Miracle-dz/UniFAR-FTUs huggingface dataset:
SEAL-lab Technical Report – No . 2015-001 ( April 29 , 2016 ) Processing-in-Memory in ReRAM-based Main Memory

Process in memory (PIM) is a promising solution to address the “memory wall” issue, and most of the proposed PIM architectures integrate logic with memory. However, as long as the computation is done by logic, it needs to access memory for data. In this work, we propose a novel PIM system, utilizing metal-oxide resistive random access memory (ReRAM) as main memory and also for computation. As an emerging non-volatile memory, ReRAM has been considered as a promising candidate for future memory architecture, thanks to its high density, fast read access, and low leakage power. In addition, it has the capability to represent synaptic weights and execute neural networks with its crossbar architecture. Our proposed design is based on a ReRAM-based main memory system. In the proposed design, a portion of ReRAM crossbar arrays can be configured either as normal memory or to be used for neural computation (hence a morphable PIM structure). We design the required peripheral circuits by computing carefully to minimize the area overhead, and also to provide a hardware-software interface for the developers so that they can easily configure the proposed design for various neural network tasks. We evaluate the proposed design on two sets of benchmarks, and compare it with a CPU-only solution and two neural process unit (NPU) solutions (using NPUs as co-processors and as PIMprocessors through 3D stacking). The experiment results show that the proposed design achieve the best energyefficiency with the highest speedup, which is ∼9000× faster than the CPU-only solution for large neural networks. In addition, it is also a low-overhead PIM solution, with only < 3.8% area overhead compared to the normal ReRAM chips.
Clustering-Guided Sparse Structural Learning for Unsupervised Feature Selection

Many pattern analysis and data mining problems have witnessed high-dimensional data represented by a large number of features, which are often redundant and noisy. Feature selection is one main technique for dimensionality reduction that involves identifying a subset of the most useful features. In this paper, a novel unsupervised feature selection algorithm, named clustering-guided sparse structural learning (CGSSL), is proposed by integrating cluster analysis and sparse structural analysis into a joint framework and experimentally evaluated. Nonnegative spectral clustering is developed to learn more accurate cluster labels of the input samples, which guide feature selection simultaneously. Meanwhile, the cluster labels are also predicted by exploiting the hidden structure shared by different features, which can uncover feature correlations to make the results more reliable. Row-wise sparse models are leveraged to make the proposed model suitable for feature selection. To optimize the proposed formulation, we propose an efficient iterative algorithm. Finally, extensive experiments are conducted on 12 diverse benchmarks, including face data, handwritten digit data, document data, and biomedical data. The encouraging experimental results in comparison with several representative algorithms and the theoretical analysis demonstrate the efficiency and effectiveness of the proposed algorithm for feature selection.
Those who walk the talk: the role of administrative professionals in transforming universities into strategic actors

The group of administrators working in the Swedish higher education sector is undergoing considerable change. National statistics show that the average educational level is increasing and that more staff are being recruited from the private sector. This article discusses the implications of such changes on life and work in academia. In particular, it points to a link between new administrative roles and new demands placed on universities to become more coherent, goal-oriented organisations – so-called strategic actors. The article builds on national statistics and an in-depth interview study of administrative professionals from the areas of internationalisation, business liaison and research funding support, at three technical universities in Sweden. The study indicates that administrative professionals can have considerable impact on the management of a university. Lacking formal decision-making power, their influence tends to be indirect. Nevertheless, they can fulfil important roles as guardians of a holistic perspective, reminding internal stakeholders of the organisational aims of the university. Their role requires a set of competences and experiences that includes academic background as well as an attitude of self-reflection, sensitivity and judgement. In conclusion, it is argued that administrative professionals can play a crucial part in transforming universities into strategic actors. Their function merits more attention as it touches upon important issues of power and strategic direction in contemporary higher education.
A Fully Convolutional Neural Network for Cardiac Segmentation Input DICOM Image Output Segmentation Mask

Automated cardiac segmentation from magnetic resonance imaging datasets is an essential step in the timely diagnosis and management of cardiac pathologies. We propose to tackle the problem of automated left and right ventricle segmentation through the application of a deep fully convolutional neural network architecture. Our model is efficiently trained end-to-end in a single learning stage from wholeimage inputs and ground truths to make inference at every pixel. To our knowledge, this is the first application of a fully convolutional neural network architecture for pixel-wise labeling in cardiac magnetic resonance imaging. Numerical experiments demonstrate that our model is robust to outperform previous fully automated methods across multiple evaluation measures on a range of cardiac datasets. Moreover, our model is fast and can leverage commodity compute resources such as the graphics processing unit to enable state-of-the-art cardiac segmentation at massive scales. The models and code are available at https://github.com/vuptran/cardiac-segmentation.
Relationships With Individuals Facing Eating Disorder Symptoms

