Miracle-dz/UniFAR-FTUs huggingface dataset:
High Performance Architecture using Speculative Threads and Dynamic Memory Management Hardware

With the advances in very large scale integration (VLSI) technology, hundreds of billions of transistors can be packed into a single chip. With the increased hardware budget, how to take advantage of available hardware resources becomes an important research area. Some researchers have shifted from control flow Von-Neumann architecture back to dataflow architecture again in order to explore scalable architectures leading to multi-core systems with several hundreds of processing elements. In this dissertation, I address how the performance of modern processing systems can be improved, while attempting to reduce hardware complexity and energy consumptions. My research described here tackles both central processing unit (CPU) performance and memory subsystem performance. More specifically I will describe my research related to the design of an innovative decoupled multithreaded architecture that can be used in multi-core processor implementations. I also address how memory management functions can be off-loaded from processing pipelines to further improve system performance and eliminate cache pollution caused by runtime management functions.
Quantum Approach to Fast Protein-Folding Time*

In the traditional random-conformational-search model, various hypotheses with a series of meta-stable intermediate states were often proposed to resolve the Levinthal paradox. Here we introduce a quantum strategy to formulate protein folding as a quantum walk on a definite graph, which provides us a general framework without making hypotheses. Evaluating it by the mean of first passage time, we find that the folding time via our quantum approach is much shorter than the one obtained via classical random walks. This idea is expected to evoke more insights for future studies.
Demographics of Planetesimals Formed by the Streaming Instability

The streaming instability (SI) is a mechanism to aerodynamically concentrate solids in protoplanetary disks and facilitate the formation of planetesimals. Recent numerical modeling efforts have demonstrated the increasing complexity of the initial mass distribution of planetesimals. To better constrain this distribution, we conduct SI simulations including self-gravity with the highest resolution hitherto. To subsequently identify all of the self-bound clumps, we develop a new clump-finding tool, Planetesimal Analyzer. We then apply a maximum likelihood estimator to fit a suite of parameterized models with different levels of complexity to the simulated mass distribution. To determine which models are best-fitting and statistically robust, we apply three model selection criteria with different complexity penalties. We find that the initial mass distribution of clumps is not universal regarding both the functional forms and parameter values. Our model selection criteria prefer models different from those previously considered in the literature. Fits to multi-segment power-law models break to a steeper distribution above masses close to those of 100 km collapsed planetesimals, similar to observed size distributions in the Kuiper Belt. We find evidence for a turnover at the low-mass end of the planetesimal mass distribution in our high-resolution run. Such a turnover is expected for gravitational collapse, but had not previously been reported.
Variational Quantum Fidelity Estimation

Computing quantum state fidelity will be important to verify and characterize states prepared on a quantum computer. In this work, we propose novel lower and upper bounds for the fidelity F(ρ,σ) based on the ``truncated fidelity'' F(ρm,σ), which is evaluated for a state ρm obtained by projecting ρ onto its m-largest eigenvalues. Our bounds can be refined, i.e., they tighten monotonically with m. To compute our bounds, we introduce a hybrid quantum-classical algorithm, called Variational Quantum Fidelity Estimation, that involves three steps: (1) variationally diagonalize ρ, (2) compute matrix elements of σ in the eigenbasis of ρ, and (3) combine these matrix elements to compute our bounds. Our algorithm is aimed at the case where σ is arbitrary and ρ is low rank, which we call low-rank fidelity estimation, and we prove that no classical algorithm can efficiently solve this problem under reasonable assumptions. Finally, we demonstrate that our bounds can detect quantum phase transitions and are often tighter than previously known computable bounds for realistic situations.
$\Delta$-transitivity for several transformations and an application to the coboundary problem

Given a compact and complete metric space $X$ with several continuous transformations $T_1, T_2, \ldots T_H: X \to X,$ we find sufficient conditions for the existence of a point $x\in X$ such that $(x,x,\ldots,x)\in X^H$ has dense orbit for the transformation $$\mathcal T:=T_1\times T_2\times\cdots\times T_H. $$ We use these conditions together with Liv\v{s}ic theorem, to obtain that for $\alpha$-H\"older maps $f_1,f_2,\ldots,f_H: X\to \mathbb{R},$ the product $\prod_{i=1}^H f_i(x_i)$ is a smooth coboundary with respect to $\mathcal T$ is equivalent to the existence of a non-empty open subset $U \subset X$ such that $$\sup_{N} \sup_{x\in U}\left| \sum_{j=0}^{N} \prod_{i=1}^H f_i (T_i^{j} x) \right| < \infty. $$
Relaxed Control and Gamma-Convergence of Stochastic Optimization Problems with Mean Field

