Miracle-dz/UniFAR-FTUs huggingface dataset:
Error rate control for classification rules in multiclass mixture models

Abstract In the context of finite mixture models one considers the problem of classifying as many observations as possible in the classes of interest while controlling the classification error rate in these same classes. Similar to what is done in the framework of statistical test theory, different type I and type II-like classification error rates can be defined, along with their associated optimal rules, where optimality is defined as minimizing type II error rate while controlling type I error rate at some nominal level. It is first shown that finding an optimal classification rule boils down to searching an optimal region in the observation space where to apply the classical Maximum A Posteriori (MAP) rule. Depending on the misclassification rate to be controlled, the shape of the optimal region is provided, along with a heuristic to compute the optimal classification rule in practice. In particular, a multiclass FDR-like optimal rule is defined and compared to the thresholded MAP rules that is used in most applications. It is shown on both simulated and real datasets that the FDR-like optimal rule may be significantly less conservative than the thresholded MAP rule.
BiQUE: Biquaternionic Embeddings of Knowledge Graphs

Knowledge graph embeddings (KGEs) compactly encode multi-relational knowledge graphs (KGs). Existing KGE models rely on geometric operations to model relational patterns. Euclidean (circular) rotation is useful for modeling patterns such as symmetry, but cannot represent hierarchical semantics. In contrast, hyperbolic models are effective at modeling hierarchical relations, but do not perform as well on patterns on which circular rotation excels. It is crucial for KGE models to unify multiple geometric transformations so as to fully cover the multifarious relations in KGs. To do so, we propose BiQUE, a novel model that employs biquaternions to integrate multiple geometric transformations, viz., scaling, translation, Euclidean rotation, and hyperbolic rotation. BiQUE makes the best trade-offs among geometric operators during training, picking the best one (or their best combination) for each relation. Experiments on five datasets show BiQUE’s effectiveness.
Finite-State Mutual Dimension

In 2004, Dai, Lathrop, Lutz, and Mayordomo defined and investigated the finite-state dimension (a finite-state version of algorithmic dimension) of a sequence <tex>$S$</tex> and, in 2018, Case and Lutz defined and investigated the mutual (algorithmic) dimension between two sequences <tex>$S$</tex> and <tex>$T$</tex>. In this paper, we propose a definition for the lower and upper finite-state mutual dimensions <tex>$mdim_{FS}(S:T)$</tex> and <tex>$Mdim_{FS}(S:T)$</tex> between two sequences <tex>$S$</tex> and <tex>$T$</tex>. Intuitively, the finite-state dimension of a sequence <tex>$S$</tex> represents the density of finite-state information contained within <tex>$S$</tex>, while the finite-state mutual dimension between two sequences <tex>$S$</tex> and <tex>$T$</tex> represents the density of finite-state information shared by <tex>$S$</tex> and <tex>$T$</tex>. Thus “finite-state mutual dimension” can be viewed as a “finite-state” version of mutual dimension and as a “mutual” version of finite-state dimension. The main results of this investigation are as follows. First, we show that finite-state mutual dimension, defined using information-lossless finite-state compressors, has all of the properties expected of a measure of mutual information. Next, we prove that finite-state mutual dimension may be characterized in terms of block mutual information rates. Finally, we provide necessary and sufficient conditions for two normal sequences <tex>$R_{1}$</tex> and <tex>$R_{2}$</tex> to achieve <tex>$mdim_{FS}(R_{1}:R_{2})=Mdim_{FS}(R_{1}:R_{2})=0$</tex>.
Spatially Coupled PLDPC-Hadamard Convolutional Codes

