Miracle-dz/UniFAR-FTUs huggingface dataset:
Covariate-adjusted nonlinear regression

In this paper, we propose a covariate-adjusted nonlinear regression model. In this model, both the response and predictors can only be observed after being distorted by some multiplicative factors. Because of nonlinearity, existing methods for the linear setting cannot be directly employed. To attack this problem, we propose estimating the distorting functions by nonparametrically regressing the predictors and response on the distorting covariate; then, nonlinear least squares estimators for the parameters are obtained using the estimated response and predictors. Root n-consistency and asymptotic normality are established. However, the limiting variance has a very complex structure with several unknown components, and confidence regions based on normal approximation are not efficient. Empirical likelihood-based confidence regions are proposed, and their accuracy is also verified due to its self-scale invariance. Furthermore, unlike the common results derived from the profile methods, even when plug-in estimates are used for the infinite-dimensional nuisance parameters (distorting functions), the limit of empirical likelihood ratio is still chi-squared distributed. This property eases the construction of the empirical likelihood-based confidence regions. A simulation study is carried out to assess the finite sample performance of the proposed estimators and confidence regions. We apply our method to study the relationship between glomerular filtration rate and serum creatinine.
Design and torque-mode control of a cable-driven rotary series elastic actuator for subject-robot interaction

To achieve a compact, lightweight and compliant actuator design for intrinsically safe subject-robot interactions, a Bowden cable-driven rotary series elastic actuator is proposed in this paper. To realize the zero output torque control of the proposed actuator in the presence of variable friction of the Bowden cable, a disturbance observer based torque-mode control algorithm is developed and analyzed. The effectiveness of the proposed design is verified by experiments with a human subject.
Authoring Illustrations of Human Movements by Iterative Physical Demonstration

Illustrations of human movements are used to communicate ideas and convey instructions in many domains, but creating them is time-consuming and requires skill. We introduce DemoDraw, a multi-modal approach to generate these illustrations as the user physically demonstrates the movements. In a Demonstration Interface, DemoDraw segments speech and 3D joint motion into a sequence of motion segments, each characterized by a key pose and salient joint trajectories. Based on this sequence, a series of illustrations is automatically generated using a stylistically rendered 3D avatar annotated with arrows to convey movements. During demonstration, the user can navigate using speech and amend or re-perform motions if needed. Once a suitable sequence of steps has been created, a Refinement Interface enables fine control of visualization parameters. In a three-part evaluation, we validate the effectiveness of the generated illustrations and the usability of DemoDraw. Our results show 4 to 7-step illustrations can be created in 5 or 10 minutes on average.
The matter of the stick: Storying/(re)storying children’s literacies in the forest

Children's intra-actions with the natural world offer an important lens to revisit notions of literacies. They allow for a decentring of humans – here children – as actors. Also, forest schools and nature-based learning programmes are increasingly erupting across North America, although more commonplace in Europe for a longer period. In this presentation of our research, we feature a storying/(re)storying of data from a yearlong research study of children's entanglements with the forest as a more-than-human world. We ask what we might learn if educators, children and researchers think with sticks, not separate from, but in relation to sticks? Eight preschool children, two educators and two researchers ventured into the forest twice a week over the course of a year, documenting their interactions with a mosaic of data generation tools, such as notebooks, iPads, Go-Pro cameras. The forest offered diverse materials that provoked “thing-matter-energy- child-assemblages” that were significant for the children's play and literacy framing. Through post-humanist theorizing, we have paid particular attention to the stick within the children's forest play and illustrate the ways in which the stick was entangled with children’s bodies, relations, identities and discourses. The stick was a catalyst, a friend, a momentary and changing text, an agentic force acting relationally with children's play and stories. The post humanism storying/(re)storying of the children's encounters in the forest with sticks invites infinite possibilities for literacy teaching and learning. How might educators foster such relations, enquiring with and alongside children with an openness toward what the sticks (forests) might teach us?
Are binary codings universal?

It is common sense to notice that one needs fewer digits to code numbers in ternary than in binary; new names are about log32 times shorter. Is this trade-off a consequence of the special coding scheme? The answer is negative. More generally, we argue that the answer to the question, stated in the title and formulated to the first author by C. Rackhoff, is in fact negative. The conclusion will be achieved by studying the role of the size of the alphabet in constructing instantaneous codes for all natural numbers, and defining random strings and sequences. We show that there is no optimal instantaneous code for all positive integers, and the binary is the worst possible. Codes over a fixed alphabet can be indefinitely improved themselves, but only “slightly”; in contrast, changing the size of the alphabet determines a significant, not linear, improvement. The key relation describing the above phenomenon can be expressed in terms of Chaitin complexity: changing the size of the coding alphabet from q to Q, 2 ≤ q < Q, results in an improvement of the complexity by a factor og log q. As a consequence, a string avoiding consistently a fixed letter is not random. In binary, this corresponds to a trivial situation. In the nonbinary case the distinction is relevant: more than 3.2n ternary strings of length n are not random (many of these strings are binary random). This phenomenon is even sharper for infinite sequences.
Net and pin distribution for 3D package global routing

