Miracle-dz/UniFAR-FTUs huggingface dataset:
$L^{1}$ metric geometry of potentials with prescribed singularities on compact Kähler manifolds

Given $(X,\omega)$ compact Kahler manifold and $\psi\in\mathcal{M}^{+}\subset PSH(X,\omega)$ a model type envelope with non-zero mass, i.e. a fixed potential determing some singularities such that $\int_{X}(\omega+dd^{c}\psi)^{n}>0$, we prove that the $\psi-$relative finite energy class $\mathcal{E}^{1}(X,\omega,\psi)$ becomes a complete metric space if endowed with a distance $d$ which generalizes the well-known $d_{1}$ distance on the space of Kahler potentials. Moreover, for $\mathcal{A}\subset \mathcal{M}^{+}$ total ordered, we equip the set $X_{\mathcal{A}}:=\bigsqcup_{\psi\in\overline{\mathcal{A}}}\mathcal{E}^{1}(X,\omega,\psi)$ with a natural distance $d_{\mathcal{A}}$ which coincides with the distance $d$ on $\mathcal{E}^{1}(X,\omega,\psi)$ for any $\psi\in\overline{\mathcal{A}}$. We show that $\big(X_{\mathcal{A}},d_{\mathcal{A}}\big)$ is a complete metric space. As a consequence, assuming $\psi_{k}\searrow \psi$ and $\psi_{k},\psi\in \mathcal{M}^{+}$, we also prove that $\big(\mathcal{E}^{1}(X,\omega,\psi_{k}),d\big)$ converges in a Gromov-Hausdorff sense to $\big(\mathcal{E}^{1}(X,\omega,\psi),d\big)$ and that there exists a direct system $\Big\langle\big(\mathcal{E}^{1}(X,\omega,\psi_{k}),d\big),P_{k,j}\Big\rangle$ in the category of metric spaces whose direct limit is dense into $\big(\mathcal{E}^{1}(X,\omega,\psi),d\big)$.
Climate Policy under Spatial Heat Transport: Cooperative and Noncooperative Regional Outcomes

We build a novel stochastic dynamic regional integrated assessment model (IAM) of the climate and economic system including a number of important climate science elements that are missing in most IAMs. These elements are spatial heat transport from the Equator to the Poles, sea level rise, permafrost thaw and tipping points. We study optimal policies under cooperation and noncooperation between two regions (the North and the Tropic-South) in the face of risks and recursive utility. We introduce a new general computational algorithm to find feedback Nash equilibrium. Our results suggest that when the elements of climate science are ignored, important policy variables such as the optimal regional carbon tax and adaptation could be seriously biased. We also find the regional carbon tax is significantly smaller in the feedback Nash equilibrium than in the social planner's problem in each region, and the North has higher carbon taxes than the Tropic-South.
ACUTE-EVAL: Improved Dialogue Evaluation with Optimized Questions and Multi-turn Comparisons

While dialogue remains an important end-goal of natural language research, the difficulty of evaluation is an oft-quoted reason why it remains troublesome to make real progress towards its solution. Evaluation difficulties are actually two-fold: not only do automatic metrics not correlate well with human judgments, but also human judgments themselves are in fact difficult to measure. The two most used human judgment tests, single-turn pairwise evaluation and multi-turn Likert scores, both have serious flaws as we discuss in this work. We instead provide a novel procedure involving comparing two full dialogues, where a human judge is asked to pay attention to only one speaker within each, and make a pairwise judgment. The questions themselves are optimized to maximize the robustness of judgments across different annotators, resulting in better tests. We also show how these tests work in self-play model chat setups, resulting in faster, cheaper tests. We hope these tests become the de facto standard, and will release open-source code to that end.
A Novel Hierarchical Binary Tagging Framework for Joint Extraction of Entities and Relations

