Miracle-dz/UniFAR-FTUs huggingface dataset:
Few-Shot Action Localization without Knowing Boundaries

Learning to localize actions in long, cluttered, and untrimmed videos is a hard task, that in the literature has typically been addressed assuming the availability of large amounts of annotated training samples for each class -- either in a fully-supervised setting, where action boundaries are known, or in a weakly-supervised setting, where only class labels are known for each video. In this paper, we go a step further and show that it is possible to learn to localize actions in untrimmed videos when a) only one/few trimmed examples of the target action are available at test time, and b) when a large collection of videos with only class label annotation (some trimmed and some weakly annotated untrimmed ones) are available for training; with no overlap between the classes used during training and testing. To do so, we propose a network that learns to estimate Temporal Similarity Matrices (TSMs) that model a fine-grained similarity pattern between pairs of videos (trimmed or untrimmed), and uses them to generate Temporal Class Activation Maps (TCAMs) for seen or unseen classes. The TCAMs serve as temporal attention mechanisms to extract video-level representations of untrimmed videos, and to temporally localize actions at test time. To the best of our knowledge, we are the first to propose a weakly-supervised, one/few-shot action localization network that can be trained in an end-to-end fashion. Experimental results on THUMOS14 and ActivityNet1.2 datasets, show that our method achieves performance comparable or better to state-of-the-art fully-supervised, few-shot learning methods.
Time series analysis of real-world complex systems - climate, finance, proteins, and physiology

Background– Heartbeat fluctuations exhibit temporal structure with robust long-range correlations, fractal and nonlinear features, which have been found to break down with pathologic conditions, reflecting changes in the mechanism of neuroautonomic control. It has been hypothesized that these features change and even break down also with advanced age, suggesting fundamental alterations in cardiac control with aging. Here we test this hypothesis. Methods and Results– We analyze heartbeat interval recordings from two independent databases: (a) 19 healthy young (avg. age 25.7 years) and 16 healthy elderly subjects (avg. age 73.8 years) during 2h under resting conditions from the Fantasia database; and (b) 29 healthy elderly subjects (avg. age 75.9 years) during ≈ 8h of sleep from the SHHS database, and the same subjects recorded 5 years later. We quantify: (1) The average heart rate 〈RR〉; (2) the standard deviation (SD) σRR and σ∆RR of the heartbeat intervals RR and their increments ∆RR; (3) the long-range correlations in RR as measured by the scaling exponent αRR using the Detrended Fluctuation Analysis; (4) fractal linear and nonlinear properties as represented by the scaling exponents αsign and αmag for the time series of the sign and magnitude of ∆RR; (5) the nonlinear fractal dimension D(k) of RR using the Fractal Dimension Analysis. We find: (1) No significant difference in 〈RR〉 (P > 0.05); (2) a significant difference in σRR and σ∆RR for the Fantasia groups (P < 10−4) but no significant change with age between the elderly SHHS groups (P < 0.5); (3) no significant change in the fractal measures αRR, αsign (P > 0.14), αmag (P > 0.12), and D(k) with age. Conclusions– Our findings do not support the hypothesis that fractal linear and nonlinear characteristics of heartbeat dynamics break down with advanced age in healthy subjects. While our results indeed show a reduced SD of heart-
Asynchronous Distributed Optimization with Redundancy in Cost Functions

