Miracle-dz/UniFAR-FTUs huggingface dataset:
DARER: Dual-task Temporal Relational Recurrent Reasoning Network for Joint Dialog Sentiment Classification and Act Recognition

The task of joint dialog sentiment classification (DSC) and act recognition (DAR) aims to simultaneously predict the sentiment label and act label for each utterance in a dialog.In this paper, we put forward a new framework which models the explicit dependencies via integrating prediction-level interactions other than semantics-level interactions, more consistent with human intuition.Besides, we propose a speaker-aware temporal graph (SATG) and a dual-task relational temporal graph (DRTG) to introduce temporal relations into dialog understanding and dual-task reasoning. To implement our framework, we propose a novel model dubbed DARER, which first generates the context-, speaker- and temporal-sensitive utterance representations via modeling SATG, then conducts recurrent dual-task relational reasoning on DRTG, in which process the estimated label distributions act as key clues in prediction-level interactions.Experiment results show that DARER outperforms existing models by large margins while requiring much less computation resource and costing less training time.Remarkably, on DSC task in Mastodon, DARER gains a relative improvement of about 25% over previous best model in terms of F1, with less than 50% parameters and about only 60% required GPU memory.
Quantifying Privacy Risks of Masked Language Models Using Membership Inference Attacks

The wide adoption and application of Masked language models (MLMs) on sensitive data (from legal to medical) necessitates a thorough quantitative investigation into their privacy vulnerabilities. Prior attempts at measuring leakage of MLMs via membership inference attacks have been inconclusive, implying potential robustness of MLMs to privacy attacks.In this work, we posit that prior attempts were inconclusive because they based their attack solely on the MLM’s model score. We devise a stronger membership inference attack based on likelihood ratio hypothesis testing that involves an additional reference MLM to more accurately quantify the privacy risks of memorization in MLMs. We show that masked language models are indeed susceptible to likelihood ratio membership inference attacks: Our empirical results, on models trained on medical notes, show that our attack improves the AUC of prior membership inference attacks from 0.66 to an alarmingly high 0.90 level.
Leveraging Initial Hints for Free in Stochastic Linear Bandits

We study the setting of optimizing with bandit feedback with additional prior knowledge provided to the learner in the form of an initial hint of the optimal action. We present a novel algorithm for stochastic linear bandits that uses this hint to improve its regret to $\tilde O(\sqrt{T})$ when the hint is accurate, while maintaining a minimax-optimal $\tilde O(d\sqrt{T})$ regret independent of the quality of the hint. Furthermore, we provide a Pareto frontier of tight tradeoffs between best-case and worst-case regret, with matching lower bounds. Perhaps surprisingly, our work shows that leveraging a hint shows provable gains without sacrificing worst-case performance, implying that our algorithm adapts to the quality of the hint for free. We also provide an extension of our algorithm to the case of $m$ initial hints, showing that we can achieve a $\tilde O(m^{2/3}\sqrt{T})$ regret.
Unveiling an Old Disk around a Massive Young Leaking Blueberry in SDSS-IV MaNGA

Extreme emission-line galaxies, such as blue compact dwarfs (BCDs), Green Peas (GPs), and blueberries in the local universe are potential candidates for understanding the nature of galaxies that reionized the early universe. Being low-mass, metal-poor starburst systems, they are understood to be local analogs of the high-redshift Lyman continuum and Lyα emitters (LAEs). Even with their proximity to us, we know little about their spatially resolved properties; while most blueberries and GPs are indeed compact, they remain unresolved. Here, we report the detection of a disk-like lower-surface-brightness (LSB) stellar host with a very old population around a blueberry LAE system using broad i-band imaging and integral field spectroscopic data from the SDSS and SDSS-IV MaNGA surveys, respectively. The LSB stellar host is structurally similar to that observed around local starburst BCDs. Furthermore, the kinematics of the studied blueberry source bears signs of misalignment between the gas and stellar components. Our findings establish an intriguing thread connecting the blueberry and an LSB disk with an old stellar population and suggest that blueberries and their high-redshift counterparts such as GPs do not represent peculiar cases of dwarf galaxy evolution. In fact, with respect to the structural properties of their host galaxies, they are compatible with a common evolutionary track of the main population of local BCDs.
NeReF: Neural Refractive Field for Fluid Surface Reconstruction and Implicit Representation

