Miracle-dz/UniFAR-FTUs huggingface dataset:
Noise Variance Estimation Using Asymptotic Residual in Compressed Sensing

In compressed sensing, the measurement is usually contaminated by additive noise, and hence the information of the noise variance is often required to design algorithms. In this paper, we propose an estimation method for the unknown noise variance in compressed sensing problems. The proposed method called asymptotic residual matching (ARM) estimates the noise variance from a single measurement vector on the basis of the asymptotic result for the $\ell_{1}$ optimization problem. Specifically, we derive the asymptotic residual corresponding to the $\ell_{1}$ optimization and show that it depends on the noise variance. The proposed ARM approach obtains the estimate by comparing the asymptotic residual with the actual one, which can be obtained by the empirical reconstruction without the information of the noise variance. Simulation results show that the proposed noise variance estimation outperforms a conventional method based on the analysis of the ridge regularized least squares. We also show that, by using the proposed method, we can achieve good reconstruction performance in compressed sensing even when the noise variance is unknown.
Ethereum's Recursive Length Prefix in ACL2

Recursive Length Prefix (RLP) is used to encode a wide variety of data in Ethereum, including transactions. The work described in this paper provides a formal specification of RLP encoding and a verified implementation of RLP decoding, developed in the ACL2 theorem prover. This work has led to improvements to the Ethereum documentation and additions to the Ethereum test suite.
A Fine-grained View on Stable Many-to-one Matching Problems with Lower and Upper Quotas

In the NP-hard Hospital Residents problem with lower and upper quotas (HR-QLU), the goal is to find a stable matching of residents to hospitals where the number of residents matched to a hospital is either between its lower and upper quota or zero. We analyze this problem from a parameterized complexity perspective using several natural parameters such as the number of hospitals and the number of residents. Moreover, answering an open question of Biró et al. [TCS 2010], we present an involved polynomial-time algorithm that finds a stable matching (if it exists) on instances with maximum lower quota two. Alongside HR-QLU, we also consider two closely related models of independent interest, namely, the special case of HR-QLU where each hospital has only a lower quota but no upper quota and the variation of HR-QLU where hospitals do not have preferences over residents, which is also known as the House Allocation problem with lower and upper quotas. Last, we investigate the parameterized complexity of these three NP-hard problems when preferences may contain ties.
Hexagonal Run-Length Zero Capacity Region—Part I: Analytical Proofs

The zero capacity region for hexagonal <inline-formula> <tex-math notation="LaTeX">$(d,k)$ </tex-math></inline-formula> run-length constraints is known for many, but not all, <inline-formula> <tex-math notation="LaTeX">$d$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>. The pairs <inline-formula> <tex-math notation="LaTeX">$(d,k)$ </tex-math></inline-formula> for which it has been unproven whether the capacity is zero or positive consist of: (i) <inline-formula> <tex-math notation="LaTeX">$k=d+2$ </tex-math></inline-formula> when <inline-formula> <tex-math notation="LaTeX">$d\ge 2$ </tex-math></inline-formula>; (ii) <inline-formula> <tex-math notation="LaTeX">$k=d+3$ </tex-math></inline-formula> when <inline-formula> <tex-math notation="LaTeX">$d \ge 1$ </tex-math></inline-formula>; (iii) <inline-formula> <tex-math notation="LaTeX">$k=d+4$ </tex-math></inline-formula> when either <inline-formula> <tex-math notation="LaTeX">$d=4$ </tex-math></inline-formula> or <inline-formula> <tex-math notation="LaTeX">$d$ </tex-math></inline-formula> is odd and <inline-formula> <tex-math notation="LaTeX">$d \ge 3$ </tex-math></inline-formula>; and (iv) <inline-formula> <tex-math notation="LaTeX">$k=d+5$ </tex-math></inline-formula> when <inline-formula> <tex-math notation="LaTeX">$d=4$ </tex-math></inline-formula>. Here, we prove that the capacity is zero for all of case (i), and for case (ii) whenever <inline-formula> <tex-math notation="LaTeX">$d \ge 7$ </tex-math></inline-formula>. The method used in this paper is to reduce an infinite search space of valid labelings to a finite set of configurations that we exhaustively examine using backtracking. In Part II of this two-part series, we use automated procedures to prove that the capacity is zero in case (i) when <inline-formula> <tex-math notation="LaTeX">$2 \le d \le 9$ </tex-math></inline-formula>, in case (ii) when <inline-formula> <tex-math notation="LaTeX">$3 \le d \le 11$ </tex-math></inline-formula>, and in case (iii) when <inline-formula> <tex-math notation="LaTeX">$d \in \{ 4,5,7,9 \}$ </tex-math></inline-formula>, and that the capacity is positive in case (ii) when <inline-formula> <tex-math notation="LaTeX">$d \in \{1,2\}$ </tex-math></inline-formula>, in case (iii) when <inline-formula> <tex-math notation="LaTeX">$d = 3$ </tex-math></inline-formula>, and in case (iv). Thus, the only remaining unknown cases are now when <inline-formula> <tex-math notation="LaTeX">$k=d+4$ </tex-math></inline-formula>, for any odd <inline-formula> <tex-math notation="LaTeX">$d \ge 11$ </tex-math></inline-formula>.
Multi-term and Multi-task Affect Analysis in the Wild

