Miracle-dz/UniFAR-FTUs huggingface dataset:
Wire packing: a strong formulation of crosstalk-aware chip-level track/layer assignment with an efficient integer programming solution

By focusing on chip-wide slices of the global routing grid, making a few mild geometric assumptions about layer use, and suitably abstracting pin details, we derive an extremely efficient integer linear programming (ILP) formulation for track/layer assignment. The key technical insight is to model all constraints - both geometric and crosstalk - as cliques in an appropriate conflict graph; these cliques can be extracted quickly from the interval structures of wires in a slice. We develop a "strong" linear relaxation of this problem that almost always yields the integral optimum; this solution gives us directly the maximum number of wires that can pack legally without crosstalk risk. Experiments on synthetic netlists that match statistics of wire layouts from industrial 0.25um designs demonstrate that we can pack 100 - 1000 wires optimally, or with at worst a very few overflows, in seconds.
Issues in Predicting the Reliability of Composed Components

Abstract : Availability is one of the most frequently specified quality attributes for computerized systems and the computation of availability requires knowledge about the reliability of the system. Although much research has been devoted to software system reliability, much work remains to be done in identifying ways to predict reliability of assemblies of components. We are designing an experiment for use as a foundation for creating a reliability prediction-enabled component technology (PECT), which is to be used to produce systems that are predictably reliable by construction; in the course of that work we have recognized the need to evolve combinatorial reliability models for use in computing reliability of assemblies based on the reliabilities of constituent components. In this paper, we describe and discuss aspects of current models that need to be adapted and how they affect the design of our experiment.
Linguistic Rule Extraction by Genetics-Based Machine Learning

This paper shows how linguistic classification knowledge can be extracted from numerical data for pattern classification problems with many continuous attributes by genetic algorithms. Classification knowledge is extracted in the form of linguistic if-then rules. In this paper, emphasis is placed on the simplicity of the extracted knowledge. The simplicity is measured by two criteria: the number of extracted linguistic rules and the length of each rule (i.e., the number of antecedent conditions involved in each rule). The classification ability of extracted linguistic rules, which is measured by the classification rate on given training patterns, is also considered. Thus our task is formulated as a linguistic rule extraction problem with three objectives: to maximize the classification rate, to minimize the number of extracted linguistic rules, and to minimize the length of each rule. For tackling this problem, we propose a multi-objective genetics-based machine learning (GBML) algorithm, which is a hybrid algorithm of Michigan approach and Pittsburgh approach. Our hybrid algorithm is basically a Pittsburgh-style algorithm with variable string length. A Michigan-style algorithm is combined as a kind of mutation for partially modifying each string.
On the Calculation of the Incomplete MGF With Applications to Wireless Communications

The incomplete moment generating function (IMGF) has paramount relevance in communication theory, since it appears in a plethora of scenarios when analyzing the performance of communication systems. We here present a general method for calculating the IMGF of any arbitrary fading distribution. Then, we provide exact closed-form expressions for the IMGF of the very general <inline-formula> <tex-math notation="LaTeX">$\kappa $ </tex-math></inline-formula>-<inline-formula> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula> shadowed fading model, which includes the popular <inline-formula> <tex-math notation="LaTeX">$\kappa $ </tex-math></inline-formula>-<inline-formula> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$\eta $ </tex-math></inline-formula>-<inline-formula> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula>, Rician shadowed, and other classical models as particular cases. We illustrate the practical applicability of this result by analyzing several scenarios of interest in wireless communications: 1) physical layer security in the presence of an eavesdropper; 2) outage probability analysis with interference and background noise; 3) channel capacity with side information at the transmitter and the receiver; and 4) average bit-error rate with adaptive modulation, when the fading on the desired link can be modeled by any of the aforementioned distributions.
Comparison between Partial Least Squares Regression and Support Vector Machine for Freeway Incident Detection

This paper presents the development of automatic incident detection (AID) models based on the partial least squares regression (PLSR), and compare it with support vector machine classifier which has exhibited good performance for freeway incident detection. The performance of AID algorithms is evaluated using the common criteria of detection rate, false alarm rate, and mean time to detection. Moreover, the curve of receiver operating characteristic (ROC) is also used to compare the detection performance. Simulated traffic data and real data collected at the 1-880 freeway in California were used in these experiments. Traffic flow parameters, such as volume, speed, occupancy and time headway both at upstream and downstream, and derived data generated from basic traffic flow parameters are used to build the PLSR model and SVM models. Several experiments using the original data or derived data have been performed to make comparisons between PLSR and SVM. The problem resulted from imbalance data and its influence on detection performance is also discussed. The test results have demonstrated that the PLSR has great potential to detect incident.
Q A ] 1 F eb 2 00 0 Geometrical Tools for Quantum Euclidean Spaces

