Miracle-dz/UniFAR-FTUs huggingface dataset:
A Cover Time Study of a non-Markovian Algorithm

Given a traversal algorithm, cover time is the expected number of steps needed to visit all nodes in a given graph. A smaller cover time means a higher exploration efficiency of traversal algorithm. Although random walk algorithms have been studied extensively in the existing literature, there has been no cover time result for any non-Markovian method. In this work, we stand on a theoretical perspective and show that the negative feedback strategy (a count-based exploration method) is better than the naive random walk search. In particular, the former strategy can locally improve the search efficiency for an arbitrary graph. It also achieves smaller cover times for special but important graphs, including clique graphs, tree graphs, etc. Moreover, we make connections between our results and reinforcement learning literature to give new insights on why classical UCB and MCTS algorithms are so useful. Various numerical results corroborate our theoretical findings.
Deterministic entangling gates with nonlinear quantum photonic interferometers

The quantum computing paradigm in photonics currently relies on the multi-port interference in linear optical devices, which is intrinsically based on probabilistic measurements outcome and thus non-deterministic. Devising a fully deterministic, universal, and practically achievable quantum computing platform based on integrated photonic circuits is still an open challenge. Here we propose to exploit weakly nonlinear photonic devices to implement deterministic entangling quantum gates, following the definition of dual rail photonic qubits. It is shown that a universal set of single- and two-qubit gates can be designed by a suitable concatenation of few optical interferometric elements, with optimal fidelities arbitrarily close to 100% theoretically demonstrated through a bound constrained optimization algorithm. The actual realization would require the concatenation of a few tens of elementary operations, as well as on-chip optical nonlinearities that are compatible with some of the existing quantum photonic platforms, as it is finally discussed.
Object-Centric Learning for Real-World Videos by Predicting Temporal Feature Similarities

Unsupervised video-based object-centric learning is a promising avenue to learn structured representations from large, unlabeled video collections, but previous approaches have only managed to scale to real-world datasets in restricted domains. Recently, it was shown that the reconstruction of pre-trained self-supervised features leads to object-centric representations on unconstrained real-world image datasets. Building on this approach, we propose a novel way to use such pre-trained features in the form of a temporal feature similarity loss. This loss encodes semantic and temporal correlations between image patches and is a natural way to introduce a motion bias for object discovery. We demonstrate that this loss leads to state-of-the-art performance on the challenging synthetic MOVi datasets. When used in combination with the feature reconstruction loss, our model is the first object-centric video model that scales to unconstrained video datasets such as YouTube-VIS.
An unconditional Montgomery Theorem for Pair Correlation of Zeros of the Riemann Zeta Function

Assuming the Riemann Hypothesis (RH), Montgomery proved a theorem concerning pair correlation of zeros of the Riemann zeta-function. One consequence of this theorem is that, assuming RH, at least $67.9\%$ of the nontrivial zeros are simple. Here we obtain an unconditional form of Montgomery's theorem and show how to apply it to prove the following result on simple zeros: Assuming all the zeros $\rho=\beta+i\gamma$ of the Riemann zeta-function such that $T^{3/8}<\gamma\le T$ satisfy $|\beta-1/2|<1/(2\log T)$, %lie in the thin box $\{s=\sigma +it: |\sigma-1/2|<1/(2\log T),\ T^{3/8}<t\le T\}$, then, as $T$ tends to infinity, at least $61.7\%$ of these zeros are simple. The method of proof neither requires nor provides any information on whether any of these zeros are on or not on the critical line where $\beta=1/2$. We also obtain the same result under the weaker assumption of a strong zero-density hypothesis.
Generalization Performance of Transfer Learning: Overparameterized and Underparameterized Regimes

Transfer learning is a useful technique for achieving improved performance and reducing training costs by leveraging the knowledge gained from source tasks and applying it to target tasks. Assessing the effectiveness of transfer learning relies on understanding the similarity between the ground truth of the source and target tasks. In real-world applications, tasks often exhibit partial similarity, where certain aspects are similar while others are different or irrelevant. To investigate the impact of partial similarity on transfer learning performance, we focus on a linear regression model with two distinct sets of features: a common part shared across tasks and a task-specific part. Our study explores various types of transfer learning, encompassing two options for parameter transfer. By establishing a theoretical characterization on the error of the learned model, we compare these transfer learning options, particularly examining how generalization performance changes with the number of features/parameters in both underparameterized and overparameterized regimes. Furthermore, we provide practical guidelines for determining the number of features in the common and task-specific parts for improved generalization performance. For example, when the total number of features in the source task's learning model is fixed, we show that it is more advantageous to allocate a greater number of redundant features to the task-specific part rather than the common part. Moreover, in specific scenarios, particularly those characterized by high noise levels and small true parameters, sacrificing certain true features in the common part in favor of employing more redundant features in the task-specific part can yield notable benefits.
Variable Radiance Field for Real-Life Category-Specifc Reconstruction from Single Image