This paper considers the problem of asynchronous distributed multi-agent optimization on server-based system architecture. In this problem, each agent has a local cost, and the goal for the agents is to collectively find a minimum of their aggregate cost. A standard algorithm to solve this problem is the iterative distributed gradient-descent (DGD) method being implemented collaboratively by the server and the agents. In the synchronous setting, the algorithm proceeds from one iteration to the next only after all the agents complete their expected communication with the server. However, such synchrony can be expensive and even infeasible in real-world applications. We show that waiting for all the agents is unnecessary in many applications of distributed optimization, including distributed machine learning, due to redundancy in the cost functions (or {\em data}). Specifically, we consider a generic notion of redundancy named $(r,\epsilon)$-redundancy implying solvability of the original multi-agent optimization problem with $\epsilon$ accuracy, despite the removal of up to $r$ (out of total $n$) agents from the system. We present an asynchronous DGD algorithm where in each iteration the server only waits for (any) $n-r$ agents, instead of all the $n$ agents. Assuming $(r,\epsilon)$-redundancy, we show that our asynchronous algorithm converges to an approximate solution with error that is linear in $\epsilon$ and $r$. Moreover, we also present a generalization of our algorithm to tolerate some Byzantine faulty agents in the system. Finally, we demonstrate the improved communication efficiency of our algorithm through experiments on MNIST and Fashion-MNIST using the benchmark neural network LeNet.
What training reveals about neural network complexity

This work explores the Benevolent Training Hypothesis (BTH) which argues that the complexity of the function a deep neural network (NN) is learning can be deduced by its training dynamics. Our analysis provides evidence for BTH by relating the NN's Lipschitz constant at different regions of the input space with the behavior of the stochastic training procedure. We first observe that the Lipschitz constant close to the training data affects various aspects of the parameter trajectory, with more complex networks having a longer trajectory, bigger variance, and often veering further from their initialization. We then show that NNs whose 1st layer bias is trained more steadily (i.e., slowly and with little variation) have bounded complexity even in regions of the input space that are far from any training point. Finally, we find that steady training with Dropout implies a training- and data-dependent generalization bound that grows poly-logarithmically with the number of parameters. Overall, our results support the intuition that good training behavior can be a useful bias towards good generalization.
Vertical Structure of Galactic Disk Kinematics from LAMOST K Giants

We examine the vertical structure of Galactic disk kinematics over a Galactocentric radial distance range of R = 5–15 kpc and up to 3 kpc away from the Galactic plane, using the K-type giants surveyed by LAMOST. Based on robust measurements of three-dimensional velocity moments, a wobbly disk is detected in a phenomenological sense. An outflow dominates the radial motion of the inner disk, while in the outer disk there exist alternate outward and inward flows. The vertical bulk velocities is a combination of breathing and bending modes. A contraction-like breathing mode with amplitudes increasing with the distance to the plane and an upward bending mode dominate the vertical motion outside R 0, and there are reversed breathing and bending modes at R < R 0, with amplitudes much smaller than those outside R 0. The mean azimuthal velocity decreases with the increasing distance to the plane, with gradients shallower for larger R. Stars in the south disk are rotating faster than stars in the north. The velocity ellipsoid orientation differs between different R: in the range of 5<R<9kpc , the gradient of the tilt angle with respect to arctan(Z/R) decreases from ∼0.83 for the inner disk to ∼0.52 for the outer disk; within 9<R<15kpc , the tilt of velocity ellipsoid deviates from vertical antisymmetry. A clear flaring signature is found for both north and south disks based on the observed vertical structures of velocity ellipsoid.
Gravity-driven Magnetic Field at ∼1000 au Scales in High-mass Star Formation

A full understanding of high-mass star formation requires the study of one of the most elusive components of the energy balance in the interstellar medium: magnetic fields. We report Atacama Large Millimeter/submillimeter Array (ALMA) 1.2 mm, high-resolution (700 au) dust polarization and molecular line observations of the rotating hot molecular core embedded in the high-mass star-forming region IRAS 18089−1732. The dust continuum emission and magnetic field morphology present spiral-like features resembling a whirlpool. The velocity field traced by the H13CO+ (J = 3−2) transition line reveals a complex structure with spiral filaments that are likely infalling and rotating, dragging the field with them. We have modeled the magnetic field and find that the best model corresponds to a weakly magnetized core with a mass-to-magnetic-flux ratio (λ) of 8.38. The modeled magnetic field is dominated by a poloidal component, but with an important contribution from the toroidal component that has a magnitude of 30% of the poloidal component. Using the Davis–Chandrasekhar–Fermi method, we estimate a magnetic field strength of 3.5 mG. At the spatial scales accessible to ALMA, an analysis of the energy balance of the system indicates that gravity overwhelms turbulence, rotation, and the magnetic field. We show that high-mass star formation can occur in weakly magnetized environments, with gravity taking the dominant role.
A critical look at the current train/test split in machine learning