We apply one of the formalisms of noncommutative geometry to Rq , the quantum space covariant under the quantum group SOq(N). Over R N q there are two SOq(N)-covariant differential calculi. For each we find a frame, a metric and two torsion-free covariant derivatives which are metric compatible up to a conformal factor and which have a vanishing linear curvature. This generalizes results found in a previous article for the case of Rq. As in the case N = 3, one has to slightly enlarge the algebra R N q ; for N odd one needs only one new generator whereas for N even one needs two. As in the particular case N = 3 there is a conformal ambiguity in the natural metrics on the differential calculi over Rq . While in our previous article the frame was found ‘by hand’, here we disclose the crucial role of the quantum group covariance and exploit it in the construction. As an intermediate step, we find a homomorphism from the cross product of Rq with Uqso(N) into R N q , an interesting result in itself. LMU-TPW 99-17, MPI-PhT/99-45 Preprint 99-52 Dip. Matematica e Applicazioni, Università di Napoli
CryptographersDevelopers Theory Standards Library API Code Samples Documentation Vulnerable Programs

While many essential cryptographic primitives have sound theoretical constructions and associated implementations, their security guarantees do not always hold in practice. We find that these security flaws do not usually exist in the libraries themselves, but instead in how developers use these libraries. We select the most popular cryptographic libraries from five programming languages (C, C++, Java, Python, and Go) and examine the properties that encourage or discourage cryptographic misuse. We then derive recommendations for library designers to follow to reduce this misuse. We find that the C++ NaCl cryptographic library is the safest. As an intermediate solution to more dangerous libraries, we present a proof-of-concept linter for PyCrypto which warns developers of potential cryptographic misuse in their programs.
Three Papers on Maximum Likelihood in Exponential Families

General methods for obtaining maximum likelihood estimates in exponential families are demonstrated using a constrained autologistic model for estimating relatedness from DNA fingerprint data. The novel features are the use of constrained optimization and two new algorithms for maximum likelihood estimation. The first, the "phase I" algorithm determines the support of the MLE in the closure of the exponential family (a distribution in the family conditioned on a face of the convex support of the natural statistic) when the MLE does not exist in the traditional sense (a point in the natural parameter space). The second, the maximum Monte Carlo likelihood algorithm uses the Metropolis algorithm or the Gibbs sampler to obtain estimates when exact calculation of the likelihood is not possible. Separate papers on each algorithm accompany the paper illustrating a complete analysis using these new methods.
Applying data mining techniques for descriptive phrase extraction in digital document collections

Traditionally, texts have been analysed using various information retrieval-related methods, such as full-text analysis and natural language processing. However, only few examples of data mining in text, particularly in full text, are available. In this paper, we show that general data mining methods are applicable to text analysis tasks such as descriptive phrase extraction. Moreover, we present a general framework for text mining. The framework follows the general knowledge discovery process, thus containing steps from preprocessing to utilization of the results. The data mining method that we apply is based on generalized episodes and episode rules. We give concrete examples of how to preprocess texts based on the intended use of the discovered results and we introduce a weighting scheme that helps in pruning out redundant or non-descriptive phrases. We also present results from real-life data experiments.
Signal Codes: Convolutional Lattice Codes

The coded modulation scheme proposed in this paper has a simple construction: an integer sequence, representing the information, is convolved with a fixed, continuous-valued, finite impulse response (FIR) filter to generate the codeword - a lattice point. Due to power constraints, the code construction includes a shaping mechanism inspired by precoding techniques such as the Tomlinson-Harashima filter. We naturally term these codes “convolutional lattice codes” or alternatively “signal codes” due to the signal processing interpretation of the code construction. Surprisingly, properly chosen short FIR filters can generate good codes with large minimal distance. Decoding can be done efficiently by sequential decoding or for better performance by bidirectional sequential decoding. Error analysis and simulation results indicate that for the additive white Gaussian noise (AWGN) channel, convolutional lattice codes with computationally reasonable decoders can achieve low error rate close to the channel capacity.
Mobile Video Communications Based on Fast DVC to H.264 Transcoding

