Miracle-dz/UniFAR-FTUs huggingface dataset:
Issues in Model-Based Flow Control

This report examines potential stability problems associated with a model based apporach to TCP-friendly flow control for non-TCP traffic. In specific, such an approach involves using a TCP-friendly formula that estimates the throughput of a TCP session with the same end-to-end traffic characteristics as the non-TCP connection under consideration. The inputs to this formula include the round trip time, the timeout value, and the packet loss fraction of the connection. This paper shows that estimating the loss fraction per transmitted packet highly depends on the current transmission rate of the connection as well as the actual loss fraction of the path. Thus the estimated loss fraction can contain errors which result in inaccurate estimation of the corresponding TCP throughput. This inaccuracy can push the transmission rate of the non-TCP connection away from the fair share of the bottleneck bandwidth on the end-to-end path, so that under steady state, the connection ends up receiving either over-allocation or under-allocation of bandwidth.
Filtering on hidden Markov models

In this letter, we propose a novel approach to adapt the hidden Markov model (HMM) parameters when the original feature vector sequences are transformed by a causal finite impulse response (FIR) filter. Our approach enables us to be free from the requirement of retraining the whole recognition parameters when the feature vectors are changed and makes it sufficient to adapt the parameters to the given FIR filter coefficients. Performance of the proposed technique is evaluated and compared to that of the retrained HMM parameters based on the continuous digit recognition experiments.
Designing Scalable Partitioned Architectures

A clustered or partitioned microarchitecture implements a number of small computational tiles, connected by an interconnect fabric. The small size of each tile (cluster) enables high clock speed. The tiles together support a large window of in-flight instructions for a program, thereby also enabling high instruction-level parallelism (ILP). A billion-transistor processor of the future can potentially accommodate hundreds of tiles and a comparable number of independent threads. Such systems will likely have to employ intelligent allocation of processor resources across threads in order to maximize throughput. Before such allocation policies can be considered, it is important to understand the scalability (or lack of scalability) of single-thread performance as computational resources are increased. This paper identifies two significant bottlenecks for highly-clustered microarchitectures: inter-cluster register communication and latencies to access a centralized data cache. To alleviate these bottlenecks, we first examine a technique that balances the communication-parallelism trade-off by dynamically enabling clusters depending on the program’s ILP characteristics. To tolerate the long latency for L1 cache access, we evaluate and compare the effects of cache decentralization and prefetch from the L1 cache. We show that the combination of all of these techniques can yield a performance improvement of 16% for
Survey and Comparative Analysis on Entropy Usage for Several Applications in Computer Vision

This paper presents a thorough study of different types of entropies. Application and comparison of various entropies have been considered with their effectiveness and suitability in different applications being explored. The usage of entropy in the fields of image thresholding, image reconstruction, image segmentation, and incorporation of entropy in tackling real life problems have been mentioned categorically. A comparative analysis of different forms of entropy accordingly to their suitability for various applications has been discussed.
A Survey of Parallelism in Communication Subsystems

This paper surveys parallelism in communication subsystems. A model that describes parallelism in terms of its form and its level, or granularity , is presented. The model describes high–level par allelism in software as well as low–level, or fine–grain parallelism in dedicated hardware. Major projects in the literature are categorized in this model. The forms of parallelism and critical per formance issues in parallel protocol implementations are discussed.
A New MLS Chaotic System and Its Sliding Mode Synchronization Control

A new four-dimensional system is proposed in this paper, which is named modified Lorenz-Stenflo (MLS) system. The system is constructed by replacing one of quadratic nonlinear terms of Lorenz-Stenflo system with a PWL function. The simple structure makes the MLS system much easier to be realized with electronic circuits. Based on adaptive control and sliding mode control methods, an adaptive sliding mode controller is derived to synchronize two MLS chaotic systems. Meanwhile the uncertain parameters in response system are identified. The error systems are deduced by backstepping method. Numerical simulations are used to verify the effectiveness of the obtained controller.
Role expectations and agency in the audit tendering process