The randomized or cross-validated split of training and testing sets has been adopted as the gold standard of machine learning for decades. The establishment of these split protocols are based on two assumptions: (i)-fixing the dataset to be eternally static so we could evaluate different machine learning algorithms or models; (ii)-there is a complete set of annotated data available to researchers or industrial practitioners. However, in this article, we intend to take a closer and critical look at the split protocol itself and point out its weakness and limitation, especially for industrial applications. In many real-world problems, we must acknowledge that there are numerous situations where assumption (ii) does not hold. For instance, for interdisciplinary applications like drug discovery, it often requires real lab experiments to annotate data which poses huge costs in both time and financial considerations. In other words, it can be very difficult or even impossible to satisfy assumption (ii). In this article, we intend to access this problem and reiterate the paradigm of active learning, and investigate its potential on solving problems under unconventional train/test split protocols. We further propose a new adaptive active learning architecture (AAL) which involves an adaptation policy, in comparison with the traditional active learning that only unidirectionally adds data points to the training pool. We primarily justify our points by extensively investigating an interdisciplinary drug-protein binding problem. We additionally evaluate AAL on more conventional machine learning benchmarking datasets like CIFAR-10 to demonstrate the generalizability and efficacy of the new framework.
Measuring and Improving BERT’s Mathematical Abilities by Predicting the Order of Reasoning.

Imagine you are in a supermarket. You have two bananas in your basket and want to buy four apples. How many fruits do you have in total? This seemingly straightforward question can be challenging for data-driven language models, even if trained at scale. However, we would expect such generic language models to possess some mathematical abilities in addition to typical linguistic competence. Towards this goal, we investigate if a commonly used language model, BERT, possesses such mathematical abilities and, if so, to what degree. For that, we fine-tune BERT on a popular dataset for word math problems, AQuA-RAT, and conduct several tests to understand learned representations better. Since we teach models trained on natural language to do formal mathematics, we hypothesize that such models would benefit from training on semi-formal steps that explain how math results are derived. To better accommodate such training, we also propose new pretext tasks for learning mathematical rules. We call them (Neighbor) Reasoning Order Prediction (ROP or NROP). With this new model, we achieve significantly better outcomes than data-driven baselines and even on-par with more tailored models.
Online Learning Robust Control of Nonlinear Dynamical Systems

In this work we address the problem of the online robust control of nonlinear dynamical systems perturbed by disturbance. We study the problem of attenuation of the total cost over a duration $T$ in response to the disturbances. We consider the setting where the cost function (at a particular time) is a general continuous function and adversarial, the disturbance is adversarial and bounded at any point of time. Our goal is to design a controller that can learn and adapt to achieve a certain level of attenuation. We analyse two cases (i) when the system is known and (ii) when the system is unknown. We measure the performance of the controller by the deviation of the controller's cost for a sequence of cost functions with respect to an attenuation $\gamma$, $R^p_t$. We propose an online controller and present guarantees for the metric $R^p_t$ when the maximum possible attenuation is given by $\overline{\gamma}$, which is a system constant. We show that when the controller has preview of the cost functions and the disturbances for a short duration of time and the system is known $R^p_T(\gamma) = O(1)$ when $\gamma \geq \gamma_c$, where $\gamma_c = \mathcal{O}(\overline{\gamma})$. We then show that when the system is unknown the proposed controller with a preview of the cost functions and the disturbances for a short horizon achieves $R^p_T(\gamma) = \mathcal{O}(N) + \mathcal{O}(1) + \mathcal{O}((T-N)g(N))$, when $\gamma \geq \gamma_c$, where $g(N)$ is the accuracy of a given nonlinear estimator and $N$ is the duration of the initial estimation period. We also characterize the lower bound on the required prediction horizon for these guarantees to hold in terms of the system constants.
Stabilizing Label Assignment for Speech Separation by Self-Supervised Pre-Training

