Miracle-dz/UniFAR-FTUs huggingface dataset:
Traffic Engineering with Precomputed Pathbooks

This paper addresses a major challenge in traffic engineering: the selection of a set of paths that minimizes routing cost for a random traffic matrix. We introduce the concept of pathbook: a small set of paths to which we restrict routing. The use of pathbook accelerates centralized traffic engineering algorithms, and therefore is appealing for instantiating, configuring, and optimizing large software-based networks. However, restricting routing to a few paths may lead to higher cost or infeasibility. To this end, we introduce the problem of pathbook design, wherein we search for a pathbook of constrained size that minimizes the expected routing cost of the random traffic matrix, which represents a prediction of the future traffic. The pathbook design problem is of combinatorial nature, and we show that it is NP-hard. We then study its convex relaxation for which we propose an optimal algorithm based on the projected subgradient method. For large networks, the subgradient vector is of prohibitive dimensions, hence we propose a coordinate-descent method using the Gauss-Southwell rule, which prescribes a move along the direction of largest subgradient element. We test the performance of our solution on dynamic traffic matrices from GEANT and find that our Gauss-Southwell pathbooks can accelerate standard methods by two orders of magnitude.
A Linear Dynamic Range Receiver With Timing Discrimination for Pulsed TOF Imaging LADAR Application

This paper presents a linear and wide dynamic range (DR) receiver for pulsed time-of-flight imaging laser detection and ranging application, which can capture the pulsed echo intensity. The alternative leading edge timing discrimination scheme with two threshold voltages by differential voltage shift is utilized to compensate the walk error, and thus accurately obtain timing information. The proposed receiver was implemented and fabricated in a 0.18-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> CMOS technology. The receiver achieves a high differential transimpedance gain of 106 dB<inline-formula> <tex-math notation="LaTeX">$\Omega $ </tex-math></inline-formula>, a wide differential output swing of about 1.8 V, an input-referred noise current of 4.55 pA/Hz<sup>0.5</sup> and a minimum detectable signal of about <inline-formula> <tex-math notation="LaTeX">$0.28~\mu $ </tex-math></inline-formula>Arms at SNR = 5, leading to a linear DR of 66 dB with a 3.3-V power supply. The area of the receiver chip is equal to <inline-formula> <tex-math notation="LaTeX">$0.95\times0.95$ </tex-math></inline-formula> mm<sup>2</sup>.
Reengineering Challenges into Opportunities for Managing Modern Libraries A Case Study of AC Joshi Library Panjab University Chandigarh

The revolution in Information and Communication Technologies (ICT) has embraced every nook and corner of life. Constant adaptation of changes in technology is essential for survival and success. ICT has completely changed the ways in which the information is being managed and accessed. Library professionals are required to actively incorporate new technologies into their service aspect to satisfy information needs of the users. This article enumerates the major challenges imposed by the ICT and how Panjab University Library has reengineered these intricacies into opportunities. This is a case study of Panjab University Library (PUL), which being a pivotal of intellectual life, is trying innovatively to bridge the gap imposed by the technology to the operational environment.
Enhanced Image Decoding via Edge-Preserving Generative Adversarial Networks

Lossy image compression usually introduces undesired compression artifacts, such as blocking, ringing and blurry effect{###} S, especially in low bit rate coding scenarios. Although many algorithms have been proposed to reduce these compression artifacts, most of them are based on image local smoothness prior, which usually leads to over-smoothing around the areas with distinct structures, e.g., edges and textures. In this paper, we propose a novel framework to enhance the perceptual quality of decoded images by well preserving the edge structures and predicting visually pleasing textures. Firstly, we propose an edge-preserving generative adversarial network (EP-GAN) to achieve edge restoration and texture generation simultaneously. Then, we elaborately design an edge fidelity regularization term to guide our network, which jointly utilizes the signal fidelity, feature fidelity and adversarial constraint to reconstruct high quality decoded images. Experimental results demonstrate that the proposed EP-GAN is able to efficiently enhance decoded images at low bit rate and reconstruct more perceptually pleasing images with abundant textures and sharp edges.
Adaptive Compressive Sensing and Data Recovery for Periodical Monitoring Wireless Sensor Networks

The development of compressive sensing (CS) technology has inspired data gathering in wireless sensor networks to move from traditional raw data gathering towards compression based gathering using data correlations. While extensive efforts have been made to improve the data gathering efficiency, little has been done for data that is gathered and recovered data with unknown and dynamic sparsity. In this work, we present an adaptive compressive sensing data gathering scheme to capture the dynamic nature of signal sparsity. By only re-sampling a few measurements, the current sparsity as well as the new sampling rate can be accurately determined, thus guaranteeing recovery performance and saving energy. In order to recover a signal with unknown sparsity, we further propose an adaptive step size variation integrated with a sparsity adaptive matching pursuit algorithm to improve the recovery performance and convergence speed. Our simulation results show that the proposed algorithm can capture the variation in the sparsities of the original signal and obtain a much longer network lifetime than traditional raw data gathering algorithms.
Refining Attention: A Sequential Attention Model for Image Captioning