Due to a dearth of qualitative research on the experience of friends, nonparental family members, and romantic dating partners of those struggling with eating disorder symptoms, we undertook a phenomenological inquiry into this issue. After interviewing 12 participants who had a friend, family member, or romantic dating partner with eating disorder symptoms, we identified four main themes: (a) compassionate emotions toward the individual with eating disorder symptoms with a subtheme of concomitant emotions of frustration and/or anger, (b) sharing the burden of concern for the struggling individual, (c) intervening with boundaries, and (d) detrimental impact of the eating disorder on the relationship with the struggling individual and the participant’s own body image and/or eating behaviors.
B Cell-Deficient (μMT) Mice Have Alterations in the Cytokine Microenvironment of the Gut-Associated Lymphoid Tissue (GALT) and a Defect in the Low Dose Mechanism of Oral Tolerance1

Peripheral immune tolerance following i.v. administration of Ag has been shown to occur in the absence of B cells. Because different mechanisms have been identified for i.v. vs low dose oral tolerance and B cells are a predominant component of the gut-associated lymphoid tissue (GALT) they may play a role in tolerance induction following oral Ag. To examine the role of B cells in oral tolerance we fed low doses of OVA or myelin oligodendrocyte glycoprotein to B cell-deficient (μMT) and wild-type C57BL/6 mice. Results showed that the GALT of naive wild-type and μMT mice was characterized by major differences in the cytokine microenvironment. Feeding low doses of 0.5 mg OVA or 250 μg myelin oligodendrocyte glycoprotein resulted in up-regulation of IL-4, IL-10, and TGF-β in the GALT of wild-type but not μMT mice. Upon stimulation of popliteal node cells, in vitro induction of regulatory cytokines TGF-β and IL-10 was observed in wild-type but not μMT mice. Greater protection against experimental autoimmune encephalomyelitis was found in wild-type mice. Oral tolerance in μMT and wild-type mice was found to proceed by different mechanisms. Anergy was observed from 0.5 mg to 250 ng in μMT mice but not in wild-type mice. Increased Ag was detected in the lymph of μMT mice. No cytokine-mediated suppression was found following lower doses from 100 ng to 500 pg in either group. These results demonstrate the importance of the B cell for the induction of cytokine-mediated suppression associated with low doses of Ag.
Journal of Neuroscience Methods Chronic Detachable Headphones for Acoustic Stimulation in Freely Moving Animals

Keywords: Spatial hearing Sound localization Sound lateralization Headphone Ferret Binaural Interaural level difference Bone cement a b s t r a c t A growing number of studies of auditory processing are being carried out in awake, behaving animals , creating a need for precisely controlled sound delivery without restricting head movements. We have designed a system for closed-field stimulus presentation in freely moving ferrets, which comprises lightweight, adjustable headphones that can be consistently positioned over the ears via a small, skull-mounted implant. The invasiveness of the implant was minimized by simplifying its construction and using dental adhesive only for attaching it to the skull, thereby reducing the surgery required and avoiding the use of screws or other anchoring devices. Attaching the headphones to a chronic implant also reduced the amount of contact they had with the head and ears, increasing the willingness of the animals to wear them. We validated sound stimulation via the headphones in ferrets trained previously in a free-field task to localize stimuli presented from one of two loudspeakers. Noise bursts were delivered binaurally over the headphones and interaural level differences (ILDs) were introduced to allow the sound to be lateralized. Animals rapidly transferred from the free-field task to indicate the perceived location of the stimulus presented over headphones. They showed near perfect lateralization with a 5 dB ILD, matching the scores achieved in the free-field task. As expected, the ferrets' performance declined when the ILD was reduced in value. This closed-field system can easily be adapted for use in other species, and provides a reliable means of presenting closed-field stimuli whilst monitoring behavioral responses in freely moving animals.
A quick survey on selected approaches for preparing programmable networks

Currently, new protocols or architectures related to core network layer or network forwarding equipment are hard to deploy. And the core network evolves slowly. To solve this problem, various programmable Internet architectures and approaches are proposed. Programmable networks allow network researchers (not only the equipment vendors like Cisco and Juniper) to program and manage their customized architectures or protocols. This paper firstly describes the ossification with the current Internet architecture. And then it analyzes several most representative approaches for programmable networks with their mechanisms, advantages, and shortcomings. By the analysis above, this paper at last discusses the future research trends and gives a detailed description of the key issues in the future research of the programmable networks.
Interfacial gauge methods for incompressible fluid dynamics