Reconstructing category-specific objects from a single image is a challenging task that requires inferring the geometry and appearance of an object from a limited viewpoint. Existing methods typically rely on local feature retrieval based on re-projection with known camera intrinsic, which are slow and prone to distortion at viewpoints distant from the input image. In this paper, we present Variable Radiance Field (VRF), a novel framework that can efficiently reconstruct category-specific objects from a single image without known camera parameters. Our key contributions are: (1) We parameterize the geometry and appearance of the object using a multi-scale global feature extractor, which avoids frequent point-wise feature retrieval and camera dependency. We also propose a contrastive learning-based pretraining strategy to improve the feature extractor. (2) We reduce the geometric complexity of the object by learning a category template, and use hypernetworks to generate a small neural radiance field for fast and instance-specific rendering. (3) We align each training instance to the template space using a learned similarity transformation, which enables semantic-consistent learning across different objects. We evaluate our method on the CO3D dataset and show that it outperforms existing methods in terms of quality and speed. We also demonstrate its applicability to shape interpolation and object placement tasks.
K-Nearest-Neighbor Resampling for Off-Policy Evaluation in Stochastic Control

In this paper, we propose a novel $K$-nearest neighbor resampling procedure for estimating the performance of a policy from historical data containing realized episodes of a decision process generated under a different policy. We provide statistical consistency results under weak conditions. In particular, we avoid the common assumption of identically and independently distributed transitions and rewards. Instead, our analysis allows for the sampling of entire episodes, as is common practice in most applications. To establish the consistency in this setting, we generalize Stone's Theorem, a well-known result in nonparametric statistics on local averaging, to include episodic data and the counterfactual estimation underlying off-policy evaluation (OPE). By focusing on feedback policies that depend deterministically on the current state in environments with continuous state-action spaces and system-inherent stochasticity effected by chosen actions, and relying on trajectory simulation similar to Monte Carlo methods, the proposed method is particularly well suited for stochastic control environments. Compared to other OPE methods, our algorithm does not require optimization, can be efficiently implemented via tree-based nearest neighbor search and parallelization, and does not explicitly assume a parametric model for the environment's dynamics. Numerical experiments demonstrate the effectiveness of the algorithm compared to existing baselines in a variety of stochastic control settings, including a linear quadratic regulator, trade execution in limit order books, and online stochastic bin packing.
Log-based Anomaly Detection based on EVT Theory with feedback

System logs play a critical role in maintaining the reliability of software systems. Fruitful studies have explored automatic log-based anomaly detection and achieved notable accuracy on benchmark datasets. However, when applied to large-scale cloud systems, these solutions face limitations due to high resource consumption and lack of adaptability to evolving logs. In this paper, we present an accurate, lightweight, and adaptive log-based anomaly detection framework, referred to as SeaLog. Our method introduces a Trie-based Detection Agent (TDA) that employs a lightweight, dynamically-growing trie structure for real-time anomaly detection. To enhance TDA's accuracy in response to evolving log data, we enable it to receive feedback from experts. Interestingly, our findings suggest that contemporary large language models, such as ChatGPT, can provide feedback with a level of consistency comparable to human experts, which can potentially reduce manual verification efforts. We extensively evaluate SeaLog on two public datasets and an industrial dataset. The results show that SeaLog outperforms all baseline methods in terms of effectiveness, runs 2X to 10X faster and only consumes 5% to 41% of the memory resource.
Motivic monodromy and p-adic cohomology theories

We build a unified framework for the study of monodromy operators and weight filtrations of cohomology theories for varieties over a local field. As an application, we give a streamlined definition of Hyodo-Kato cohomology without recourse to log-geometry, as predicted by Fontaine, and we produce an induced Clemens-Schmid chain complex.
KAJIAN KEMAMPUAN GENERALISASI SUPPORT VECTOR MACHINE DALAM PENGENALAN JENIS SPLICE SITES PADA BARISAN DNA

Study on Generalization Capability of Support Vector Machine in Splice Site Type Recognition of DNA Sequence. Recently, support vector machine has become a popular model as machine learning. A particular advantage of SVM over other machine learning is that it can be analyzed theoretically and at same time can achieve a good performance when applied to real problems. This paper will describe analytically the using of SVM to solve pattern recognition problem with a preliminary case study in determining the type of splice site on the DNA sequence, particularity on the generalization capability. The result obtained show that SVM has a good generalization capability of around 95.4 %. Keywords: Support vector machine, generalization test, pattern recognition, splice sites, DNA
Moment explosion and tail asymptotics for the ckls process

