Miracle-dz/UniFAR-FTUs huggingface dataset:
Promise Problems Meet Pseudodeterminism

The Acceptance Probability Estimation Problem (APEP) is to additively approximate the acceptance probability of a Boolean circuit. This problem admits a probabilistic approximation scheme. A central question is whether we can design a pseudodeterministic approximation algorithm for this problem: a probabilistic polynomial-time algorithm that outputs a canonical approximation with high probability. Recently, it was shown that such an algorithm would imply that every approximation algorithm can be made pseudodeterministic (Dixon, Pavan, Vinodchandran; ITCS 2021). The main conceptual contribution of this work is to establish that the existence of a pseudodeterministic algorithm for APEP is fundamentally connected to the relationship between probabilistic promise classes and the corresponding standard complexity classes. In particular, we show the following equivalence: every promise problem in PromiseBPP has a solution in BPP if and only if APEP has a pseudodeterministic algorithm. Based on this intuition, we show that pseudodeterministic algorithms for APEP can shed light on a few central topics in complexity theory such as circuit lowerbounds, probabilistic hierarchy theorems, and multi-pseudodeterminism.
Learning One Representation to Optimize All Rewards

We introduce the forward-backward (FB) representation of the dynamics of a reward-free Markov decision process. It provides explicit near-optimal policies for any reward specified a posteriori. During an unsupervised phase, we use reward-free interactions with the environment to learn two representations via off-the-shelf deep learning methods and temporal difference (TD) learning. In the test phase, a reward representation is estimated either from observations or an explicit reward description (e.g., a target state). The optimal policy for that reward is directly obtained from these representations, with no planning. We assume access to an exploration scheme or replay buffer for the first phase. The corresponding unsupervised loss is well-principled: if training is perfect, the policies obtained are provably optimal for any reward function. With imperfect training, the sub-optimality is proportional to the unsupervised approximation error. The FB representation learns long-range relationships between states and actions, via a predictive occupancy map, without having to synthesize states as in model-based approaches. This is a step towards learning controllable agents in arbitrary black-box stochastic environments. This approach compares well to goal-oriented RL algorithms on discrete and continuous mazes, pixel-based MsPacman, and the FetchReach virtual robot arm. We also illustrate how the agent can immediately adapt to new tasks beyond goal-oriented RL.
Image Classifiers for Network Intrusions

This research recasts the network attack dataset from UNSW-NB15 as an intrusion detection problem in image space. Using one-hot-encodings, the resulting grayscale thumbnails provide a quarter-million examples for deep learning algorithms. Applying the MobileNetV2's convolutional neural network architecture, the work demonstrates a 97% accuracy in distinguishing normal and attack traffic. Further class refinements to 9 individual attack families (exploits, worms, shellcodes) show an overall 56% accuracy. Using feature importance rank, a random forest solution on subsets show the most important source-destination factors and the least important ones as mainly obscure protocols. The dataset is available on Kaggle.
A Systematic Review of Reproducibility Research in Natural Language Processing

Against the background of what has been termed a reproducibility crisis in science, the NLP field is becoming increasingly interested in, and conscientious about, the reproducibility of its results. The past few years have seen an impressive range of new initiatives, events and active research in the area. However, the field is far from reaching a consensus about how reproducibility should be defined, measured and addressed, with diversity of views currently increasing rather than converging. With this focused contribution, we aim to provide a wide-angle, and as near as possible complete, snapshot of current work on reproducibility in NLP,
FLIP: A flexible initializer for arbitrarily-sized parametrized quantum circuits

