Miracle-dz/UniFAR-FTUs huggingface dataset:
SWIM: selective write-verify for computing-in-memory neural accelerators

Computing-in-Memory architectures based on non-volatile emerging memories have demonstrated great potential for deep neural network (DNN) acceleration thanks to their high energy efficiency. However, these emerging devices can suffer from significant variations during the mapping process (i.e., programming weights to the devices), and if left undealt with, can cause significant accuracy degradation. The non-ideality of weight mapping can be compensated by iterative programming with a write-verify scheme, i.e., reading the conductance and rewriting if necessary. In all existing works, such a practice is applied to every single weight of a DNN as it is being mapped, which requires extensive programming time. In this work, we show that it is only necessary to select a small portion of the weights for write-verify to maintain the DNN accuracy, thus achieving significant speedup. We further introduce a second derivative based technique SWIM, which only requires a single pass of forward and backpropagation, to efficiently select the weights that need write-verify. Experimental results on various DNN architectures for different datasets show that SWIM can achieve up to 10x programming speedup compared with conventional full-blown write-verify while attaining a comparable accuracy.
Structural and Semantic Contrastive Learning for Unsupervised Node Representation Learning

Graph Contrastive Learning (GCL) recently has drawn much research interest for learning generalizable node representations in a self-supervised manner. In general, the contrastive learning process in GCL is performed on top of the representations learned by a graph neural network (GNN) backbone, which transforms and propagates the node contextual information based on its local neighborhoods. However, nodes sharing similar characteristics may not always be geographically close, which poses a great challenge for unsupervised GCL efforts since they have inherent limitations in capturing such global graph knowledge. In this work, we go beyond the existing unsupervised GCL counterparts and address their limitations by proposing a simple yet effective framework – Simple Neural Networks with Structural and Semantic Contrastive Learning (S 3 -CL). Notably, by virtue of the proposed structural and semantic contrastive learning algorithms, even a simple neural network is able to learn expressive node representations that preserve valuable global structural and semantic patterns. Our experiments demonstrate that the node representations learned by S 3 -CL achieve superior performance on different downstream tasks compared to the state-of-the-art GCL methods.
Nonadiabatic dissociation of molecular condensates: competition between multi-mode reaction channels

We formulate and solve a model describing a nonadiabatic dissociation process of molecular condensates. The dynamical process includes multi-mode reaction channels, and the competition between channel parameters leads to a non-trivial dependence on critical components such as path interference and symmetries. We find an analytical solution under CPT symmetry, where C is charge conjugation, P is parity, and T is time-reversal symmetry. Our solution predicts a population imbalance between atomic modes that is exponentially sensitive to channel parameters. However, a weakly broken symmetry alters the population at each atomic mode and can reverse the population imbalance. Our solution also demonstrates a strong quantum correlation between atomic modes that leads to the spontaneous production of atoms in a multi-mode squeezed state. Moreover, our results are applicable in solving time-dependent PT-symmetric quantum mechanics, which naturally manifests in the Heisenberg picture.
Revisiting the Evaluation Metrics of Paraphrase Generation

Paraphrase generation is an important NLP task that has achieved signiﬁcant progress recently. However, one crucial problem is overlooked, ‘how to evaluate the quality of paraphrase? ’. Most existing paraphrase generation models use reference-based metrics (e.g., BLEU) from neural machine translation (NMT) to evaluate their generated paraphrase. Such metrics’ reliability is hardly evaluated, and they are only plausible when there exists a standard reference. Therefore, this paper ﬁrst answers one fundamental question, ‘Are existing metrics reliable for paraphrase generation? ’. We present two conclusions that disobey conventional wisdom in paraphrasing generation: (1) existing metrics poorly align with human annotation in system-level and segment-level paraphrase evaluation. (2) reference-free metrics outperform reference-based metrics, indicating that the standard references are unnecessary to evaluate the para-phrase’s quality. Such empirical ﬁndings expose a lack of reliable automatic evaluation metrics. Therefore, this paper proposes BB-Score, a reference-free metric that can reﬂect the generated paraphrase’s quality. BBScore consists of two sub-metrics: S3C score and SelfBLEU, which correspond to two criteria for paraphrase evaluation: semantic preservation and diversity. By connecting two sub-metrics, BBScore signiﬁcantly outperforms existing paraphrase evaluation metrics.
Comparing feature sets and machine learning models for prediction of solar flares: Topology, physics, and model complexity