We propose a new type of ultimate-Shannon-limit-approaching codes called spatially coupled protograph-based low-density parity-check Hadamard convolutional codes (SC-PLDPCH-CCs), which are constructed by spatially coupling PLDPC-Hadamard block codes. We develop an efficient decoding algorithm that combines pipeline decoding and layered scheduling for the decoding of SC-PLDPCH-CCs, and analyze the latency and complexity of the decoder. To estimate the decoding thresholds of SC-PLDPCH-CCs, we first propose a layered protograph extrinsic information transfer (PEXIT) algorithm to evaluate the thresholds of spatially coupled PLDPC-Hadamard terminated codes (SC-PLDPCH-TDCs) with a moderate coupling length. With the use of the proposed layered PEXIT method, we develop a genetic algorithm to find good SC-PLDPCH-TDCs in a systematic way. Then we extend the coupling length of these SC-PLDPCH-TDCs to form good SC-PLDPCH-CCs. Results show that our constructed SC-PLDPCH-CCs can achieve comparable thresholds to the block code counterparts. Simulations illustrate the superiority of the SC-PLDPCH-CCs over the block code counterparts and other state-of-the-art low-rate codes in terms of error performance. For the rate-0.00295 SC-PLDPCH-CC, a bit error rate of 10−5 is achieved at $E_{b}/N_{0} = -1.465$ dB, which is only 0.125 dB from the ultimate Shannon limit.
Who says like a style of Vitamin ∗ : Towards Syntax-Aware Dialogue Summarization using Multi-task Learning

Abstractive dialogue summarization is a challenging task for several reasons. First, most of the important pieces of information in a conversation are scattered across utterances through multi-party interactions with different textual styles. Second, dialogues are often informal structures, wherein different individuals express personal perspectives, unlike text summarization, tasks that usually target formal documents such as news articles. To address these issues, we focused on the association between utterances from individual speakers and unique syntactic structures. Speakers have unique textual styles that can contain linguistic information, such as voiceprint. Therefore, we constructed a syntax-aware model by leveraging linguistic information ( i.e., POS tagging), which alleviates the above issues by inherently distinguishing sentences uttered from individual speakers. We employed multi-task learning of both syntax-aware information and dialogue summarization. To the best of our knowledge, our approach is the ﬁrst method to apply multi-task learning to the dialogue summarization task. Experiments on a SAMSum corpus (a large-scale dialogue summarization corpus) demonstrated that our method improved upon the vanilla model. We further analyze the costs and beneﬁts of our approach relative to base-line models.
VideoCLIP: Contrastive Pre-training for Zero-shot Video-Text Understanding

We present VideoCLIP, a contrastive approach to pre-train a unified model for zero-shot video and text understanding, without using any labels on downstream tasks. VideoCLIP trains a transformer for video and text by contrasting temporally overlapping positive video-text pairs with hard negatives from nearest neighbor retrieval. Our experiments on a diverse series of downstream tasks, including sequence-level text-video retrieval, VideoQA, token-level action localization, and action segmentation reveal state-of-the-art performance, surpassing prior work, and in some cases even outperforming supervised approaches. Code is made available at https://github.com/pytorch/fairseq/examples/MMPT.
TrajectoTree: Trajectory Optimization Meets Tree Search for Planning Multi-contact Dexterous Manipulation

Dexterous manipulation tasks often require contact switching, where fingers make and break contact with the object. We propose a method that plans trajectories for dexterous manipulation tasks involving contact switching using contact-implicit trajectory optimization (CITO) augmented with a high-level discrete contact sequence planner. We first use the high-level planner to find a sequence of finger contact switches given a desired object trajectory. With this contact sequence plan, we impose additional constraints in the CITO problem. We show that our method finds trajectories approximately 7 times faster than a general CITO baseline for a four-finger planar manipulation scenario. Furthermore, when executing the planned trajectories in a full dynamics simulator, we are able to more closely track the object pose trajectories planned by our method than those planned by the baselines.
Robust Temporal Ensembling for Learning with Noisy Labels

Successful training of deep neural networks with noisy labels is an essential capability as most real-world datasets contain some amount of mislabeled data. Left unmitigated, label noise can sharply degrade typical supervised learning approaches. In this paper, we present robust temporal ensembling (RTE), which combines robust loss with semi-supervised regularization methods to achieve noise-robust learning. We demonstrate that RTE achieves state-of-the-art performance across the CIFAR-10, CIFAR-100, ImageNet, WebVision, and Food-101N datasets, while forgoing the recent trend of label filtering and/or fixing. Finally, we show that RTE also retains competitive corruption robustness to unforeseen input noise using CIFAR-10-C, obtaining a mean corruption error (mCE) of 13.50% even in the presence of an 80% noise ratio, versus 26.9% mCE with standard methods on clean data.
Distributionally Robust Frequency Constrained Scheduling for an Integrated Electricity-Gas System

