Energy Efficiency Maximization for C-RANs: Discrete Monotonic Optimization, Penalty, and l0-Approximation Methods

We study downlink of multiantenna cloud radio access networks (C-RANs) with finite-capacity fronthaul links. The aim is to propose joint designs of beamforming and remote radio head (RRH)-user association, subject to constraints on users' quality-of-service, limited capacity of fronthaul links and transmit power, to maximize the system energy efficiency. To cope with the limited-capacity fronthaul we consider the problem of RRH-user association to select a subset of users that can be served by each RRH. Moreover, different to the conventional power consumption models, we take into account the dependence of baseband signal processing power on the data rate, as well as the dynamics of the efficiency of power amplifiers. The considered problem leads to a mixed binary integer program (MBIP) which is difficult to solve. Our first contribution is to derive a globally optimal solution for the considered problem by customizing a discrete branch-reduce-and-bound (DBRB) approach. Since the global optimization method requires a high computational effort, we further propose two suboptimal solutions able to achieve the near optimal performance but with much reduced complexity. To this end, we transform the design problem into continuous (but inherently nonconvex) programs by two approaches: penalty and \ell_{0}-approximation methods. These resulting continuous nonconvex problems are then solved by the successive convex approximation framework. Numerical results are provided to evaluate the effectiveness of the proposed approaches.Comment: IEEE Transaction on Signal Processing, September 2018 (15 pages, 12 figures

PROVISION CAPACITY OF SERVICE DELIVERY FACILITIES FOR CHILDREN WITH HEARING LOSS IN HAI PHONG, VIETNAM

Objective: Hearing loss is a commonly occurring disability that affects 466 million people worldwide. This study aimed at determining the actual situations of early intervention delivery facilities for children with hearing loss. Out of this affected population, 7% are children (34 million) who, along with their families, grapple with the serious lifelong problems that accompany the disease. Methods: This descriptive cross-sectional study was conducted with facilities investigated consisting of a school for the deaf, hospitals, an audiology center, and a social agency in Hai Phong province from January 2013 to December 2014. A sample composed of 353 children was also recruited. Results: The examined facilities suffer from shortcomings in provision capacity, which manifest in deficient materials, supplies and equipment, and human resources; the lack of interdisciplinary coordination of activities; inadequate community awareness; and the unaddressed need for early detection and intervention. The conditions of most of the children (98%) were detected by their families, and among those who were clinically diagnosed, the majority (76.8%) received such diagnosis at central hospitals. Hearing impairment among the children were detected, diagnosed, and subjected to intervention at a very late stage (on average, at ages 22.3, 34, and 32.5 months, respectively), thereby compelling up to 63.6% of the families to struggle with their children’s hearing loss. Conclusion: Solutions to current interventions are needed to enhance service delivery systems and guarantee early detection as well as timely and appropriate treatment

Attentive Deep Neural Networks for Legal Document Retrieval

Legal text retrieval serves as a key component in a wide range of legal text processing tasks such as legal question answering, legal case entailment, and statute law retrieval. The performance of legal text retrieval depends, to a large extent, on the representation of text, both query and legal documents. Based on good representations, a legal text retrieval model can effectively match the query to its relevant documents. Because legal documents often contain long articles and only some parts are relevant to queries, it is quite a challenge for existing models to represent such documents. In this paper, we study the use of attentive neural network-based text representation for statute law document retrieval. We propose a general approach using deep neural networks with attention mechanisms. Based on it, we develop two hierarchical architectures with sparse attention to represent long sentences and articles, and we name them Attentive CNN and Paraformer. The methods are evaluated on datasets of different sizes and characteristics in English, Japanese, and Vietnamese. Experimental results show that: i) Attentive neural methods substantially outperform non-neural methods in terms of retrieval performance across datasets and languages; ii) Pretrained transformer-based models achieve better accuracy on small datasets at the cost of high computational complexity while lighter weight Attentive CNN achieves better accuracy on large datasets; and iii) Our proposed Paraformer outperforms state-of-the-art methods on COLIEE dataset, achieving the highest recall and F2 scores in the top-N retrieval task.Comment: Preprint version. The official version will be published in Artificial Intelligence and Law journa

Sub-optimal Deep Pipelined Implementation of MIMO Sphere Detector on FPGA

Sphere detector (SD) is an effective signal detection approach for the wireless multiple-input multiple-output (MIMO) system since it can achieve near-optimal performance while reducing significant computational complexity. In this work, we proposed a novel SD architecture that is suitable for implementation on the hardware accelerator. We first perform a statistical analysis to examine the distribution of valid paths in the SD search tree. Using the analysis result, we then proposed an enhanced hybrid SD (EHSD) architecture that achieves quasi-ML performance and high throughput with a reasonable cost in hardware. The fine-grained pipeline designs of 4 × 4 and 8 × 8 MIMO system with 16-QAM modulation delivers throughput of 7.04 Gbps and 14.08 Gbps on the Xilinx Virtex Ultrascale+ FPGA, respectively