In this paper, we study the net and pin distribution problem for global routing targeting three dimensional packaging layout via system-on-package (SOP). The routing environment for the new emerging mixed-signal SOP technology is more advanced than that of the conventional PCB or MCM technology - pins are located at all layers of SOP packaging substrate rather than the top-most layer only. This is the first work to formulate and solve the multi-layer net and pin distribution for layer, wirelength, and crosstalk minimization.
TRAF Family Proteins Link PKR with NF-κB Activation

ABSTRACT The double-stranded RNA (dsRNA)-dependent protein kinase PKR activates NF-κB via the IκB kinase (IKK) complex, but little is known about additional molecules that may be involved in this pathway. Analysis of the PKR sequence enabled us to identify two putative TRAF-interacting motifs. The viability of such an interaction was further suggested by computer modeling. Here, we present evidence of the colocalization and physical interaction between PKR and TRAF family proteins in vivo, as shown by immunoprecipitation and confocal microscopy experiments. This interaction is induced upon PKR dimerization. Most importantly, we show that the binding between PKR and TRAFs is functionally relevant, as observed by the absence of NF-κB activity upon PKR expression in cells genetically deficient in TRAF2 and TRAF5 or after expression of TRAF dominant negative molecules. On the basis of sequence information and mutational and computer docking analyses, we favored a TRAF-PKR interaction model in which the C-terminal domain of TRAF binds to a predicted TRAF interaction motif present in the PKR kinase domain. Altogether, our data suggest that TRAF family proteins are key components located downstream of PKR that have an important role in mediating activation of NF-κB by the dsRNA-dependent protein kinase.
LAZY LEARNING : A LOCAL METHOD FOR SUPERVISED LEARNING

The traditional approach to supervised learning is global modeling which describes the relationship between the input and the output with an analytical function over the whole input domain. What makes global modeling appealing is the nice property that even for huge datasets, a parametric model can be stored in a small memory. Also, the evaluation of the parametric model requires a short program that can be executed in a reduced amount of time. Nevertheless, modeling complex input/output relations often requires the adoption of global nonlinear models, whose learning procedures are typically slow and analytically intractable. In particular, validation methods, which address the problem of assessing a global model on the basis of a finite amount of noisy samples, are computationally prohibitive. For these reasons, in recent years, interest has grown in pursuing alternatives to global modeling techniques. A demonstration is the popularity which approaches based on the divide-and-conquer strategy have gained in the research community. The divide-andconquer strategy consists in attacking a complex problem by dividing it into simpler problems whose solutions can be combined to yield a solution to the original problem. Instances of the divide-and-conquer approach are local modeling techniques. These techniques do not fit the whole dataset but perform the prediction of the output for specific test input values, also called queries. For that purpose, the database of observed input/output data is always kept in memory and the output prediction is obtained by interpolating the samples in the neighborhood of the query point. " # $ % & ' )(* +, -. & -/ # This chapter presents a set of novel local modeling techniques, called Lazy Learning techniques, and their application to a number of experimental problems. We show that, once a Lazy Learning perspective is adopted, the use of conventional linear techniques in nonlinear settings is easy and effective. A local Lazy Learning technique makes possible the adoption of linear methods to the widest range, not simply in the parametric identification procedure but also in the model validation step. In terms of applications, we show that promising areas of application for these algorithms are regression modeling and data mining. Experimental results on simulated and real-world tasks, as well as the successful participation to two international competitions for learning techniques, demonstrate that these new techniques are competitive with existing methods and can be considered as a potential alternative to state-of-the-art approaches in a number of practical domains.
Music information retrieval research

