Miracle-dz/UniFAR-FTUs huggingface dataset:
SMT-Based Bounded Model Checking for Embedded ANSI-C Software

Propositional bounded model checking has been applied successfully to verify embedded software, but remains limited by increasing propositional formula sizes and the loss of high-level information during the translation preventing potential optimizations to reduce the state space to be explored. These limitations can be overcome by encoding high-level information in theories richer than propositional logic and using SMT solvers for the generated verification conditions. Here, we propose the application of different background theories and SMT solvers to the verification of embedded software written in ANSI-C in order to improve scalability and precision in a completely automatic way. We have modified and extended the encodings from previous SMT-based bounded model checkers to provide more accurate support for variables of finite bit width, bit-vector operations, arrays, structures, unions, and pointers. We have integrated the CVC3, Boolector, and Z3 solvers with the CBMC front-end and evaluated them using both standard software model checking benchmarks and typical embedded software applications from telecommunications, control systems, and medical devices. The experiments show that our ESBMC model checker can analyze larger problems than existing tools and substantially reduce the verification time.
The Do-Calculus Revisited

The do-calculus was developed in 1995 to facilitate the identification of causal effects in non-parametric models. The completeness proofs of [Huang and Valtorta, 2006] and [Shpitser and Pearl, 2006] and the graphi-cal criteria of [Tian and Shpitser, 2010] have laid this identification problem to rest. Recent explorations unveil the usefulness of the do-calculus in three additional areas: mediation analysis [Pearl, 2012], trans-portability [Pearl and Bareinboim, 2011] and meta-synthesis. Meta-synthesis (freshly coined) is the task of fusing empirical results from several diverse studies , conducted on heterogeneous populations and under different conditions, so as to synthesize an estimate of a causal relation in some target environment, potentially different from those under study. The talk surveys these results with emphasis on the challenges posed by meta-synthesis. For background material,
A Comparison of Two- and Three-Dimensional Wave Breaking

The influence of directionality on wave-packet evolution and in particular on the onset of breaking was explored through laboratory experiment. Lateral tapering was applied to the input signal to produce a range of crest lengths, with greater directionality created by diffraction for the shorter crests. The wave shape, local and global wave steepness, and surface displacement spectra were used to characterize the wave fields. The observations suggest that directionality can accelerate or suppress the onset of breaking, and additionally can influence both the local wave steepness at breaking as well as the breaking severity. Directionality, however, did not alter the observed up-frequency energy transfer associated with wave focusing. When no breaking occurred this energy shift was completely reversed. With breaking the shifted energy was lost, that is, passed from the wave to the turbulent energy field. The short-crested wave packet lost 16% of its energy as a result of breaking, while a comparable two-dimensional breaker lost 22%.
Comprehensive Performance Study of Magneto Cantilevers as a Candidate Model for Biological Sensors used in Lab ‐ on ‐ a ‐ Chip Applications

In recent years, demand for biological sensors which are capable of fast and accurate detection of minor amounts of pathogens in real‐time form has been intensified. Acoustic wave (AW) devices whose performance is determined by mass sensitivity parameters and quality factor are used in biological sensors as platforms with high quality. Yet, current AW devices are facing many challenges such as the low value of their quality factor in practical applications and also their difficulty to use in liquids. The main focus of this article is to study on the magnetostrictive sensors which include milli/microcantilever (MSMC) type. In comparison with AW devices, MSMC has a lot of advantages; (1) its actuation and sensing unit is wirelessly controlled. (2) Its fabrication process is easy. (3) It works well in liquids. (4) It has a high‐quality factor (in the air > 500). Simulation results demonstrate that the amount of quality factor depends on environment properties (density and viscosity), MSMC geometry, and its resonant behavior of harmonic modes.
Codes for DNA Sequence Profiles

We consider the problem of storing and retrieving information from synthetic DNA media. We introduce the DNA storage channel and model the read process through the use of profile vectors. We provide an asymptotic analysis of the number of profile vectors and propose new asymmetric coding techniques to combat the effects of synthesis and sequencing noise. Furthermore, we construct two families of codes for this new channel model.
Wavelet-BasedTransformations for Nonlinear SignalProcessing RobertD