Recently, there has been growing interest in the use of machine-learning methods for predicting solar flares. Initial efforts along these lines employed comparatively simple models, correlating features extracted from observations of sunspot active regions with known instances of flaring. Typically, these models have used physics-inspired features that have been carefully chosen by experts in order to capture the salient features of such magnetic field structures. Over time, the sophistication and complexity of the models involved has grown. However, there has been little evolution in the choice of feature sets, nor any systematic study of whether the additional model complexity is truly useful. Our goal is to address these issues. To that end, we compare the relative prediction performance of machine-learning-based, flare-forecasting models with varying degrees of complexity. We also revisit the feature set design, using topological data analysis to extract shape-based features from magnetic field images of the active regions. Using hyperparameter training for fair comparison of different machine-learning models across different feature sets, we show that simpler models with fewer free parameters \textit{generally perform better than more-complicated models}, ie. , powerful machinery does not necessarily guarantee better prediction performance. Secondly, we find that \textit{abstract, shape-based features contain just as much useful information}, for the purposes of flare prediction, as the set of hand-crafted features developed by the solar-physics community over the years. Finally, we study the effects of dimensionality reduction, using principal component analysis, to show that streamlined feature sets, overall, perform just as well as the corresponding full-dimensional versions.
Point cloud completion on structured feature map with feedback network

In this paper, we tackle the challenging problem of point cloud completion from the perspective of feature learning. Our key observation is that to recover the underlying structures as well as surface details given a partial input, a fundamental component is a good feature representation that can capture both global structure and local geometric details. Towards this end, we ﬁrst propose FSNet, a feature structuring module that can adaptively aggregate point-wise features into a 2D structured feature map by learning multiple latent patterns from local regions. We then integrate FSNet into a coarse-to-ﬁne pipeline for point cloud completion. Speciﬁcally, a 2D convolutional neural network is adopted to decode feature maps from FSNet into a coarse and complete point cloud. Next, a point cloud upsampling network is used to generate dense point cloud from the partial input and the coarse intermediate output. To eﬃciently exploit the local structures and enhance the point distribution uniformity, we propose IFNet, a point upsampling module with self-correction mechanism that can progressively reﬁne details of the generated dense point cloud. We conduct both qualitative and quantitative experiments on ShapeNet, MVP, and KITTI datasets, which demonstrate that our method outperforms state-of-the-art point cloud completion approaches.
On the ideas of the origin of eukaryotes: a critical review

The origin and early evolution of eukaryotes is one of the major transitions in the evolution of life on earth (Smith and Szathamary, 1995). One of its most interesting aspects is the emergence of cellular organelles, their dynamics, their functions, and their divergence. Cell compartmentalization and architecture in prokaryotes is a less understood complex property. In eukaryotes it is related to cell size, specific genomic architecture, evolution of cell cycles, biogenesis of membranes and endosymbiotic processes. Explaining cell evolution through form and function demands an interdisciplinary approach focused on microbial diversity, phylogenetic and functional cell biology. Two centuries of views on eukaryotic origin have completed the disciplinary tools necessarily to answer these questions. We have moved from Haeckel’s SCALA NATURAE to the un-rooted tree of life. However the major relations among cell domains are still elusive and keep the nature of eukaryotic ancestor enigmatic. Here I present a review on state of art views of eucaryogenesis; the background and perspectives of different disciplines involved in this topic Islas-Morales P.F. & Jiménez-García, Ideas on the origin of eukaryotes: a critical review
Total consensus under high reproductive-variance conditions

