Miracle-dz/UniFAR-FTUs huggingface dataset:
A META-THEORY OF A COGNITIVE MODEL OF LEARNING PROCESS BASED ON TRANS-MULTIPLE ABILITIES

Learning is a complex entity that involves not only the cognitive faculties such as thinking, reasoning and judgment, but also elicits conative and affective responses. In this paper, the authors examined three cognitive models of the learning process – the modal model (Atkinson & Shiffrin, 1971), the process model (Sternberg, 1969) and the interactive model (Neisser, 1976) – and put forth their meta-theoretical model based on trans-multiple abilities (TMA) to explain the process of learning. The meta-theory of the TMA model of learning process takes into consideration of three mental potentials – cognition, conation and affect – that form the core components of the mind (Poland, 1974) and their interactions joined by sensation within the mental domains of episodic, semantic and procedural memories. It also introduces the developmental range of learning from peri-level (at concrete level) via rebus level to apo-level (at
The impact of temperamental dimensions on change in symptoms of oppositional defiant disorder from preschool to first grade

Oppositional defiant disorder (ODD) is often present already at preschool age. Previous research has established the association between temperament and broad categories of behavioral disorders. However, no longitudinal research has studied the potential impact of temperament on changes in ODD symptoms in preschool and early school years. Two birth cohorts of 4-year olds living in the city of Trondheim, Norway, were screened for emotional and behavioral problems and a subsample oversampled for such problems was drawn to take part in the study; 82.1% consented. Parents of 1000 children were interviewed with the Preschool Age Psychiatric Assessment, and ratings of children's temperament were provided using the Child Behavior Questionnaire (CBQ). Children were reassessed after 2 years (N = 797). The temperamental dimensions Surgency (SU) and Negative Affectivity (NA) were positively correlated with initial level of ODD symptoms, and predicted an increase in symptoms from age 4 to 6. High Effortful Control (EC) was associated with little ODD symptoms at age 4, but did not predict change in such symptoms. However, in interaction with NA, EC was associated with lower initial levels of ODD symptoms and predicted a decrease in symptoms from age 4 to 6. More precisely, the protective effect of EC was very strong for children high on NA but lower for children low to moderate on NA. The findings suggest that NA and SU function as risk factors whereas EC protects against ODD in young children. NA serves as a moderator of EC, in that among children high in NA, EC had a large protective effect, whereas among children with lower levels of NA, EC did protect to a lesser degree against ODD symptoms. Results of this study have theoretical implications linking temperament to ODD in preschoolers, and clinical applications utilizing temperament assessment to identify children at risk, prevent development of ODD and match treatment modalities to the child’s specific temperamental strengths and weaknesses.
Your Policy Regularizer is Secretly an Adversary

Policy regularization methods such as maximum entropy regularization are widely used in reinforcement learning to improve the robustness of a learned policy. In this paper, we show how this robustness arises from hedging against worst-case perturbations of the reward function, which are chosen from a limited set by an imagined adversary. Using convex duality, we characterize this robust set of adversarial reward perturbations under KL and alpha-divergence regularization, which includes Shannon and Tsallis entropy regularization as special cases. Importantly, generalization guarantees can be given within this robust set. We provide detailed discussion of the worst-case reward perturbations, and present intuitive empirical examples to illustrate this robustness and its relationship with generalization. Finally, we discuss how our analysis complements and extends previous results on adversarial reward robustness and path consistency optimality conditions.
Online Encrypted Skype Identification Based on an Updating Mechanism