When compared to fault-tolerant quantum computational strategies, variational quantum algorithms stand as one of the candidates with the potential of achieving quantum advantage for real-world applications in the near term. However, the optimization of the circuit parameters remains arduous and is impeded by many obstacles such as the presence of barren plateaus, many local minima in the optimization landscape, and limited quantum resources. A non-random initialization of the parameters seems to be key to the success of the parametrized quantum circuits (PQC) training. Drawing and extending ideas from the field of meta-learning, we address this parameter initialization task with the help of machine learning and propose FLIP: a FLexible Initializer for arbitrarily-sized Parametrized quantum circuits. FLIP can be applied to any family of PQCs, and instead of relying on a generic set of initial parameters, it is tailored to learn the structure of successful parameters from a family of related problems which are used as the training set. The flexibility advocated to FLIP hinges in the possibility of predicting the initialization of parameters in quantum circuits with a larger number of parameters from those used in the training phase. This is a critical feature lacking in other meta-learning parameter initializing strategies proposed to date. We illustrate the advantage of using FLIP in three scenarios: a family of problems with proven barren plateaus, PQC training to solve max-cut problem instances, and PQC training for finding the ground state energies of 1D Fermi-Hubbard models.
Scaling of dynamical susceptibility at the onset of rigidity for disordered viscoelastic matter

The onset of rigidity in interacting liquids, as they undergo a transition to a disordered solid, is associated with a dramatic rearrangement of the low-frequency vibrational spectrum. In this letter, we derive scaling forms for the singular dynamical response of disordered viscoelastic networks near both jamming and rigidity percolation. Using effective-medium theory, we extract critical exponents, invariant scaling combinations and analytical formulas for universal scaling functions near these transitions. Our scaling forms describe the behavior in space and time near the various onsets of rigidity, for rigid and floppy phases and the crossover region, including diverging length and time scales at the transitions. We expect that these behaviors can be measured in systems ranging from colloidal suspensions to anomalous charge-density fluctuations of “strange” metals.
S-AT GCN: Spatial-Attention Graph Convolution Network based Feature Enhancement for 3D Object Detection

3D object detection plays a crucial role in environmental perception for autonomous vehicles, which is the prerequisite of decision and control. This paper analyses partition-based methods' inherent drawbacks. In the partition operation, a single instance such as a pedestrian is sliced into several pieces, which we call it the partition effect. We propose the Spatial-Attention Graph Convolution (S-AT GCN), forming the Feature Enhancement (FE) layers to overcome this drawback. The S-AT GCN utilizes the graph convolution and the spatial attention mechanism to extract local geometrical structure features. This allows the network to have more meaningful features for the foreground. Our experiments on the KITTI 3D object and bird's eye view detection show that S-AT Conv and FE layers are effective, especially for small objects. FE layers boost the pedestrian class performance by 3.62\% and cyclist class by 4.21\% 3D mAP. The time cost of these extra FE layers are limited. PointPillars with FE layers can achieve 48 PFS, satisfying the real-time requirement.
Modeling multivariate cyber risks: deep learning dating extreme value theory

Modeling cyber risks has been an important but challenging task in the domain of cyber security, which is mainly caused by the high dimensionality and heavy tails of risk patterns. Those obstacles have hindered the development of statistical modeling of the multivariate cyber risks. In this work, we propose a novel approach for modeling the multivariate cyber risks which relies on the deep learning and extreme value theory. The proposed model not only enjoys the high accurate point predictions via deep learning but also can provide the satisfactory high quantile predictions via extreme value theory. Both the simulation and empirical studies show that the proposed approach can model the multivariate cyber risks very well and provide satisfactory prediction performances.
GVINS: Tightly Coupled GNSS-Visual-Inertial for Smooth and Consistent State Estimation

—Visual-Inertial odometry is known to suffer from drifting especially over long-term runs. In this paper, we present GVINS, a non-linear optimization based system that tightly fuses GNSS raw measurements with visual and inertial information for real-time and drift-free state estimation. The proposed system combines merits from VIO and GNSS system, thus is able to achieve both local smoothness and global consistency. To associate global measurements with local states, a coarse-to-ﬁne initialization procedure is proposed to efﬁciently online calibrate the transformation and initialize GNSS states from only a short window of measurements. The GNSS pseudorange and Doppler shift measurements are modelled and optimized under a factor graph framework along with visual and inertial constraints. For complex and GNSS-unfriendly area, the degenerate cases are discussed and carefully handled to ensure robustness. The engineering challenges involved in the system are also included to facilitate relevant GNSS fusion researches. Thanks to the tightly-coupled multi-sensor approach and system design, our estimator is able to recover the position and orientation in the global Earth frame, even with less than 4 satellites being tracked. We extensively evaluate the proposed system on simulation and real-world experiments, and the result demonstrates that our system substantially eliminates the drift of VIO and preserves the accuracy in spite of noisy GNSS measurements.
Physicochemical characteristics and renneting properties of camels’ milk: A comparison with goats’, ewes’ and cows’ milks