Star-shaped branching patterns of genealogies are common in marine species. High-fecundity marine populations are characterized by low ratios of effective to actual population size, which reflect high variance in reproductive success among parents in mass spawns. When extreme reproduction events occur, offspring from very few parents dominate the population (whereby multiple mergers, or subsets of lineages with star-like trees, arise) and thus, the loss of genetic diversity is significant. Under high reproductive-variance conditions (assuming that reproduction occurs by sampling from the Pareto distribution), this paper explores the distribution of heterozygosity across generations. The result shows that zero heterozygosity is not achieved, implying that the populations may decline without evident loss of genetic variation. It is also found that there are singularities in the heterozygosity distributions. However, in the case of high reproductive variance, the locations of the singular points subtly deviate from those of the case where reproduction occurs by Wright-Fisher sampling.
Plasticity and evolvability under environmental variability: the joint role of fitness-based selection and niche-limited competition

The diversity and quality of natural systems have been a puzzle and inspiration for communities studying artificial life. It is now widely admitted that the adaptation mechanisms enabling these properties are largely influenced by the environments they inhabit. Organisms facing environmental variability have two alternative adaptation mechanisms operating at different timescales: plasticity, the ability of a phenotype to survive in diverse environments and evolvability, the ability to adapt through mutations. Although vital under environmental variability both mechanisms are associated with fitness costs hypothesized to render them unnecessary in stable environments. In this work, we study the interplay between environmental dynamics and adaptation in a minimal model of the evolution of plasticity and evolvability. We experiment with different types of environments characterized by the presence of niches and a climate function that determines the fitness landscape. We empirically show that environmental dynamics affect plasticity and evolvability differently and that the presence of diverse ecological niches favors adaptability even in stable environments. We perform ablation studies of the selection mechanisms to separate the role of fitness-based selection and niche-limited competition. Results obtained from our minimal model allow us to propose promising research directions in the study of open-endedness in biological and artificial systems.
End-to-End Training of Both Translation Models in the Back-Translation Framework

Semi-supervised learning algorithms in neural machine translation (NMT) have signiﬁ-cantly improved translation quality compared to the supervised learning methods by us-ing additional monolingual corpora. Among them, back-translation is a theoretically well-structured and cutting-edge method. Given two pre-trained NMT models between source and target languages, one NMT model translates a monolingual sentence to a latent sentence, and the other reconstructs the mono-lingual input sentence given the latent sentence. Based on this auto-encoding framework, previous work tried to apply the variational auto-encoder’s (VAE) training framework to the back-translation. However, the discrete property of the latent sentence made it impossible to use backpropagation in the end-to-end fashion. In this paper, we pro-pose a categorical reparameterization trick that makes NMT models generate differentiable sentences . Based on the proposed method, end-to-end learning is possible so that two NMT models for the back-translation can be trained as a uniﬁed model. In addition, we propose several regularization techniques that are especially advantageous to this framework. Our experiments demonstrate that our method can achieve better BLEU scores than the previous baseline, on the datasets of the WMT18 translation task.
Adiabatic Quantum Computing for Multi Object Tracking

Multi-Object Tracking (MOT) is most often approached in the tracking-by-detection paradigm, where object detections are associated through time. The association step naturally leads to discrete optimization problems. As these optimization problems are often NP-hard, they can only be solved exactly for small instances on current hardware. Adiabatic quantum computing (AQC) offers a solution for this, as it has the potential to provide a considerable speedup on a range of NP-hard optimization problems in the near future. However, current MOT formulations are unsuitable for quantum computing due to their scaling properties. In this work, we therefore propose the first MOT formulation designed to be solved with AQC. We employ an Ising model that represents the quantum mechanical system implemented on the AQC. We show that our approach is competitive compared with state-of-the-art optimization-based approaches, even when using of-the-shelf integer programming solvers. Finally, we demonstrate that our MOT problem is already solvable on the current generation of real quantum computers for small examples, and analyze the properties of the measured solutions.
Linear Tur\'{a}n numbers of acyclic quadruple systems