Speech separation has been well developed, with the very successful permutation invariant training (PIT) approach, although the frequent label assignment switching happening during PIT training remains to be a problem when better convergence speed and achievable performance are desired. In this paper, we propose to perform self-supervised pre-training to stabilize the label assignment in training the speech separation model. Experiments over several types of self-supervised approaches, several typical speech separation models and two different datasets showed that very good improvements are achievable if a proper self-supervised approach is chosen.
Deep Learning Statistical Arbitrage

Statistical arbitrage identifies and exploits temporal price differences between similar assets. We propose a unifying conceptual framework for statistical arbitrage and develop a novel deep learning solution, which finds commonality and time-series patterns from large panels in a data-driven and flexible way. First, we construct arbitrage portfolios of similar assets as residual portfolios from conditional latent asset pricing factors. Second, we extract the time series signals of these residual portfolios with one of the most powerful machine learning time-series solutions, a convolutional transformer. Last, we use these signals to form an optimal trading policy, that maximizes risk-adjusted returns under constraints. We conduct a comprehensive empirical comparison study with daily large cap U.S. stocks. Our optimal trading strategy obtains a consistently high out-of-sample Sharpe ratio and substantially outperforms all benchmark approaches. It is orthogonal to common risk factors, and exploits asymmetric local trend and reversion patterns. Our strategies remain profitable after taking into account trading frictions and costs. Our findings suggest a high compensation for arbitrageurs to enforce the law of one price.
Self-supervised Graph-level Representation Learning with Local and Global Structure

This paper studies unsupervised/self-supervised whole-graph representation learning, which is critical in many tasks such as molecule properties prediction in drug and material discovery. Existing methods mainly focus on preserving the local similarity structure between different graph instances but fail to discover the global semantic structure of the entire data set. In this paper, we propose a unified framework called Local-instance and Global-semantic Learning (GraphLoG) for self-supervised whole-graph representation learning. Specifically, besides preserving the local similarities, GraphLoG introduces the hierarchical prototypes to capture the global semantic clusters. An efficient online expectation-maximization (EM) algorithm is further developed for learning the model. We evaluate GraphLoG by pre-training it on massive unlabeled graphs followed by fine-tuning on downstream tasks. Extensive experiments on both chemical and biological benchmark data sets demonstrate the effectiveness of the proposed approach.
Fine-grained Out-of-Distribution Detection with Mixup Outlier Exposure

Enabling out-of-distribution (OOD) detection for DNNs is critical for their safe and reliable operation in the “open world”. Unfortunately, current works in both methodology and evaluation focus on rather contrived detection problems, and only consider a coarse level of granularity w.r. t. : 1) the in-distribution (ID) classes, and 2) the OOD data’s “closeness” to the ID data. We posit that such settings may be poor approximations of many real-world tasks that are naturally ﬁne-grained (e.g., bird species classiﬁcation), and thus the reported detection abilities may be over-estimates. Differently, in this work we make granularity a top priority and focus on ﬁne-grained OOD detection . We start by carefully constructing ﬁve novel ﬁne-grained test environments in which existing methods are shown to have difﬁculties. We then propose a new DNN training algorithm, Mixup Outlier Exposure (MixupOE), which leverages an outlier distribution and principles from vicinal risk minimization. Finally, we perform extensive experiments and analyses in our custom test environments and demonstrate that MixupOE can consistently improve ﬁne-grained detection performance, establishing a strong baseline in these more realistic and challenging OOD detection settings.
An Empirical Study of Assumptions in Bayesian Optimisation