Effects of calpastain (CAST) polymorphisms on carcass and meat quality traits in Mongcai pigs

Calpastain (CAST) activity plays a major role in muscle growth and proteolytic changes post-mortem and the CAST gene has been considered as a candidate gene for carcass and pork quality characteristics. The aim of this study was to analyze the association of two polymorphisms namely CAST_HinfI (allele A and B) and CAST_MspI (allele C and D) with carcass and meat quality traits in Mongcai, a Vietnamese indigenous pig breed. Polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) was used to genotype the animals at these loci. Results indicate that the CAST_HinfI single nucleotide polymorphism (SNP) had a low frequency of allele A as compared to allele B, while the C and D allele distribution was almost the same for the CAST_MspI SNP. In the association analysis, significant effects on dressing percentage of carcass were detected. The CAST_HinfI locus was associated with the pH24, while the CAST_MspI position was in association with pH45 min, drip loss48 and redness color. Additional analysis showed a variation in muscle fiber type composition with higher proportion of IIx fiber in pigs with AB genotype (P < 0.05). Three constructed haplotypes namely AB/CD, AB/DD and BB/CC also had significant effects on carcass, type IIa and IIb fiber percentages.Keywords: Association, carcass, pork quality, Vietnamese local pi

Ordered Mesoporous Carbons as Novel and Efficient Adsorbent for Dye Removal from Aqueous Solution

Ordered mesoporous carbons (OMCs) were successfully synthesized by using hard template and soft template methods. These materials were characterized by XRD, TEM, and N2 adsorption-desorption Brunauer-Emmett-Teller (BET). From the obtained results, it is revealed that the obtained OMCs samples showed high surface area (>1000 m2/g) with high pore volume, mainly mesopore volume (1.2–2.4 cm3/g). Moreover, OMCs samples had similar structure of the SBA-15 silica and exhibited high MB adsorption capacity with qm of 398 mg·g−1 for OMCs synthesis with hard template and 476 mg·g−1 for OMCs synthesis with soft template, respectively. From kinetics investigation, it is confirmed that MB adsorption from aqueous solution obeys the pseudo-second-order kinetic equation

Transitions in diatom assemblages and pigments through dry and wet season conditions in the Red River, Hanoi (Vietnam)

Background and aims – Biomonitoring is an important tool for assessing river water quality, but is not routinely applied in tropical rivers. Marked hydrological changes can occur between wet and dry season conditions in the tropics. Thus, a prerequisite for ecological assessment is that the influence of ‘natural’ hydrological change on biota can be distinguished from variability driven by water quality parameters of interest. Here we aimed to (a) assess seasonal changes in water quality, diatoms and algal assemblages from river phytoplankton and artificial substrates through the dry-wet season transition (February–July 2018) in the Red River close to Hanoi and (b) evaluate the potential for microscopic counts and high-performance liquid chromatography (HPLC) analysis of chlorophyll and carotenoid pigments for biomonitoring in large tropical rivers.Methods – River water (phytoplankton) and biofilms grown on artificial glass substrates were sampled monthly through the dry (February–April) to wet (May–August) season transition and analysed via microscopic and HPLC techniques.Key results – All phototrophic communities shifted markedly between the dry and wet seasons. Phytoplankton concentrations were low (ca. thousands of cells/mL) and declined as the wet season progressed. The dominant phytoplankton taxa were centric diatoms (Aulacoseira granulata and Aulacoseira distans) and chlorophytes (Scenedesmus and Pediastrum spp.), with chlorophytes becoming more dominant in the wet season. Biofilm diatoms were dominated by Melosira varians, and areal densities declined in the wet season when fast-growing pioneer diatom taxa (e.g. Achnanthidium minutissimum, Planothidium lanceolatum) and non-degraded Chlorophyll a concentrations increased, suggesting active phytobenthos growth in response to scour damage. Otherwise, a-phorbins were very abundant in river seston and biofilms indicating in situ Chlorophyll a degradation which may be typical of tropical river environments. The very large range of total suspended solids (reaching > 120 mg L-1) and turbidity appears to be a key driver of photoautotrophs through control of light availability.Conclusions – Hydrological change and associated turbidity conditions exceed nutrient influences on photoautotrophs at inter-seasonal scales in this part of the Red River. Inter-seasonal differences might be a useful measure for biomonitoring to help track how changes in suspended solids, a major water quality issue in tropical rivers, interact with other variables of interest