Purpose - Tenders are generally considered important in auditing research and practice, and are associated with significant difficulties for the auditing profession, as well as for individual auditors in the context of client relationships. The purpose of this paper is to explore the way auditors respond to complex client expectations related to the audit tendering process. Design/methodology/approach - Using a role-theory perspective, the authors analyzed 75 client evaluations of auditors, along with in-depth interviews with high-level auditors. Findings - The authors present a theoretical framework of audit tenders that identifies different formal and informal practices auditors may employ in response to evolving client expectations that arise throughout different phases of the tendering process, and elucidate relevant conditions that may enable or impede these practices. Research limitations/implications - First, the study reveals that, in the context of auditor-client relationships, expectations cannot be considered stable, but may vary significantly throughout the tendering process. Second, the study indicates that auditors are not only determined by the formal tendering procedures, but are also influenced by their level of agency. Practical implications - The framework has practical value by providing individual auditors guidance in managing their client relationships and, further, top management guidance for creating favorable conditions for auditors to meet client needs. Originality/value - The findings provide a deeper and more nuanced understanding of the autonomy auditors have in responding to the contemporary pressures exerted on them.
Reducing the Logistical Footprint of Forward Resuscitative Surgical Units Using a Patient-Driven Model of Clinical Events.

Abstract : Moving towards a concept of medical organization that achieves the objectives of Marine Corps guidance such as Operational Maneuver from the Sea brings particular challenges when examining forward resuscitative surgical units. Surgical units must be small enough to perform abbreviated, staged surgical procedures in forward areas to accommodate today's operational requirements for greater mobility. The primary objective of this paper was to reduce the surgical footprint by developing a model of the far forward medical supply stream that linked specific clinical requirements to individual medical treatment items. One hundred fifteen clinical Subject Matter Experts (SMEs) with operational experience assisted in the development of Marine Corps specific surgical treatment profiles for 72 of the 350 DEPMEDS Patient Conditions (PCs). Medical consumables and equipment were then assigned to each of the operating room tasks, establishing the clinical requirement for each item. A patient generating model (PATGEN) was used to determine which PCs would present as candidates for forward surgery. This information was loaded into the model and used to generate the medical items required for the operating room equipment AMAL 639 and the operating room consumable AMAL 640. When compared to the current Marine Corps operating room AMALs, the proposed AMALs resulted in reductions of 15.0% in weight and 13.2% in cube for the equipment AMAL 639 and 22.4% reductions in weight and 36.3% in cube for the consumable AMAL 640. This approach, which also resulted in an increase in operating room clinical capability, produced an audit trail for each item that allows medical logisticians to substantially improve the AMAL configurations because only items that can be clinically related to a treatment task conducted in theater are considered for inclusion in the AMALs.
A generalization of Amdahl's law and relative conditions of parallelism

In this work I present a generalization of Amdahl's law on the limits of a parallel implementation with many processors. In particular I establish some mathematical relations involving the number of processors and the dimension of the treated problem, and with these conditions I define, on the ground of the reachable speedup, some classes of parallelism for the implementations. I also derive a condition for obtaining superlinear speedup. The used mathematical technics are those of differential calculus. I describe some examples from classical problems offered by the specialized literature on the subject.
Cascaded Multilevel Inverter With Regeneration Capability and Reduced Number of Switches

Multilevel converters are a very interesting alternative for medium and high power drives. One of the more flexible topologies of this type is the cascaded multicell converter. This paper proposes the use of a single-phase reduced cell suitable for cascaded multilevel converters. This cell uses a reduced single-phase active rectifier at the input and an H-bridge inverter at the output side. This topology presents a very good performance, effectively controlling the waveform of the input current and of the output voltage and allowing operation in the motoring and regenerative mode. The results presented in this paper confirm that this medium voltage inverter effectively eliminates low frequency input current harmonics at the primary side of the transformer and operates without problems in regenerative mode.
Visual target tracking via weighted non-sparse representation and online metric learning

In this paper, we propose online metric learning tracking method that consider visual tracking as a similarity measurement problem, and incorporates adaptive metric learning and generative histogram model based on non-sparse linear representation into the target tracking framework. We propose a generative histogram model based on non-sparse linear representation, which make full use of the non-sparse coefficients to discriminate between the target and the background. The similarity metric is adaptively learned online to maximize the margin of the distance between the foreground target and background. A bi-linear graph is defined accordingly to propagate the label of each sample. The model can also self-update using the more confident new samples. Numerous experiments on various challenging videos demonstrate that the proposed tracker performs favorably against several state-of-the-art algorithms.
Introduction to the ISO Specification Language LOTOS 25

LOTOS is a specification language that has been specifically developed for the formal description of the OSI (Open Systems Interconnection) architecture, although it is applicable to distributed, concurrent systems in general. In LOTOS a system is seen as a set of processes which interact and exchange data with each other and with their environment. LOTOS is expected to become an ISO international standard by 1988.
Towards a synthesis semantics for SystemC channels

