Miracle-dz/UniFAR-FTUs huggingface dataset:
A 1.8-mW CMOS ΣΔ Modulator with Integrated Mixter for A/D Conversion of IF signals

In this paper, the design of a continuous-time baseband sigma-delta modulator with an integrated mixer for intermediate-frequency (IF) analog-to-digital conversion is presented. This highly linear IF modulator digitizes a GSM channel at intermediate frequencies up to 50 MHz. The sampling rate is not related to the input IF and is 13.0 MHz in this design. Power consumption is 1.8 mW from a 2.5-V supply. Measured dynamic range is 82 dB, and third-order intermodulation distor- tion is 84 dB for two 6-dBV IF input tones. Two modulators in quadrature configuration provide 200-kHz GSM bandwidth. Active area of a single IF modulator is 0.2 mm in 0.35- m CMOS.
Erythrocyte cAMP in Determining Frequency of Acute Pain Episodes in Sickle Cell Disease Patients from Odisha State, India

Abstract Vaso-occlusive crisis (VOC) occurs more frequently during stress in sickle cell disease patients. Epinephrine released during stress increases adhesion of sickled red blood cells (RBCs) to endothelium and to leukocytes, a process mediated through erythrocyte cyclic adenosine monophosphate (cAMP). Increased adhesion of sickled RBCs retards blood flow through the capillaries and promotes vaso-occlusion. Therefore, we examined the association of RBC-cAMP levels with frequency of acute pain episodes in sickle cell disease subjects. Using a case control study design, we measured RBC-cAMP levels, fetal hemoglobin (Hb F), α-thalassemia (α-thal) and other hematological parameters at baseline (sham treated) and after stimulation with epinephrine. The cases consisted of sickle cell disease subjects with three or more acute pain episodes in the last 12 months, and those without a single acute pain episode in the last 12 months were considered as controls. Significantly higher cAMP values were found in cases than the controls, in both sham treated (p < 0.001) and epinephrine treated RBCs (p < 0.001) by Wilcoxon Rank Sum test. However, significant association of cAMP values was observed both on univariate [odds ratio (OR): 4.8, 95% confidence interval (95% CI): 1.51-15.19, p < 0.008) and multivariate logistic regression analyses only in epinephrine treated (OR: 5.07, 95% CI: 1.53-16.82, p < 0.008) but not in sham-treated RBCs. In the covariates, Hb F consistently showed protective effects in univariate as well as in multivariate analyses. Frequent acute pain episodes are associated with higher cAMP levels than those with less frequent pain episodes, only after stimulation with epinephrine but not with baseline level.
Best Practice Guidelines and Essential Methodological Steps to Conduct Rigorous and Systematic Meta-Reviews.

BACKGROUND A growing body of primary study and systematic review literature evaluates interventions and phenomena in applied and health psychology. Reviews of reviews (i.e., meta-reviews) systematically synthesise and utilise this vast and often overwhelming literature; yet, currently there are few practical guidelines for meta-review authors to follow. OBJECTIVE The aim of this article is to provide an overview of the best practice guidelines for all research synthesis and to detail additional specific considerations and methodological details for the best practice of conducting a rigorous meta-review. METHODS This article provides readers with six systematic and practical steps along with accompanying examples to address with rigor the unique challenges that arise when authors familiar with systematic review methods begin a meta-review: (a) detailing a clear scope, (b) identifying synthesis literature through strategic searches, (c) considering datedness of the literature, (d) addressing overlap among included reviews, (e) choosing and applying review quality tools, and (f) appropriate options for handling the synthesis and reporting of the vast amount of data collected in a meta-review. CONCLUSIONS We have curated best practice recommendations and practical tips for conducting a meta-review. We anticipate that assessments of meta-review quality will ultimately formalise best-method guidelines.
Expiration of Historical Databases (Extended Abstract)

We present a technique for automatic expiration of data in a historical data warehouse that preserves answers to a known andfied set ofjrst-order queries. In addition, we show that for queries with output size bounded by afunction of the active data domain size (the number of values that have ever appeared in the warehouse), the size of the portion of the data warehouse history needed to answer the queries is also bounded by afunction of the active data domain size and therefore does not depend on the age of the warehouse (the length of the history).
Bootstrapping Language Associated with Biomedical Entities The AID Group at TREC Genomics 2007

