Miracle-dz/UniFAR-FTUs huggingface dataset:
Development and optimization of an injectable formulation of copper diethyldithiocarbamate, an active anticancer agent

Copper diethyldithiocarbamate (Cu(DDC)2) is the active anticancer agent generated when disulfiram (DSF) is provided in the presence of copper. To date, research directed toward repurposing DSF as an anticancer drug has focused on administration of DSF and copper in combination, efforts that have proven unsuccessful in clinical trials. This is likely due to the inability to form Cu(DDC)2 at relevant concentrations in regions of tumor growth. Little effort has been directed toward the development of Cu(DDC)2 because of the inherent aqueous insolubility of the complex. Here, we describe an injectable Cu(DDC)2 formulation prepared through a method that involves synthesis of Cu(DDC)2 inside the aqueous core of liposomes. Convection-enhanced delivery of a Cu(DDC)2 formulation prepared using 1,2-distearoyl-sn-glycero-3-phosphocholine (DSPC)/cholesterol liposomes into a rat model of F98 glioma engendered a 25% increase in median survival time relative to vehicle-treated animals. In a murine subcutaneous MV-4–11 model, treatment resulted in a 45% reduction in tumor burden when compared to controls. Pharmacokinetic studies indicated that the Cu(DDC)2 was rapidly eliminated after intravenous administration while the liposomes remained in circulation. To test whether liposomal lipid composition could increase Cu(DDC)2 circulation lifetime, a number of different formulations were evaluated. Studies demonstrated that liposomes composed of DSPC and 1,2-distearoyl-sn-glycero-3-phosphoethanolamine-n-(carboxy[polyethylene glycol]-2000) (95:5) enhanced Cu(DDC)2 concentrations in the circulation as reflected by a 4.2-fold increase in plasma AUC(0−∞) relative to the DSPC/cholesterol formulation. The anticancer activity of this Cu(DDC)2 formulation was subsequently evaluated in the MV-4–11 model. At its maximum tolerated dose, this formulation exhibited comparable activity to the DSPC/cholesterol formulation. This is the first report demonstrating the therapeutic effects of an injectable Cu(DDC)2 formulation in vivo.
An MPLS-based fat tree Network-on-Chip systems

This paper describes a Fat tree based Network-on-Chip system using MPLS networking technique. A discrete-event simulator has been developed in C++ in in order to demonstrate the applicability of the MPLS to Network-on-Chip systems. The correctness and the efficiency of the MPLS based Fat network-on-chip system is measured against well-known wormhole with virtual channels technique. The results of the MPLS-based NoC in terms of throughput and latency are clearly proves the applicability of the MPLS to implement future NoC designs.
Illumination suppression for illumination invariant face recognition

This paper describes a multiresolution based method for face recognition under illumination variation. The idea of using the double-density dual-tree complex wavelet transform (DD-DTCWT) for illumination invariant face recognition is motivated by the structure of the DD-DTCWT; in addition to the shift-invariance and directionality, the transformation contains more number of wavelets in each level. Assuming that an input image can be considered as a combination of illumination and reflectance, we use a tunable logarithmic function to obtain a representative image. The image is then decomposed into several frequency subbands via DD-DTCWT. Because the illumination mostly lies in the low-frequency part of the images, the high-frequency subbands are thresholded to construct a mask. Principal component analysis (PCA) and the extreme learning machine (ELM) are used for dimensionality reduction and classification, respectively. Experimental results are presented to illustrate the effectiveness of the proposed method.
Estimation of the Fire Interval Distribution for Los Angeles County, California

This paper describes a new method for estimating the fire interval distribution of a region using historical wildfire boundary data. The new procedure does not assume a specific parametric model and can adapt to various types of fire interval relationships. The estimator first averages the proportions of certain age classes which burn across time and weights these proportions by the amount of available fuel. Then, rather than fit known theoretical models, we use local linear smoothing methods to ascertain the overall relationship between fuel age and burning. In Los Angeles County, California, detailed information on wildfires has been made available through the use of geographic information systems (GIS) technology. The proposed procedure is applied to GIS data covering the years 1878 - 1996 and reveals an apparent nonlinear threshold - type relationship with burn area. The result is compared with two well - known parametric models and the relationship appears to conform to the well known Olson model for a fire interval distribution.
Improved Look-direction Interference Suppression in NLMS Generalized Sidelobe Canceler using Dynamic Weight Behavior