Motivated by the deep layer limit of residual neural networks, we study the relaxed control and Gamma-convergence of a class of mean ﬁeld stochastic optimization problems based on their sample idealization. We ﬁrst establish the existence of optimal relaxed solutions to such a class of mean ﬁeld optimization problems in the case of a ﬁnite training sample. The core of the paper is to show that, when the sample capacity is large, the minimizer of the sampled relaxed optimization problem converges to the minimizer of the limiting relaxed optimization problem using Gamma-convergence. To prove the Gamma-convergence of sampled objective functionals, we establish general convergence properties of related empirical processes arising from the ﬁnite sample controlled model. Then, we connect the limit of the large sampled objective functional to the unique solution of a nonlinear Fokker-Plank-Kolmogorov (FPK) equation in a random environment. We prove the uniqueness of solutions to the FPK equation in the trajectory sense.
Supporting adults to address their literacy needs using e-learning

Many adults need help with literacy learning. This is extremely challenging for the tertiary education sector and workplace-situated learning organisations. E-learning may be an effective and efficient way to improve the delivery of teaching basic skills learners. Our research study included five embedded case studies within one tertiary institution, and a series of stakeholder interviews with representatives across New Zealand. The study found that e-learning opens up greater interaction between adults’ study, work, home, and community environments, simply because the learning environment can be extended into those places.
Information Bottleneck Decoding of Rate-Compatible 5G-LDPC Codes

The new 5G communications standard increases data rates and supports low-latency communication that places constraints on the computational complexity of channel decoders. 5G low-density parity-check (LDPC) codes have the so-called protograph-based raptor-like (PBRL) structure which offers inherent rate-compatibility and excellent performance. Practical LDPC decoder implementations use message-passing decoding with finite precision, which becomes coarse as complexity is more severely constrained. Performance degrades as the precision becomes more coarse. Recently, the information bottleneck (IB) method was used to design mutual-information-maximizing lookup tables that replace conventional finite-precision node computations. The IB approach exchanges messages represented by integers with very small bit width. This paper extends the IB principle to the flexible class of PBRL LDPC codes as standardized in 5G. The extensions include puncturing and rate-compatible IB decoder design. As an example of the new approach, a 4-bit information bottleneck decoder is evaluated for PBRL LDPC codes over a typical range of rates. Frame error rate simulations show that the proposed scheme outperforms offset min-sum decoding algorithms and operates very close to double-precision sum-product belief propagation decoding.
Learning Discriminative features using Center Loss and Reconstruction as Regularizer for Speech Emotion Recognition

This paper proposes a Convolutional Neural Network (CNN) inspired by Multitask Learning (MTL) and based on speech features trained under the joint supervision of softmax loss and center loss, a powerful metric learning strategy, for the recognition of emotion in speech. Speech features such as Spectrograms and Mel-frequency Cepstral Coefficient s (MFCCs) help retain emotion-related low-level characteristics in speech. We experimented with several Deep Neural Network (DNN) architectures that take in speech features as input and trained them under both softmax and center loss, which resulted in highly discriminative features ideal for Speech Emotion Recognition (SER). Our networks also employ a regularizing effect by simultaneously performing the auxiliary task of reconstructing the input speech features. This sharing of representations among related tasks enables our network to better generalize the original task of SER. Some of our proposed networks contain far fewer parameters when compared to state-of-the-art architectures.
Global Software Development: Managing Virtual Teams and Environments

From the Publisher: Following the life cycle of a typical software development project, Global Software Development looks at the growing need for global software development and the foundations of development strategy. Beginning with a description of the necessary first steps in setting up the development environment and project team, the book progresses through development, emphasizing the differences between traditional and virtual management. The final chapter presents three case studies that illustrate the principles and activities described in the preceding chapters. Dale Karolak makes if easier for managers to understand what to consider when managing a virtual project and offers a broad spectrum of information that enhances the reader's understanding of global software development.
Differentially Private Summation with Multi-Message Shuffling

In recent work, Cheu et al. (Eurocrypt 2019) proposed a protocol for $n$-party real summation in the shuffle model of differential privacy with $O_{\epsilon, \delta}(1)$ error and $\Theta(\epsilon\sqrt{n})$ one-bit messages per party. In contrast, every local model protocol for real summation must incur error $\Omega(1/\sqrt{n})$, and there exist protocols matching this lower bound which require just one bit of communication per party. Whether this gap in number of messages is necessary was left open by Cheu et al. In this note we show a protocol with $O(1/\epsilon)$ error and $O(\log(n/\delta))$ messages of size $O(\log(n))$ per party. This protocol is based on the work of Ishai et al.\ (FOCS 2006) showing how to implement distributed summation from secure shuffling, and the observation that this allows simulating the Laplace mechanism in the shuffle model.
Deep RGB-D Canonical Correlation Analysis For Sparse Depth Completion