Nowadays, mobile devices demand multimedia services such as video communications due to the advances in mobile communications systems (such us 4G) and the integration of video cameras into mobile devices. However, these devices have some limitations of computing power, resources and complexity constraints for performing complex algorithms. For this reason, in order to establish a video communications between mobile devices, it is necessary to use low complex encoding techniques. In traditional video codecs (such as H.264/AVC (ISO/IEC, 2003)) these low complexity requirements have not been met because H.264/AVC is more complex at the encoder side. Then, mobile video communications based on H.264/AVC low complexity imply a penalty in terms of Rate – Distortion (RD). However, Distributed Video Coding (DVC) (Girod et al., 2005), and particularly Wyner-Ziv (WZ) video coding (Aaron et al., 2002), provides a novel video paradigm where the complexity of the encoder is reduced by shifting the complexity of the encoder to the decoder (Brites et al., 2008). Taking into account the benefits of both paradigms, recently WZ to H.26X transcoders have been proposed in the multimedia community to support mobile-to-mobile video communications. The transcoding framework provides a scheme where transmitter and receiver execute lower complexity algorithms and the majority of the computation is moved to the network where the transcoder is allocated. This complexity is thus assumed by a transcoder, which has more resources and no battery limitations. Nevertheless, for real time communications it is necessary to perform this conversion from WZ to H.264/AVC with a short delay, and then the transcoding process must be executed as efficiently as possible.
Gait event detection through neuromorphic spike sequence learning

We present a novel sampling and processing method for detecting gait events from an insole pressure sensor. Inspired by how tactile data is processed in the brain, we propose the use of timing, instead of intensity, as our event detection feature. By sacrificing the need for accurate intensity measurements, it is possible to achieve superior temporal resolution, which is arguably more important given the need for timely feedback. In this paper, we demonstrate temporally accurate gait-event detection of 1.2±7ms (mean and standard deviation) for heel-strike and 0.2± 14ms for toe-off events compared to the reference system, and a success rate of above 97% in most trials, using only 1 bit of pressure information per channel. Our method thus has the potential to achieve much lower computational complexity and bandwidth, both of which are key to low-cost, portable solutions for prosthetics, exoskeletons or long-term gait monitoring applications.
A RSSI-Based Distributed Weighted Search Localization Algorithm for WSNs

In order to solve the node localization problem in wireless sensor networks, we propose a novel distributed weighted search localization algorithm (WSLA) in this paper. The WSLA adopts a modified received signal strength indicator-based range model to estimate the distances between nodes, utilizes the results of a centroid localization algorithm as the search initial point, and employs a new weighted search method to compute the positions of nodes in a distributed and recursive manner. The key ideas of the WSLA include a node localization precision classification scheme, a processing scheme for special nodes, and weight-based searches. Compared with three state-of-art localization algorithms—namely, maximum likelihood estimation (MLE), edge-based second-order cone programming + nonconvex sequential greedy (ESOCP + NCSG), and particle swarm optimization (PSO)—the simulation results show that localization performance of the WSLA is superior to that of MLE, ESOCP + NCSG, and PSO.
Dynamic Programming and Clique Based Approaches for Protein Threading with Profiles and Constraints

The protein threading problem with profiles is known to be efficiently solvable using dynamic programming. In this paper, we consider a variant of the protein threading problem with profiles in which constraints on distances between residues are given. We prove that protein threading with profiles and constraints is NP-hard. Moreover, we show a strong hardness result on the approximation of an optimal threading satisfying all the constraints. On the other hand, we develop two practical algorithms: CLIQUETHREAD and BBDPTHREAD. CLIQUETHREAD reduces the threading problem to the maximum edge-weight clique problem, whereas BBDPTHREAD combines dynamic programming and branch-and-bound techniques. We perform computational experiments using protein structure data in PDB (Protein Data Bank) using simulated distance constraints. The results show that constraints are useful to improve the alignment accuracy of the target sequence and the template structure. Moreover, these results also show that BBDPTHREAD is in general faster than CLIQUETHREAD for larger size proteins whereas CLIQUETHREAD is useful if there does not exist a feasible threading.
On the Effectiveness of Group Dialectical Behavior Therapy in the Enhancement of Distress Tolerance and Emotional Regulation in Substance Abusers

Objective: The aim of this study was to investigate the effectiveness of group dialectical behavior therapy in the improvement of distress tolerance and emotion regulation among substance abusers. Method: A quasiexperimental study along with pretest-posttest and control group was used for the conduct of this study. The population of the study consisted of all addicts referring to addiction treatment center of Zahedshahr (Omid Center) in summer 2012. From among this population, the number of 28 addicts was selected via convenience sampling as the participants of the study and was randomly assigned to two experimental and control groups. The participants responded to Difficulties in Emotion Regulation Scale and Distress Tolerance Scale as pretest, posttest, and follow-up test. The experimental group received group dialectical behavior therapy for 20 one-hour sessions whereas the control group received only Naltrexone drug. Results: The results showed that dialectical behavior therapy was effective in increasing distress tolerance and emotion regulation in substance abusers. Conclusion: Dialectical behavior therapy training can improve distress tolerance and emotion regulation, which could be the main reason for the continued use of drugs.
Two-chamber lattice model for thermodiffusion in polymer solutions