In an adaptive generalized sidelobe canceler interference signals are canceled when the reference inputs, or auxiliary beams, contain information about those interference signals while not containing information about the mainlobe signal of interest. Assuming a wide sense stationary scenario, the behavior and performance of this adaptive system approach the behavior and performance of the corresponding optimal (Wiener) solution as the adaptive filter stepsize approaches zero. We show here that, with narrowband interferers from the look-direction, time-varying adaptive weight behavior may lead to better performance – in terms of estimating the signal of interest – than that associated with the stationary Wiener solution.
Visualizing Dynamics: from t-SNE to SEMI-MDPs

Deep Reinforcement Learning (DRL) is a trending field of research, showing great promise in many challenging problems such as playing Atari, solving Go and controlling robots. While DRL agents perform well in practice we are still missing the tools to analayze their performance and visualize the temporal abstractions that they learn. In this paper, we present a novel method that automatically discovers an internal Semi Markov Decision Process (SMDP) model in the Deep Q Network's (DQN) learned representation. We suggest a novel visualization method that represents the SMDP model by a directed graph and visualize it above a t-SNE map. We show how can we interpret the agent's policy and give evidence for the hierarchical state aggregation that DQNs are learning automatically. Our algorithm is fully automatic, does not require any domain specific knowledge and is evaluated by a novel likelihood based evaluation criteria.
Target Coverage in Wireless Sensor Networks: A Learning Automata Approach

To cover a set of targets with known locations within an area with limited or prohibited ground access using a wireless sensor network, one approach is to deploy the sensors remotely, from an aircraft. In this approach, the lack of precise sensor placement is compensated by redundant deployment of sensor nodes. This redundancy can also be used for extending the lifetime of the network, if a proper scheduling mechanism is available for scheduling the active and sleep times of sensor nodes in such a way that each node be in active mode only if it is required to. In this paper, we propose an efficient scheduling method based on learning automata, in which each node is equipped with a learning automaton, which helps the node to select its proper state (active or sleep), at any given time. To study the performance of the proposed method, computer simulations are conducted. Results of these simulations show that the proposed scheduling method can better prolong the lifetime of the network in comparison to similar existing methods such as Greedy-MSC
Designing as if trust mattered

Historically, builders of CSCW tools have not focused on the creation of trust among users as a critical design goal. Yet, high levels of trust produce a number of benefits, including reduced coordination and communication costs. These benefits are particularly salient and desirable for geographically distributed workers. Therefore, to the extent that support for geographically distributed work is a key CSCW goal, CSCW tools should include features that enhance trust. This paper draws on the experience of a geographically distributed organization to identify aspects of long distance relationships associated with increased trust. Survey data collected within the organization showed that non-work interaction was positively correlated with trust to the same degree as more predictable factors, such as personal familiarity with distant sites and level of overarching shared group identity. In addition, interactive communication media had an unexpectedly positive relationship to non-work interaction, compared to less interactive media, which were negatively related. We conclude with a discussion of how CSCW tools can use these findings to improve production and maintenance of trust in geographically distributed organizations.
The Clinical and Prognostic Significance of Activated AKT-mTOR Pathway in Human Astrocytomas

Astrocytomas, the most common type of gliomas, and especially grade IV glioblastomas are “endowed” with strong proliferation and invasion potentials, high recurrence rate, and poor patients' prognosis. Aberrant signaling of AKT-mTOR (mammalian target of rapamycin) has been implicated in carcinogenesis. This paper is focused on the impact of deregulated AKT-mTOR signaling components in the clinical outcome and prognosis of human astrocytomas. Current therapeutic targeting of astrocytomas with AKT-mTOR inhibitors in preclinical and clinical stage is also discussed, including future perspectives regarding the management of these devastating tumors.
Representational Forms and Modes of Conception; an Approach to the History of Architectural Drawing

After briefly outlining several examples from recent historical research which begin to establish the relativity of design processes during several different epochs, this paper suggests that a new synthetic approach to the study of architectural representation and its influence on thought is needed to bridge the gap between architectural history, intellectual history and design methodology. Such an approach would be based upon the concept of studying relationships between representation, conception, and perception, or more simply, between modes of conception as they appear historically, and the particular forms of representation used by designers. Drawing, when studied as a language of thought rather than simply a medium of expression, may yield clues to the intellectual history of architecture which have gone unnoticed under standard techniques of historical inquiry.
Implementation of deep learning algorithm for automatic detection of brain tumors using intraoperative IR-thermal mapping data