The machine learning algorithm is gaining prominence in traffic identification research as it offers a way to overcome the shortcomings of port-based and deep packet inspection, especially for P2P-based Skype. However,recent studies have focused mainly on traffic identification based on a full-packet dataset, which poses great challenges to identifying online network traffic. This study aims to provide a new flow identification algorithm by taking the sampled flow records as the object. The study constructs flow records from a Skype set as the dataset, considers the inherent NETFLOW and extended flow metrics as features, and uses a fast correlation-based filter algorithm to select highly correlated features. The study also proposes a new NFI method that adopts a Bayesian updating mechanism to improve the classifier model. The experimental results show that the proposed scheme can achieve much better identification performance than existing state-of-the-art traffic identification methods, and a typical feature metric is analyzed in the sampling environment. The NFI method improves identification accuracy and reduces false positives and false negatives compared to other methods.
Quantum Cryptanalysis of Farfalle and (Generalised) Feistel Network

Farfalle is a permutation-based pseudo-random function which has been proposed by G. Bertoni et al. in 2017. In this work, we show that by observing suitable inputs to Farfalle, one can derive various constructions of a periodic function with a period that involves a secret key. As this admits the application of Simon’s algorithm in the so-called Q2 attack model, we further show that in the case when internal rolling function is linear, then the secret key can be extracted under feasible assumptions. Furthermore, using the provided constructions of periodic functions for Farfalle, we show that one can mount forgery attacks on the session-supporting mode for authenticated encryption (Farfalle-SAE) and the synthetic initial value AE mode (Farfalle-SIV). In addition, as the wide block cipher mode Farfalle-WBC is a 4-round Feistel scheme, a quantum distinguisher is constructed in the case when input branches are containing at last two blocks, where length of one block corresponds to the size of a permutation employed in Farfalle (a similar attack can be mounted to Farfalle-WBC-AE). And ﬁnally, we consider the problem of extracting a secret round key out of diﬀerent periods obtained from a (Generalized) Feistel scheme (GFN), which has not been addressed in any of the previous works which consider the application of Simon’s (or Simon-Grover) algorithm to round reduced versions of GFNs. In this part, we assume that the key is added to an input of an inner function utilized in the round function of a given GFN. By applying two diﬀer-ent interpolation formulas, we show that one can extract the round key by utilizing amount of diﬀerent periods which is closely related to the polynomial/algebraic degree of underlying inner function.
A fast randomized algorithm for computing a hybrid CUR-type Tucker decomposition

The paper develops a fast randomized algorithm for computing a hybrid CUR-type decomposition of tensors in the Tucker representation. Speciﬁcally, to obtain the factor matrices, random sampling techniques are utilized to accelerate the procedure of constructing the classical matrix decompositions, that are, the interpolatory decomposition and singular value decomposition. Compared with the non-random algorithm, the proposed algorithm has advantages in speed with lower computational cost while keeping a high degree of accuracy. We establish a detailed probabilistic error analysis for the algo-rithm and provide numerical results that show the promise of our approach
Runtime Software Patching: Taxonomy, Survey and Future Directions

Runtime software patching aims to minimize or eliminate service downtime, user interruptions and potential data losses while deploying a patch. Due to modern software systems' high variance and heterogeneity, no universal solutions are available or proposed to deploy and execute patches at runtime. Existing runtime software patching solutions focus on specific cases, scenarios, programming languages and operating systems. This paper aims to identify, investigate and synthesize state-of-the-art runtime software patching approaches and gives an overview of currently unsolved challenges. It further provides insights on multiple aspects of runtime patching approaches such as patch scales, general strategies and responsibilities. This study identifies seven levels of granularity, two key strategies providing a conceptual model of three responsible entities and four capabilities of runtime patching solutions. Through the analysis of the existing literature, this research also reveals open issues hindering more comprehensive adoption of runtime patching in practice. Finally, it proposes several crucial future directions that require further attention from both researchers and practitioners.
Analog Quantum Simulation of the Dynamics of Open Quantum Systems with Quantum Dots and Microelectronic Circuits