Visual attention is widely applied to image captioning. Previous works put visual attention and linguistic word into a long short-term memory network together, but neglect the sequential relation of attention at different time steps during word prediction. Moreover, the abstraction degree of visual attention is usually different from that of linguistic word. To address these issues, a sequential attention model is proposed in this work to handle visual attention by considering the corresponding sequential relation, and hence the internal relation among attention at each word prediction step is well utilized to enhance the visual information during sentence decoding. The experimental results on the benchmark MSCOCO and Flickr30K datasets show that the proposed model achieves excellent performances with 108.1 and 34.9 respectively on the evaluation criteria of CIDEr and BLEU-4 for MSCOCO.
Accelerometer-Based Tilt Determination for Rigid Bodies With a Nonaccelerated Pivot Point

An estimation algorithm is proposed for determining pitch and roll angles (tilt), angular velocities, and angular accelerations of a rigid body with a nonaccelerated pivot point. The estimation uses only accelerometer measurements. It is based on a kinematic model of the rigid body and is, therefore, independent of its dynamics; only the mounting positions of the sensors need to be known. Simulation results indicate a significant performance increase compared with an existing method, a claim which is supported by experimental results.
Global vs. localized search: A comparison of database selection methods in a hierarchical environment

In this work, we compare standard global IR searching with more localized techniques to address the database selection problem. We conduct a series of experiments to compare the retrieval effectiveness of three separate search modes using a hierarchically structured data environment of textual databse representations. The data environment is represented as a tree-like structure containing over 15,000 unique databases and approximately 100,000 total leaf nodes. The search modes consist of varying degrees of browse and search, from a global search at the root node to a refined search at a sub-node using dynamically-calculated inverse document frequencies (idfs) to score the candidate databases for probable relevance. Our findings indicate that a browse plus search approach that relies upon localized searching from sub-nodes in this environment produces the most effective results.
Kalman particle filtering algorithm for symmetric alpha-stable distribution signals with application to high frequency time difference of arrival geolocation

In this study, a non-linear filtering algorithm for state estimation with symmetric alpha-stable (SαS) noise is presented. The dynamic system model investigated here can be described by a linear state-space equation and a non-linear observation equation. The contribution of this study can be summarised as follows. First, particle filtering approach is employed for coarse estimation of the unknown parameters and then Kalman filter is performed to achieve better estimation. Second, SαS noise is considered as the additive disturbance in the observed signal and Gaussian approximation is used to compute the characteristics. Third, the calculation complexity is analysed according to the proposed algorithm. The proposed method is compared with the standard particle filter, extended Kalman filter and unscented Kalman filter for static parameter estimation of a periodic signal. As a practical application, the proposed method is used in high frequency source localisation based on time difference of arrival measurements.
Impact of oral rehabilitation on the quality of life of partially dentate elders in a randomised controlled clinical trial: 2 year follow-up

Objective This randomised clinical trial aimed to compare the impact of two different tooth replacement strategies for partially dentate older patients namely; removable partial dentures (RPDs) and functionally orientated treatment based on the shortened dental arch (SDA) concept, on Oral Health-related Quality of Life (OHrQOL). Methods 89 patients completed a randomised clinical trial. Patients were recruited in two centres: Cork University Dental Hospital (CUDH) and a Geriatric Day Hospital (SFDH). 44 patients were randomly allocated to the RPD group and 45 to the SDA group where adhesive bridgework was used to provide 10 pairs of occluding contacts. The impact of treatment on OHrQOL was used as the primary outcome measure. Each patient completed the Oral Health Impact Profile (OHIP-14) at baseline, 1, 6, 12 and 24 months after treatment. Results Both treatment groups reported improvements in OHIP-14 scores at 24 months (p<0.05). For the SDA group OHIP-14 scores improved by 8.0 scale points at 12 months (p<0.001) and 5.9 scale points at 24 months (p<0.05). For the RPD group OHIP-14 scores improved by 5.7 scale points at 12 months (p<0.05) and 4.2 scale points at 24 months (p<0.05). Analysis using ANCOVA showed that there were significant between group differences recorded in both treatment centres. 24 months after intervention the SDA group recorded better OHIP-14 scores by an average of 2.9 points in CUDH (p<0.0001) and by an average of 7.9 points in SFDH (p<0.0001) compared to the RPD group. Conclusions Patients in the SDA group maintained their improvements in OHrQOL scores throughout the 24 month study period. For the RPD group the initial improvement in OHrQOL score began to diminish after 6 months, particularly for those treated in SFDH. Thus, the benefits of functionally orientated treatment increased over time, particularly for the older, more systemically unwell cohort in SFDH.
Applications of PageRank to Function Comparison and Malware Classification