Human affect recognition is an important factor in human-computer interaction. However, the development of method for in-the-wild data is still nowhere near enough. in this paper, we introduce the affect recognition method that was submitted to the Affective Behavior Analysis in-the-wild (ABAW) 2020 Contest. In our approach, since we considered that affective behaviors have different time window features, we generated features and averaged labels using short-term, medium-term, and long-term time windows from video images. Then, we generated affect recognition models in each time window, and esembled each models. In addition,we fuseed the VA and EXP models, taking into account that Valence, Arousal, and Expresion are closely related. The features were trained by gradient boosting, using the mean, standard deviation, max-range, and slope in each time winodows. We achieved the valence-arousal score: 0.495 and expression score: 0.464 on the validation set.
Convergence of Ramanujan expansions, I [Multiplicativity on Ramanujan clouds]

We call $R_G(a):=\sum_{q=1}^{\infty}G(q)c_q(a)$ the 'Ramanujan series', of coefficient $G:$N$\to$C, where $c_q(a)$ is the well-known Ramanujan sum. We study the convergence of this series (a preliminary step, to study Ramanujan expansions and define $G$ a 'Ramanujan coefficient' when $R_G(a)$ converges pointwise, in all natural $a$. Then, $R_G:$N$\to$C is well defined ('w-d'). The 'Ramanujan cloud' of a fixed $F:$N$\to$C is $ :=${$G:N\to C|R_G \; w-d, F=R_G$}. (See the Appendix.) We study in detail the multiplicative Ramanujan coefficients $G$ : their $ $ subset is called the 'multiplicative Ramanujan cloud', $ _M$. Our first main result, the "Finiteness convergence Theorem", for $G$ multiplicative, among other properties equivalent to "$R_G$ well defined", reduces the convergence test to a finite set, i.e., $R_G$ w-d is equivalent to: $R_G(a)$ converges for all $a$ dividing $N(G)\in$N, that we call the "Ramanujan conductor". Our second main result, the "Finite Euler product explicit formula", for multiplicative Ramanujan coefficients $G$, writes $F=R_G$ as a finite Euler product; thus, $F$ is a semi-multiplicative function (following Rearick definition) and this product is the Selberg factorization for $F$. In particular, we have: $F(a)=R_G(a)$ converges absolutely, being finite (of length depending on non-zero $p-$adic valuations of $a$). Our third main result, called the "Multiplicative Ramanujan clouds", studies the important subsets of $ _M$; also giving, for all multiplicative $F$, the 'canonical Ramanujan coefficient' $G_F\in _M$, proving: Any multiplicative $F$ has a finite Ramanujan expansion with multiplicative coefficients.
Noise induced effects at nano-structured thin films growth during deposition in plasma-condensate devices

