluation of linear polynomials modulo 2 with hybrid security. We then introduce a general protocol using modified versions of the XOR oblivious transfer protocols to evaluate linear polynomials modulo 2 with partial information-theoretic security. When combined with the ability to perform arbitrary quantum computation, this would lead to deterministic interactive two-party computation which is quite secure in the information-theoretic sense when the allowed set of inputs is large. For the task of classical function evaluation, although the quantum computation approach is still usable, we also discuss purely classical post-processing methods based on the proposed linear polynomial evaluation protocols.
Take a Break in the Middle: Investigating Subgoals towards Hierarchical Script Generation

Goal-oriented Script Generation is a new task of generating a list of steps that can fulfill the given goal. In this paper, we propose to extend the task from the perspective of cognitive theory. Instead of a simple flat structure, the steps are typically organized hierarchically - Human often decompose a complex task into subgoals, where each subgoal can be further decomposed into steps. To establish the benchmark, we contribute a new dataset, propose several baseline methods, and set up evaluation metrics. Both automatic and human evaluation verify the high-quality of dataset, as well as the effectiveness of incorporating subgoals into hierarchical script generation. Furthermore, We also design and evaluate the model to discover subgoal, and find that it is a bit more difficult to decompose the goals than summarizing from segmented steps.
Machine Learning Recommendation System For Health Insurance Decision Making In Nigeria

The uptake of health insurance has been poor in Nigeria, a significant step to improving this includes improved awareness, access to information and tools to support decision making. Artificial intelligence (AI) based recommender systems have gained popularity in helping individuals find movies, books, music, and different types of products on the internet including diverse applications in healthcare. The content-based methodology (item-based approach) was employed in the recommender system. We applied both the K-Nearest Neighbor (KNN) and Cosine similarity algorithm. We chose the Cosine similarity as our chosen algorithm after several evaluations based of their outcomes in comparison with domain knowledge. The recommender system takes into consideration the choices entered by the user, filters the health management organization (HMO) data by location and chosen prices. It then recommends the top 3 HMOs with closest similarity in services offered. A recommendation tool to help people find and select the best health insurance plan for them is useful in reducing the barrier of accessing health insurance. Users are empowered to easily find appropriate information on available plans, reduce cognitive overload in dealing with over 100 options available in the market and easily see what matches their financial capacity.
Dark matter condensates as highly nonlocal solitons: instanbility in the Schwarzschild metric and laboratory analog

Theories on the bosonic nature of dark matter are a promising alternative to the cold dark matter model. Here we consider a dark matter halo in the state of a Bose-Einstein condensate, subject to the gravitation of a black hole. In the low energy limit, we bring together the general relativity in the Schwarzschild metric and the quantum description of the Bose-Einstein condensate. The model is solvable in the Fermi normal coordinates with the so called highly nonlocal approximation and describes tidal deformations in the condensate wave function. The black hole deforms the localized condensate until the attraction of the compact object overcomes the self-gravitation and destabilizes the solitonic dark matter. Moreover, the model can be implemented as a gravitational analog in the laboratory; the time-dependent potential generated by the galactic black hole can be mimicked by an optical trap acting on a conventional condensate. The results open the way to new laboratory simulators for quantum gravitational effects.
CLEME: Debiasing Multi-reference Evaluation for Grammatical Error Correction

Evaluating the performance of Grammatical Error Correction (GEC) systems is a challenging task due to its subjectivity. Designing an evaluation metric that is as objective as possible is crucial to the development of GEC task. However, mainstream evaluation metrics, i.e., reference-based metrics, introduce bias into the multi-reference evaluation by extracting edits without considering the presence of multiple references. To overcome this issue, we propose Chunk-LEvel Multi-reference Evaluation (CLEME), designed to evaluate GEC systems in the multi-reference evaluation setting. CLEME builds chunk sequences with consistent boundaries for the source, the hypothesis and references, thus eliminating the bias caused by inconsistent edit boundaries. Furthermore, we observe the consistent boundary could also act as the boundary of grammatical errors, based on which the F$_{0.5}$ score is then computed following the correction independence assumption. We conduct experiments on six English reference sets based on the CoNLL-2014 shared task. Extensive experiments and detailed analyses demonstrate the correctness of our discovery and the effectiveness of CLEME. Further analysis reveals that CLEME is robust to evaluate GEC systems across reference sets with varying numbers of references and annotation style.
The noise level in linear regression with dependent data

We derive upper bounds for random design linear regression with dependent ($\beta$-mixing) data absent any realizability assumptions. In contrast to the strictly realizable martingale noise regime, no sharp instance-optimal non-asymptotics are available in the literature. Up to constant factors, our analysis correctly recovers the variance term predicted by the Central Limit Theorem -- the noise level of the problem -- and thus exhibits graceful degradation as we introduce misspecification. Past a burn-in, our result is sharp in the moderate deviations regime, and in particular does not inflate the leading order term by mixing time factors.