We classify . NET files as either benign or malicious by examining certain directed graphs extracted from the files via decompilation. Each graph is viewed probabilistically as a Markov chain where each node heuristically represents the possible state of the running file, and by computing the PageRank vector (Perron vector with transport) we can assign a probability measure over the nodes of the given graph. We train a random forest with features derived from computing Lebesgue antiderivatives of functions defined over the vertex sets of these directed graphs against the PageRank measure. The model was trained on 2.5 million samples of . NET and has an accuracy of 98.3% on test data. The median time needed for decompilation and scoring was 24ms.
The role of perceived friend depression and perceived stigma on self-reported depression among individuals who use drugs.

Depression and substance use are highly comorbid, and stigmatized, mental health conditions influenced by social network factors. The objective of this study was to explore the role of perceived depression and perceived mental health stigma among friends on self-reported depression over time. Between 2009 and 2012, 527 participants who used drugs completed baseline and 6-month follow-up surveys in Baltimore, Maryland. Logistic regression was used to assess whether perceived depression and stigma among friends were associated with personal depressive status before and after accounting for depressive status at baseline. A total of 309 participants (58.6%) had depressive symptoms at baseline, while 207 participants (39.3%) had depressive symptoms at follow-up. Not accounting for baseline depression, perceiving friends as being depressed (adjusted odds ratio [AOR] = 1.32, 95% confidence interval [CI] [1.12, 1.56]) and as having stigmatizing attitudes toward mental health conditions (AOR = 1.92, 95% CI [1.31, 2.79]) were associated with increased odds of personal depression. After accounting for baseline depression, the relationship between personal depression and perceived stigma was marginally significant, while the relationship between personal depression and perceived depression among friends was attenuated. These results suggest that baseline depression is the largest predictor of follow-up depression. Future research should explore whether specific aspects of perceived stigma independently account for personal depression.
Go with the Flow: Recurrent Networks for Wind Time Series Multi-Step Forecasting

One of the ways of reducing the effects of Climate Change is to rely on renewable energy sources. Their intermittent nature makes necessary to obtain a mid-long term accurate forecasting. Wind Energy prediction is based on the ability to forecast wind speed. This has been a problem approached using different methods based on the statistical properties of the wind time series. Wind Time series are non-linear and non-stationary, making their forecasting very challenging. Deep neural networks have shown their success recently for problems involving sequences with non-linear behavior. In this work, we perform experiments comparing the capability of different neural network architectures for multi-step forecasting obtaining a 12 hours ahead prediction using data from the National Renewable Energy Laboratory's WIND dataset
Design of a Simplified Neuro-Fuzzy-GA-based IM Drive Deploying Linearization Approach

This paper focuses on the design and implementation of a genetic algorithm (GA) tuned modified simple neuro-fuzzy control (NFC) for optimal performance of induction motor (IM) drive employing feedback linearization (FBL) approach. The intuitive FBL with the proposed simplified NFC with GA (SNFC-GA) substantially reduces the torque chattering and improves the speed response of the IM drive. This new technique also has the main advantage of improved computational efficiency by reducing computational burden over classical NFC and thus, fit for real-time industrial applications. Moreover, the GA searches the optimal parameters of the simplified NFC in order to ensure the global convergence of error. The efficacy of the proposed controller using FBL IM drive is investigated in simulation as well as in experiment, and it is evident that the robust and optimal dynamic performance of the system is achieved in terms of parameter variations and external load disturbance.
Smart-U: Smart Utensils Know what You Eat

Mobile sensing enables unobtrusive monitoring of our daily activities, sleep quality, breathing and heart rate, revolutionizing the health-care system. Dietary information is also a critical dimension for health management but has no convenient solution yet. In this paper, we ask whether we can track meal composition unobtrusively. We introduce Smart-U, a new utensil design that can recognize meal composition during the intake process, without user intervention or on-body instruments. Smart-U makes use of the fact that light spectra reflected by foods are dependent on the food ingredients. By analyzing the reflected light spectra, Smart-U can recognize what food is on top of the utensil. We describe the prototype design of Smart-U and the food recognition algorithm. We demonstrate that Smart-U can recognize 20 types of foods with 93 % accuracy. It can work robustly under different conditions. We envision that Smart-U can enable automatic food intake tracking, and provide personalized food suggestions based on nutrient recommendations and prior consumption. In the long run, Smart-U can contribute to the study of chronic diseases.
New Insight to the Effects of Heat Treatment in Air on the Permeation Properties of Thin Pd77%Ag23% Membranes