The efficiency of deep machine learning for automatic delineation of tumor areas has been demonstrated for intraoperative neuronavigation using active IR-mapping with the use of the cold test. The proposed approach employs a matrix IR-imager to remotely register the space-time distribution of surface temperature pattern, which is determined by the dynamics of local cerebral blood flow. The advantages of this technique are non-invasiveness, zero risks for the health of patients and medical staff, low implementation and operational costs, ease and speed of use. Traditional IR-diagnostic technique has a crucial limitation - it involves a diagnostician who determines the boundaries of tumor areas, which gives rise to considerable uncertainty, which can lead to diagnosis errors that are difficult to control. The current study demonstrates that implementing deep learning algorithms allows to eliminate the explained drawback.
MRF solutions for probabilistic optical flow formulations

We propose an efficient, non-iterative method for estimating optical flow. We develop a probabilistic framework that is appropriate for describing the inherent uncertainty in the brightness constraint due to errors in image derivative computation. We separate the flow into two 1D representations and pose the problem of flow estimation as one of solving for the most probable configuration of 1D labels in an Markov random fields (MRF) with linear clique potentials. The global optimum for this problem can be efficiently solved for using the maximum flow computation in a graph. We develop this formulation and describe how the use of the probabilistic framework, the parametrisation and MRF formulation together enables one to capture the desirable properties for flow estimation, especially preserving motion discontinuities. We demonstrate the performance of our algorithm and compare our results with that of other algorithms described by Barron et. al. (1994).
Caring to Try: How Building Students’ Developmental Assets Can Promote School Engagement and Success

Both a sense of belonging and a belief in their own competency seem to be missing from the school experiences of disengaged, underachieving students. Building students’ developmental assets is a promising practice for reconnecting students and supporting achievement. Looking at schools through an assets lens can promote concrete changes in school organization, curriculum and instruction, cocurricular programs, support services, and community partnerships that make the whole environment more conducive to great teaching and learning.
Illumination and Reflection Maps : Simulated Objects in Simulated and Real Environments Gene

Blinn and Newell introduced reflection maps for computer simulated mirror highlights. This paper extends their method to cover a wider class of reflectance models. Panoramic images of real, painted and simulated environments are used as illumination maps that are convolved (blurred) and transformed to create reflection maps. These tables of reflected light values are used to efficiently shade objects in an animation sequence. Shaders based on point illumination may be improved in a straightforward manner to use reflection maps. Shading is by table-lookup, and the number of calculations per pixel is constant regardless of the complexity of the reflected scene. Antialiased mapping further improves image quality. The resulting pictures have many of the reality cues associated with ray-tracing but at greatly reduced computational cost. The geometry of highlights is less exact than in ray-tracing, and multiple surface reflections are not explicitly handled. The color of diffuse reflections can be rendered more accurately than in ray-tracing. CR
A Novel Passive Road Side Unit Detection Scheme in Vehicular Networks

The data dissemination and content delivery is a challenging research subject in the Internet of Things (IoT). Especially, in the Vehicular Networks environment, the network topology has the transient nature, due to the fast-moving velocity of the vehicles. One potential solution to this task is to improve the probability of the successful communication and content delivery. Hence, in this paper, we propose a novel and simple passive road side unit (RSU) localization scheme to estimate the location of the RSU, by which the vehicle can pre-determine the desired RSU to communicate based on its own position and routing information. By exploring the Doppler effects of the received signal, the RSU location estimator is derived by the maximum likelihood estimation method. Experimental results verify the correctness of the proposed estimation scheme.
A Psychoanalytic Discursive Psychology: from consciousness to unconsciousness

This article presents the position for a Psychoanalytic Discursive Psychology. This position combines two elements: an action-theory of language, derived from Wittgenstein’s later philosophy, and a revised Freudian concept of repression. According to Wittgenstein and most contemporary discursive psychologists, language is to be understood as action, rather than being assumed to be an outward expression of inner, unobservable cognitive processes. However, a critical approach demands more than an interactional analysis of language acts: it requires an analysis of ideology. Because what is left unsaid can be as ideologically important as what is said, there is a need to investigate socially reproduced unconsciousness. This means taking the notion of repression seriously. Whereas Freud imagined repression to be an inner psychic process, it can, by contrast, be seen as an activity that is constituted within everyday language. In this respect, language is fundamentally both expressive and repressive. The social and psychological significance of a Psychoanalytic Discursive Psychology are discussed.
Exploring subject positions and multiple temporalities through an Iranian migrant mother's family photograph albums