Existing neural reconstruction schemes such as Neural Radiance Field (NeRF) are largely focused on modeling opaque objects. We present a novel neural refractive field(NeReF) to recover wavefront of transparent fluids by simultaneously estimating the surface position and normal of the fluid front. Unlike prior arts that treat the reconstruction target as a single layer of the surface, NeReF is specifically formulated to recover a volumetric normal field with its corresponding density field. A query ray will be refracted by NeReF according to its accumulated refractive point and normal, and we employ the correspondences and uniqueness of refracted ray for NeReF optimization. We show NeReF, as a global optimization scheme, can more robustly tackle refraction distortions detrimental to traditional methods for correspondence matching. Furthermore, the continuous NeReF representation of wavefront enables view synthesis as well as normal integration. We validate our approach on both synthetic and real data and show it is particularly suitable for sparse multi-view acquisition. We hence build a small light field array and experiment on various surface shapes to demonstrate high fidelity NeReF reconstruction.
Holographic representation of the unified early and late universe via a viscous dark fluid

In this paper, we apply the holographic principle for describing in a unifying way the early and the late-time universe, when the general equation of state contains a bulk viscosity. We use the idea of a generalized cut-off holographic dark energy introduced by Nojiri and Odintsov (2006, 2017), and study the evolution of the universe when the equation of state has two power-law asymptotes. Analytical expressions for the infrared cut-offs in terms of the particle horizon are obtained. The energy conservation laws as derived from the holographic point of view, are given for various forms of the thermodynamic parameters and for various forms of the bulk viscosity. As a result, we obtain a unifying description of the early and the late-time universe in the presence of a viscous holographic fluid.
Recent Formation of a Spiral Disk Hosting Progenitor Globular Clusters at the Center of the Perseus Brightest Cluster Galaxy. I. Spiral Disk

We address the nature and origin of a spiral disk at the center of NGC 1275, the giant elliptical galaxy at the center of the Perseus cluster, that spans a radius of ∼5 kpc. By comparing stellar absorption lines measured in long-slit optical spectra with synthetic spectra for single stellar populations, we find that fitting of these lines requires two stellar populations: (i) a very young population that peaks in radial velocity at ±250 km s−1 of the systemic velocity within a radius of ∼720 pc of the nucleus, a 1σ velocity dispersion significantly lower than 140 km s−1, and an age of 0.15 ± 0.05 Gyr; and (ii) a very old population having a constant radial velocity with a radius corresponding to the systemic velocity, a much broader velocity dispersion of ∼250 km s−1, and an age of around 10 Gyr. We attribute the former to a post-starburst population associated with the spiral disk, and the latter to the main stellar body of NGC 1275 along the same sight line. If the spiral disk is the remnant of a cannibalized galaxy, then its progenitor would have had to retain an enormous amount of gas in the face of intensive ram-pressure stripping so as to form a total initial mass in stars of ∼3 × 109 M ⊙. More likely, the central spiral originally comprised a gaseous body accreted over the distant past from a residual cooling flow, before experiencing a starburst ∼0.15 Gyr ago to form its stellar body.
Fate of quantum black holes