Extracting relational triples from unstructured text is crucial for large-scale knowledge graph construction. However, few existing works excel in solving the overlapping triple problem where multiple relational triples in the same sentence share the same entities. We propose a novel Hierarchical Binary Tagging (HBT) framework derived from a principled problem formulation. Instead of treating relations as discrete labels as in previous works, our new framework models relations as functions that map subjects to objects in a sentence, which naturally handles overlapping triples. Experiments show that the proposed framework already outperforms state-of-the-art methods even its encoder module uses a randomly initialized BERT encoder, showing the power of the new tagging framework. It enjoys further performance boost when employing a pretrained BERT encoder, outperforming the strongest baseline by 25.6 and 45.9 absolute gain in F1-score on two public datasets NYT and WebNLG, respectively. In-depth analysis on different types of overlapping triples shows that the method delivers consistent performance gain in all scenarios.
Waveform of gravitational waves in the ghost-free parity-violating gravities

Gravitational waves (GWs) provide an excellent opportunity to test the gravity in the strong gravitational fields. In this article, we calculate the waveform of GWs, produced by the coalescence of compact binaries, in an extension of the Chern-Simons gravity by including higher derivatives of the coupling scalar field. By comparing the two circular polarization modes, we find the effects of amplitude birefringence and velocity birefringence of GWs in their propagation caused by the parity violation in gravity, which are explicitly presented in the GW waveforms by the amplitude and phase modifications, respectively. Combining the two modes, we obtain the GW waveforms in the Fourier domain and find that the deviations from those in general relativity are dominated by effects of velocity birefringence of GWs. In addition, we also map the effects of the parity violation on the waveform onto the parametrized post-Einsteinian (PPE) framework and identify explicitly the PPE parameters.
Respiratory Rate Estimation from Face Videos

Vital signs, such as heart rate (HR), heart rate variability (HRV), respiratory rate (RR), are important indicators for a person's health. Vital signs are traditionally measured with contact sensors, and may be inconvenient and cause discomfort during continuous monitoring. Commercial cameras are promising contact-free sensors, and remote photoplethysmography (rPPG) have been studied to remotely monitor heart rate from face videos. For remote RR measurement, most prior art was based on small periodical motions of chest regions caused by breathing cycles, which are vulnerable to subjects' voluntary movements. This paper explores remote RR measurement based on rPPG obtained from face videos. The paper employs motion compensation, two-phase temporal filtering, and signal pruning to capture signals with high quality. The experimental results demonstrate that the proposed framework can obtain accurate RR results and can provide HR, HRV and RR measurement synergistically in one framework.
Semantic Role Labeling with Iterative Structure Refinement

Modern state-of-the-art Semantic Role Labeling (SRL) methods rely on expressive sentence encoders (e.g., multi-layer LSTMs) but tend to model only local (if any) interactions between individual argument labeling decisions. This contrasts with earlier work and also with the intuition that the labels of individual arguments are strongly interdependent. We model interactions between argument labeling decisions through iterative refinement. Starting with an output produced by a factorized model, we iteratively refine it using a refinement network. Instead of modeling arbitrary interactions among roles and words, we encode prior knowledge about the SRL problem by designing a restricted network architecture capturing non-local interactions. This modeling choice prevents overfitting and results in an effective model, outperforming strong factorized baseline models on all 7 CoNLL-2009 languages, and achieving state-of-the-art results on 5 of them, including English.
Cumulative Quality Modeling for HTTP Adaptive Streaming

HTTP Adaptive Streaming has become the de facto choice for multimedia delivery. However, the quality of adaptive video streaming may fluctuate strongly during a session due to throughput fluctuations. So, it is important to evaluate the quality of a streaming session over time. In this article, we propose a model to estimate the cumulative quality for HTTP Adaptive Streaming. In the model, a sliding window of video segments is employed as the basic building block. Through statistical analysis using a subjective dataset, we identify four important components of the cumulative quality model, namely the minimum window quality, the last window quality, the maximum window quality, and the average window quality. Experiment results show that the proposed model achieves high prediction performance and outperforms related quality models. In addition, another advantage of the proposed model is its simplicity and effectiveness for deployment in real-time estimation. Our subjective dataset as well as the source code of the proposed model have been made publicly available at https://sites.google. com/site/huyenthithanhtran1191/cqmdatabase.
C-Differentials, Multiplicative Uniformity, and (Almost) Perfect c-Nonlinearity