Nonlinearitiesareoftenencounteredin theanalysisandprocessingof real-worldsignals.In this paper , weintroducetwo new structuresfor nonlinearsignalprocessing. Thenew structuressimplify theanalysis,design,andimplementationof nonlinearfilters andcanbeappliedto obtainmorereliableestimates of higher-orderstatistics.Both structuresarebasedon a two-stepdecompositionconsistingof a linear orthogonalsignalexpansionfollowedby scalarpolynomialtransformationsof theresultingsignalcoefficients.Mostexisting approachesto nonlinearsignalprocessingcharacterizethenonlinearityin thetime domainor frequency domain;in our framework any orthogonalsignalexpansioncanbeemployed.In fact,therearegoodreasonsfor characterizingnonlinearityusingmoregeneralsignalrepresentations like thewavelettransform.Waveletexpansionsoftenprovideveryconcisesignalrepresentationandthereby cansimplify subsequent nonlinearanalysisandprocessing.Waveletsalsoenablelocal nonlinearanalysisandprocessingin both time andfrequency, which canbeadvantageousin non-stationaryproblems. Moreover, weshow thatthewaveletdomainofferssignificanttheoreticaladvantagesover classicaltime or frequency domainapproachesto nonlinearsignalanalysisandprocessing. Thisworkwassupportedby theNationalScienceFoundation,grantno.MIP–9457438,andtheOfficeof Naval Research, grant no.N00014–95–1– 0849.
Image Acquisition View Plan Execution Operator Manual Initialization Images + GPS position UAV Safety Intervention Region of Interest Geometry Estimation Confidence Prediction View Planning Laptop

In this paper we present an autonomous system for acquiring close-range high-resolution images that maximize the quality of a later-on 3D reconstruction with respect to coverage, ground resolution and 3D uncertainty. In contrast to previous work, our system uses the already acquired images to predict the confidence in the output of a dense multi-view stereo approach without executing it. This confidence encodes the likelihood of a successful reconstruction with respect to the observed scene and potential camera constellations. Our prediction module runs in real-time and can be trained without any externally recorded ground truth. We use the confidence prediction for on-site quality assurance and for planning further views that are tailored for a specific multi-view stereo approach with respect to the given scene. We demonstrate the capabilities of our approach with an autonomous Unmanned Aerial Vehicle (UAV) in a challenging outdoor scenario.
Episodic Memory Question Answering

the improvement in egomotion estimation by retraining the VO model for our setup. We also note how, due to its cumulative nature (accumulating noise along the trajectory), the error metrics are much higher for the egomotion predictions compared to the independent noise models (where noise is independently added to the pose at each step).
Accumulation of prokaryotic remains during organic matter diagenesis in surface sediments off Peru

Bacterial biomarkers (D‐amino acids and muramic acid) were investigated in surface sediments (0–1 cm) at 22 stations in the Peru margin. Concentrations were used to quantitatively estimate the relative importance of peptidoglycan in the preservation of prokaryotic remains during diagenesis. The bacterial imprint in organic matter was also evident from low molar ratios (average of 1.6) of glucosamine and galactosamine. Estimates of the fraction of biomarkers associated with intact peptidoglycan showed that peptidoglycan became a progressively less important component of prokaryotic remains with ongoing diagenesis, whereas other prokaryotic biomolecules increased in importance. The exact nature of these biomolecules remains unknown, but constant ratios between individual D‐amino acids and amino sugars observed in this study provide novel information on their molecular composition. Examination of total hydrolyzable amino acids revealed systematic compositional changes with increasing water depth and age of the sediment, reflecting increased diagenetic alteration of the organic matter. The time scale integrated in the upper 1 cm of the sediment was ca. 2 yr at the shallow sites and up to 20 yr at the deeper stations. The alteration stages of organic matter ranged from coastal and ocean margin sediments at shallow water depth to organic matter as degraded as hemipelagic surface sediments at greater water depths.
Avaliação do transporte público urbano realizado por ônibus: uma abordagem exploratória

Due to the arise of new forms of transportation, businessmen and governments that operate urban buses have been more and more interested in assessing the quality of transportation according to the users’ satisfaction. Since the users have experienced aspects related to categories that concern the transportation services, such as vehicles, roads/bus stops, service, time and values, they are able to evaluate the quality of transportation according to these categories. In this context, this article purposes a methodological approach for evaluating the public urban transport carried out by bus according to the users’ perception. Supported on seven dimensions of service quality, the resultant model is composed by 33 criteria which were organized into a questionnaire. In spite of the exploratory context of the study, the results showed that the proposed approach and the resultant model constitute a simple and viable tool for the analysis of the problem. We hope this model is able to aid managers in decision making processes.
An Improved Sphere-Packing Bound for Finite-Length Codes on Symmetric Memoryless Channels