We perform a comprehensive study of noise-induced effects in a stochastic model of reaction-diffusion type, describing nano-structured thin films growth at condensation. We introduce an external flux of adsorbate between neighbour monoatomic layers caused by the electrical field presence near substrate in plasma-condensate devices. We take into account that the strength of the electric field fluctuates around its mean value. We discuss a competing influence of the regular and stochastic parts of the external flux onto the dynamics of adsorptive system. It will be shown that the introduced fluctuations induce first-order phase transition in a homogeneous system, govern the pattern formation in a spatially extended system; these parts of the flux control the dynamics of the patterning, spatial order, morphology of the surface, growth law of the mean size of adsorbate islands, type and linear size of surface structures. The influence of the intensity of fluctuations onto scaling and statistical properties of the nano-structured surface is analysed in detail. This study provides an insight into the details of noise induced effects at pattern formation processes in anisotropic adsorptive systems.
Apollo: An Adaptive Parameter-wise Diagonal Quasi-Newton Method for Nonconvex Stochastic Optimization

In this paper, we introduce Apollo, a quasi-Newton method for nonconvex stochastic optimization, which dynamically incorporates the curvature of the loss function by approximating the Hessian via a diagonal matrix. Importantly, the update and storage of the diagonal approximation of Hessian is as efficient as adaptive first-order optimization methods with linear complexity for both time and memory. To handle nonconvexity, we replace the Hessian with its rectified absolute value, which is guaranteed to be positive-definite. Experiments on three tasks of vision and language show that Apollo achieves significant improvements over other stochastic optimization methods, including SGD and variants of Adam, in term of both convergence speed and generalization performance. The implementation of the algorithm is available at this https URL.
Dynamic slicing for deep neural networks

Program slicing has been widely applied in a variety of software engineering tasks. However, existing program slicing techniques only deal with traditional programs that are constructed with instructions and variables, rather than neural networks that are composed of neurons and synapses. In this paper, we introduce NNSlicer, the first approach for slicing deep neural networks based on data-flow analysis. Our method understands the reaction of each neuron to an input based on the difference between its behavior activated by the input and the average behavior over the whole dataset. Then we quantify the neuron contributions to the slicing criterion by recursively backtracking from the output neurons, and calculate the slice as the neurons and the synapses with larger contributions. We demonstrate the usefulness and effectiveness of NNSlicer with three applications, including adversarial input detection, model pruning, and selective model protection. In all applications, NNSlicer significantly outperforms other baselines that do not rely on data flow analysis.
Towards a Measure of Individual Fairness for Deep Learning

Deep learning has produced big advances in artificial intelligence, but trained neural networks often reflect and amplify bias in their training data, and thus produce unfair predictions. We propose a novel measure of individual fairness, called prediction sensitivity, that approximates the extent to which a particular prediction is dependent on a protected attribute. We show how to compute prediction sensitivity using standard automatic differentiation capabilities present in modern deep learning frameworks, and present preliminary empirical results suggesting that prediction sensitivity may be effective for measuring bias in individual predictions.
Knowledge Fusion Transformers for Video Action Recognition

We introduce Knowledge Fusion Transformers for video action classification. We present a self-attention based feature enhancer to fuse action knowledge in 3D inception based spatio-temporal context of the video clip intended to be classified. We show, how using only one stream networks and with little or, no pretraining can pave the way for a performance close to the current state-of-the-art. Additionally, we present how different self-attention architectures used at different levels of the network can be blended-in to enhance feature representation. Our architecture is trained and evaluated on UCF-101 and Charades dataset, where it is competitive with the state of the art. It also exceeds by a large gap from single stream networks with no to less pretraining.
Waveform Design and Accurate Channel Estimation for Frequency-Hopping MIMO Radar-Based Communications