Power systems are shifted from conventional bulk generation toward renewable generation. This trend leads to the frequency security problem due to the decline of system inertia. On the other hand, natural gas-fired units are frequently scheduled to provide operational flexibility due to their fast adjustment ability. The interdependence between the power system and natural gas system is thus intensified. In this context, this paper considers the frequency constrained scheduling problem from the perspective of an integrated electricity-gas system under wind power uncertainty. A distributionally robust (DR) joint chance-constrained optimization model is proposed to co-optimize the unit commitment and virtual inertia provision from wind farm systems. This model incorporates both frequency constraints and natural gas system (NGS) operational constraints and addresses the wind power uncertainty by designing DR joint chance constraints. It is shown that this model admits a mixed-integer second-order cone programming. Case studies demonstrate that the proposed method can provide a highly reliable and computationally efficient solution and show the importance of incorporating NGS operational constraints in the frequency constrained scheduling problem.
A Glioblastoma PDE-ODE model including chemotaxis and vasculature

In this work we analyse a PDE-ODE problem modelling the evolution of a Glioblastoma, which includes chemotaxis term directed to vasculature. First, we obtain some a priori estimates for the (possible) solutions of the model. In particular, under some conditions on the parameters, we obtain that the system does not develop blow-up at finite time. In addition, we design a fully discrete finite element scheme for the model which preserves some pointwise estimates of the continuous problem. Later, we make an adimensional study in order to reduce the number of parameters. Finally, we detect the main parameters determining different width of the ring formed by proliferative and necrotic cells and different regular/irregular behaviour of the tumor surface.
A Comprehensive Survey and Performance Analysis of Activation Functions in Deep Learning

—Neural networks have shown tremendous growth in recent years to solve numerous problems. Various types of neural networks have been introduced to deal with different types of problems. However, the main goal of any neural network is to transform the non-linearly separable input data into more linearly separable abstract features using a hierarchy of layers. These layers are combinations of linear and nonlinear functions. The most popular and common non-linearity layers are activation functions (AFs), such as Logistic Sigmoid, Tanh, ReLU, ELU, Swish and Mish. In this paper, a comprehensive overview and survey is presented for AFs in neural networks for deep learning. Different classes of AFs such as Logistic Sigmoid and Tanh based, ReLU based, ELU based, and Learning based are covered. Several characteristics of AFs such as output range, monotonicity, and smoothness are also pointed out. A performance comparison is also performed among 18 state-of-the-art AFs with different networks on different types of data. The insights of AFs are presented to beneﬁt the researchers for doing further research and practitioners to select among different choices. The code used for experimental comparison is released at: https://github.com/shivram1987/ActivationFunctions.
Dense matter equation of state and phase transitions from a generalized Skyrme model

Skyrmion crystals are the field configurations which minimize the energy per baryon in the infinitely large topological charge sector of the Skyrme model, at least for sufficiently high density. They are, therefore, an important tool to describe the ground state of cold, symmetric nuclear matter at high density regimes. In this work, we analyze different crystalline phases and the existence of phase transitions between them within the generalized Skyrme model, with the ultimate goal of describing symmetric nuclear matter in a wide regime of densities. Furthermore, we propose a new energy-minimizing phase for densities lower than the nuclear saturation point ($n_0$) which also presents a good qualitative behavior in the zero density limit, thereby improving the description of strongly interacting matter in the region $n
Gaia GraL: Gaia DR2 Gravitational Lens Systems. VII. XMM-Newton Observations of Lensed Quasars