This paper derives an improved sphere-packing (ISP) bound for finite-length codes whose transmission takes place over symmetric memoryless channels. We first review classical results, i.e., the 1959 sphere-packing (SP59) bound of Shannon for the Gaussian channel, and the 1967 sphere-packing (SP67) bound of Shannon et al. for discrete memoryless channels. A recent improvement on the SP67 bound, as suggested by Valembois and Fossorier, is also discussed. These concepts are used for the derivation of a new lower bound on the decoding error probability (referred to as the ISP bound) which is uniformly tighter than the SP67 bound and its recent improved version. The ISP bound is applicable to symmetric memoryless channels, and some of its applications are exemplified. Its tightness is studied by comparing it with bounds on the ML decoding error probability, and computer simulations of iteratively decoded turbo-like codes. The paper also presents a technique which performs the entire calculation of the SP59 bound in the logarithmic domain, thus facilitating the exact calculation of this bound for moderate to large block lengths without the need for the asymptotic approximations provided by Shannon.
Data Lake Organization

We consider the problem of building an organizational directory of data lakes to support effective user navigation. The organization directory is defined as an acyclic graph that contains nodes representing sets of attributes and edges indicating subset relationships between nodes. A probabilistic model is constructed to model user navigational behaviour. The model also predicts the likelihood of users finding relevant tables in a data lake given an organization. We formulate the data lake organization problem as an optimization over the organizational structure in order to maximize the expected likelihood of discovering tables by navigating. An approximation algorithm is proposed with an analysis of its error bound. The effectiveness and efficiency of the algorithm are evaluated on both synthetic and real data lakes. Our experiments show that our algorithm constructs organizations that outperform many existing organizations including an existing hand-curated taxonomy, a linkage graph, and a common baseline organization. We have also conducted a formal user study which shows that navigation can help users discover relevant tables that are not easily accessible by keyword search queries. This suggests that keyword search and navigation using an organization are complementary modalities for data discovery in data lakes.
State of the Art of Adaptive Cruise Control and Stop and Go Systems

—This paper presents the state of the art of Adaptive Cruise Control (ACC) and Stop and Go systems as well as Intelligent Transportation Systems enhanced with inter vehicle communication. The sensors used in these systems and the level of their current technology are introduced. Simulators related to ACC and Stop and Go (S&G) systems are also surveyed and the MEKAR simulator is presented. Finally, future trends of ACC and Stop and Go systems and their advantages are emphasized
Detecting and Diagnosing Adversarial Images with Class-Conditional Capsule Reconstructions

Adversarial examples raise questions about whether neural network models are sensitive to the same visual features as humans. Most of the proposed methods for mitigating adversarial examples have subsequently been defeated by stronger attacks. Motivated by these issues, we take a different approach and propose to instead detect adversarial examples based on class-conditional reconstructions of the input. Our method uses the reconstruction network proposed as part of Capsule Networks (CapsNets), but is general enough to be applied to standard convolutional networks. We find that adversarial or otherwise corrupted images result in much larger reconstruction errors than normal inputs, prompting a simple detection method by thresholding the reconstruction error. Based on these findings, we propose the Reconstructive Attack which seeks both to cause a misclassification and a low reconstruction error. While this attack produces undetected adversarial examples, we find that for CapsNets the resulting perturbations can cause the images to appear visually more like the target class. This suggests that CapsNets utilize features that are more aligned with human perception and address the central issue raised by adversarial examples.
On Breast Cancer Detection : An Application of Machine Learning Algorithms on the Wisconsin Diagnostic Dataset Abien

This paper presents a comparison of six machine learning (ML) algorithms: GRU-SVM[4], Linear Regression, Multilayer Perceptron (MLP), Nearest Neighbor (NN) search, Softmax Regression, and Support Vector Machine (SVM) on the Wisconsin Diagnostic Breast Cancer (WDBC) dataset[22] bymeasuring their classification test accuracy and their sensitivity and specificity values. The said dataset consists of features which were computed from digitized images of FNA tests on a breast mass[22]. For the implementation of the ML algorithms, the dataset was partitioned in the following fashion: 70% for training phase, and 30% for the testing phase. The hyper-parameters used for all the classifiers were manually assigned. Results show that all the presented ML algorithms performed well (all exceeded 90% test accuracy) on the classification task. The MLP algorithm stands out among the implemented algorithms with a test accuracy of ≈99.04% Lastly, the results are comparable with the findings of the related studies[18, 23].
Aspects of Predicative Algebraic Set Theory III : Sheaves