In this paper, we propose our Correlation For Completion Network (CFCNet), an end-to-end deep learning model that uses the correlation between two data sources to perform sparse depth completion. CFCNet learns to capture, to the largest extent, the semantically correlated features between RGB and depth information. Through pairs of image pixels and the visible measurements in a sparse depth map, CFCNet facilitates feature-level mutual transformation of different data sources. Such a transformation enables CFCNet to predict features and reconstruct data of missing depth measurements according to their corresponding, transformed RGB features. We extend canonical correlation analysis to a 2D domain and formulate it as one of our training objectives (i.e. 2d deep canonical correlation, or "2D2CCA loss"). Extensive experiments validate the ability and flexibility of our CFCNet compared to the state-of-the-art methods on both indoor and outdoor scenes with different real-life sparse patterns. Codes are available at: this https URL.
Plan-Driven Approaches Are Alive and Kicking in Agile Global Software Development

Background: Agile methods are no longer restricted to small projects and co-located teams. The last decade has seen the spread of agile into large scale, distributed and regulated domains. Many case studies show successful agile adoption in GSD, however, taken as a whole, it remains unclear how widespread this trend is, and what form the agile adoption takes in a global software development (GSD) setting. Aims: Our objective is to gain a deeper understanding of how organisations adopt agile development methods in distributed settings. Specifically we aim to plot the current development process landscape in GSD. Method: We analyse industrial survey data from 33 different countries collected as part of the HELENA project that explored the wider use of hybrid development approaches in software development. We extract and analyse the results of 263 surveys completed by participants involved in globally distributed projects. Results: In our sample, 72% of globally distributed projects implement a mix of both agile and traditional approaches (termed ‘hybrid’). 25% of GSD projects are predominantly agile, with only very few (2%) opting for traditional approaches. GSD projects that used only agile methods tended to be very large. Conclusions: Globally Distributed Software Development (and project size) is not a barrier to adopting agile practices. Yet, to facilitate project coordination and general project management, many adopt traditional approaches, resulting in a hybrid approach that follows defined rules.
Learning Reward Functions by Integrating Human Demonstrations and Preferences

Our goal is to accurately and efficiently learn reward functions for autonomous robots. Current approaches to this problem include inverse reinforcement learning (IRL), which uses expert demonstrations, and preference-based learning, which iteratively queries the user for her preferences between trajectories. In robotics however, IRL often struggles because it is difficult to get high-quality demonstrations; conversely, preference-based learning is very inefficient since it attempts to learn a continuous, high-dimensional function from binary feedback. We propose a new framework for reward learning, DemPref, that uses both demonstrations and preference queries to learn a reward function. Specifically, we (1) use the demonstrations to learn a coarse prior over the space of reward functions, to reduce the effective size of the space from which queries are generated; and (2) use the demonstrations to ground the (active) query generation process, to improve the quality of the generated queries. Our method alleviates the efficiency issues faced by standard preference-based learning methods and does not exclusively depend on (possibly low-quality) demonstrations. In numerical experiments, we find that DemPref is significantly more efficient than a standard active preference-based learning method. In a user study, we compare our method to a standard IRL method; we find that users rated the robot trained with DemPref as being more successful at learning their desired behavior, and preferred to use the DemPref system (over IRL) to train the robot.
Heating of the Solar Wind by Ion Acoustic Waves

Calculations are made of the energy supplied to the solar wind by the rapid decay of density fluctuations, identified as ion acoustic waves. It is shown that this process supplies an appreciable fraction, perhaps nearly all, of the observed heating of the solar wind. This process may be an important step in the conversion of magnetic turbulence to particle energy.
Synthesis of Asynchronous Controllers for Heterogeneous Systems

There are two synchronization mechanisms used in digital systems: synchronous and asynchronous. Synchronous or asynchronous refers to whether the system events occur in lock-step based on a clock or not. Today''s system components typically employ the synchronous paradigm primarily because of the availability of the rich set of design tools and algorithms and, perhaps, because of the designers'' perception of ``ease of design'''' and the lack of alternatives. Even so, the interfaces among the system components do not strictly adhere to the synchronous paradigm because of the cost benefit of mixing modules operating at different clock rates and modules with asynchronous interfaces. This thesis addresses the problem of how to synthesize controllers operating in heterogeneous systems - systems with components employing different synchronization mechanisms. We introduce a new design style called extended-burst-mode. The extended-burst-mode design style covers a wide spectrum of sequential circuits ranging from delay-insensitive to synchronous. We can synthesize multiple-input change asynchronous finite state machines, and many circuits that fall in the gray area between synchronous and asynchronous which are difficult or impossible to synthesize automatically using existing methods. Our implementation of extended-burst-mode machines uses standard combinational logic, generates low-latency outputs and guarantees freedom from hazards at the gate level. We present a complete set of automated sequential synthesis algorithms: hazard-free state assignment, hazard-free state minimization, and critical-race-free state encoding. We also describe two radically different hazard-free combinational synthesis methods: two-level sums-of-products implementation and multiplexor trees implementation. Existing theories for hazard-free combinational synthesis are extended to handle non-monotonic input changes. A set of requirements for freedom from logic hazards is presented for each combinational synthesis method. Experimental data from a large set of examples are presented and compared to competing methods, whenever possible. To demonstrate the effectiveness of the design style and the synthesis tool, the design of a commercial-scale SCSI controller data path is presented. This design is functionally compatible with an existing high performance commercial chip and meets the ANSI SCSI-2 standard.
An intelligent heuristic search method for flow-shop problems