Frequency-hopping (FH) MIMO radar-based dual-function radar communication (FH-MIMO DFRC) enables communication symbol rate to exceed radar pulse repetition frequency, which requires accurate estimations of timing offset and channel parameters. The estimations, however, are challenging due to unknown, fast-changing hopping frequencies and the multiplicative coupling between timing offset and channel parameters. In this article, we develop accurate methods for a single-antenna communication receiver to estimate timing offset and channel for FH-MIMO DFRC. First, we design a novel FH-MIMO radar waveform, which enables a communication receiver to estimate the hopping frequency sequence (HFS) used by radar, instead of acquiring it from radar. Importantly, the novel waveform incurs no degradation to radar ranging performance. Then, via capturing distinct HFS features, we develop two estimators for timing offset and derive mean squared error lower bound of each estimator. Using the bounds, we design an HFS that renders both estimators applicable. Furthermore, we develop an accurate channel estimation method, reusing the single hop for timing offset estimation. Validated by simulations, the accurate channel estimates attained by the proposed methods enable the communication performance of DFRC to approach that achieved based on perfect timing and ideal knowledge of channel.
The design and implementation of Language Learning Chatbot with XAI using Ontology and Transfer Learning

In this paper, we proposed a transfer learning-based English language learning chatbot, whose output generated by GPT-2 can be explained by corresponding ontology graph rooted by fine-tuning dataset. We design three levels for systematically English learning, including phonetics level for speech recognition and pronunciation correction, semantic level for specific domain conversation, and the simulation of free-style conversation in English - the highest level of language chatbot communication as free-style conversation agent. For academic contribution, we implement the ontology graph to explain the performance of free-style conversation, following the concept of XAI (Explainable Artificial Intelligence) to visualize the connections of neural network in bionics, and explain the output sentence from language model. From implementation perspective, our Language Learning agent integrated the mini-program in WeChat as front-end, and fine-tuned GPT-2 model of transfer learning as back-end to interpret the responses by ontology graph.
Equilibrium Orientation and Adsorption of an Ellipsoidal Janus Particle at a Fluid–Fluid Interface

We investigate the equilibrium orientation and adsorption process of a single, ellipsoidal Janus particle at a fluid–fluid interface. The particle surface comprises equally sized parts that are hydrophobic or hydrophilic. We present free energy models to predict the equilibrium orientation and compare the theoretical predictions with lattice Boltzmann simulations. We find that the deformation of the fluid interface strongly influences the equilibrium orientation of the Janus ellipsoid. The adsorption process of the Janus ellipsoid can lead to different final orientations determined by the interplay of particle aspect ratio and particle wettablity contrast.
Genesis of ER Stress in Huntington’s Disease

Abstract Recent research has identified ER stress as a major mechanism implicated in cytotoxicity in many neurodegenerative diseases, among them Huntington’s disease. This genetic disorder is of late-onset, progressive and fatal, affecting cognition and movement. There is presently no cure nor any effective therapy for the disease. This review focuses on recent findings that shed light on the mechanisms of the advent and development of ER stress in Huntington’s disease and on its implications, highlighting possible therapeutic avenues that are being or could be explored.
Introduction to Lambda

The bacteriophages form a diverse collection of viruses that multiply in bacterial cells. This book describes the variety called λ and some of its relatives, which multiply in Escherichia coli. Chapter 1 presents general characteristics of the λ group, and explains how λ came to be the subject of intensive study. The classical phages destroy their host cells by lysis. Lambda causes lysis too, but can also propagate in a form that permits joint multiplication of phage and host. Phages possessing this added potentiality are called temperate, in distinction from the broad class of intemperate or virulent species, which cannot reproduce without destroying their host cells. Lambda is also one of a few well known genetic elements, called episomes, that are able to multiply in the cell either autonomously or as part of the bacterial chromosome. In fact λ and the fertility agent F of E. coli are historical prototypes of the class. Campbell (1969) presents the comparative biology of episomes. For newcomers to phage research, Stent’s Molecular Biology of Bacterial Viruses (1963) can be recommended as a source book. THE LIFE CYCLE Lambda phage particles are about half protein and half DNA. Each contains one double-stranded DNA molecule encapsulated in an icosahedral head, 0.05 μ in diameter, from which projects a tubular tail, 0.15 μ long (Chapter 14). The DNA molecule is made from the usual four bases and weighs about 31 million daltons. Phage particles can be prepared in milligram quantities for analysis of various sorts. Lambda is.
Colonization of decomposing Sphagnum moss litter by mycorrhizal roots in two types of peatland ecosystems

