Miracle-dz/UniFAR-FTUs huggingface dataset:
A simple implementation of determinant decision diagram

Determinant decision diagram (DDD) uses a Binary Decision Diagram (BDD) to represent the Laplace expansion of a determinant. It is used as the core computation engine in some modern symbolic circuit simulators. The traditional implementations rely on a BDD package for the common-data sharing operations in which symbol ordering plays an essential role. This paper proposes a simple implementation method which does not use any BDD package. Sharing is implemented by directly hashing minors, while the requirement on symbol ordering is weakened to an expansion ordering. The basic mechanism used is a natural formulation of layered expansion which is analogous to manual expansion of a determinant, hence it is easily understood. The simplified DDD construction method not only makes the DDD implementation straightforward, but also results in greater efficiency. A simulator developed based on this new method solves the μa725 op-amp circuit in a few seconds by flat expansion.
Particle graphics on reconfigurable hardware

Particle graphics simulations are well suited for modeling complex phenomena such as water, cloth, explosions, fire, smoke, and clouds. They are normally realized in software as part of an interactive graphics application. The computational complexity of particle graphics simulations restricts the number of particles that can be updated in software at interactive frame rates. This article presents the design and implementation of a hardware particle graphics engine for accelerating real-time particle graphics simulations. We explore the design process, implementation issues, and limitations of using field-programmable gate arrays (FPGAs) for the acceleration of particle graphics. The FPGA particle engine processes million-particle systems at a rate from 47 to 112 million particles per second, which represents one to two orders of magnitude speedup over a 2.8 GHz CPU. Using three FPGAs, a maximum sustained performance of 112 million particles per second was achieved.
PHAD: packet header anomaly detection for identifying hostile network traffic

We describe an experimental packet header anomaly detector (PHAD) that learns the normal range of values for 33 fields of the Ethernet, IP, TCP, UDP, and ICMP protocols. On the 1999 DARPA off-line intrusion detection evaluation data set (Lippmann et al. 2000), PHAD detects 72 of 201 instances (29 of 59 types) of attacks, including all but 3 types that exploit the protocols examined, at a rate of 10 false alarms per day after training on 7 days of attack-free internal network traffic. In contrast to most other network intrusion detectors and firewalls, only 8 attacks (6 types) are detected based on anomalous IP addresses, and none by their port numbers. A number of variations of PHAD were studied, and the best results were obtained by examining packets and fields in isolation, and by using simple nonstationary models that estimate probabilities based on the time since the last event rather than the average rate of events.
AdaMast: A Drum Sound Recognizer based on Adaptation and Matching of Spectrogram Templates

This paper describes a template-matching-based system, called AdaMast, that detects onset times of the bass drum, snare drum, and hi-hat cymbals in polyphonic audio signals of popular songs. AdaMast uses the power spectrograms of the drum sounds as templates. However, there are two main problems in transcribing drum sounds in the presence of other sounds. The first problem is that actual drum-sound spectrograms cannot be prepared as templates beforehand for each song. The second problem is that power spectrograms of sound mixtures including the drum sound are greatly different from the template (pure drum-sound spectrogram). To solve the first problem, a template-adaptation algorithm is built into AdaMast. To solve the second problem, a distance measure used in the template matching is designed to be robust to the spectral overlapping of other sounds. The test results in Audio Drum Detection Contest were 72.8%, 70.2%, and 57.4% in transcribing the bass drums, snare drums, and hi-hat cymbals, respectively, and AdaMast won the contest.
Becoming a “Better” Father: Supporting the Needs of Incarcerated Fathers

Given the importance of fathering to the well-being and development of children, paternal incarceration has a major impact on children and families. Drawing on interviews with 64 incarcerated fathers in New South Wales, Australia, this article explores their experiences. The men’s childhood familial separation and disconnection is frequently repeated in adulthood, with limited contact with their own families even when not in custody. Despite barriers to connection, the interviewees express strong aspirations to be “good” fathers and to achieve a “better life” for their children. The absence of stable models of responsive fathering in early life is a common theme that has implications for the development of education and support programs for imprisoned fathers.
Convex treemaps with bounded aspect ratio