Sputtered Pd77%Ag23% membranes of thickness 2.2–8.5 µm were subjected to a three-step heat treatment in air (HTA) to investigate the relation between thickness and the reported beneficial effects of HTA on hydrogen transport. The permeability experiments were complimented by volumetric hydrogen sorption measurements and atomic force microscopy (AFM) imaging in order to relate the observed effects to changes in hydrogen solubility and/or structure. The results show that the HTA—essentially an oxidation-reduction cycle—mainly affects the thinner membranes, with the hydrogen flux increasing stepwise upon HTA of each membrane side. The hydrogen solubility is found to remain constant upon HTA, and the change must therefore be attributed to improved transport kinetics. The HTA procedure appears to shift the transition from the surface to bulk-limited transport to lower thickness, roughly from ~5 to ≤2.2 µm under the conditions applied here. Although the surface topography results indicate that HTA influences the surface roughness and increases the effective membrane surface area, this cannot be the sole explanation for the observed hydrogen flux increase. This is because considerable surface roughening occurs during hydrogen permeation (no HTA) as well, but not accompanied by the same hydrogen flux enhancement. The latter effect is particularly pronounced for thinner membranes, implying that the structural changes may be dependent on the magnitude of the hydrogen flux.
Structure Learning Under Missing Data

Causal discovery is the problem of learning the structure of a graphical causal model that approximates the true generating process that gave rise to observed data. In practical problems, including in causal discovery problems, missing data is a very common issue. In such cases, learning the true causal graph entails estimating the full data distribution, samples from which are not directly available. Attempting to instead apply existing structure learning algorithms to samples drawn from the observed data distribution, containing systematically missing entries, may well result in incorrect inferences due to selection bias. In this paper we discuss adjustments that must be made to existing structure learning algorithms to properly account for missing data. We first give an algorithm for the simpler setting where the underlying graph is unknown, but the missing data model is known. We then discuss approaches to the much more difficult case where only the observed data is given with no other additional information on the missingness model known. We validate our approach by simulations, showing that it outperforms standard structure learning algorithms in all of these settings.
A Conceptual High Level Controller to Walk with Active Foot Prostheses/Orthoses

In this paper, the goal is to develop a high level controller for active prosthetic feet that can continuously estimate the ankle motion based on the shank motion. The proposed controller does not require speed determination, gait percent identification, input data manipulation, look-up tables or switching rules. To do this, the Gaussian process (GP) regression is used. The performance of the controller has been tested for walking speed of 0.6, 0.9, 1.2, 1.4 and 1.6 m/s. The results showed that the controller had lower estimation quality when input was only shank angular velocity or shank angle. However, the aggregated angular velocity and angle input resulted in high output estimation quality. Furthermore, for each speed, the estimation quality was more acceptable when the controller was trained for it. Accordingly, when the high level controller was tested without previous training, the estimation quality was less acceptable.
Synthesis of Macrocycles Derived from Substituted Triazines

A triazine ring derivatized with morpholine, an N‐alkyl‐N′‐BOC‐hydrazine (alkyl=isopropyl or benzyl) and the diethylacetal of glycinylpropionaldehyde undergoes spontaneous dimerization in good yields upon acid‐catalyzed deprotection. The resulting 24‐member macrocycles can be characterized by NMR spectroscopy, mass spectrometry, and single crystal X‐ray diffraction. In the solid state, both homodimers adopt a taco‐like conformation. Although each shows π–π stacking between the triazine rings, different patterns of hydrogen bonds emerge. The crystal structure of the isopropyl dimer shows that it includes two molecules of trifluoracetic acid per macrocycle. The trifluoroacetate anion charge balances the protonated triazines, which engage in bifurcated hydrogen bonds with the carbonyl acceptor of the distant glycine. This carbonyl also forms a hydrogen bond with the NH of the proximate glycine. The crystal structure of the benzyl derivative does not include trifluoracetic acid. Instead, two hydrogen bonds form, each between a glycine NH and the lone pair of the C=N nitrogen of the hydrazine group. In the solid state, both molecules present the alkyl side chains and morpholine groups in close proximity. A heterodimer is accessible in approximately statistical yields—along with both homodimers—by mixing the two protected monomers prior to subjecting them to deprotection.
Robust PI observer design for hybrid heterogeneous multiple models with delayed measurements

This paper deals with the problem of state estimation for hybrid systems modelled via multiple models and subject to delayed measurements. In contrast to the most popular results found in the multiple model literature, we consider heterogeneous multiple models, also known as decoupled multiple model, which allow to use submodels whose state space dimension can be different. On the basis of this multiple model a proportional-integral observer (PIO) is designed in order to cope with the robust state estimation with respect to disturbances. Sufficient delay-independent conditions for ensuring robust performances (attenuation level) and dynamic performances (exponential convergence) of the estimation error are provided in terms of LMIs using the Lyapunov-Krasovskii method. The validity of the proposed methodology is illustrated by an academic example.