In this paper we define a new (output) multiplicative differential, and the corresponding <italic>c</italic>-differential uniformity. With this new concept, even for characteristic 2, there are perfect <inline-formula> <tex-math notation="LaTeX">${c}$ </tex-math></inline-formula>-nonlinear (PcN) functions. We first characterize the <inline-formula> <tex-math notation="LaTeX">${c}$ </tex-math></inline-formula>-differential uniformity of a function in terms of its Walsh transform. We further look at some of the known perfect nonlinear (PN) functions and show that only one remains a PcN function, under a different condition on the parameters. In fact, the <italic>p</italic>-ary Gold PN function increases its <italic>c</italic>-differential uniformity significantly, under some conditions on the parameters. We then precisely characterize the <italic>c</italic>-differential uniformity of the inverse function (in any dimension and characteristic), relevant for the Rijndael (and Advanced Encryption Standard) block cipher.
Measuring the neutron star equation of state with gravitational waves: The first forty binary neutron star merger observations

Gravitational waves from binary neutron star coalescences contain rich information about matter at supranuclear densities encoded by the neutron star equation of state. We can measure the equation of state by analyzing the tidal interactions between neutron stars, which is quantified by the tidal deformability. Multiple merger events are required to probe the equation of state over a range of neutron star masses. The more events included in the analysis, the stronger the constraints on the equation of state. In this paper, we build on previous work to explore the constraints that LIGO and Virgo are likely to place on the neutron star equation of state by combining the first forty binary neutron star detections, a milestone we project to be reached during the first year of accumulated design-sensitivity data. We carry out Bayesian inference on a realistic mock dataset of binaries to obtain posterior distributions for neutron star tidal parameters. In order to combine posterior samples from multiple observations, we employ a random forest regressor, which allows us to efficiently interpolate the likelihood distribution. Assuming a merger rate of 1540 Gpc$^{-3}$ yr$^{-1}$ and a LIGO-Virgo detector network operating for one year at the sensitivity of the third-observation run, plus an additional eight months of design sensitivity, we find that the radius of a 1.4 $M_\odot$ neutron star can be constrained to $\sim 10$% at 90% confidence. At the same time, the pressure at twice the nuclear saturation density can be constrained to $\sim 45$ % at 90% confidence. Finally, we add an appendix following publication of the paper in the journal, showing the posterior distribution of the maximum neutron star mass allowed by the equation of state. We find that the maximum mass can be constrained to $\sim 0.3$ $M_\odot$ at 90% confidence.
Is two greater than one?: Analyzing Multipath TCP over Dual-LTE in the Wild

Multipath TCP (MPTCP) is a standardized TCP extension which allows end-hosts to simultaneously exploit all of their network interfaces. The recent proliferation of dual-SIM mobile phones makes multi-LTE MPTCP setup an attractive option. We perform extensive measurements of MPTCP over two LTE connections in low and high-speed mobility scenarios over five months, both in controlled and in-the-wild environments. Our findings indicate that MPTCP performance decreases at high speeds due to increased frequency of signal strength drops and handovers. Both LTE paths experience frequent changes which result in a sub-optimal subflow utilization. We also find that while path changes are unpredictable, their impact on MPTCP follows a deterministic trend. Finally, we show that both application traffic patterns and congestion control variants impact MPTCP adaptability at high speeds.
Optimal Causal Rate-Constrained Sampling of the Wiener Process