Treemaps are a popular technique to visualize hierarchical data. The input is a weighted tree T where the weight of each node is the sum of the weights of its children. A treemap for T is a hierarchical partition of a rectangle into simply connected regions, usually rectangles. Each region represents a node of T and the area of each region is proportional to the weight of the corresponding node. An important quality criterium for treemaps is the aspect ratio of its regions. Unfortunately, one cannot bound the aspect ratio if the regions are restricted to be rectangles. Hence Onak and Sidiropoulos introduced polygonal partitions, which use convex polygons. We are the first to obtain convex partitions with optimal aspect ratio O(depth(T )). We also consider the important special case that depth(T ) = 1, that is, single-level treemaps. We show how to construct convex singlelevel treemaps that use only four simple shapes for the regions and have aspect ratio at most 34/7.
Risk prediction models: I. Development, internal validation, and assessing the incremental value of a new (bio)marker

Prediction models are increasingly used to complement clinical reasoning and decision making in modern medicine in general, and in the cardiovascular domain in particular. Developed models first and foremost need to provide accurate and (internally and externally) validated estimates of probabilities of specific health conditions or outcomes in targeted patients. The adoption of such models must guide physician's decision making and an individual's behaviour, and consequently improve individual outcomes and the cost-effectiveness of care. In a series of two articles we review the consecutive steps generally advocated for risk prediction model research. This first article focuses on the different aspects of model development studies, from design to reporting, how to estimate a model's predictive performance and the potential optimism in these estimates using internal validation techniques, and how to quantify the added or incremental value of new predictors or biomarkers (of whatever type) to existing predictors. Each step is illustrated with empirical examples from the cardiovascular field.
Noise-robust voice conversion based on spectral mapping on sparse space

This paper presents a voice conversion (VC) technique for noisy environments based on a sparse representation of speech. In our previous work, we discussed an exemplar-based VC technique for noisy environments. In that report, source exemplars and target exemplars are extracted from the parallel training data, having the same texts uttered by the source and target speakers. The input source signal is represented using the source exemplars and their weights. Then, the converted speech is constructed from the target exemplars and the weights related to the source exemplars. However, this exemplar-based approach needs to hold all training exemplars (frames) and it requires high computation times to obtain the weights of the source exemplars. In this paper, we propose a framework to train the basis matrices of source and target exemplars so that they have a common weight matrix. By using the basis matrices instead of the exemplars, the VC is performed with lower computation times than with the exemplar-based method. The effectiveness of this method was confirmed by comparing its effectiveness, in speaker conversion experiments using noise-added speech data, with the effectiveness of an exemplar-based method and a conventional Gaussian mixture model (GMM)-based method.
Wearout-aware compiler-directed register assignment for embedded systems

Although constant technology scaling has resulted in considerable benefits, smaller device dimensions, higher operating temperatures and electric fields have also contributed to faster device aging due to wearout. Not only does this result in the shortening of processor lifetimes, it leads to faster wearout resultant performance degradation with operating time. Instead of taking a reactive approach towards reliability awareness, we propose a pre-emptive route toward wearout mitigation. Given the significant thermal and stress variation across the components of microprocessors, in this work we focus on one of the most likely candidates for overheating and hence reliability failures, the register file. We propose different wearout-aware compiler-directed register assignment techniques that distribute the stress induced wearout throughout the register file, with the aim of improving the lifetime of the register file, with negligible performance overhead. We compare our results with a state-of-the-art thermal-aware compilation scheme to show the clear advantage our proposed wearout-aware scheme has over thermal-aware schemes in terms of lifetime improvement that can reach up to 20% for Bias Temperature Instability.
Poster: Exploring the Need for Sensor Learning and Collaboration in IoT-based Parking Systems

The need to find parking contributes to road congestion and leads to unnecessary fuel consumption. Of all emerging parking systems, Internet-of-Things (IoT)-based systems have demonstrated the feasibility of real-time delivery of parking availability using magnetic sensors. However, existing magnetic-based methods are prone to false positives caused by electromagnetic fields emitted from surrounding electric facilities. In this study, we conducted a 3-month data collection in a parking area. We identified the need to introduce learning and collaboration into the design of our detection algorithm which recognizes learned patterns associated with car arrivals or departures, and to filter out unreliable events based on spatial and temporal features.
Non-native Speech Learning in Older Adults

Though there is an extensive literature investigating the ability of younger adults to learn non-native phonology, including investigations into individual differences in younger adults’ lexical tone learning, very little is known about older adults’ ability to learn non-native phonology, including lexical tone. There are several reasons to suspect that older adults would use different learning mechanisms when learning lexical tone than younger adults, including poorer perception of dynamic pitch, greater reliance on working memory capacity in second language learning, and poorer category learning in older adulthood. The present study examined the relationships among older adults’ baseline sensitivity for pitch patterns, working memory capacity, and declarative memory capacity with their ability to learn to associate tone with lexical meaning. In older adults, baseline pitch pattern sensitivity was not associated with generalization performance. Rather, older adults’ learning performance was best predicted by declarative memory capacity. These data suggest that training paradigms will need to be modified to optimize older adults’ non-native speech sound learning success.
Scalable Software Defined Monitoring for Service Provider DevOps