We introduce a general setup for the analog quantum simulation of the dynamics of open quantum systems based on semiconductor quantum dots electrically connected to a chain of quantum RLC electronic circuits. The dots are chosen to be in the regime of spin-charge hybridization to enhance their sensitivity to the RLC circuits while mitigating the detrimental eﬀects of unwanted noise. In this context, we establish an experimentally realizable map between the hybrid system and a qubit coupled to thermal harmonic environments of arbitrary complexity that enables the analog quantum simulation of open quantum systems. We assess the utility of the simulator by numerically exact emulations that indicate that the experimental setup can faithfully mimic the intended target even in the presence of its natural inherent noise. We further provide a detailed analysis of the physical requirements on the quantum dots and the RLC circuits needed to experimentally realize this proposal that indicates that the simulator can be created with existing technology. The approach can exactly capture the eﬀects of highly structured non-Markovian quantum environments typical of photosynthesis and chemical dynamics, and oﬀer clear potential advantages over conventional and even quantum computation. The proposal opens a general path for eﬀective quantum dynamics simulations based on semiconductor quantum dots.
Learning Personalized Item-to-Item Recommendation Metric via Implicit Feedback

This paper studies the item-to-item recommendation problem in recommender systems from a new perspective of metric learning via implicit feedback. We develop and investigate a personalizable deep metric model that captures both the internal contents of items and how they were interacted with by users. There are two key challenges in learning such model. First, there is no explicit similarity annotation, which deviates from the assumption of most metric learning methods. Second, these approaches ignore the fact that items are often represented by multiple sources of meta data and different users use different combinations of these sources to form their own notion of similarity. To address these challenges, we develop a new metric representation embedded as kernel parameters of a probabilistic model. This helps express the correlation between items that a user has interacted with, which can be used to predict user interaction with new items. Our approach hinges on the intuition that similar items induce similar interactions from the same user, thus fitting a metric-parameterized model to predict an implicit feedback signal could indirectly guide it towards finding the most suitable metric for each user. To this end, we also analyze how and when the proposed method is effective from a theoretical lens. Its empirical effectiveness is also demonstrated on several real-world datasets.
Nonlinear Fano resonance without a continuum

A new mechanism of Fano resonance induced by distant discrete levels in atoms or molecules is proposed and experimentally veriﬁed by Doppler-free Lamb-dip spectroscopy of vibration-rotational transitions of CO 2 . The observed spectrum has an asymmetric proﬁle and its amplitude increases quadratically with the probe laser power. Our results appeal to further investigations of Fano resonances and facilitate a broad range of topics based on narrow transitions.
Transformer-based Multimodal Information Fusion for Facial Expression Analysis

Human affective behavior analysis has received much attention in human-computer interaction (HCI). In this paper, we introduce our submission to the CVPR 2022 Competition on Affective Behavior Analysis in-the-wild (ABAW). To fully exploit affective knowledge from multiple views, we utilize the multimodal features of spoken words, speech prosody, and facial expression, which are extracted from the video clips in the Aff-Wild2 dataset. Based on these features, we propose a unified transformer-based multimodal framework for Action Unit detection and also expression recognition. Specifically, the static vision feature is first encoded from the current frame image. At the same time, we clip its adjacent frames by a sliding window and extract three kinds of multimodal features from the sequence of images, audio, and text. Then, we introduce a transformer-based fusion module that integrates the static vision features and the dynamic multimodal features. The cross-attention module in the fusion module makes the output integrated features focus on the crucial parts that facilitate the downstream detection tasks. We also leverage some data balancing techniques, data augmentation techniques, and postprocessing methods to further improve the model performance. In the official test of ABAW3 Competition, our model ranks first in the EXPR and AU tracks. The extensive quantitative evaluations, as well as ablation studies on the Aff-Wild2 dataset, prove the effectiveness of our proposed method.
SORPTION OF AQUEOUS FE(II) ON GOETHITE INVESTIGATED WITH STABLE OXYGEN ISOTOPES A Thesis Presented By