A great variability in physicochemical and microscopic characteristics of individual milk has been observed leading to the presence of differences in kinetics of the renneting reaction and in drainage ability. Furthermore, this study shows that casein concentration, total calcium content and mean diameter of casein micelles are the principal variables explaining the major part of the variation in the renneting properties. Finally, several relationships between coagulation characteristics, drainage ability and some compositional parameters of these kinds of milk were highlighted.
Testing for the Network Small-World Property

Researchers have long observed that the"small-world"property, which combines the concepts of high transitivity or clustering with a low average path length, is ubiquitous for networks obtained from a variety of disciplines including social sciences, biology, neuroscience, and ecology. However, we find three shortcomings of the currently popular definition and detection methods rendering the concept less powerful. First, the classical definition combines high transitivity with a low average path length in a rather ad-hoc fashion which confounds the two separate aspects. We find that in several cases, networks get flagged as"small world"by the current methodology solely because of their high transitivity. Second, the detection methods lack a formal statistical inference, and third, the comparison is typically performed against simplistic random graph models as the baseline which ignores well-known network characteristics. We propose three innovations to address these issues. First, we decouple the properties of high transitivity and low average path length as separate events to test for. Second, we define the property as a statistical test between a suitable null model and a superimposed alternative model. Third, the test is performed using parametric bootstrap with several null models to allow a wide range of background structures in the network. In addition to the bootstrap tests, we also propose an asymptotic test under the Erd\"{o}s-Ren\'{y}i null model for which we provide theoretical guarantees on the asymptotic level and power. Applying the proposed methods on a large number of network datasets, we uncover new insights about their small-world property.
Deep Dynamic Neural Network to trade-off between Accuracy and Diversity in a News Recommender System

The news recommender systems are marked by a few unique challenges specific to the news domain. These challenges emerge from rapidly evolving readers' interests over dynamically generated news items that continuously change over time. News reading is also driven by a blend of a reader's long-term and short-term interests. In addition, diversity is required in a news recommender system, not only to keep the reader engaged in the reading process but to get them exposed to different views and opinions. In this paper, we propose a deep neural network that jointly learns informative news and readers' interests into a unified framework. We learn the news representation (features) from the headlines, snippets (body) and taxonomy (category, subcategory) of news. We learn a reader's long-term interests from the reader's click history, short-term interests from the recent clicks via LSTMSs and the diversified reader's interests through the attention mechanism. We also apply different levels of attention to our model. We conduct extensive experiments on two news datasets to demonstrate the effectiveness of our approach.
The SpiNNaker 2 Processing Element Architecture for Hybrid Digital Neuromorphic Computing

This paper introduces the processing element architecture of the second generation SpiNNaker chip, implemented in 22nm FDSOI. On circuit level, the chip features adaptive body biasing for near-threshold operation, and dynamic voltage-and-frequency scaling driven by spiking activity. On system level, processing is centered around an ARM M4 core, similar to the processor-centric architecture of the first generation SpiNNaker. To speed operation of subtasks, we have added accelerators for numerical operations of both spiking (SNN) and rate based (deep) neural networks (DNN). PEs communicate via a dedicated, custom-designed network-on-chip. We present three benchmarks showing operation of the whole processor element on SNN, DNN and hybrid SNN/DNN networks.
A new Indirect method for Detection of Metabolites of Polycyclic Aromatic Compounds in Urine