Technology trends such as Cloud, SDN, and NFV are transforming the telecommunications business, promising higher service flexibility and faster deployment times. They also allow for increased programmability of the infrastructure layers. We propose to split selected monitoring control functionality onto node-local control planes, thereby taking advantage of processing capabilities on programmable nodes. Our software defined monitoring approach provides telecom operators with a way to handle the trade off between high-granular monitoring information versus network and computation loads at central control and management layers. To illustrate the concept, a link rate monitoring function is implemented using node-local control plane components. Furthermore, we introduce a messaging bus for simple and flexible communication between monitoring function components as well as control and management systems. We investigate scalability gains with a numerical analysis, demonstrating that our approach would generate thousand fold less monitoring traffic while providing similar information granularity as a naive SNMP implementation or an Open Flow approach.
PowerSC : A SystemC-based Framework for Power Estimation

Although SystemC is considered the most promising language for system-on-chip functional modeling, it doesn’t come with power modeling capabilities. This work presents PowerSC, a novel power estimation framework which instruments SystemC for power characterization, modeling and estimation. Since it is entirely based on SystemC, PowerSC allows consistent power modeling from the highest to the lowest abstraction level. Besides, the framework’s API provides facilities to integrate alternative modeling techniques, either at the same or at different abstraction levels. As a result, the required power evaluation infrastructure is reduced to a minimum: the standard SystemC library, the PowerSC library itself and a C++ compiler. Although RTL power macromodeling is a mature research topic, it is not yet broadly accepted in the industrial environment. One of the main reasons impairing its widespread use as a power estimation paradigm is that each macromodeling technique makes some assumptions that lead to some sort of intrinsic limitation, thereby affecting its accuracy. Therefore, alternative macromodeling methods can be envisaged as part of a power modeling toolkit from which the most suitable method for a given component should be automatically selected. This paper describes a new multi-model power estimation engine that selects the macromodeling technique leading to the least estimation error for a given system component depending on its input-vector stream properties. A proper selection function is built after component characterization and used during estimation. Experimental results show that our multi-model engine improves the robustness of power analysis, reducing significantly the average and the maximum estimation errors, as compared to conventional single-model estimation.
VSSA: a service-oriented vehicular social-networking platform for transportation efficiency

This paper proposes a novel service-oriented vehicular social networking platform called VSSA for transportation efficiency. VSSA consists of two layers: service layer and application layer. The service layer is implemented based on the service-oriented architecture (SOA) to ease application developments. By extending and composing the web services in this layer, application developers can efficiently and flexibly develop different new functions or applications for transportation scenarios. The application layer is oriented to vehicular users and it integrates with functions of mobile social networks. With dynamic and automatic service collaboration support, it enables people to easily collaborate and help each other through their mobile devices in transportation situations. Moreover, VSSA provides a context-awareness mechanism to support automatically and intelligently predicting the potential incoming traffic congestions.
VBLAST detection based on quadratic zero-one programming

Maximum likelihood detection for VBLAST architectures is equivalent to an integer quadratic optimization problem. Branch and bound (BB) algorithm is a simple method for solving quadratic problem, though other methods exist. In this paper, we use a depth-first BB algorithm for VBLAST detection. For reducing detection complexity, we use a preprocessing stage before running the BB algorithm. We also show that the stage can be improved with a simple modification which leads to lower detection complexity.
Bringing Elton and Grinnell Together: a quantitative framework to represent the biogeography of ecological interaction networks

Biogeography has traditionally focused on the spatial distribution and abundance of species. Both are driven by the way species interact with one another, but also by the way these interactions vary across time and space. Here, we call for an integrated approach, adopting the view that community structure is best represented as a network of ecological interactions, and show how it translates to biogeography questions. We propose that the ecological niche should encompass the effect of the environment on species distribution (the Grinnellian dimension of the niche) and on the ecological interactions among them (the Eltonian dimension). Starting from this concept, we develop a quantitative theory to explain turnover of interactions in space and time i.e. a novel approach to interaction distribution modelling. We apply this framework to host parasite interactions across Europe and find that two aspects of the environment (temperature and precipitation) exert a strong imprint on species co-occurrence, but not on species interactions. Even where species co-occur, interaction proves to be stochastic rather than deterministic, adding to variation in realized network structure. We also find that a large majority of host-parasite pairs are never found together, thus precluding any inferences regarding their probability to interact. This first attempt to explain variation of network structure at large spatial scales opens new perspectives at the interface of species distribution modelling and community ecology.
Prediction for human transcription start site using diversity measure with quadratic discriminant