We study the quantum dynamics of the Lema\^itre-Tolman-Bondi space-times using a polymer quantization prescription based on loop quantum cosmology that incorporates fundamental discreteness. By solving an effective equation derived from this quantization, we find analytical solutions for the Oppenheimer-Snyder and thin-shell collapse models, and numerical solutions for a variety of asymptotically flat collapsing dust profiles. Our study (i) tracks the formation, evolution and disappearance of dynamical horizons, (ii) shows that matter undergoes a non-singular bounce that results in an outgoing shock wave, (iii) determines black hole lifetime to be proportional to the square its mass, and (iv) provides a conformal diagram that substantially modifies the standard"information loss"picture by resolving the singularity and replacing the event horizon by transient apparent horizons.
The principle of maximum entropy and the distribution of mass in galaxies

We do not have a final answer to the question of why galaxies choose a particular internal mass distribution. Here we examine whether the distribution is set by thermodynamic equilibrium (TE). Traditionally, TE is discarded for a number of reasons including the inefficiency of two-body collisions to thermalize the mass distribution in a Hubble time, and the fact that the mass distribution maximizing the classical Boltzmann-Gibbs entropy is unphysical. These arguments are questionable. In particular, when the Tsallis entropy that describes self-gravitating systems is used to define TE, the mass distributions that result (i.e., the polytropes) are physically sensible. This work spells out this and other arguments for TE, and presents the polytropes and their properties. It puts forward empirical evidence for the mass distribution observed in galaxies to be consistent with polytropes. It compares polytropes with Sérsic functions and it shows how the DM halos resulting from cosmological numerical simulations become polytropes when efficient collisions are allowed. It also discusses pathways to thermalization bypassing two-body collisions. It finally outlines future developments including deciphering whether or not DM particles collide efficiently.
This paper is included in the Proceedings of the 19th USENIX Symposium on Networked Systems Design and Implementation

Permissioned ledger systems allow a consortium of members that do not trust one another to execute transactions safely on a set of replicas. Such systems typically use Byzantine fault tolerance (BFT) protocols to distribute trust, which only ensures safety when fewer than 1/3 of the replicas misbehave. Providing guarantees beyond this threshold is a challenge: current systems assume that the ledger is corrupt and fail to identify misbehaving replicas or hold the members that operate them accountable—instead all members share the blame. We describe IA-CCF, a new permissioned ledger system that provides individual accountability . It can assign blame to the individual members that operate misbehaving replicas regardless of the number of misbehaving replicas or members. IA-CCF achieves this by signing and logging BFT protocol messages in the ledger, and by using Merkle trees to provide clients with succinct, universally-verifiable receipts as evidence of successful transaction execution. Anyone can audit the ledger against a set of receipts to discover inconsistencies and identify replicas that signed contradictory statements. IA-CCF also supports changes to consortium membership and replicas by tracking signing keys using a sub-ledger of governance transactions. IA-CCF provides strong disincentives to misbehavior with low overhead: it executes 47,000 tx/s while providing clients with receipts in two network round trips.
Taxonomy of Machine Learning Safety: A Survey and Primer

The open-world deployment of Machine Learning (ML) algorithms in safety-critical applications such as autonomous vehicles needs to address a variety of ML vulnerabilities such as interpretability, verifiability, and performance limitations. Research explores different approaches to improve ML dependability by proposing new models and training techniques to reduce generalization error, achieve domain adaptation, and detect outlier examples and adversarial attacks. However, there is a missing connection between ongoing ML research and well-established safety principles. In this article, we present a structured and comprehensive review of ML techniques to improve the dependability of ML algorithms in uncontrolled open-world settings. From this review, we propose the Taxonomy of ML Safety that maps state-of-the-art ML techniques to key engineering safety strategies. Our taxonomy of ML safety presents a safety-oriented categorization of ML techniques to provide guidance for improving dependability of the ML design and development. The proposed taxonomy can serve as a safety checklist to aid designers in improving coverage and diversity of safety strategies employed in any given ML system.
Overcoming Catastrophic Forgetting beyond Continual Learning: Balanced Training for Neural Machine Translation