We present XMM-Newton X-ray observations of nine confirmed lensed quasars at 1 ≲ z ≲ 3 identified by the Gaia Gravitational Lens program. Eight systems are strongly detected, with 0.3–8.0 keV fluxes F 0.3−8.0 ≳ 5 ×10−14 erg cm−2 s−1. Modeling the X-ray spectra with an absorbed power law, we derive power-law photon indices and 2–10 keV luminosities for the eight detected quasars. In addition to presenting sample properties for larger quasar population studies and for use in planning for future caustic-crossing events, we also identify three quasars of interest: a quasar that shows evidence of flux variability from previous ROSAT observations, the most closely separated individual lensed sources resolved by XMM-Newton, and one of the X-ray brightest quasars known at z > 3. These sources represent the tip of the discoveries that will be enabled by SRG/eROSITA.
Large-scale Vortices in Rapidly Rotating Rayleigh–Bénard Convection at Small Prandtl number

One prominent feature in the atmospheres of Jupiter and Saturn is the appearance of large-scale vortices (LSVs). However, the mechanism that sustains these LSVs remains unclear. One possible mechanism is that these LSVs are driven by rotating convection. Here, we present numerical simulation results on a rapidly rotating Rayleigh–Bénard convection at a small Prandtl number Pr = 0.1 (close to the turbulent Prandtl numbers of Jupiter and Saturn). We identified four flow regimes in our simulation: multiple small vortices, a coexisting large-scale cyclone and anticyclone, large-scale cyclone, and turbulence. The formation of LSVs requires that two conditions be satisfied: the vertical Reynolds number is large ( Rez≥400 ), and the Rossby number is small (Ro ≤ 0.4). A large-scale cyclone first appears when Ro decreases to be smaller than 0.4. When Ro further decreases to be smaller than 0.1, a coexisting large-scale cyclone and anticyclone emerges. We have studied the heat transfer in rapidly rotating convection. The result reveals that the heat transfer is more efficient in the anticyclonic region than in the cyclonic region. Besides, we find that the 2D effect increases and the 3D effect decreases in transporting convective flux as the rotation rate increases. We find that aspect ratio has an effect on the critical Rossby number in the emergence of LSVs. Our results provide helpful insights into understanding the dynamics of LSVs in gas giants.
Detection of a Multiphase Intragroup Medium: Results from the COS-IGrM Survey

We present the results of the Cosmic Origins Spectrograph-Intragroup Medium (COS-IGrM) Survey that used the COS on the Hubble Space Telescope to observe a sample of 18 UV bright quasars, each probing the IGrM of a galaxy group. We detect Lyα, C ii, N v, Si ii, Si iii, and O vi in multiple sightlines. The highest ionization species detected in our data is O vi, which was detected in eight out of 18 quasar sightlines. The wide range of ionization states observed provide evidence that the IGrM is patchy and multiphase. We find that the O vi detections generally align with radiatively cooling gas between 105.8 and 106 K. The lack of O vi detections in 10 of the 18 groups illustrates that O vi may not be the ideal tracer of the volume filling component of the IGrM. Instead, it either exists at trace levels in a hot IGrM or is generated in the boundary between the hotter IGrM and cooler gas.
Assessing the goodness of fit of linear regression via higher-order least squares

We introduce a simple diagnostic test for assessing the goodness of fit of linear regression, and in particular for detecting hidden confounding. We propose to evaluate the sensitivity of the regression coefficient with respect to changes of the marginal distribution of covariates by comparing the so-called higher-order least squares with the usual least squares estimates. In spite of its simplicity, this strategy is extremely general and powerful. Specifically, we show that it allows to distinguish between confounded and unconfounded predictor variables as well as determining ancestor variables in structural equation models.
Harmonic Analysis of Social Cognition