The flow-shop problem has been a problem for production management ever since the manufacturing industry began. This problem involves the scheduling of production processes for manufacturing orders (referred to as jobs), where each job requires the same processing sequence and thus the same processing facilities (referred to as machines), although the processing time for a job on a machine may be different from that of a different job. The objective is to find a schedule that can complete all the jobs in the shortest possible time under the constraints of machine availability. The fact that more than one job may be chosen to be processed on a machine at any one time has made this problem combinatorial in nature. The most unwanted characteristic of a combinatorial problem is the explosive growth of the solution space and hence the computation time. In mathematical terms, the number of possible schedules for n jobs and m machines is (n\). Early research in this area is mainly based on Johnson's theorem (Johnson, 1954). For flow-shop problems with only two machines or three machines with certain characteristics and an arbitrary number of jobs, Johnson's theorem provides simple procedures for finding an optimal solution. The optimal solution is the sequence of an arbitrary number of jobs that will minimise the maximum flow-time (makespan or schedule time). Unfortunately, this theorem does not extend to general three-machine problems or problems involving more than three machines. The aim of this research is to define a new solution procedure (algorithm) for finding an optimal solution for the three-machine flow-shop problem. In this research, a significant contribution has been made by developing an algorithm that can solve
Management gurus and management fashions : a dramatistic inquiry

Since the 1980s, popular management thinkers, or management gurus, have promoted a number of performance improvement programs or management fashions that have greatly influenced both the pre-occupations of academic researchers and the everyday conduct of organizational life. This thesis provides a rhetorical critique of the management guru and management fashion phenomenon with a view to building on the important theoretical progress that has recently been made by a small, but growing, band of management researchers. Fantasy theme analysis, a dramatistically-based method of rhetorical criticism, is conducted on three of the most important management fashions to have emerged during the 1990s: the reengineering movement promoted by Michael Hammer and James Champy; the effectiveness movement led by Stephen Covey; and the learning organization movement inspired by Peter Senge and his colleagues. The study discovers that the rhetorical visions for all three movements possess strong dramatic qualities. It suggests that managers and other followers find these visions compelling because they can readily relate to the gurus' organizational dramas which contain a familiar cast of characters, a clear and well-developed plot line and a meaningful setting. While they share strong dramatic qualities, the underlying rhetorical appeal of each vision is rooted in three quite distinctive master analogues or deep structures: pragmatic (reengineering), righteous (effectiveness) and social (learning organization). In addition to its theoretical and empirical contributions, it is hoped that this study will help to stimulate a critical dialogue between practitioners and academics about the sources of the underlying appeal of management gurus and management fashions and their effect upon the quality of management and organizational learning.
Analysis of MRC receivers for asynchronous MC-CDMA with channel estimation errors

The combination of code-division multiple-access (CDMA) and multicarrier modulation has been proposed for high data-rate wireless communication systems. In this paper, a novel practical technique based on alternative Gaussian approximation (AGA) for determining the bit error rate (BER) of maximal-ratio combining (MRC) receivers for MC-CDMA systems in frequency selective Rayleigh fading channels is presented. The impacts of the channel estimation errors are also taken into account in both the conventional MRC receivers and MRC based parallel interference cancellation (PIC) receivers. The analysis assumes that different subcarriers experience independent fading channels. The accuracy of the proposed approach based on AGA is verified by computer simulations. The results of this paper can be applied to link design of the future MC-CDMA based wireless communication systems.
Documentary and Corpus Approaches to Sign Language Research

In this chapter, we discuss some key aspects of methodology associated with sign language documentation and corpus based approaches to sign language research. We first introduce the field of sign language corpus linguistics, carefully defining the term ‘corpus’ in this context, and discussing the emergence of technology that has made this new approach to sign language research possible. We then discuss specific details of the methodology involved in corpus building, such as the recruitment of participants, the selection of language activities for the corpus, and the set up for filming. We move onto a discussion of annotation for corpora, with a focus on the use of ID glossing. We close with a brief discussion of online archiving and accessibility.