This is the third in a series of papers on algebraic set theory, the aim of which is to develop a categorical semantics for constructive set theories, including predicative ones, based on the notion of a “predicative category with small maps”. In the first paper in this series [8] we discussed how these predicative categories with small maps provide a sound and complete semantics for constructive set theory. In the second one [10], we explained how realizability extensions of such predicative categories with small maps can be constructed. The purpose of the present paper is to do the same for sheaf-theoretic extensions. This program was summarised in [11], where we announced the results that we will present and prove here.
Learning Fine-grained Image Similarity with Deep Ranking Supplemental Materials

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Clusters and maps of science journals based on bi-connected graphs in Journal Citation Reports

The aggregated journal‐journal citation matrix derived from Journal Citation Reports 2001 can be decomposed into a unique subject classification using the graph‐analytical algorithm of bi‐connected components. This technique was recently incorporated in software tools for social network analysis. The matrix can be assessed in terms of its decomposability using articulation points which indicate overlap between the components. The articulation points of this set did not exhibit a next‐order network of “general science” journals. However, the clusters differ in size and in terms of the internal density of their relations. A full classification of the journals is provided in the Appendix. The clusters can also be extracted and mapped for the visualization.
Viterbi Decoder

The use of forward error correction (FEC) technique is known to be an e ective way to increase the reliability of the digital communication and to improve the capacity of a channel. Convolutional encoder at the transmitter associated with the Viterbi decoder at the receiver has been a predominant FEC technique because of its high e ciency and robustness. However, the Viterbi decoder consumes large resources due to its complexity, and the decoding load has increased with newer communication standards; as a result, the hardware solution has become crucial for higher performance and cost e ectiveness. A Viterbi Decoder for IEEE 802.16 WiMax Standards (constraint length K = 7, supporting rates of 1/2, 2/3, 3/4, 5/6) has been implemented in Bluespec on an FPGA. The design is based on MATLAB source code of Viterbi decoder. The decoder throughput has been boosted by using parallel and pipelined architecture to reach over 150Mb/s. Bluespec and FPGA played a key role in that it remarkably reduces design e ort as well as veri cation time through high-level synthesis.
S UMM N : A Multi-Stage Summarization Framework for Long Input Dialogues and Documents

Text summarization helps readers capture 001 salient information from documents, news, in-002 terviews, and meetings. However, most state-003 of-the-art pretrained language models (LM) 004 are unable to efﬁciently process long text 005 for many summarization tasks. In this pa-006 per, we propose S UMM N , a simple, ﬂexi-007 ble, and effective multi-stage framework for 008 input texts that are longer than the maxi-009 mum context length of typical pretrained LMs. 010 S UMM N ﬁrst splits the data samples and gener-011 ates a coarse summary in multiple stages and 012 then produces the ﬁnal ﬁne-grained summary 013 based on it. Our framework can process in-014 put text of arbitrary length by adjusting the 015 number of stages, while keeping the LM in-016 put size ﬁxed. Moreover, it can deal with 017 both single-source documents and dialogues, 018 and it can be used on top of different back-019 bone abstractive summarization models. To 020 the best of our knowledge, S UMM N is the 021 ﬁrst multi-stage split-then-summarize frame-022 work for long input summarization. Our ex-023 periments demonstrate that S UMM N outper-024 forms previous state-of-the-art methods by im-025 proving ROUGE scores on three long meet-026 ing summarization datasets AMI, ICSI, and 027 QMSum, two long TV series datasets from 028 SummScreen, and a long document summa-029 rization dataset GovReport. Our data and code 030 are available at https://github.com/ 031 ANONYMOUS/Summ-N . 032
Qualitative Research in Information Systems

Qualitative research involves the use of qualitative data, such as interviews, documents, and participant observation, to understand and explain social phenomena. As the focus of information systems research shifts from technological to managerial and organizational issues, qualitative research methods become increasingly useful. This example of "living scholarship" within MISQ Discovery's worldwide web archive provides an overview of qualitative research for the newcomer and a set of resources for those more experienced. The work discusses philosophical perspectives that can inform qualitative research, qualitative research methods, techniques, and modes of analysis. Links to citation lists, Internet resources, software tools, and calls for papers are also included.
Minimax rates of estimation for high-dimensional linear regression over l q-balls

Consider the high-dimensional linear regression modely = Xβ∗ +w, where y ∈ R is an observation vector, X ∈ R is a design matrix with d > n, the quantity β∗ ∈ R is an unknown regression vector, and w ∼ N (0, σI) is additive Gaussian noise. This paper studies the minimax rates of convergence for estimating β∗ in either l2-loss and l2-prediction loss, assuming that β∗ belongs to an lq-ball Bq(Rq) for some q ∈ [0, 1]. It is shown that under suitable regularity conditions on the design matrix X, the minimax optimal rate in l2-loss and l2-prediction loss scales asRq (