The TREC Genomics 2007 task in- cluded recognizing topic-specific entities in the re- turned passages. To address this task, we have de- signed and implemented a novel data-driven ap- proach by combining information extraction with language modeling techniques. Instead of using an exhaustive list of all possible instances for an entity type, we look at the language usage around each entity type and use that as a classifier to de- termine whether or not a piece of text discusses such an entity type. We do so by comparing it with language models of the passages. E.g., given the entity type "genes", our approach can measure the gene-iness of a piece of text. Our algorithm works as follows. Given an en- tity type, it first uses Hearst patterns to extract instances of the type. To extract more instances, we look for new contextual patterns around the instances and use them as input for a bootstrap- ping method, in which new instances and patterns are discovered iteratively. Afterwards, all discov- ered instances and patterns are used to find the sentences in the collection which are most on par with the requested entity type. A language model is then generated from these sentences and, at re- trieval time, we use this model to rerank retrieved passages. As to the results of our submitted runs, we find that our baseline run performs well above the median of all participant's scores. Additionally, we find that applying our proposed method helps those en- tity types most for which there are unambiguous patterns and numerous instances.
Applications of Graph Integration to Function Comparison and Malware Classification

We classify . NET files as either benign or malicious by examining directed graphs derived from the set of functions comprising the given file. Each graph is viewed probabilistically as a Markov chain where each node represents a code block of the corresponding function, and by computing the PageRank vector (Perron vector with transport), a probability measure can be defined over the nodes of the given graph. Each graph is vectorized by computing Lebesgue antiderivatives of hand-engineered functions defined on the vertex set of the given graph against the PageRank measure. Files are subsequently vectorized by aggregating the set of vectors corresponding to the set of graphs resulting from decompiling the given file. The result is a fast, intuitive, and easy-to-compute glass-box vectorization scheme, which can be leveraged for training a standalone classifier or to augment an existing feature space. We refer to this vectorization technique as PageRank Measure Integration Vectorization (PMIV). We demonstrate the efficacy of PMIV by training a vanilla random forest on 2.5 million samples of decompiled. NET, evenly split between benign and malicious, from our in-house corpus and compare this model to a baseline model which leverages a text-only feature space. The median time needed for decompilation and scoring was 24ms. 11Code available at https://github.com/gtownrocks/grafuple
Evolutionary Multi-Objective Optimization Driven by Generative Adversarial Networks

Recently, increasing works have proposed to drive evolutionary algorithms using machine learning models. Usually, the performance of such model based evolutionary algorithms is highly dependent on the training qualities of the adopted models. Since it usually requires a certain amount of data (i.e. the candidate solutions generated by the algorithms) for model training, the performance deteriorates rapidly with the increase of the problem scales, due to the curse of dimensionality. To address this issue, we propose a multi-objective evolutionary algorithm driven by the generative adversarial networks (GANs). At each generation of the proposed algorithm, the parent solutions are first classified into real and fake samples to train the GANs; then the offspring solutions are sampled by the trained GANs. Thanks to the powerful generative ability of the GANs, our proposed algorithm is capable of generating promising offspring solutions in high-dimensional decision space with limited training data. The proposed algorithm is tested on 10 benchmark problems with up to 200 decision variables. Experimental results on these test problems demonstrate the effectiveness of the proposed algorithm.
Trust-Region Variational Inference with Gaussian Mixture Models

Many methods for machine learning rely on approximate inference from intractable probability distributions. Variational inference approximates such distributions by tractable models that can be subsequently used for approximate inference. Learning sufficiently accurate approximations requires a rich model family and careful exploration of the relevant modes of the target distribution. We propose a method for learning accurate GMM approximations of intractable probability distributions based on insights from policy search by establishing information-geometric trust regions for principled exploration. For efficient improvement of the GMM approximation, we derive a lower bound on the corresponding optimization objective enabling us to update the components independently. The use of the lower bound ensures convergence to a local optimum of the original objective. The number of components is adapted online by adding new components in promising regions and by deleting components with negligible weight. We demonstrate on several domains that we can learn approximations of complex, multi-modal distributions with a quality that is unmet by previous variational inference methods, and that the GMM approximation can be used for drawing samples that are on par with samples created by state-of-the-art MCMC samplers while requiring up to three orders of magnitude less computational resources.
The complexity of the first-order theory of pure equality

We will find a lower bound on the recognition complexity of the theories that are nontrivial relative to equality (or equational-nontrivial), namely, each of these theories is consistent with the formula, whose sense is that there exist two various elements at least. However, at first, we will obtain a lower bound on the computational complexity for the first-order theory of Boolean algebra that has only two elements. For this purpose, we will code the long-continued deterministic Turing machine computations by the relatively short-length quantified Boolean formulae; the modified Stockmeyer and Meyer method will appreciably be used for this simulation. Then, we will construct a polynomial reduction of the theory of this Boolean algebra to the first-order theory of pure equality. Keywords: Computational complexity, the theory of equality, the coding of computations, simulation by means formulae, polynomial time, polynomial space, lower complexity bound
The impact of private and work-related smartphone usage on interruptibility