The accurate identification of promoter regions and transcription start sites is a challenge to the construction of human transcription regulation networks. Thus, an efficient prediction method based on theoretical formulation is necessary for this purpose. We used the method of increment diversity with quadratic discriminant analysis (IDQD) to predict transcription start sites (TSS). The method produced sensitivity and positive predictive value of more than 65% with positives to negatives ratio of 1:58. The performance evaluation using Receiver Operator Characteristics (ROC) showed an auROC (area under ROC) of greater than 96%. The evaluation by Precision Recall Curves (PRC) showed an auPRC (area under PRC) of about 26% for positives to negatives ratio of 1:679 and about 64% for positives to negatives ratio of 1:113. The results documented in this approach are either better or comparable to other known methods.
Enterprise Knowledge Graphs: A Backbone of Linked Enterprise Data

Semantic technologies in enterprises have recently received increasing attention from both the research and industrial side. The concept of Linked Enterprise Data (LED) describes a framework to incorporate benefits of semantic technologies into enterprise IT environments. However, LED still remains an abstract idea lacking a point of origin, i.e., station zero from which it comes to existence. In this paper we argue and demonstrate that Enterprise Knowledge Graphs (EKGs) might be considered as an embodiment of LED lifting corporate information management to a semantic level which ultimately allows for real artificial intelligence applications. By EKG we refer to a semantic network of concepts, properties, individuals and links representing and referencing foundational and domain knowledge relevant for an enterprise. Although the concept of EKGs was not invented yesterday, both enterprise and semantic communities have not yet come up with a formal comprehensive framework for designing such graphs. In this paper we aim to join the dots between the expanding interest in EKGs expressed by those communities and the lack of blueprints for realizing the EKGs. A thorough study of the key design concepts provides a multi-dimensional aspects matrix from which an enterprise is able to choose specific features of the highest priority. We emphasize the importance of various data fusion approaches, e.g., unified and federated. In the extensive evaluation section we investigate the effect of the chosen approach on the EKG performance along several dimensions, e.g., basic reasoning and OWL entailment which account for machine understanding of the EKG data, and access control subsystem which is of the utmost importance in large enterprises.
Refinement of the Sphere-Packing Bound: Asymmetric Channels

We provide a refinement of the sphere-packing bound for constant composition codes over asymmetric discrete memoryless channels that improves the subexponential factor in front of the exponent. The order of our subexponential factor is Ω(N<sup>-0.5(1+ε+ρ</sup><sup>R</sup><sup>*</sup>)) for any ϵ > 0, where ρ<sub>R</sub>* is the left derivative of the sphere-packing exponent at rate R and N is the blocklength.
Gain- and Loss-of-Function Mutations in the Breast Cancer Gene GATA3 Result in Differential Drug Sensitivity

Patterns of somatic mutations in cancer genes provide information about their functional role in tumourigenesis, and thus indicate their potential for therapeutic exploitation. Yet, the classical distinction between oncogene and tumour suppressor may not always apply. For instance, TP53 has been simultaneously associated with tumour suppressing and promoting activities. Here, we uncover a similar phenomenon for GATA3, a frequently mutated, yet poorly understood, breast cancer gene. We identify two functional classes of frameshift mutations that are associated with distinct expression profiles in tumours, differential disease-free patient survival and gain- and loss-of-function activities in a cell line model. Furthermore, we find an estrogen receptor-independent synthetic lethal interaction between a GATA3 frameshift mutant with an extended C-terminus and the histone methyltransferases G9A and GLP, indicating perturbed epigenetic regulation. Our findings reveal important insights into mutant GATA3 function and breast cancer, provide the first potential therapeutic strategy and suggest that dual tumour suppressive and oncogenic activities are more widespread than previously appreciated.
Eigen-prosody analysis for robust speaker recognition under mismatch handset environment

A novel eigen-prosody analysis approach is proposed for robust speaker recognition under a mismatch handset environment. The idea is to convert the prosodic contours of a speaker's speech into sequences of prosody symbols, and transform the speaker recognition problem into a full-text document retrieval-similar task. Experimental results on the HTIMIT corpus have shown that, even though only few training/test data are available, about 32.2% relative error rate reduction could be achieved compared with the conventional Gaussian mixture model/cepstral mean subtraction approach.