In a previous article (cf. [1]) the author obtained a Tauberian result which relates explicitly the moment generating function (MGF) and the complementary cumulative distribution function of a random variable whose MGF is finite only on part of the real line. In this paper we first give a new Tauberian result which is an" improvement" of the one obtained in [1]. We then consider the CKLS process defined by the one dimensional stochastic differential equation: $$ dVt = (a -- bVt)dt + $\sigma$V p t dWt. $$ We first show that the MGF of $V^{2(1--p)}\_t$ explodes at a critical moment ${\mu}^\ast\_t$ which is independent from p. We then show that this MGF is given as a sum of the MGF of a Cox-Ingersoll-Ross process plus an extra term which is given by a nonlinear partial differential equation (PDE) on $\partial$t and $\partial$x. We then solve this nonlinear PDE to obtain an asymptotic expansion for the MGF of $V^{2(1--p)}\_t$ near its critical moment. Then a sharp asymptotic expansion for the cumulative distribution function of $V^{2(1--p)}\_t$ is deduced using the Tauberian formulated in the beginning of the paper.
Minimizing Energy Consumption in MU-MIMO via Antenna Muting by Neural Networks with Asymmetric Loss

Transmit antenna muting (TAM) in multiple-user multiple-input multiple-output (MU-MIMO) networks allows reducing the power consumption of the base station (BS) by properly utilizing only a subset of antennas in the BS. In this paper, we consider the downlink transmission of an MU-MIMO network where TAM is formulated to minimize the number of active antennas in the BS while guaranteeing the per-user throughput requirements. To address the computational complexity of the combinatorial optimization problem, we propose an algorithm called neural antenna muting (NAM) with an asymmetric custom loss function. NAM is a classification neural network trained in a supervised manner. The classification error in this scheme leads to either sub-optimal energy consumption or lower quality of service (QoS) for the communication link. We control the classification error probability distribution by designing an asymmetric loss function such that the erroneous classification outputs are more likely to result in fulfilling the QoS requirements. Furthermore, we present three heuristic algorithms and compare them with the NAM. Using a 3GPP compliant system-level simulator, we show that NAM achieves $\sim73\%$ energy saving compared to the full antenna configuration in the BS with $\sim95\%$ reliability in achieving the user throughput requirements while being around $1000\times$ and $24\times$ less computationally intensive than the greedy heuristic algorithm and the fixed column antenna muting algorithm, respectively.
Data Augmentation for Improving Tail-traffic Robustness in Skill-routing for Dialogue Systems

Large-scale conversational systems typically rely on a skill-routing component to route a user request to an appropriate skill and interpretation to serve the request. In such system, the agent is responsible for serving thousands of skills and interpretations which create a long-tail distribution due to the natural frequency of requests. For example, the samples related to play music might be a thousand times more frequent than those asking for theatre show times. Moreover, inputs used for ML-based skill routing are often a heterogeneous mix of strings, embedding vectors, categorical and scalar features which makes employing augmentation-based long-tail learning approaches challenging. To improve the skill-routing robustness, we propose an augmentation of heterogeneous skill-routing data and training targeted for robust operation in long-tail data regimes. We explore a variety of conditional encoder-decoder generative frameworks to perturb original data fields and create synthetic training data. To demonstrate the effectiveness of the proposed method, we conduct extensive experiments using real-world data from a commercial conversational system. Based on the experiment results, the proposed approach improves more than 80% (51 out of 63) of intents with less than 10K of traffic instances in the skill-routing replication task.
Correlated Noise in Epoch-Based Stochastic Gradient Descent: Implications for Weight Variances

Stochastic gradient descent (SGD) has become a cornerstone of neural network optimization, yet the noise introduced by SGD is often assumed to be uncorrelated over time, despite the ubiquity of epoch-based training. In this work, we challenge this assumption and investigate the effects of epoch-based noise correlations on the stationary distribution of discrete-time SGD with momentum, limited to a quadratic loss. Our main contributions are twofold: first, we calculate the exact autocorrelation of the noise for training in epochs under the assumption that the noise is independent of small fluctuations in the weight vector; second, we explore the influence of correlations introduced by the epoch-based learning scheme on SGD dynamics. We find that for directions with a curvature greater than a hyperparameter-dependent crossover value, the results for uncorrelated noise are recovered. However, for relatively flat directions, the weight variance is significantly reduced. We provide an intuitive explanation for these results based on a crossover between correlation times, contributing to a deeper understanding of the dynamics of SGD in the presence of epoch-based noise correlations.
Knowledge Detection by Relevant Question and Image Attributes in Visual Question Answering