There is a long history of music librarianship in the domain of printed Western classical music. Special schemes have been developed to aid in the organisation and retrieval of musical works, and existing schemes have been widely used to include these types of documents in larger physical library collections. However the advent of digital consumer technology in the form of MP3 players and mobile phones, combined with the enormous impact of the internet and the digitisation and ease of compression of audio files has brought new formats and types of user interaction to the fore. This has led to an explosion in music information retrieval research, concentrating on how to most beneficially use computers to organise, search, and retrieve music information and recordings from large digital collections. Many of us today carry around music collections of thousands of digitised music recordings and access all manner of types of music on the web but still are unsure what to listen to next – the enormous size of these collections and the instant accessibility of 8 million Western pop, classical, jazz and folk songs can cause confusion and trepidation. Where the classical music researcher would previously consult academic texts and visit a specialist music library, or the post-rock listener would read NME and visit the Rough Trade shop for advice on what's coming up, now we access music through hand-held devices and laptops. The issue is no longer 'I hope I can find that Velvet Underground live album somewhere this year, I wonder what it sounds like' but 'which Velvet Underground live track shall I read about / download / stream now? '. Efficient and effective automated retrieval tools can help users access globally distributed and personal digital music libraries. Recent research in this area generally involves extracting musical features from audio files and using these features to make comparisons between pieces of music to determine whether or not they are similar. Enormous data resources from internet useage and unfeasibly large music collections are used for machine learning to enable autotagging. The web is trawled for socio-cultural information, and algorithms are developed and tested by a thriving scientific community. Although some of this work is hampered by the fear of breaking copyright regulations great steps are being made to allow users to engage more actively with digital music recordings in almost utopian ways. This chapter examines how the special nature of music impacts …
A Quantity Based Aggregation Control Model for Graph Databases

The Quantity Based Aggregation (QBA) policy is very simple, yet understudied by the scientific community. The QBA policy can be stated as follows: given a set of sensitive objects, no more than a limited number of these objects should be disclosed. In [1], [2], the authors defined a QBA control model for relational databases supporting projection-selection queries on single relations. In [3]–[6], the authors defined a QBA control model for (cadastral) graph databases supporting point & click queries. The work we present in this paper can be seen as both an interpretation for graph datasets of the model defined in [1], [2], a generalization of the model defined in [3]–[6] to arbitrary queries. We use Prolog to specify, develop a prototype our model.
National survey on intra-laboratory turnaround time for some most common routine and stat laboratory analyses in 479 laboratories in China

Introduction To investigate the state of the art of intra-laboratory turnaround time (intra-TAT), provide suggestions and find out whether laboratories accredited by International Organization for Standardization (ISO) 15189 or College of American Pathologists (CAP) will show better performance on intra-TAT than non-accredited ones. Materials and methods 479 Chinese clinical laboratories participating in the external quality assessment programs of chemistry, blood gas, and haematology tests organized by the National Centre for Clinical Laboratories in China were included in our study. General information and the median of intra-TAT of routine and stat tests in last one week were asked in the questionnaires. Results The response rate of clinical biochemistry, blood gas, and haematology testing were 36% (479 / 1307), 38% (228 / 598), and 36% (449 / 1250), respectively. More than 50% of laboratories indicated that they had set up intra-TAT median goals and almost 60% of laboratories declared they had monitored intra-TAT generally for every analyte they performed. Among all analytes we investigated, the intra-TAT of haematology analytes was shorter than biochemistry while the intra-TAT of blood gas analytes was the shortest. There were significant differences between median intra-TAT on different days of the week for routine tests. However, there were no significant differences in median intra-TAT reported by accredited laboratories and non-accredited laboratories. Conclusions Many laboratories in China are aware of intra-TAT control and are making effort to reach the target. There is still space for improvement. Accredited laboratories have better status on intra-TAT monitoring and target setting than the non-accredited, but there are no significant differences in median intra-TAT reported by them.
First Experimental In Vivo Model of Enhanced Dengue Disease Severity through Maternally Acquired Heterotypic Dengue Antibodies

Dengue (DEN) represents the most serious arthropod-borne viral disease. DEN clinical manifestations range from mild febrile illness to life-threatening hemorrhage and vascular leakage. Early epidemiological observations reported that infants born to DEN-immune mothers were at greater risk to develop the severe forms of the disease upon infection with any serotype of dengue virus (DENV). From these observations emerged the hypothesis of antibody-dependent enhancement (ADE) of disease severity, whereby maternally acquired anti-DENV antibodies cross-react but fail to neutralize DENV particles, resulting in higher viremia that correlates with increased disease severity. Although in vitro and in vivo experimental set ups have indirectly supported the ADE hypothesis, direct experimental evidence has been missing. Furthermore, a recent epidemiological study has challenged the influence of maternal antibodies in disease outcome. Here we have developed a mouse model of ADE where DENV2 infection of young mice born to DENV1-immune mothers led to earlier death which correlated with higher viremia and increased vascular leakage compared to DENV2-infected mice born to dengue naïve mothers. In this ADE model we demonstrated the role of TNF-α in DEN-induced vascular leakage. Furthermore, upon infection with an attenuated DENV2 mutant strain, mice born to DENV1-immune mothers developed lethal disease accompanied by vascular leakage whereas infected mice born to dengue naïve mothers did no display any clinical manifestation. In vitro ELISA and ADE assays confirmed the cross-reactive and enhancing properties towards DENV2 of the serum from mice born to DENV1-immune mothers. Lastly, age-dependent susceptibility to disease enhancement was observed in mice born to DENV1-immune mothers, thus reproducing epidemiological observations. Overall, this work provides direct in vivo demonstration of the role of maternally acquired heterotypic dengue antibodies in the enhancement of dengue disease severity and offers a unique opportunity to further decipher the mechanisms involved.
The Image Quality and Radiation Dose of 100-kVp versus 120-kVp ECG-Gated 16-Slice CT Coronary Angiography