Neural networks tend to gradually forget the previously learned knowledge when learning multiple tasks sequentially from dynamic data distributions. This problem is called catastrophic forgetting, which is a fundamental challenge in the continual learning of neural networks. In this work, we observe that catastrophic forgetting not only occurs in continual learning but also affects the traditional static training. Neural networks, especially neural machine translation models, suffer from catastrophic forgetting even if they learn from a static training set. To be specific, the final model pays imbalanced attention to training samples, where recently exposed samples attract more attention than earlier samples. The underlying cause is that training samples do not get balanced training in each model update, so we name this problem imbalanced training. To alleviate this problem, we propose Complementary Online Knowledge Distillation (COKD), which uses dynamically updated teacher models trained on specific data orders to iteratively provide complementary knowledge to the student model. Experimental results on multiple machine translation tasks show that our method successfully alleviates the problem of imbalanced training and achieves substantial improvements over strong baseline systems.
Open-Ended Knowledge Tracing

Knowledge tracing refers to the problem of estimating each student’s knowledge component/skill mastery level from their past responses to questions in educational applications. One direct beneﬁt knowledge tracing methods provide is the ability to predict each student’s performance on the future questions. However, one key limitation of most existing knowledge tracing methods is that they treat student responses to questions as binary-valued, i.e., whether the responses are correct or incorrect. Response correctness analysis/prediction is easy to navigate but loses important information, especially for open-ended questions: the exact student responses can potentially provide much more information about their knowledge states than only response correctness. In this paper, we present our ﬁrst exploration into open-ended knowledge tracing , i.e., the analysis and prediction of students’ open-ended responses to questions in the knowledge tracing setup. We ﬁrst lay out a generic framework for open-ended knowledge tracing before detailing its application to the domain of computer science education with programming questions. We deﬁne a series of evaluation metrics in this domain and conduct a series of quantitative and qualitative experiments to test the boundaries of open-ended knowledge tracing methods on a real-world student code dataset.
Learn to Match with No Regret: Reinforcement Learning in Markov Matching Markets

We study a Markov matching market involving a planner and a set of strategic agents on the two sides of the market. At each step, the agents are presented with a dynamical context, where the contexts determine the utilities. The planner controls the transition of the contexts to maximize the cumulative social welfare, while the agents aim to find a myopic stable matching at each step. Such a setting captures a range of applications including ridesharing platforms. We formalize the problem by proposing a reinforcement learning framework that integrates optimistic value iteration with maximum weight matching. The proposed algorithm addresses the coupled challenges of sequential exploration, matching stability, and function approximation. We prove that the algorithm achieves sublinear regret.
Localized bulging of an inflated rubber tube with fixed ends

When a rubber tube with free ends is inflated under volume control, the pressure will first reach a maximum and then decrease monotonically to approach a constant asymptote. The pressure maximum corresponds to the initiation of a localized bulge and is predicted by a bifurcation condition, whereas the asymptote is the Maxwell pressure corresponding to a ‘two-phase’ propagation state. By contrast, when the tube is first pre-stretched and then has its ends fixed during subsequent inflation, the pressure as a function of the bulge amplitude has both a maximum and a minimum, and the behaviour on the right ascending branch has previously not been fully understood. We show that for all values of the pre-stretch and tube length, the ascending branches all converge to a single curve that is dependent only on the ratio of the tube thickness to the outer radius. This curve represents the Maxwell state to be approached in each case (if Euler buckling or axisymmetric wrinkling does not occur first), but this state is pressure-dependent, in contrast to the free-ends case. We also demonstrate experimentally that localized bulging cannot occur when the pre-stretch is sufficiently large and investigate what strain-energy functions can predict this observed phenomenon. This article is part of the theme issue ‘The Ogden model of rubber mechanics: Fifty years of impact on nonlinear elasticity’.
InsightNet: non-contact blood pressure measuring network based on face video