A new class of numerical algorithms facilitates accurate computational modeling of intricate fluid interface phenomena. Designing numerical methods for incompressible fluid flow involving moving interfaces, for example, in the computational modeling of bubble dynamics, swimming organisms, or surface waves, presents challenges due to the coupling of interfacial forces with incompressibility constraints. A class of methods, denoted interfacial gauge methods, is introduced for computing solutions to the corresponding incompressible Navier-Stokes equations. These methods use a type of “gauge freedom” to reduce the numerical coupling between fluid velocity, pressure, and interface position, allowing high-order accurate numerical methods to be developed more easily. Making use of an implicit mesh discontinuous Galerkin framework, developed in tandem with this work, high-order results are demonstrated, including surface tension dynamics in which fluid velocity, pressure, and interface geometry are computed with fourth-order spatial accuracy in the maximum norm. Applications are demonstrated with two-phase fluid flow displaying fine-scaled capillary wave dynamics, rigid body fluid-structure interaction, and a fluid-jet free surface flow problem exhibiting vortex shedding induced by a type of Plateau-Rayleigh instability. The developed methods can be generalized to other types of interfacial flow and facilitate precise computation of complex fluid interface phenomena.
Safety Assessment of Roundup Ready Cotton Event 1445

Executive Summary Using modern biotechnology, Monsanto Company has developed Roundup Ready  cotton plants that confer tolerance to Roundup  herbicide by the production of the CP4 5-enolpyruvylshikimate-3-phosphate synthase (EPSPS) protein. Three genes were introduced into the cotton genome to produce this product: the cp4 epsps gene derived from the common soil bacterium Agrobacterium sp. strain CP4 which encodes the naturally Roundup tolerant EPSPS protein, the target site for Roundup herbicide action; the neomycin phosphotransferase II (nptII) gene which provides a plant selectable marker; and the 3 " (9)-O-aminoglycoside adenylyltransferase (aad) gene, a bacterial selectable marker. The cp4 epsps and nptII genes are expressed in the plants; the aad gene-product is not produced in the plants as this gene is under the control of a bacterial promoter. Results from field experiments conducted in 1992, 1993 and 1994 at over 65 locations throughout the United States and commercial production of Roundup Ready cotton for four years on six million acres in United States alone, have demonstrated that cotton containing the Roundup Ready gene is tolerant to topical applications of Roundup herbicide up to the four-leaf stage of growth. Roundup herbicide has been shown to be highly effective in controlling the majority of annual and perennial grasses and broad-leafed weeds. Growers planting Roundup Ready cotton are able to reduce the number of herbicides used to control the economically destructive weeds that grow in their fields (Culpepper and York, 1998). The following summary provides information on the methods used to develop Roundup Ready cotton, the proteins produced, as well as an overview of the food, feed and environmental safety studies that have been conducted on Roundup Ready cotton. This summary includes: molecular characterization of the DNA inserted into Roundup Ready cotton, an assessment of the safety of the produced proteins, compositional analyses of the food and feed components to evaluate substantial equivalence to conventional cotton varieties, and an assessment of the environmental safety of this product. These studies demonstrate the food, feed and environmental safety of Roundup Ready cotton by establishing the safety of the CP4 EPSPS and NPTII proteins to humans, animals and other non-target organisms; establishing the nutritional equivalence and wholesomeness of Roundup Ready cottonseed compared to cottonseed from conventional varieties; and confirming the lack of a negative impact of Roundup Ready cotton on the environment.
SitAndReach Test to Predict Mobility in Patients Recovering from Acute Stroke