In this paper, we argue that some fundamental concepts and tools of signal processing may be effectively applied to represent and interpret social cognition processes. From this viewpoint, individuals or, more generally, social stimuli are thought of as a weighted sum of harmonics with different frequencies: Low frequencies represent general categories such as gender, ethnic group, nationality, etc., whereas high frequencies account for personal characteristics. Individuals are then seen by observers as the output of a filter that emphasizes a certain range of high or low frequencies. The selection of the filter depends on the social distance between the observing individual or group and the person being observed as well as on motivation, cognitive resources and cultural background. Enhancing low- or high-frequency harmonics is not on equal footing, the latter requiring supplementary energy. This mirrors a well-known property of signal processing filters. More generally, in the light of this correspondence, we show that several established results of social cognition admit a natural interpretation and integration in the signal processing language. While the potential of this connection between an area of social psychology and one of information engineering appears considerable (compression, information retrieval, filtering, feedback, feedforward, sampling, aliasing, etc.), in this paper we shall limit ourselves to laying down what we consider the pillars of this bridge on which future research may be founded.
Generative Probabilistic Image Colorization

We propose Generative Probabilistic Image Colorization, a diffusion-based generative process that trains a sequence of probabilistic models to reverse each step of noise corruption. Given a line-drawing image as input, our method suggests multiple candidate colorized images. Therefore, our method accounts for the ill-posed nature of the colorization problem. We conducted comprehensive experiments investigating the colorization of line-drawing images, report the influence of a score-based MCMC approach that corrects the marginal distribution of estimated samples, and further compare different combinations of models and the similarity of their generated images. Despite using only a relatively small training dataset, we experimentally develop a method to generate multiple diverse colorization candidates which avoids mode collapse and does not require any additional constraints, losses, or re-training with alternative training conditions. Our proposed approach performed well not only on color-conditional image generation tasks using biased initial values, but also on some practical image completion and inpainting tasks.
Effects of Aqueous Organic Coatings on the Interfacial Transport of Atmospheric Species

Species must interact with air—aqueous interfaces in order to transport between either phase, however organic coated water surfaces are ubiquitous in the environment, and the physical and chemical processes that occur at organic coated aqueous surfaces are often different than those at pure air—water interfaces. Three studies were performed investigating the transport of species across air—aqueous interfaces with organic coatings in an effort to gain further insight into these processes. Gas and solution phase absorption spectroscopy were used to study the effect of octanol coatings on the formation of molecular iodine (I2) by the heterogeneous ozonation of iodide and its partitioning between phases. Compared to uncoated solutions, the presence of octanol monolayers had a minor effect on the total amount of I2 produced, however, it did significantly enhance the gas to solution partitioning of I2. Incoherent broadband cavity-enhanced absorption spectroscopy (IBBC-EAS) was used to measure the gas-phase nitrogen dioxide (NO2) evolved via
Training Feedback Spiking Neural Networks by Implicit Differentiation on the Equilibrium State

Spiking neural networks (SNNs) are brain-inspired models that enable energy-efficient implementation on neuromorphic hardware. However, the supervised training of SNNs remains a hard problem due to the discontinuity of the spiking neuron model. Most existing methods imitate the backpropagation framework and feedforward architectures for artificial neural networks, and use surrogate derivatives or compute gradients with respect to the spiking time to deal with the problem. These approaches either accumulate approximation errors or only propagate information limitedly through existing spikes, and usually require information propagation along time steps with large memory costs and biological implausibility. In this work, we consider feedback spiking neural networks, which are more brain-like, and propose a novel training method that does not rely on the exact reverse of the forward computation. First, we show that the average firing rates of SNNs with feedback connections would gradually evolve to an equilibrium state along time, which follows a fixed-point equation. Then by viewing the forward computation of feedback SNNs as a black-box solver for this equation, and leveraging the implicit differentiation on the equation, we can compute the gradient for parameters without considering the exact forward procedure. In this way, the forward and backward procedures are decoupled and therefore the problem of non-differentiable spiking functions is avoided. We also briefly discuss the biological plausibility of implicit differentiation, which only requires computing another equilibrium. Extensive experiments on MNIST, Fashion-MNIST, N-MNIST, CIFAR-10, and CIFAR-100 demonstrate the superior performance of our method for feedback models with fewer neurons and parameters in a small number of time steps. Our code is avaiable at https://github.com/pkuxmq/IDE-FSNN.