A linear $r$-uniform hypergraph is called acycilc if it can be constructed starting from one single edge then at each step adding a new edge that intersect the union of the vertices of the previous edges in at most one vertex. Recently, Gy\'{a}rf\'{a}s, Ruszink\'{o} and S\'{a}rk\''{o}zy initiated the study of the linear Tur\'{a}n numbers of acyclic linear triple systems. In this paper, we extend their results to linear quadruple systems. Here, we concentrate on small trees, paths and matchings. For the case of small trees, we find that for a linear tree $T$, $ex^{lin}_{4}(n,T)$ relates to difficult problems on Steiner system $S(2,4,n)$ For example, we show that $ex^{lin}_{4}(n, P_4)\le \frac{5n}{4}$ with equality holds if and only if the linear quadruple system is the disjoint union of $S(2,4,16)$. Denote by $E^{+}_{4}$ the linear tree consisting of three pairwise disjoint quadruples and a fourth one intersecting all of them. We prove that $12\lfloor\frac{n-4}{9}\rfloor\le ex^{lin}_{4}(n, E^{+}_4)\le \frac{14(n-s)}{9}$, where $s$ is the number of vertices in $G$ with degree at least 8. Denote by $M_k$ and $P_k$ the set of $k$ pairwise disjoint quadruples and the linear path with $k$ quadruples, respectively. For the case of paths, we show that $ex^{lin}_{4}(n, P_k)\le 2.5kn$. For the case of matchings, we prove that for fixed $k$ and sufficiently large $n$, $ex^{lin}_{4}(n, M_k)=g(n,k)$ where $g(n,k$) denotes the maximum number of quadruples that can intersect $k-1$ vertices in a linear quadruple system on $n$ vertices.
Algorithmic fair allocation of indivisible items

The theory of algorithmic fair allocation is at the center of multi-agent systems and economics in recent decades due to its industrial and social importance. At a high level, the problem is to assign a set of items that are either goods or chores to a set of agents so that every agent is happy with what she obtains. In this survey, we focus on indivisible items, for which exact fairness as measured by envy-freeness and proportionality cannot be guaranteed. One main theme in the recent research agenda is designing algorithms that approximately achieve fairness criteria. We aim at presenting a comprehensive survey of recent progress through the prism of algorithms, highlighting the ways to relax fairness notions and common techniques to design algorithms, as well as the most interesting questions for future research.
Solving Hamiltonian Cycle Problem using Quantum $\mathbb{Z}_2$ Lattice Gauge Theory

Abstract The Hamiltonian cycle (HC) problem in graph theory is a well-known NP-complete problem. We present an approach in terms of Z2 lattice gauge theory (LGT) defined on the lattice with the graph as its dual. When the coupling parameter g is less than the critical value gc, the ground state is a superposition of all configurations with closed strings of spins in a same singlespin state, which can be obtained by using an adiabatic quantum algorithm with time complexity O( 1 g c √
DEEP VQA BASED ON A NOVEL HYBRID TRAINING METHODOLOGY

In recent years, deep learning techniques have been widely applied to video quality assessment (VQA), showing signiﬁ-cant potential to achieve higher correlation performance with subjective opinions compared to conventional approaches. However, these methods are often developed based on limited training materials and evaluated through cross validation, due to the lack of large scale subjective databases. In this context, this paper proposes a new hybrid training methodology, which generates large volumes of training data by using quality indices from an existing perceptual quality metric, VMAF, as training targets, instead of actual subjective opinion scores. An additional shallow CNN is also employed for temporal pooling, which was trained based on a small subjective video database. The resulting Deep Video Quality Metric (based on Hybrid Training), DVQM-HT, has been fully tested on eight HD subjective video databases, and consistently exhibits higher correlation with perceptual quality compared to other deep quality assessment methods, with an average SROCC value of 0.8263.
Single Trajectory Nonparametric Learning of Nonlinear Dynamics