We consider the following communication scenario. An encoder causally observes the Wiener process and decides when and what to transmit about it. A decoder makes real-time estimation of the process using causally received codewords. We determine the causal encoding and decoding policies that jointly minimize the mean-square estimation error, under the long-term communication rate constraint of R bits per second. We show that an optimal encoding policy can be implemented as a causal sampling policy followed by a causal compressing policy. We prove that the optimal encoding policy samples the Wiener process once the innovation passes either $\sqrt{\frac{1}{R}}$ or $-\sqrt{\frac{1}{R}}$, and compresses the sign of the innovation (SOI) using a 1-bit codeword. The SOI coding scheme achieves the operational distortion-rate function, which is equal to $D^{\mathrm{o}\mathrm{p}}(R)= \displaystyle \frac{1}{6R}$. Surprisingly, this is significantly better than the distortion-rate tradeoff achieved in the limit of infinite delay by the best non-causal code. This is because the SOI coding scheme leverages the free timing information supplied by the zero-delay channel between the encoder and the decoder. The key to unlock that gain is the event-triggered nature of the SOI sampling policy. In contrast, the distortion-rate tradeoffs achieved with deterministic sampling policies are much worse: we prove that the causal informational distortion-rate function in that scenario is as high as $D_{\mathrm{D}\mathrm{E}\mathrm{T}}(R) = \displaystyle \frac{5}{6R}$. It is achieved by the uniform sampling policy with the sampling interval $\displaystyle \frac{1}{R}$. In either case, the optimal strategy is to sample the process as fast as possible and to transmit 1-bit codewords to the decoder without delay.
On the Downstream Performance of Compressed Word Embeddings

Compressing word embeddings is important for deploying NLP models in memory-constrained settings. However, understanding what makes compressed embeddings perform well on downstream tasks is challenging-existing measures of compression quality often fail to distinguish between embeddings that perform well and those that do not. We thus propose the eigenspace overlap score as a new measure. We relate the eigenspace overlap score to downstream performance by developing generalization bounds for the compressed embeddings in terms of this score, in the context of linear and logistic regression. We then show that we can lower bound the eigenspace overlap score for a simple uniform quantization compression method, helping to explain the strong empirical performance of this method. Finally, we show that by using the eigenspace overlap score as a selection criterion between embeddings drawn from a representative set we compressed, we can efficiently identify the better performing embedding with up to 2× lower selection error rates than the next best measure of compression quality, and avoid the cost of training a model for each task of interest.
A Systematic Review on Learning and Suggesting Source Code Changes in Version History

Software systems are in continuous evolution through source code changes to fixing bugs, adding new functionalities and improving the internal architecture. All these practices are recorded in the version history, which can be reused as an advantage in the development process. We conducted a systematic review related to primary studies about source code changes. The search approach identified 2410 studies, up to and including 2012, which resulted in a final set of 40 selected papers. We grouped the studies according to each established research question. This review investigates how source code changes, which were performed in the past of software, can support the improvement of the software project. The main implication of this review is that, source code changes as examples, to support the improvement of coding practice during the development process, in which we presented some relevant strategies to guide each step, since identifying until the suggesting of source code changes.
Formal Methods and CyberSecurity

Formal methods have been largely thought of in the context of safety-critical systems, where they have achieved major acceptance. Tens of millions of people trust their lives every day to such systems, based on formal proofs rather than ``we haven't found a bug'' (yet! ). Why is ``we haven't found a bug'' an acceptable basis for systems trusted with hundreds of millions of people's personal data? This paper looks at some of the issues in CyberSecurity, and the extent to which formal methods, ranging from ``fully verified'' to better tool support, could help. Alas The Royal Society (2016) only recommended formal methods in the limited context of ``safety critical applications'': we suggest this is too limited.
A proof of N.Takahashi's conjecture on genus zero Gromov-Witten theory of $(\mathbb{P}^2,E)$

We prove N. Takahashi’s conjecture on genus 0 Gromov-Witten theory of P relative to a smooth cubic. Using the fact that the same scattering diagram arises in the tropical computation of the relevant Gromov-Witten invariants by Gabele, and in the description of the wall-crossing behavior of coherent sheaves on P, we reduce N. Takahashi’s conjecture to a known statement about moduli spaces of Gieseker semistable sheaves of dimension 1 on P.
No Sign of G2's Encounter Affecting Sgr A*'s X-Ray Flaring Rate from Chandra Observations