Blood pressure indicates cardiac function and peripheral vascular resistance and is critical for disease diagnosis. Traditionally, blood pressure data are mainly acquired through contact sensors, which require high maintenance and may be inconvenient and unfriendly to some people (e.g., burn patients). In this paper, an eﬃcient non-contact blood pressure measurement network based on face videos is proposed for the ﬁrst time. An innovative oversampling training strategy is proposed to handle the unbalanced data distribution. The input video sequences are ﬁrst normalized and converted to our proposed YUVT color space. Then, the Spatio-temporal slicer encodes it into a multi-domain Spatio-temporal mapping. Finally, the neural network computation module, used for high-dimensional feature extraction of the multi-domain spatial feature mapping, after which the extracted high-dimensional features are used to enhance the time-domain feature association using LSTM, is computed by the blood pressure classiﬁer to obtain the blood pressure measurement intervals. Combining the output of feature extraction and the result after classiﬁcation, the blood pressure calculator, calculates the blood pressure measurement values. The so-lution uses a blood pressure classiﬁer to calculate blood pressure intervals, which can help the neural network distinguish between the high-dimensional features of diﬀerent blood pressure intervals and alleviate the overﬁtting phenomenon.
Student Become Decathlon Master in Retinal Vessel Segmentation via Dual-teacher Multi-target Domain Adaptation

Unsupervised domain adaptation has been proposed recently to tackle the so-called domain shift between training data and test data with different distributions. However, most of them only focus on single-target domain adaptation and cannot be applied to the scenario with multiple target domains. In this paper, we propose RVms, a novel unsupervised multi-target domain adaptation approach to segment retinal vessels (RVs) from multimodal and multicenter retinal images. RVms mainly consists of a style augmentation and transfer (SAT) module and a dual-teacher knowledge distillation (DTKD) module. SAT augments and clusters images into source-similar domains and source-dissimilar domains via Bezier and Fourier transformations. DTKD utilizes the augmented and transformed data to train two teachers, one for source-similar domains and the other for source-dissimilar domains. Afterwards, knowledge distillation is performed to iteratively distill different domain knowledge from teachers to a generic student. The local relative intensity transformation is employed to characterize RVs in a domain invariant manner and promote the generalizability of teachers and student models. Moreover, we construct a new multimodal and multicenter vascular segmentation dataset from existing publicly-available datasets, which can be used to benchmark various domain adaptation and domain generalization methods. Through extensive experiments, RVms is found to be very close to the target-trained Oracle in terms of segmenting the RVs, largely outperforming other state-of-the-art methods.
Low Mass Stars as Tracers of Star and Cluster Formation

We review the use of young low mass stars and protostars, or young stellar objects (YSOs), as tracers of star formation. Observations of molecular clouds at visible, infrared, radio and X-ray wavelengths can identify and characterize the YSOs populating these clouds, with the ability to detect deeply embedded objects at all evolutionary stages. Surveys with the Spitzer, Herschel, XMM-Newton and Chandra space telescopes have measured the spatial distribution of YSOs within a number of nearby (<2.5 kpc) molecular clouds, showing surface densities varying by more than three orders of magnitude. These surveys have been used to measure the spatially varying star formation rates and efficiencies within clouds, and when combined with maps of the molecular gas, have led to the discovery of star-forming relations within clouds. YSO surveys can also characterize the structures, ages, and star formation histories of embedded clusters, and they illuminate the relationship of the clusters to the networks of filaments, hubs and ridges in the molecular clouds from which they form. Measurements of the proper motions and radial velocities of YSOs trace the evolving kinematics of clusters from the deeply embedded phases through gas dispersal, providing insights into the factors that shape the formation of bound clusters. On 100 pc scales that encompass entire star-forming complexes, Gaia is mapping the young associations of stars that have dispersed their natal gas and exist alongside molecular clouds. These surveys reveal the complex structures and motions in associations, and show evidence for supernova driven expansions. Remnants of these associations have now been identified by Gaia, showing that traces of star-forming structures can persist for a few hundred million years.