Effect of Ethics and Integrity on Good Public Procurement System

This study aimed at exposing the effect of ethics and integrity on good public procurement system in Nigeria. Data for the study were collected through a structured questionnaire administered on eighty-two (82) officers of the Bureau for Public Procurement in Abuja, Nigeria. The data generated were analysed with the Pearson Product Moment Coefficient of Correlation. Our findings revealed that ethics, accountability, and transparency of public procurement system in Nigeria. In view of the above, the paper recommended that fairness and impartiality should be enshrined in public procurement in Nigeria; there should be proper re-orientation of public procurement officers on the need for consistency and transparency in procurement procedure; and regulatory authorities should ensure that legislative obligations and policies on public procurement are fully enforced.Key words: Ethics, integrity, good public procurement, accountability, transparency

High Fidelity State Transfer Over an Unmodulated Linear XY Spin Chain

We provide a class of initial encodings that can be sent with a high fidelity over an unmodulated, linear, XY spin chain. As an example, an average fidelity of ninety-six percent can be obtained using an eleven-spin encoding to transmit a state over a chain containing ten-thousand spins. An analysis of the magnetic field dependence is given, and conditions for field optimization are provided.Comment: Replaced with published version. 8 pages, 5 figure

Hidden dependence of spreading vulnerability on topological complexity

Many dynamical phenomena in complex systems concern spreading that plays out on top of networks with changing architecture over time -- commonly known as temporal networks. A complex system's proneness to facilitate spreading phenomena, which we abbreviate as its `spreading vulnerability', is often surmised to be related to the topology of the temporal network featured by the system. Yet, cleanly extracting spreading vulnerability of a complex system directly from the topological information of the temporal network remains a challenge. Here, using data from a diverse set of real-world complex systems, we develop the `entropy of temporal entanglement' as a novel and insightful quantity to measure topological complexities of temporal networks. We show that this parameter-free quantity naturally allows for topological comparisons across vastly different complex systems. Importantly, by simulating three different types of stochastic dynamical processes playing out on top of temporal networks, we demonstrate that the entropy of temporal entanglement serves as a quantitative embodiment of the systems' spreading vulnerability, irrespective of the details of the processes. In being able to do so, i.e., in being able to quantitatively extract a complex system's proneness to facilitate spreading phenomena from topology, this entropic measure opens itself for applications in a wide variety of natural, social, biological and engineered systems.Comment: 15 pages, 9 figures, to appear in Phys. Rev.

Interstellar: Using Halide's Scheduling Language to Analyze DNN Accelerators

We show that DNN accelerator micro-architectures and their program mappings represent specific choices of loop order and hardware parallelism for computing the seven nested loops of DNNs, which enables us to create a formal taxonomy of all existing dense DNN accelerators. Surprisingly, the loop transformations needed to create these hardware variants can be precisely and concisely represented by Halide's scheduling language. By modifying the Halide compiler to generate hardware, we create a system that can fairly compare these prior accelerators. As long as proper loop blocking schemes are used, and the hardware can support mapping replicated loops, many different hardware dataflows yield similar energy efficiency with good performance. This is because the loop blocking can ensure that most data references stay on-chip with good locality and the processing units have high resource utilization. How resources are allocated, especially in the memory system, has a large impact on energy and performance. By optimizing hardware resource allocation while keeping throughput constant, we achieve up to 4.2X energy improvement for Convolutional Neural Networks (CNNs), 1.6X and 1.8X improvement for Long Short-Term Memories (LSTMs) and multi-layer perceptrons (MLPs), respectively.Comment: Published as a conference paper at ASPLOS 202

Exploration of Computational Methods for Classification of Movement Intention During Human Voluntary Movement from Single Trial EEG

Objective: To explore effective combinations of computational methods for the prediction of movement intention preceding the production of self-paced right and left hand movements from single trial scalp electroencephalogram (EEG). Methods: Twelve naïve subjects performed self-paced movements consisting of three key strokes with either hand. EEG was recorded from 128 channels. The exploration was performed offline on single trial EEG data. We proposed that a successful computational procedure for classification would consist of spatial filtering, temporal filtering, feature selection, and pattern classification. A systematic investigation was performed with combinations of spatial filtering using principal component analysis (PCA), independent component analysis (ICA), common spatial patterns analysis (CSP), and surface Laplacian derivation (SLD); temporal filtering using power spectral density estimation (PSD) and discrete wavelet transform (DWT); pattern classification using linear Mahalanobis distance classifier (LMD), quadratic Mahalanobis distance classifier (QMD), Bayesian classifier (BSC), multi-layer perceptron neural network (MLP), probabilistic neural network (PNN), and support vector machine (SVM). A robust multivariate feature selection strategy using a genetic algorithm was employed. Results: The combinations of spatial filtering using ICA and SLD, temporal filtering using PSD and DWT, and classification methods using LMD, QMD, BSC and SVM provided higher performance than those of other combinations. Utilizing one of the better combinations of ICA, PSD and SVM, the discrimination accuracy was as high as 75%. Further feature analysis showed that beta band EEG activity of the channels over right sensorimotor cortex was most appropriate for discrimination of right and left hand movement intention. Conclusions: Effective combinations of computational methods provide possible classification of human movement intention from single trial EEG. Such a method could be the basis for a potential brain-computer interface based on human natural movement, which might reduce the requirement of long-term training. Significance: Effective combinations of computational methods can classify human movement intention from single trial EEG with reasonable accuracy

Co-translational protein targeting facilitates centrosomal recruitment of PCNT during centrosome maturation in vertebrates.

As microtubule-organizing centers of animal cells, centrosomes guide the formation of the bipolar spindle that segregates chromosomes during mitosis. At mitosis onset, centrosomes maximize microtubule-organizing activity by rapidly expanding the pericentriolar material (PCM). This process is in part driven by the large PCM protein pericentrin (PCNT), as its level increases at the PCM and helps recruit additional PCM components. However, the mechanism underlying the timely centrosomal enrichment of PCNT remains unclear. Here, we show that PCNT is delivered co-translationally to centrosomes during early mitosis by cytoplasmic dynein, as evidenced by centrosomal enrichment of PCNT mRNA, its translation near centrosomes, and requirement of intact polysomes for PCNT mRNA localization. Additionally, the microtubule minus-end regulator, ASPM, is also targeted co-translationally to mitotic spindle poles. Together, these findings suggest that co-translational targeting of cytoplasmic proteins to specific subcellular destinations may be a generalized protein targeting mechanism

Improving Islet Engraftment by Gene Therapy

Islet cell transplantation is currently the only feasible long-term treatment option for patients with type 1 diabetes. However, the majority of transplanted islets experience damage and apoptosis during the isolation process, a blood-mediated inflammatory microenvironment in the portal vein upon islet infusion, hypoxia induced by the low oxygenated milieu, and poor-revascularization-mediated lack of nutrients, and impaired hormone modulation in the local transplanted site. Strategies using genetic modification methods through overexpression or silencing of those proteins involved in promoting new formation of blood vessels or inhibition of apoptosis may overcome these hurdles and improve islet engraftment outcomes