In this paper we propose a synthesis semantics for SystemC™ channels, which contribute to a clear separation between computation (algorithm) and communication, whereas communication related parts are modelled through either primitive or hierarchical channels. We present a synthesisable replacement for SystemC primitive channels that allows deterministic access scheduling and user-constrained refinement for HW/HW and HW/SW communication. We demonstrate the feasibility of our approach through synthesis and exploration of a communication intensive packet switch design under consideration of different configurations and communication refinements.
Profiling Cellular Protein Complexes by Proximity Ligation with Dual Tag Microarray Readout

Patterns of protein interactions provide important insights in basic biology, and their analysis plays an increasing role in drug development and diagnostics of disease. We have established a scalable technique to compare two biological samples for the levels of all pairwise interactions among a set of targeted protein molecules. The technique is a combination of the proximity ligation assay with readout via dual tag microarrays. In the proximity ligation assay protein identities are encoded as DNA sequences by attaching DNA oligonucleotides to antibodies directed against the proteins of interest. Upon binding by pairs of antibodies to proteins present in the same molecular complexes, ligation reactions give rise to reporter DNA molecules that contain the combined sequence information from the two DNA strands. The ligation reactions also serve to incorporate a sample barcode in the reporter molecules to allow for direct comparison between pairs of samples. The samples are evaluated using a dual tag microarray where information is decoded, revealing which pairs of tags that have become joined. As a proof-of-concept we demonstrate that this approach can be used to detect a set of five proteins and their pairwise interactions both in cellular lysates and in fixed tissue culture cells. This paper provides a general strategy to analyze the extent of any pairwise interactions in large sets of molecules by decoding reporter DNA strands that identify the interacting molecules.
Learning Shape, Motion and Elastic Models in Force Space

In this paper, we address the problem of simultaneously recovering the 3D shape and pose of a deformable and potentially elastic object from 2D motion. This is a highly ambiguous problem typically tackled by using low-rank shape and trajectory constraints. We show that formulating the problem in terms of a low-rank force space that induces the deformation, allows for a better physical interpretation of the resulting priors and a more accurate representation of the actual object's behavior. However, this comes at the price of, besides force and pose, having to estimate the elastic model of the object. For this, we use an Expectation Maximization strategy, where each of these parameters are successively learned within partial M-steps, while robustly dealing with missing observations. We thoroughly validate the approach on both mocap and real sequences, showing more accurate 3D reconstructions than state-of-the-art, and additionally providing an estimate of the full elastic model with no a priori information.
Critical Thinking Training for Army Officers. Volume Three. Development and Assessment of a Web-Based Training Program

Abstract : This is the third volume of a three-volume report describing a multi-year research program to develop and validate web based training in critical thinking for Army officers. The first volume presents an overview of the research effort that developed and validated a theoretical model for the training, selected and validated eight high impact critical thinking skills for Army officers, and developed and validated the training course. The second volume describes, in more detail, the model and theoretical basis for the training and the investigations that were conducted to validate the model. This, the third volume, describes the prototype training system that was developed for two of the skills, including the functional requirements, pedagogical principles, course content, and evaluation of the training. A succeeding report (Fischer, Spiker, Harris, McPeters, & Riedel, 2008) describes an expanded training system which provides training for eight critical thinking skills for Army officers.
Human and Machine Consciousness

Consciousness is widely perceived as one of the most fundamental, interesting and difficult problems of our time. However, we still know next to nothing about the relationship between consciousness and the brain and we can only speculate about the consciousness of animals and machines. Human and Machine Consciousness presents a new foundation for the scientific study of consciousness. It sets out a bold interpretation of consciousness that neutralizes the philosophical problems and explains how we can make scientific predictions about the consciousness of animals, brain-damaged patients and machines. Gamez interprets the scientific study of consciousness as a search for mathematical theories that map between measurements of consciousness and measurements of the physical world. We can use artificial intelligence to discover these theories and they could make accurate predictions about the consciousness of humans, animals and artificial systems. Human and Machine Consciousness also provides original insights into unusual conscious experiences, such as hallucinations, religious experiences and out-of-body states, and demonstrates how ‘designer’ states of consciousness could be created in the future. Gamez explains difficult concepts in a clear way that closely engages with scientific research. His punchy, concise prose is packed with vivid examples, making it suitable for the educated general reader as well as philosophers and scientists. Problems are brought to life in colourful illustrations and a helpful summary is given at the end of each chapter. The endnotes provide detailed discussions of individual points and full references to the scientific and philosophical literature.
Robust Analysis of Movie Earnings