This thesis work is aimed at examining the oxygen a tom mixing between common iron(III) oxides and Fe(II) aq solution during the sorption process. First, O labeled iron(III) minerals were used in batch sorption reactors to study the exchan ge of oxygen atom within the Fe(II) aq – iron(III) oxide system. Results showed a much lesser extent o f oxygen mixing than reported Fe mixing from previous studies, even when dissolved Fe(II) i s present. The oxygen mixing extents are 3.95 % more for goethite and 9.66 % more for ferrihydrite than their respective controls within 14 days. A consecutive atom exchange kinetic study indicated that O exchange is not inhibited by surface Fe sorptive site saturation, and that after 40d sor ption experiment, the maximum O exchange is 54.96 % across reactors with different initial soli d loadings and Fe(II) aq levels. The second part of this work examined the influence of aqueous Fe(II) on the sorption of selenium oxyanions (selenite and selenate) in Fe(II) aq – iron(III) systems. It was found that Fe(II) aq promotes selenium oxyanion sorption, and potential electron transfer occurred at the interface of goethite and selenite. Iron exists in nature in various forms including mi nerals in underground formations, freshwater and seawater sediments and in almost any living org anisms. The diverse electrochemical properties of elemental Fe and its compounds have f ound way in many environmental applications; for example, Fe powder is used in per meable reactive barriers for groundwater remediation; Ferrate (+6 valent iron, FeO 4 ) is used as an environmentally-friendly disinfecta nt for water and wastewater treatment. In recent decad es, sorption of iron(III) oxides in reducing, Fe(II)-rich environment was extensively studied, wi th the advancement of spectroscopic techniques, researchers have found that the presenc of aqueous Fe(II) changes the surface sorption activity of Fe minerals. Evidence of heavy metal incorporation and release, electron transfer between phases and through bulk solids, an d the observation of recrystallization on the surface of the substrate mineral structures have po sitively confirmed the catalytic effects of aqueous Fe(II) on mineral-water interactions. More recent studies with Fe isotope tracers found near-complete mixing of structural Fe atoms with bu lk solution dissolved Fe(II).
Efficient Few-Shot Object Detection via Knowledge Inheritance

Few-shot object detection (FSOD), which aims at learning a generic detector that can adapt to unseen tasks with scarce training samples, has witnessed consistent improvement recently. However, most existing methods ignore the efficiency issues, e.g., high computational complexity and slow adaptation speed. Notably, efficiency has become an increasingly important evaluation metric for few-shot techniques due to an emerging trend toward embedded AI. To this end, we present an efficient pretrain-transfer framework (PTF) baseline with no computational increment, which achieves comparable results with previous state-of-the-art (SOTA) methods. Upon this baseline, we devise an initializer named knowledge inheritance (KI) to reliably initialize the novel weights for the box classifier, which effectively facilitates the knowledge transfer process and boosts the adaptation speed. Within the KI initializer, we propose an adaptive length re-scaling (ALR) strategy to alleviate the vector length inconsistency between the predicted novel weights and the pretrained base weights. Finally, our approach not only achieves the SOTA results across three public benchmarks, i.e., PASCAL VOC, COCO and LVIS, but also exhibits high efficiency with $1.8-100\times $ faster adaptation speed against the other methods on COCO/LVIS benchmark during few-shot transfer. To our best knowledge, this is the first work to consider the efficiency problem in FSOD. We hope to motivate a trend toward powerful yet efficient few-shot technique development. The codes are publicly available at https://github.com/Ze-Yang/Efficient-FSOD.
Cosmic flexion