Objective This study was conducted to assess the feasibility of performing 100-kVp electrocardiogram (ECG)-gated coronary CT angiography, as compared to 120-kVp ECG-gated coronary CT angiography. Materials and Methods We retrospectively evaluated one hundred eighty five gender- and body mass index-matched 16-slice coronary CT sets of data, which were obtained using either 100 kVp and 620 effective mAs or 120 kVp and 500 effective mAs. The density measurements (image noise, vessel density, signal-to-noise ratio [SNR] and contrast-to-noise ratio [CNR]) and the estimated radiation dose were calculated. As a preference test, two image readers were independently asked to choose one image from each pair of images. The results of both protocols were compared using the paired t-test or the Wilcoxon signed rank test. Results The 100-kVp images showed significantly more noise and a significantly higher vessel density than did the 120-kVp images. There were no significant differences in the SNR and CNR. The estimated reduction of the radiation dose for the 100-kVp protocol was 24%; 7.8 ± 0.4 mSV for 100-kVp and 10.1 ± 1.0 mSV for 120-kVp (p < 0.001). The readers preferred the 100-kVp images for reading (reader 1, p = 0.01; reader 2, p = 0.06), with their preferences being stronger when the subject's body mass index was less than 25. Conclusion Reducing the tube kilovoltage from 120 to 100 kVp allows a significant reduction of the radiation dose without a significant change in the SNR and the CNR.
Point cloud segmentation using gradient vector flow snake

This paper presents a new method for extracting point cloud of useful main surface from 3D point cloud of an existing product or part. The 3D point cloud is sliced into 2D point cloud at different slices. For each slice, gradient vector flow (GVF) is generated. An active contour or snake is applied to capture the point cloud belonging to main surface. Points belonging to mini-structures or mini-features are repaired. Experimental results demonstrate the robustness and the effectiveness of the proposed algorithm.
ALLIANCE: an architecture for fault tolerant, cooperative control of heterogeneous mobile robots

This research addresses the problem of achieving fault tolerant cooperation within small- to medium-sized teams of heterogeneous mobile robots. We describe a novel behavior-based, fully distributed architecture, called ALLIANCE, that utilizes adaptive action selection to achieve fault tolerant cooperative control in robot missions involving loosely coupled, largely independent tasks. The robots in this architecture possess a variety of high-level functions that they can perform during a mission, and must at all times select an appropriate action based on the requirements of the mission, the activities of other robots, the current environmental conditions, and their own internal states. The software architecture allows the team members to respond robustly and reliably to unexpected environmental changes and modifications in the robot team that may occur due to mechanical failure, the learning of new skills, or the addition or removal of robots from the team by human intervention. After presenting ALLIANCE, we describe in detail our experimental results of an implementation of this architecture on a team of physical mobile robots performing a cooperative box pushing demonstration. <<ETX>>
AZALIA: an A to Z assessment of the likelihood of insider attack

The insider threat problem is increasing, both in terms of the number of incidents and their financial impact. To date, solutions have been developed to detect specific instances of insider attacks (e.g., fraud detection) and therefore use very limited information for input. In this paper we describe an architecture for an enterprise-level solution that incorporates data from multiple sources. The unique aspects of this solution include the prioritization of resources based on the business value of the protected assets, and the use of psychological indicators and language affectation analysis to predict insider attacks. The goal of this architecture is not to detect that insider abuse has occurred, but rather to determine how to prioritize monitoring activities, giving priority to scrutinizing those whose background includes access to key combinations of assets as well as those psychological/other factors that have in the past been associated with malicious insiders.
Multicriteria Reasoning Considering Reliability or Availability

Reliability and/or availability are increasingly important aspects in the design of systems, especially networks and service offerings. Optimization here is a multi-criteria process finding the right compromise between cost and quality. At the same time, results should be explored online, either as part of interactive, user-centric design tools or for Web based service negotiation. In this paper we consider a generic method for multi-criteria optimization based on reliability/availability graphs, but also provide a more specialized solution for dealing with the widely used reliability/availability block diagrams. Initial experiments show that the techniques can be applied to realistic problem sizes from the literature.