When a temperature gradient is applied to a polymer solution, the polymer typically migrates to the colder regions of the fluid as a result of thermal diffusion (Soret effect). However, in recent thermodiffusion experiments on poly(ethylene-oxide) (PEO) in a mixed ethanol/water solvent it is observed that for some solvent compositions the polymer migrates to the cold side, while for other compositions it migrates to the warm side. In order to understand this behavior, we have developed a two-chamber lattice model approach to investigate thermodiffusion in dilute polymer solutions. For a short polymer chain in an incompressible, one-component solvent we obtain exact results for the partitioning of the polymer between a warm and a cold chamber. In order to describe mixtures of PEO, ethanol, and water, we have extended this simple model to account for compressibility and hydrogen bonding between PEO and water molecules. For this complex system, we obtain approximate results for the composition in the warmer .
Angina Self-Management Plan and Quality of Life, Anxiety and Depression in Post Coronary Angioplasty Patients

Background: Coronary artery diseases are the most frequent cause of mortality in industrialized countries as well as Iran. Coronary artery disease affects patient’s quality of life (QoL) and produces some degrees of anxiety and depression. Although self-management programs have shown significant impact on chronic diseases, there is limited evidence in Iran regarding the effectiveness of these interventions, particularly in patients with coronary artery disease. Objectives: This study aimed to evaluate the effects of angina plan on QoL, anxiety, and depression in post coronary angioplasty patients referred to selected hospitals in Shiraz. Patients and Methods: This parallel randomized, controlled trial was conducted in selected hospitals in Shiraz, Iran. We enrolled 80 post coronary angioplasty eligible patients in the study. After acquisition of the informed consent, eligible patients were randomly divided into two groups: control and experimental. Pretest data were obtained by using a demographic data form and two valid and reliable questionnaires for QoL, anxiety, and depression. Blood pressure, weight, and height (to calculate body mass index) were measured too. Patient’s history of smoking, diabetes, hypercholesterolemia, hypertension, and coronary vascular involvement (with grade and severity) were obtained from their medical records. A 12-week angina plan intervention consisted of a 30 to 40 minutes of counseling interview and telephone follow up at the end of 1, 4, 8, and 12 weeks were performed for experimental group. Post-test data were obtained three months after the pretest using the same questionnaires as pretest. QoL data were analyzed by analysis of co-variance (ANCOVA). The results (before and after intervention) regarding anxiety and depression were analyzed by independent t-tests or their equivalent nonparametric Mann-Whitney test using SPSS v. 11.5. Results: There was no statistically significant difference in demographic variables between two groups. Baseline mean scores for QoL, anxiety, and depression did not differ between the two groups. There was a significant difference between the experimental and control groups in terms of changes in perception of QoL before and after the intervention. Adjusted mean ± SD of perception of QoL for the control group was 38.48 ± 13.38 and for the experimental one was 56.30 ± 13.38, with a P value of less than 0.001. The mean difference of anxiety scores (before and after intervention) in experimental and control groups were 1.15 ± 1.99 and.0. 07 ± 2.22, respectively with a P value of less than 0.01. The mean difference of depression scores (before and after intervention) in experimental and control groups were 0.4 ± 2.89 and 0.13 ± 2.76, respectively (P > 0.05). Conclusions: Our results show that the self-management angina plan was effective in improving perception of QoL and reducing anxiety. Further studies with a larger sample size and a longer follow-up period are recommended to better understand the effectiveness of this plan.
Errors on the Trail Making Test Are Associated with Right Hemispheric Frontal Lobe Damage in Stroke Patients

Measures of performance on the Trail Making Test (TMT) are among the most popular neuropsychological assessment techniques. Completion time on TMT-A is considered to provide a measure of processing speed, whereas completion time on TMT-B is considered to constitute a behavioral measure of the ability to shift between cognitive sets (cognitive flexibility), commonly attributed to the frontal lobes. However, empirical evidence linking performance on the TMT-B to localized frontal lesions is mostly lacking. Here, we examined the association of frontal lesions following stroke with TMT-B performance measures (i.e., completion time and completion accuracy measures) using voxel-based lesion-behavior mapping, with a focus on right hemispheric frontal lobe lesions. Our results suggest that the number of errors, but not completion time on the TMT-B, is associated with right hemispheric frontal lesions. This finding contradicts common clinical practice—the use of completion time on the TMT-B to measure cognitive flexibility, and it underscores the need for additional research on the association between cognitive flexibility and the frontal lobes. Further work in a larger sample, including left frontal lobe damage and with more power to detect effects of right posterior brain injury, is necessary to determine whether our observation is specific for right frontal lesions.