Cosmic flexion, like cosmic shear, is a correlation function whose signal originates from the large-scale structure of the universe. Building on the observational success of cosmic shear, along with the unprecedented quality of large-scale cosmological datasets, the time is ripe to explore the practical constraints from cosmic flexion. Unlike cosmic shear, which has a broad window function for power, cosmic flexion is only measurable on small scales and therefore can uniquely place constraints on the small-scale matter power spectrum. Here, we present a full theoretical formalism for cosmic flexion, including both flexion-flexion and shear-flexion two-point correlations. We present forecasts for measuring cosmic flexion in the Dark Energy Survey (DES), a Stage III cosmological survey, and comment on the future prospects of measuring these cosmological flexion signals in the upcoming era of Stage IV experiments.
Distributionally Robust Model Predictive Control With Total Variation Distance

This letter studies the problem of distributionally robust model predictive control (MPC) using total variation distance ambiguity sets. For a discrete-time linear system with additive disturbances, we provide a conditional value-at-risk reformulation of the MPC optimization problem that is distributionally robust in the expected cost and chance constraints. The distributionally robust chance constraint is over-approximated as a simpler, tightened chance constraint that reduces the computational burden. Numerical experiments support our results on probabilistic guarantees and computational efficiency.
Contextual Integrity Up and Down the Data Food Chain

Abstract According to the theory of contextual integrity (CI), privacy norms prescribe information flows with reference to five parameters — sender, recipient, subject, information type, and transmission principle. Because privacy is grasped contextually (e.g., health, education, civic life, etc.), the values of these parameters range over contextually meaningful ontologies — of information types (or topics) and actors (subjects, senders, and recipients), in contextually defined capacities. As an alternative to predominant approaches to privacy, which were ineffective against novel information practices enabled by IT, CI was able both to pinpoint sources of disruption and provide grounds for either accepting or rejecting them. Mounting challenges from a burgeoning array of networked, sensor-enabled devices (IoT) and data-ravenous machine learning systems, similar in form though magnified in scope, call for renewed attention to theory. This Article introduces the metaphor of a data (food) chain to capture the nature of these challenges. With motion up the chain, where data of higher order is inferred from lower-order data, the crucial question is whether privacy norms governing lower-order data are sufficient for the inferred higher-order data. While CI has a response to this question, a greater challenge comes from data primitives, such as digital impulses of mouse clicks, motion detectors, and bare GPS coordinates, because they appear to have no meaning. Absent a semantics, they escape CI’s privacy norms entirely.
Hybrid Mesh-neural Representation for 3D Transparent Object Reconstruction

We propose a novel method to reconstruct the 3D shapes of transparent objects using hand-held captured images under natural light conditions. It combines the advantage of explicit mesh and multi-layer perceptron (MLP) network, a hybrid representation, to simplify the capture setting used in recent contributions. After obtaining an initial shape through the multi-view silhouettes, we introduce surface-based local MLPs to encode the vertex displacement field (VDF) for the reconstruction of surface details. The design of local MLPs allows to represent the VDF in a piece-wise manner using two layer MLP networks, which is beneficial to the optimization algorithm. Defining local MLPs on the surface instead of the volume also reduces the searching space. Such a hybrid representation enables us to relax the ray-pixel correspondences that represent the light path constraint to our designed ray-cell correspondences, which significantly simplifies the implementation of single-image based environment matting algorithm. We evaluate our representation and reconstruction algorithm on several transparent objects with ground truth models. Our experiments show that our method can produce high-quality reconstruction results superior to state-of-the-art methods using a simplified data acquisition setup.
Multi-Mosaics: Corpus Summarizing and Exploration using multiple Concordance Mosaic Visualisations

Researchers working in areas such as lexicography, translation studies, and computational linguistics, use a combination of au-tomated and semi-automated tools to analyze the content of text corpora. Keywords, named entities, and events are often extracted automatically as the first step in the analysis. Concordancing – or the arranging of passages of a textual corpus in alphabetical order according to user-defined keywords – is one of the oldest and still most widely used forms of text analysis. This paper describes Multi-Mosaics, a tool for corpus analysis using multiple implicitly linked Concordance Mosaic visualisations. Multi-Mosaics supports examining linguistic relationships within the context windows surrounding extracted keywords.