An unusual object, G2, had its pericenter passage around Sgr A*, the 4 × 106 M⊙ supermassive black hole in the Galactic Center, in Summer 2014. Several research teams have reported evidence that, following G2's pericenter encounter, the rate of Sgr A*'s bright X-ray flares increased significantly. Our analysis carefully treats varying flux contamination from a nearby magnetic neutron star and is free from complications induced by using data from multiple X-ray observatories with different spatial resolutions. We test the scenario of an increased bright X-ray flaring rate using a massive data set from the Chandra X-ray Observatory, the only X-ray instrument that can spatially distinguish between Sgr A* and the nearby Galactic Center magnetar throughout the full extended period encompassing G2's encounter with Sgr A*. We use X-ray data from the 3 Ms observations of the Chandra X-ray Visionary Program (XVP) in 2012, as well as an additional 1.5 Ms of observations up to 2018. We use detected flares to make distributions of flare properties. Using simulations of X-ray flares accounting for important factors such as the different Chandra instrument modes, we test the null hypothesis on Sgr A*'s bright (or any flare category) X-ray flaring rate around different potential change points. In contrast to previous studies, our results are consistent with the null hypothesis; the same model parameters produce distributions consistent with the observed ones around any plausible change point.
An Experiment on Network Density and Sequential Learning

We conduct a sequential social-learning experiment where subjects take turns guessing a hidden state based on private signals and the guesses of a subset of their predecessors. A network determines the observable predecessors, and we compare subjects' accuracy on sparse and dense networks. Accuracy gains from social learning are twice as large on sparse networks compared to dense networks. Models of naive inference where agents ignore correlation between observations predict this comparative static in network density, while the finding is difficult to reconcile with rational-learning models. The full paper can be found at https://arxiv.org/abs/1909.02220
Graph-Based Decoding Model for Functional Alignment of Unaligned fMRI Data

Aggregating multi-subject functional magnetic resonance imaging (fMRI) data is indispensable for generating valid and general inferences from patterns distributed across human brains. The disparities in anatomical structures and functional topographies of human brains warrant aligning fMRI data across subjects. However, the existing functional alignment methods cannot handle well various kinds of fMRI datasets today, especially when they are not temporally-aligned, i.e., some of the subjects probably lack the responses to some stimuli, or different subjects might follow different sequences of stimuli. In this paper, a cross-subject graph that depicts the (dis)similarities between samples across subjects is used as a priori for developing a more flexible framework that suits an assortment of fMRI datasets. However, the high dimension of fMRI data and the use of multiple subjects makes the crude framework time-consuming or unpractical. To address this issue, we further regularize the framework, so that a novel feasible kernel-based optimization, which permits non-linear feature extraction, could be theoretically developed. Specifically, a low-dimension assumption is imposed on each new feature space to avoid overfitting caused by the high-spatial-low-temporal resolution of fMRI data. Experimental results on five datasets suggest that the proposed method is not only superior to several state-of-the-art methods on temporally-aligned fMRI data, but also suitable for dealing with temporally-unaligned fMRI data.
What is the physics behind the Larson mass–size relation?

Different studies have reported a power-law mass–size relation M ∝ Rq for ensembles of molecular clouds. In the case of nearby clouds, the index of the power-law q is close to 2. However, for clouds spread all over the Galaxy, indexes larger than 2 are reported. We show that indexes larger than 2 could be the result of line-of-sight superposition of emission that does not belong to the cloud itself. We found that a random factor of gas contamination, between 0.001 per cent and 10 per cent of the line of sight, allows to reproduce the mass–size relation with q ∼ 2.2–2.3 observed in Galactic CO surveys. Furthermore, for dense cores within a single cloud, or molecular clouds within a single galaxy, we argue that, even in these cases, there is observational and theoretical evidence that some degree of superposition may be occurring. However, additional effects may be present in each case, and are briefly discussed. We also argue that defining the fractal dimension of clouds via the mass–size relation is not adequate, since the mass is not necessarily a proxy to the area, and the size reported in M−R relations is typically obtained from the square root of the area, rather than from an estimation of the size independent from the area. Finally, we argue that the statistical analysis of finding clouds satisfying the Larson’s relations does not mean that each individual cloud is in virial equilibrium.