Inspired by the increasing desire to efﬁciently tune machine learning hyper-parameters, in this work we rigorously analyse conventional and non-conventional assumptions inherent to Bayesian optimisation. Across an extensive set of experiments we conclude that: 1) the majority of hyper-parameter tuning tasks exhibit heteroscedasticity and non-stationarity, 2) multi-objective acquisition ensembles with Pareto-front solutions signiﬁcantly improve queried conﬁgurations, and 3) robust acquisition maximisation affords empirical advantages relative to its non-robust counterparts. We hope these ﬁndings may serve as guiding principles, both for practitioners and for further research in the ﬁeld.
Theoretical, Kinetic and Mechanistic Studies of the Reaction between Dialkyl Acetylenedicarboxylates, Triphenylphosphine and Pyrrole in Organic Solvents

The kinetics of the reaction between triphenylphosphine, dialkyl acetylenedicarboxylates and pyrrole in dry organic solvents at different temperatures have been studied spectrophotometrically. The observed overall second rate constant (k2)for reaction decreases with decreasing solvent dielectric constant and media temperature; k2 follows the Arrhenius equation, and the overall reaction is first order in both the dialkyl acetylenedicarboxylate and triphenylphosphine concentrations. The proposed mechanism has been evaluated, and the activation parameters ΔG≠, ΔS≠ and ΔH≠ for the first, rate-determining step, as an elementary reaction have been determined on the basis of Eyring equation. In addition, the Z- and E-isomers have been optimized at HF and B3LYP levels with the 6-311++G** basis set; the more stable is the E-isomer, in agreement with experimental data. Furthermore, to better understand the intramolecular interactions, atoms in molecules (AIM) calculations and natural population analysis (NPA) methods have been carried out. The 1H, 13C, and 31P NMR data of these ylides are consistent with results obtained from theoretical calculations.
Long-term Survivals, Toxicities and the Role of Chemotherapy in Early-Stage Nasopharyngeal Carcinoma Patients Treated with Intensity-Modulated Radiation Therapy: A Retrospective Study with 15-Year Follow-up

Purpose This study was aimed to investigate long-term survivals and toxicities of early-stage nasopharyngeal carcinoma (NPC) in endemic area, evaluating the role of chemotherapy in stage II patients. Materials and Methods Totally 187 patients with newly diagnosed NPC and restaged American Joint Committee on Cancer/ International Union Against Cancer 8th T1-2N0-1M0 were retrospectively recruited. All received intensity-modulated radiotherapy (IMRT)±chemotherapy (CT) from 2001 to 2010. Results With 15.7-year median follow-up, 10-year locoregional recurrence-free survival, distant metastasis-free survival (DMFS), disease-specific survival (DSS), and overall survival (OS) were 93.3%, 93.5%, 92.9% and 88.2%, respectively. Multivariable analyses showed cervical lymph nodes positive and pre-treatment prognostic nutritional index ≥ 52.0 could independently predict DMFS (p=0.036 and p=0.011), DSS (p=0.014 and p=0.026), and OS (p=0.002 and p < 0.001); Charlson comorbidity index < 3 points could predict DSS (p=0.011); age > 45 years (p=0.002) and pre-treatment lactate dehydrogenase ≥ 240 U/L (p < 0.001) predicted OS. No grade 4 late toxicity happened; grade 3 late toxicities included subcutaneous fibrosis (4.3%), deafness or otitis (4.8%), skin dystrophy (2.1%), and xerostomia (1.1%). No differences on survivals were shown between IMRT+CT vs. IMRT alone in stage II patients, even in T2N1M0 (p > 0.05). Unsurprising, patients in IMRT+CT had more acute gastrointestinal reaction, myelosuppression, mucositis, late ear toxicity, and cranial nerve injury (all p < 0.05) than IMRT alone group. Conclusion Superior tumor control and satisfying long-term outcomes could be achieved with IMRT in early-stage NPC with mild late toxicities. As CT would bring more toxicities, it should be carefully performed to stage II patients.