During a 35-month study on the decomposition of Sphagnum moss litter in poor fen and pine bog forest, an intensive colonization of litter-bags by mycorrhizal roots was observed during the decomposition process. Content of mycorrhizal roots in litter-bags, expressed as % mass of roots, was generally increasing during the decomposition in pine bog forest, and fluctuating during decomposition on poor fen, although in both cases the results were statistically insignificant. Two morphotypes of ericoid roots and two morphotypes of ectomycorrhizal roots were recorded from litter-bags on poor fen during the decomposition experiment, while in pine bog forest one morphotype of ericoid and nine morphotypes of ectomycorrhizal roots were recorded. Molecular identification of mycorrhizal roots succeeded only in the case of one ericoid and six putatively ectomycorrhizal morphotypes. Most morphotypes were recorded only once during the whole 35-month decomposition period, and only one ericoid and one ectomycorrhizal morphotypes were shared between the poor fen and pine bog forest communities.
Institute of Psychology Department of Basic Psychological Processes Graduate Program in Behavioral Sciences Master’s Thesis The efficacy of retrieval practice in creating stress-resistant memories

Human memory research has shown that practicing retrieval improves long-term memory, and that stress induced before retrieval impairs memory performance. A recent study by Smith, Floerke, and Thomas (2016) has shown that this detrimental effect of stress on memory does not occur when materials are encoded via retrieval practice. In this study, we sought to replicate this protective effect of retrieval practice under conditions of stress, while controlling for item difficulty. Participants (59 undergraduate men) first studied 40 SwahiliPortuguese word pairs. Half of the word pairs were then repeatedly restudied while the other half were repeatedly retrieval practiced. Half of the pairs in each condition were high in memorability (easier), whereas the other half were low in memorability (harder). Participants were then asked about which learning strategy (restudy vs. retrieval practice) would result in better recall on a subsequent test. One week later, participants returned for a final cued-recall test. Half of the participants underwent a stress-induction protocol (modified SECPT) 25 min before the beginning of the memory test for all 40 word pairs; the other half underwent a control condition. Salivary cortisol and questionnaire responses were used to assess the efficacy of stress induction. Results showed that stress was successfully induced in the stress group. In addition, participants recalled more word pairs when learned via retrieval practice than via restudy, replicating previous findings. Unexpectedly, the retrieval practice effect was larger for easier targets than for harder targets. In addition, most participants expected to recall more items in the retrieval practice condition than in the restudy condition, in line with actual performance, but unlike previous results indicating poor metacognitive calibration in similar samples. The protective effect of retrieval practice, as reported by Smith et al (2016), was not replicated, possibly due to a floor effect in the restudy condition. The results, however, revealed an unanticipated interaction in the retrieval practice condition such that stress increased recall for harder items but decreased recall for easier items. We provide a RETRIEVAL PRACTICE CREATES MEMORIES RESISTANT TO STRESS? 15 tentative account of this finding by suggesting that retrieval practice of harder items adds a stress-related attribute to the item’s memory trace, whose retrieval is later facilitated under stress (conditionalized analysis). The results suggest that the impact of stress on memory depends both on the learning strategy and on the intrinsic difficulty of the material.
SELinux Integration into Linux Kernel.

: The protection mechanisms of many mainstream operating systems were inadequate to support confidentiality and integrity requirements for end systems. To address this problem, the National Security Agency (NSA) worked with Secure Computing Corporation (SCC) to develop a strong, flexible mandatory access control architecture based on Type Enforcement. The NSA has integrated the SELinux architecture into the Linux operating system to transfer the technology to a larger developer and user community. This paper presents the design and implementation for integrating the security mechanisms of the SELinux architecture into the Linux kernel.