In the last decade, the effects of interruptions through mobile notifications have been extensively researched in the field of Human-Computer Interaction. Breakpoints in tasks and activities, cognitive load, and personality traits have all been shown to correlate with individuals' interruptibility. However, concepts that explain interruptibility in a broader sense are needed to provide a holistic understanding of its characteristics. In this paper, we build upon the theory of social roles to conceptualize and investigate the correlation between individuals' private and work-related smartphone usage and their interruptibility. Through our preliminary study with four participants over 11 weeks, we found that application sequences on smartphones correlate with individuals' private and work roles. We observed that participants engaged in these roles tend to follow specific interruptibility strategies - integrating, combining, or segmenting private and work-related engagements. Understanding these strategies breaks new ground for attention and interruption management systems in ubiquitous computing.
Deep Lagrangian Networks for end-to-end learning of energy-based control for under-actuated systems

Applying Deep Learning to control has a lot of potential for enabling the intelligent design of robot control laws. Unfortunately common deep learning approaches to control, such as deep reinforcement learning, require an unrealistic amount of interaction with the real system, do not yield any performance guarantees, and do not make good use of extensive insights from control theory. In particular, common black-box approaches – that abandon all insight from control – are not suitable for complex robot systems.We propose a deep control approach as a bridge between the solid theoretical foundations of energy-based control and the flexibility of deep learning. To accomplish this goal, we extend Deep Lagrangian Networks (DeLaN) to not only adhere to Lagrangian Mechanics but also ensure conservation of energy and passivity of the learned representation. This novel extension is embedded within a energy control law to control under-actuated systems. The resulting DeLaN for energy control (DeLaN 4EC) is the first model learning approach using generic function approximation that is capable of learning energy control because existing approaches cannot learn the system energies directly. DeLaN 4EC exhibits excellent real-time control on the physical Furuta pendulum and learns to swing-up the pendulum while the control law using system identification does not.
A Framework for Multiaccess Support for Unreliable Internet Traffic using Multipath DCCP

Mobile nodes are typically equipped with multiple radios and can connect to multiple radio access networks (e.g. WiFi, LTE and 5G). Consequently, it is important to design mechanisms that efficiently manage multiple network interfaces for aggregating the capacity, steering of traffic flows or switching flows among multiple interfaces. While such multi-access solutions have the potential to increase the overall traffic throughput and communication reliability, the variable latencies on different access links introduce packet delay variation which has negative effect on the application quality of service and user quality of experience. In this paper, we present a new IP-compatible multipath framework for heterogeneous access networks. The framework uses Multipath Datagram Congestion Control Protocol (MP-DCCP) - a set of extensions to regular DCCP - to enable a transport connection to operate across multiple access networks, simultaneously. We present the design of the new protocol framework and show simulation and experimental testbed results that (1) demonstrate the operation of the new framework, and (2) demonstrate the ability of our solution to manage significant packet delay variation caused by the asymmetry of network paths, by applying pluggable packet scheduling or reordering algorithms.
Quasi-polynomial time approximation schemes for the Maximum Weight Independent Set Problem in H-free graphs

In the Maximum Independent Set problem we are asked to find a set of pairwise nonadjacent vertices in a given graph with the maximum possible cardinality. In general graphs, this classical problem is known to be NP-hard and hard to approximate within a factor of $n^{1-\varepsilon}$ for any $\varepsilon > 0$. Due to this, investigating the complexity of Maximum Independent Set in various graph classes in hope of finding better tractability results is an active research direction. In $H$-free graphs, that is, graphs not containing a fixed graph $H$ as an induced subgraph, the problem is known to remain NP-hard and APX-hard whenever $H$ contains a cycle, a vertex of degree at least four, or two vertices of degree at least three in one connected component. For the remaining cases, where every component of $H$ is a path or a subdivided claw, the complexity of Maximum Independent Set remains widely open, with only a handful of polynomial-time solvability results for small graphs $H$ such as $P_5$, $P_6$, the claw, or the fork. We show that for every graph $H$ for which Maximum Independent Set is not known to be APX-hard and SUBEXP-hard in $H$-free graphs, the problem admits a quasi-polynomial time approximation scheme and a subexponential-time exact algorithm in this graph class. Our algorithm works also in the more general weighted setting, where the input graph is supplied with a weight function on vertices and we are maximizing the total weight of an independent set.
Co-creating external design representations: Comparing face-to-face sketching to designing in virtual environments