Visual question answering (VQA) is a Multidisciplinary research problem that pursued through practices of natural language processing and computer vision. Visual question answering automatically answers natural language questions according to the content of an image. Some testing questions require external knowledge to derive a solution. Such knowledge-based VQA uses various methods to retrieve features of image and text, and combine them to generate the answer. To generate knowledgebased answers either question dependent or image dependent knowledge retrieval methods are used. If knowledge about all the objects in the image is derived, then not all knowledge is relevant to the question. On other side only question related knowledge may lead to incorrect answers and over trained model that answers question that is irrelevant to image. Our proposed method takes image attributes and question features as input for knowledge derivation module and retrieves only question relevant knowledge about image objects which can provide accurate answers.
Geometric pure and matter coupled supergravity theories

This thesis deals with the formulation of pure and matter coupled supergravity theories in three and four dimensions. Different supergravity Lagrangians are constructed in geometrical terms, by using the useful properties of the abelian semigroup expansion method. Furthermore, a supergravity model with partial breaking of N = 2 to N = 1 supersymmetry which, in the low energy limit, gives rise to a rigid supersymmetric theory, is presented. In Chapter 1, we briefly review General Relativity in both, Einstein and Cartan formalism. It is also revised a natural extension of Einstein theory to D-dimensions, namely the Lanczos-Lovelock theory. Then, we study the Maxwell type algebras, and we show that standard General Relativity can be obtained in a certain limit of Chern-Simons and Born-Infeld theories, invariant under these algebras. Chapter 2 deals with the supersymmetric extension of gravity. We mainly study the MacDowell-Mansouri supergravity and the AdS Chern-Simons supergravity. In Chapters 3, 4, 5, 6 and 7, we present our main results, which are based on five articles written during the doctoral research. First, we present supersymmetric extensions of the Maxwell type algebras. We show that considering different choices of semigroups, inequivalent Maxwell superalgebras are obtained, when using the S-expansion procedure. Then, we construct the N = 1 supergravity action a la MacDowell-Mansouri from the minimal Maxwell superalgebra. Interestingly, the action describes pure supergravity. Based on the AdS-Lorentz superalgebra, we also build the minimal D = 4 supergravity action which includes a generalized supersymmetric cosmological constant term. The construction of the Chern-Simons supergravity action from a generalized minimal Maxwell superalgebra is also presented. Eventually, in Chapter 7 we present the multi-vector generalization of a rigid, partially broken N = 2 supersymmetric theory as a rigid limit of a gauged N = 2 supergravity with electric and magnetic charges
Towards Commonsense Knowledge based Fuzzy Systems for Supporting Size-Related Fine-Grained Object Detection

Deep learning has become the dominating approach for object detection. To achieve accurate fine-grained detection, one needs to employ a large enough model and a vast amount of data annotations. In this paper, we propose a commonsense knowledge inference module (CKIM) which leverages commonsense knowledge to assist a lightweight deep neural network base coarse-grained object detector to achieve accurate fine-grained detection. Specifically, we focus on a scenario where a single image contains objects of similar categories but varying sizes, and we establish a size-related commonsense knowledge inference module (CKIM) that maps the coarse-grained labels produced by the DL detector to size-related fine-grained labels. Considering that rule-based systems are one of the popular methods of knowledge representation and reasoning, our experiments explored two types of rule-based CKIMs, implemented using crisp-rule and fuzzy-rule approaches, respectively. Experimental results demonstrate that compared with baseline methods, our approach achieves accurate fine-grained detection with a reduced amount of annotated data and smaller model size. Our code is available at: https://github.com/ZJLAB-AMMI/CKIM.
Organ agar serves as physiologically relevant alternative for in vivo colonization

Animal models for host-microbial interactions have proven valuable, yielding physiologically relevant data that may be otherwise difficult to obtain. Unfortunately, such models are lacking or nonexistent for many microbes. Here, we introduce organ agar, a straightforward method to enable the screening of large mutant libraries while avoiding physiological bottlenecks. We demonstrate that growth defects on organ agar were translatable to colonization deficiencies in a murine model. Specifically, we present a urinary tract infection agar model to interrogate an ordered library of Proteus mirabilis transposon mutants, with accurate prediction of bacterial genes critical for host colonization. Thus, we demonstrate the ability of ex vivo organ agar to reproduce in vivo deficiencies. This work provides a readily adoptable technique that is economical and uses substantially fewer animals. We anticipate this method will be useful for a wide variety of microorganisms, both pathogenic and commensal, in a diverse range of model host species.