This article applies recently developed nonparametric kernel regression estimation methods to quantify the conditional distribution of motion picture earnings. The nonparametric, data-driven approach allows the full range of relations among variables to be captured, including nonlinearities that usually remain hidden in parametric models. The nonparametric approach does not assume a functional form, so specification error is not an issue. This study finds that the nonparametric regression model fits the data far better than the logarithmic regression model employed by most applied researchers; it also fits the data much better than a polynomial regression model. The nonparametric model yields substantially different estimates of the elasticity of box-office revenue with respect to production budgets and opening screens, and the model also has very good out-of-sample predictive ability, making it a potentially useful tool for studio management.
Effect of Antibiotic Treatment on the Intestinal Metabolome

ABSTRACT The importance of the mammalian intestinal microbiota to human health has been intensely studied over the past few years. It is now clear that the interactions between human hosts and their associated microbial communities need to be characterized in molecular detail if we are to truly understand human physiology. Additionally, the study of such host-microbe interactions is likely to provide us with new strategies to manipulate these complex systems to maintain or restore homeostasis in order to prevent or cure pathological states. Here, we describe the use of high-throughput metabolomics to shed light on the interactions between the intestinal microbiota and the host. We show that antibiotic treatment disrupts intestinal homeostasis and has a profound impact on the intestinal metabolome, affecting the levels of over 87% of all metabolites detected. Many metabolic pathways that are critical for host physiology were affected, including bile acid, eicosanoid, and steroid hormone synthesis. Dissecting the molecular mechanisms involved in the impact of beneficial microbes on some of these pathways will be instrumental in understanding the interplay between the host and its complex resident microbiota and may aid in the design of new therapeutic strategies that target these interactions.
QACTIS-based Question Answering at TREC 2005

The QACTIS system is being developed for the eventual purpose of providing a user the capability of multilingual question-answering from multimedia. QACTIS was tested at TREC-2005 as a means of identifying its successes and limitations in answering questions specifically from English newswire text as it moves in the direction of multilingual, multimedia question answering. In this paper, we provide a complete overview of those parts of QACTIS which focus specifically on text question-answering, and we analyze the system’s performance at TREC-2005.
The Relative Performance of VAR and VECM Model

Two models are examined in this study: Vector Autoregressive Model (VAR) and Vector Error Correction Model (VECM). Based on three indices: S&P 500, Nikkei 225 (NIKKEI), and Morgan Stanley EAFE (MSCIEAFE or MSCI EAFE) Index, we implement VAR and VECM models, including the pre-estimation diagnostics, model estimation and interpretation and post-estimation tests, etc. By testing, we find that while the VECM model consistently outperforms the VAR model within sample, it is hard to tell which works better out-of-sample. Our examination suggests four conclusions: First, both models do the best job with MSCI EAFE. Second, VECM model does a better job within sample. Third, both models create less error out-of-sample than within sample. Fourth, VECM model does a better job than VAR in S&P 500 and NIKKEI.
Ranking with Unlabeled Data: A First Study

In this paper, we present a general learning framework which treats the ranking problem for various Information Retrieval tasks. We extend the training set generalization error bound proposed by Matti Kaariannan to the ranking case and show that the use of unlabeled data can be beneficial for learning a ranking function. We finally discuss open issues regarding the use of the unlabeled data during training a ranking function.
Facial expression classification based on local spatiotemporal edge and texture descriptors

Facial expressions are emotionally, socially and otherwise meaningful reflective signals in the face. Facial expressions play a critical role in human life, providing an important channel of nonverbal communication. Automation of the entire process of expression analysis can potentially facilitate human-computer interaction, making it to resemble mechanisms of human-human communication. In this paper, we present an ongoing research that aims at development of a novel spatiotemporal approach to expression classification in video. The novelty comes from a new facial representation that is based on local spatiotemporal feature descriptors. In particular, a combined dynamic edge and texture information is used for reliable description of both appearance and motion of the expression. Support vector machines are utilized to perform a final expression classification. The planned experiments will further systematically evaluate the performance of the developed method with several databases of complex facial expressions.
Evolution of black-hole intermediate-mass X-ray binaries

Abstract We propose a plausible mechanism for orbital angular momentum loss in black-hole intermediate-mass X-ray binaries, assuming that a small fraction of the transferred mass form a circumbinary disc. The disc can effectively drain orbital angular momentum from the binary, leading to the formation of compact black-hole low-mass X-ray binaries. This scenario also suggests the possible existence of luminous, persistent black hole low-mass X-ray binaries.