Abstract A frequently used means of assessing exposure to PAHs is measurement of urinary 1-hydroxypyrene and its metabolites following enzymatic deconjugation. Singh et al. [1] have reported an HPLC method which eliminates the deconjugation step, and directly measures levels of metabolites as well as free 1-hydroxypyrene. Using the samples from their study, including urine from mice fed coal tar, and urine from workers occupationally exposed to coal tar, we have developed a rapid, sensitive method for determining exposures to PAHs. The method takes advantage of the extreme mutagenicity of nitro-PAH in the Ames Assay. Urine samples were chemically nitrated and tested in Ames strain TA98. For the mouse urines, correlation of mutagenic potency with various measures of coal tar intake and excretion gave R2 values greater than 0.97. There was more scatter in the worker study, possibly reflecting differential responses of the two methods to non-coal tar PAH exposures.
How to distribute data across tasks for meta-learning?

Meta-learning models transfer the knowledge acquired from previous tasks to quickly learn new ones. They are trained on benchmarks with a fixed number of data points per task. This number is usually arbitrary and it is unknown how it affects performance at testing. Since labelling of data is expensive, finding the optimal allocation of labels across training tasks may reduce costs. Given a fixed budget of labels, should we use a small number of highly labelled tasks, or many tasks with few labels each? Should we allocate more labels to some tasks and less to others? We show that: 1) If tasks are homogeneous, there is a uniform optimal allocation, whereby all tasks get the same amount of data; 2) At fixed budget, there is a trade-off between number of tasks and number of data points per task, with a unique solution for the optimum; 3) When trained separately, harder task should get more data, at the cost of a smaller number of tasks; 4) When training on a mixture of easy and hard tasks, more data should be allocated to easy tasks. Interestingly, Neuroscience experiments have shown that human visual skills also transfer better from easy tasks. We prove these results mathematically on mixed linear regression, and we show empirically that the same results hold for few-shot image classification on CIFAR-FS and mini-ImageNet. Our results provide guidance for allocating labels across tasks when collecting data for meta-learning.
Improving Diversity of Neural Text Generation via Inverse Probability Weighting

The neural text generation suffers from the text degeneration issue such as repetition. Traditional stochastic sampling methods only focus on truncating the unreliable"tail"of the distribution, and do not address the"head"part, which we show might contain tedious or even repetitive candidates with high probability that lead to repetition loops. They also do not consider the issue that human text does not always favor high-probability words. Inspired by these, in this work we propose a heuristic sampling method. We propose to use interquartile range of the predicted distribution to determine the"head"part, then permutate and rescale the"head"with inverse probability. This aims at decreasing the probability for the tedious and possibly repetitive candidates with higher probability, and increasing the probability for the rational but more surprising candidates with lower probability. The proposed algorithm provides a reasonable permutation on the predicted distribution which enhances diversity without compromising rationality of the distribution. We use pre-trained language model to compare our algorithm with traditional methods. Results show that our algorithm can effectively increase the diversity of generated samples while achieving close resemblance to human text.
Polymer–nanoparticle interfacial interactions in polymer nanocomposites: Confinement effects on glass transition temperature and suppression of physical aging