While developments in computer-supported collaborative work tools, computer aided design and the availability of high bandwidth networks offer alternative collaborative environments for design, the alternatives have yet to be characterised in terms of their impact on the co-creation of a shared model. A specific collaborative design environment makes a commitment to the way in which the designers can communicate and co-create an external representation of the shared design model. This research focuses on the impact of alternative collaborative virtual environments on design behaviour and collaborative processes. An experiment was conducted to identify similarities and differences between co-located collaborative designing using sketches as the external representation and remote collaborative designing sessions using sketches and 3-D models. The results of this study are discussed in terms of: (1) the effect of being remotely located; (2) the effect of the type of external representations.
Intrinsic multipolar contents of nanoresonators for tailored scattering

We introduce a theoretical and computational method to design resonant objects, such as nanoantennas or meta-atoms, exhibiting tailored multipolar responses. In contrast with common approaches that rely on a multipolar analysis of the scattering response of an object upon specific excitations, we propose to engineer the intrinsic (i.e., excitation-independent) multipolar content and spectral characteristics of the natural resonances---or quasinormal modes---of the object. A rigorous numerical approach for the multipolar decomposition of resonances at complex frequencies is presented, along with an analytical model conveying a direct physical insight into the multipole moments induced in the resonator. Our design strategy is illustrated by designing a subwavelength optical resonator exhibiting a Janus resonance that provides side-dependent coupling to waveguides over the full linewidth of the resonance and on a wide angular range for linearly polarized incident plane waves. The method applies to all kinds of waves and may open new perspectives for subwavelength-scale manipulation of scattering and emission.
Sparse Regular Expression Matching

We present the first algorithm for regular expression matching that can take advantage of sparsity in the input instance. Our main result is a new algorithm that solves regular expression matching in $O\left(\Delta \log \log \frac{nm}{\Delta} + n + m\right)$ time, where $m$ is the number of positions in the regular expression, $n$ is the length of the string, and $\Delta$ is the \emph{density} of the instance, defined as the total number of active states in a simulation of the position automaton. This measure is a lower bound on the total number of active states in simulations of all classic polynomial sized finite automata. Our bound improves the best known bounds for regular expression matching by almost a linear factor in the density of the problem. The key component in the result is a novel linear space representation of the position automaton that supports state-set transition computation in near-linear time in the size of the input and output state sets.
Modeling Semantic Compositionality with Sememe Knowledge

Semantic compositionality (SC) refers to the phenomenon that the meaning of a complex linguistic unit can be composed of the meanings of its constituents. Most related works focus on using complicated compositionality functions to model SC while few works consider external knowledge in models. In this paper, we verify the effectiveness of sememes, the minimum semantic units of human languages, in modeling SC by a confirmatory experiment. Furthermore, we make the first attempt to incorporate sememe knowledge into SC models, and employ the sememe-incorporated models in learning representations of multiword expressions, a typical task of SC. In experiments, we implement our models by incorporating knowledge from a famous sememe knowledge base HowNet and perform both intrinsic and extrinsic evaluations. Experimental results show that our models achieve significant performance boost as compared to the baseline methods without considering sememe knowledge. We further conduct quantitative analysis and case studies to demonstrate the effectiveness of applying sememe knowledge in modeling SC.All the code and data of this paper can be obtained on https://github.com/thunlp/Sememe-SC.
Adding Common Randomness Can Remove the Secrecy Constraints in Communication Networks

In communication networks secrecy constraints usually incur an extra limit in capacity or generalized degrees-of-freedom (GDoF), in the sense that a penalty in capacity or GDoF is incurred due to the secrecy constraints. Over the past decades a significant amount of effort has been made by the researchers to understand the limits of secrecy constraints in communication networks. In this work, we focus on how to remove the secrecy constraints in communication networks, i.e., how to remove the GDoF penalty due to secrecy constraints. We begin with three basic settings: a two-user symmetric Gaussian interference channel with confidential messages, a symmetric Gaussian wiretap channel with a helper, and a two-user symmetric Gaussian multiple access wiretap channel. Interestingly, in this work we show that adding common randomness at the transmitters can totally remove the penalty in GDoF or GDoF region of the three settings considered here. The results reveal that adding common randomness at the transmitters is a powerful way to remove the secrecy constraints in communication networks in terms of GDoF performance. Common randomness can be generated offline. The role of the common randomness is to jam the information signal at the eavesdroppers, without causing too much interference at the legitimate receivers. To accomplish this role, a new method of Markov chain-based interference neutralization is proposed in the achievability schemes utilizing common randomness. From the practical point of view, we hope to use less common randomness to remove secrecy constraints in terms of GDoF performance. With this motivation, for most of the cases we characterize the minimal GDoF of common randomness to remove secrecy constraints, based on our derived converses and achievability.