In this article, I draw on a research study of one Iranian migrant mother's generation of selves through her material configuration of her personal photograph albums and through our verbal reading of her photographs. The research engages in a visual-material feminist ethnographic approach, and is informed by the work of Gillian Rose and the understanding that family photographs are a means by which women negotiate subject positions. In this article, I discuss an unexpected finding of my research, the significance of multiple temporalities in a migrant mother's production of selves. The photo album practices of the participant mother of this study, ‘Parvin’, depart from the common social convention of mothers arranging their photograph albums to chart family growth following the model of milestones occurring over linear developmental time. Parvin does not limit herself to linear developmental time, but rather she mixes up photographs in her post-migration family albums to generate multiple temporalities woven together by an enveloping ‘mixed’ time. Drawing on both Julia Kristeva and Homi Bhaba's theories of temporality and the subject, I suggest that Parvin produces subject positions for self and family through a continual interweaving of a multitude of pasts into the present and through a subsuming of milestones within cyclical family time. Further, I suggest that through her generation of multiple temporalities, Parvin produces the subject position of ‘accommodating mother’. Finally, I highlight the potential afforded by considering the temporal and the spatial together in studies of migrant identity.
Gains in bank mergers : Evidence from the bond markets

We present evidence that the adjusted returns of merging banks’ bonds are positive and significant across pre-merger and announcement months. The cross-sectional evidence indicates that the primary determinants of merger-related bondholder gains are diversification gains, gains associated with achieving too-big-to-fail status, and, to a lesser degree, synergy gains. We obtain the same finding when we examine the acquiring banks’ credit spreads on new debt issues both before and after the merger. We also provide the first study that shows acquirers benefit by the lower cost of funds on post-merger debt issues. Gains in bank mergers: Evidence from the bond markets
Mhbounds - Sensitivity Analysis for Average Treatment Effects

Matching has become a popular approach to estimate average treatment effects. It is based on the conditional independence or unconfoundedness assumption. Checking the sensitivity of the estimated results with respect to deviations from this identifying assumption has become an increasingly important topic in the applied evaluation literature. If there are unobserved variables which affect assignment into treatment and the outcome variable simultaneously, a hidden bias might arise to which matching estimators are not robust. We address this problem with the bounding approach proposed by Rosenbaum (2002), where mhbounds allows the researcher to determine how strongly an unmeasured variable must influence the selection process in order to undermine the implications of the matching analysis.
Importance Point Projection for GPU‐based Final Gathering

We present a practical importance‐driven method for GPU‐based final gathering. We take as input a point cloud representing directly illuminated scene geometry; we then project and splat the points to microbuffers, which store each shading pixel's occluded radiance field. We select points for projection based on importance, defined as each point's estimated contribution to a shading pixel. For each selected point, we calculate its splat size adaptively based on its importance value. The main advantage of our method is that it's simple and fast, and provides the capability to incorporate additional importance factors such as glossy reflection paths. We also introduce an image‐space adaptive sampling method, which combines adaptive image subdivision with joint bilateral upsampling to robustly preserve fine details. We have implemented our algorithm on the GPU, providing high‐quality rendering for dynamic scenes at near interactive rates.
The Development of Subjectivity in Young Children: Theoretical and Pedagogical Considerations

The author presents the outlines of a theory of subjectivity that is anchored in processes of identification. Subject formation is a continual process of becoming that is constituted by three interrelated processes: (1) intrapsychic factors within each child; (2) effects of participation in groups on the kinds of identifications and disidentifications a child adopts; and (3) effects of the discursive practices of society on the kinds of subjectivity a particular child performs. The author begins by outlining Melanie Klein's theory of the development of individual subjectivity through early object relations. Then, using neo-Kleinian writings, the effect of group membership on the child's evolving sense of subjectivity is explored. Ways in which specific discursive environments at home, at school, in popular culture and media etc. can either open up possibilities for expanded subject identification for children or limit those possibilities are then explored. Finally, the author explores the pedagogical implications of this way of thinking, focusing on the ethical responsibilities of teachers to understand the workings of otherness in subject formation so that they might create classroom communities that foster empathy and positive identity formation and diminish the capacity of children to hate and exclude others.
Autonomic management of multimodal interaction: DynaMo in action

Multimodal interaction can play a dual key role in pervasive environments because it provides naturalness for interacting with distributed, dynamic and heterogeneous digitally controlled equipment and flexibility for letting the users select the interaction modalities depending on the context. The DynaMo (Dynamic multiModality) framework is dedicated to the development and the runtime management of multimodal interaction in pervasive environments. This paper focuses on the autonomic approach of DynaMo whose originality is based on partial interaction models. The autonomic manager combines and completes partial available models at runtime in order to build multimodal interaction adapted to the current execution conditions and in conformance with the predicted models. We illustrate the autonomic solution by considering several running examples and different partial interaction models.