Given a single trajectory of a dynamical system, we analyze the performance of the nonparametric least squares estimator (LSE). More precisely, we give nonasymptotic expected $l^2$-distance bounds between the LSE and the true regression function, where expectation is evaluated on a fresh, counterfactual, trajectory. We leverage recently developed information-theoretic methods to establish the optimality of the LSE for nonparametric hypotheses classes in terms of supremum norm metric entropy and a subgaussian parameter. Next, we relate this subgaussian parameter to the stability of the underlying process using notions from dynamical systems theory. When combined, these developments lead to rate-optimal error bounds that scale as $T^{-1/(2+q)}$ for suitably stable processes and hypothesis classes with metric entropy growth of order $\delta^{-q}$. Here, $T$ is the length of the observed trajectory, $\delta \in \mathbb{R}_+$ is the packing granularity and $q\in (0,2)$ is a complexity term. Finally, we specialize our results to a number of scenarios of practical interest, such as Lipschitz dynamics, generalized linear models, and dynamics described by functions in certain classes of Reproducing Kernel Hilbert Spaces (RKHS).
Emotion Recognition among Couples: A Survey

Couples' relationships affect the physical health and emotional well-being of partners. Automatically recognizing each partner's emotions could give a better understanding of their individual emotional well-being, enable interventions and provide clinical benefits. In the paper, we summarize and synthesize works that have focused on developing and evaluating systems to automatically recognize the emotions of each partner based on couples' interaction or conversation contexts. We identified 28 articles from IEEE, ACM, Web of Science, and Google Scholar that were published between 2010 and 2021. We detail the datasets, features, algorithms, evaluation, and results of each work as well as present main themes. We also discuss current challenges, research gaps and propose future research directions. In summary, most works have used audio data collected from the lab with annotations done by external experts and used supervised machine learning approaches for binary classification of positive and negative affect. Performance results leave room for improvement with significant research gaps such as no recognition using data from daily life. This survey will enable new researchers to get an overview of this field and eventually enable the development of emotion recognition systems to inform interventions to improve the emotional well-being of couples.
Half-quantized Hall effect and power law decay of edge-current distribution

The half-quantized Hall conductance is characteristic of quantum systems with parity anomaly. Here we investigate topological and transport properties of a class of parity anomalous semimetals, in which massive Dirac fermions coexist with massless Dirac fermions in momentum space or real space, and uncovered a distinct bulk-edge correspondence that the half-quantized Hall effect is realized via the bulk massless Dirac fermions while the nontrivial Berry curvature is provided by the massive Dirac fermions. The spatial distribution of the edge current decays away from the boundary in a power law instead of an exponential law in integer quantum Hall effect. We further address physical relevance of parity anomalous semimetal to three-dimensional semi-magnetic topological insulators and two-dimensional photonic crystals.
The Iterates of the Frank-Wolfe Algorithm May Not Converge

The Frank-Wolfe algorithm is a popular method for minimizing a smooth convex function $f$ over a compact convex set $\mathcal{C}$. While many convergence results have been derived in terms of function values, hardly nothing is known about the convergence behavior of the sequence of iterates $(x_t)_{t\in\mathbb{N}}$. Under the usual assumptions, we design several counterexamples to the convergence of $(x_t)_{t\in\mathbb{N}}$, where $f$ is $d$-time continuously differentiable, $d\geq2$, and $f(x_t)\to\min_\mathcal{C}f$. Our counterexamples cover the cases of open-loop, closed-loop, and line-search step-size strategies. We do not assume \emph{misspecification} of the linear minimization oracle and our results thus hold regardless of the points it returns, demonstrating the fundamental pathologies in the convergence behavior of $(x_t)_{t\in\mathbb{N}}$.