The effects of confinement on glass transition temperature (Tg) and physical aging are measured in polystyrene (PS), poly(methyl methacrylate) (PMMA), and poly(2-vinyl pyridine) (P2VP) nanocomposites containing 10- to 15-nm-diameter silica nanospheres or 47-nm-diameter alumina nanospheres. Nanocomposites are made by spin coating films from sonicated solutions of polymer, nanofiller, and dye. The Tgs and physical aging rates are measured by fluorescence of trace levels of dye in the films. At 0.1–10 vol % nanofiller, Tg values can be enhanced or depressed relative to neat, bulk Tg (Tg,bulk) or invariant with nanofiller content. For alumina nanocomposites, Tg increases relative to Tg,bulk by as much as 16 K in P2VP, decreases by as much as 5 K in PMMA, and is invariant in PS. By analogy with thin polymer films, these results are explained by wetted P2VP–nanofiller interfaces with attractive interactions, nonwetted PMMA–nanofiller interfaces (free space at the interface), and wetted PS–nanofiller interfaces lacking attractive interactions, respectively. The presence of wetted or nonwetted interfaces is controlled by choice of solvent. For example, 0.1–0.6 vol % silica/PMMA nanocomposites exhibit Tg enhancements as large as 5 K or Tg reductions as large as 17 K relative to Tg,bulk when films are made from methyl ethyl ketone or acetic acid solutions, respectively. A factor of 17 reduction of physical aging rate relative to that of neat, bulk P2VP is demonstrated in a 4 vol % alumina/P2VP nanocomposite. This suggests that a strategy for achieving nonequilibrium, glassy polymeric systems that are stable or nearly stable to physical aging is to incorporate well-dispersed nanoparticles possessing attractive interfacial interactions with the polymer. © 2006 Wiley Periodicals, Inc. J Polym Sci Part B: Polym Phys 44: 2935–2943, 2006
A Normal Distribution Transform-Based Radar Odometry Designed For Scanning and Automotive Radars

Existing radar sensors can be classified into automotive and scanning radars. While most radar odometry (RO) methods are only designed for a specific type of radar, our RO method adapts to both scanning and automotive radars. Our RO is simple yet effective, where the pipeline consists of thresholding, probabilistic submap building, and an Normal Distribution Transform-based (NDT-based) radar scan matching. The proposed RO has been tested on two public radar datasets: the Oxford Radar RobotCar dataset and the nuScenes dataset, which provide scanning and automotive radar data respectively. The results show that our approach surpasses state-of-the-art RO using either automotive or scanning radar by reducing translational error by 51% and 30%, respectively, and rotational error by 17% and 29%, respectively. Besides, we show that our RO achieves centimeter-level accuracy as lidar odometry, and automotive and scanning RO have similar accuracy.
TransFG: A Transformer Architecture for Fine-grained Recognition

Fine-grained visual classification (FGVC) which aims at recognizing objects from subcategories is a very challenging task due to the inherently subtle inter-class differences. Most existing works mainly tackle this problem by reusing the backbone network to extract features of detected discriminative regions. However, this strategy inevitably complicates the pipeline and pushes the proposed regions to contain most parts of the objects thus fails to locate the really important parts. Recently, vision transformer (ViT) shows its strong performance in the traditional classification task. The self-attention mechanism of the transformer links every patch token to the classification token. In this work, we first evaluate the effectiveness of the ViT framework in the fine-grained recognition setting. Then motivated by the strength of the attention link can be intuitively considered as an indicator of the importance of tokens, we further propose a novel Part Selection Module that can be applied to most of the transformer architectures where we integrate all raw attention weights of the transformer into an attention map for guiding the network to effectively and accurately select discriminative image patches and compute their relations. A contrastive loss is applied to enlarge the distance between feature representations of confusing classes. We name the augmented transformer-based model TransFG and demonstrate the value of it by conducting experiments on five popular fine-grained benchmarks where we achieve state-of-the-art performance. Qualitative results are presented for better understanding of our model.
The Sombor Index of Trees and Unicyclic Graphs with Given Maximum Degree

Let $d_G(v)$ be the degree of the vertex $v$ in a graph $G$. The Sombor index of $G$ is defined as $SO(G) =\sum_{uv\in E(G)}\sqrt{d^2_G(u)+d^2_G(v)}$, which is a new degree-based topological index introduced by Gutman. Let $\mathscr{T}_{n,\Delta}$ and $\mathscr{U}_{n,\Delta}$ be the set of trees and unicyclic graphs with $n$ vertices and maximum degree $\Delta$, respectively. In this paper, the tree and the unicyclic graph with minimum Sombor index among $\mathscr{T}_{n,\Delta}$ and $\mathscr{U}_{n,\Delta}$ are characterized.