Early accurate and reliable prediction of outcome in stroke victim is important. Regaining mobility is a primary goal of patient with stroke during rehabilitation. Sitting balance has been reported to correlate with mobility and functional outcomes after stroke. Sit and Reach Test would be a useful measure of sitting balance. Thus the study is an attempt to find the efficacy of Sit and Reach Test to longitudinal study was carried in 30 patients with first ever stroke. The study Reach Test, the day patient was able to sit unsupported for 1 Independence Measure (transfer and locomotion activity items) at 3 months post strok efficient yielded that Sit and Reach Test correlated with the Functional Independence Measure Mobility scores (r= 0.958, p=0.000). Linear regression analysis showed that Sit and Reach Test could predict Functional Independence Measur Mobility scores and accounted for 13.55% of the variation. and time can predict mobility of patients recovering from acute stroke. Of these three, Sit and Reach test was found to be the most powerful predictor. Early accurate and reliable prediction of outcome in stroke victim is important. Regaining mobility is a primary goal of rehabilitation. Sitting balance has been reported to correlate with mobility and functional outcomes after stroke. Sit and Reach Test would be a useful measure of sitting balance. Thus the study is an attempt to predict mobility in patients recovering from acute stroke. Methods: A consist of measuring performance of Sit and minute post stroke and measuring mobility using Functional Results: Pearson correlation co� efficient yielded that Sit and Reach Test correlated with the Functional Independence Measure Mobility scores (r= 0.958, p=0.000). Linear regression analysis showed that Sit and Reach Test could predict Functional Independence Measur e This study reveals that Sit and Reach test, age and time can predict mobility of patients recovering from acute stroke. Of these three, Sit and Reach test was found to be .
Distributed Aggregation Schemes for Scalable Peer-to-Peer and Grid Computing

Peer-to-Peer (P2P) systems and P2P Grids are evolving as two viable distributed computing paradigms for wide-area resource sharing on the Internet. To achieve scalable distribution of processing workload among the nodes, large-scale distributed systems such as P2P Grids need to acquire some global information in a fully decentralized fashion. This paper presents efficient schemes for building distributed aggregation trees (DAT) on a structured P2P network like Chord. By leveraging the topology and routing mechanisms of Chord, the DAT trees are implicitly constructed from native Chord routing paths without membership maintenance. To balance the DAT trees, we propose a balanced routing algorithm on Chord that dynamically selects the parent of a node from its finger nodes by its distance to the root. This paper proves that this balanced routing algorithm enables the construction of almost completely balanced DATs, when nodes are evenly distributed in the Chord identifier space. We have evaluated the performance and scalability of a DAT prototype implementation with up to 8192 nodes. Our experimental results show that the balanced DAT scheme scales well to a large number of nodes and corresponding aggregation trees. Without maintaining explicit parent-child membership, it has very low overhead during node arrival and departure. We demonstrate that the DAT scheme performs well in Grid resource monitoring, P2P reputation management, distributed RDF repository, and worm signature generation systems.
Identification of live or studio versions of a song via supervised learning

We aim to distinguish between the “live” and “studio” versions of songs by using supervised techniques. We show which segments of a song are the most relevant to this classification task, and we also discuss the relative importance of audio, music and acoustic features, given this challenge. This distinction is crucial in practice since the listening experience of the user of online streaming services is often affected, depending on whether the song played is the original studio version or a secondary live recording. However, manual labelling can be tedious and challenging. Therefore, we propose to classify automatically a music data set by using Machine Learning techniques under a supervised setting. To the best of our knowledge, this issue has never been addressed before. Our proposed system is proven to perform with high accuracy on a 1066-song data set with distinct genres and across different languages.
Understanding seed selection in bootstrapping

Bootstrapping has recently become the focus of much attention in natural language processing to reduce labeling cost. In bootstrapping, unlabeled instances can be harvested from the initial labeled “seed” set. The selected seed set affects accuracy, but how to select a good seed set is not yet clear. Thus, an “iterative seeding” framework is proposed for bootstrapping to reduce its labeling cost. Our framework iteratively selects the unlabeled instance that has the best “goodness of seed” and labels the unlabeled instance in the seed set. Our framework deepens understanding of this seeding process in bootstrapping by deriving the dual problem. We propose a method called expected model rotation (EMR) that works well on not well-separated data which frequently occur as realistic data. Experimental results show that EMR can select seed sets that provide significantly higher mean reciprocal rank on realistic data than existing naive selection methods or random seed sets.
Daily cost minimization for an off-grid renewable microhybrid system installed to a residential home

Electricity prices inevitably increase in many countries due to various reasons such as growing demand, incremental costs etc. This obviously has considerable impact on electricity consumers using electrical household appliances in daily life. In Turkey, rise of electricity price is almost around 10% every year and unfortunately this leads to budge from living comfort. Use of renewable energy systems is one alternative to reduce energy cost in family budget. The recent survey has shown that Turkey is rich in renewable energy sources in order to generate low cost electricity in residential homes or similar places using a typical micro-hybrid system with very low power wind turbines, several PV panels, few storage batteries and controllable and uncontrollable electrical loads. The operational cost of this system should be reduced to competitive amount of the grid cost. This is actually a typical global minimization problem with several constraints to be solved. In this investigation, the problem was solved by the SFLA technique and the results indicated that operational cost in a PV/wind micro-hybrid renewable system is considerably reduced.