2,734 research outputs found
AHP analysis of classifying and positioning the crucial influential factors of brand establishment in the semiconductor industry
Abstract. This study categorizes the crucial influencing factors and positions them according to their importance in achieving the impact of semiconductor brand establishment on improving corporate performance and meeting customer needs. This study conducted an in-depth literature review that recognizes the crucial factors necessary for implementing influence in establishing a semiconductor brand. This study identifies five main variables and 17 subvariables, including âCustomer valueâ, âBrand equityâ, âBrand loyaltyâ, âBrand orientationâ and âBrand performanceâ, and provides expertsâ suggestions. The positioning of 17 subvariables and 5 main variables representing crucial influential factors was performed using an analytical hierarchy process (AHP) technique per their relevance in crucial influential factor implementation. The results show that 5 main variables and 17 subvariables play a vital role in the successful implementationof the impact of establishing a semiconductor brand, and âCustomer valueâ has gained more weight compared to the other main variables. âAddressing problemâ, âSuperior valueâ and âNew product developmentâ are more important than are other subvariables. The limitation of this study is that, first, although this study consults experts from the semiconductor industry and academia of various countries, their opinions are only relevant to their regions. Second, the development of this model only applies to the semiconductor industry. Third, only expert opinion variables were used for pairwise comparisons. This study compensates for the lack of key factors in establishing a semiconductor brand, using the literature and expert questionnaires to obtain the weight of each factor through the AHP method and ranking them in order of importance. It examines the overall situation of the practice of building brand comprehension, missing no factor, understanding where the key points areand using them effectively. This research advances the implementation focus of the key factors that affect the establishment of semiconductor brands. According to the results of the literature review, this study is the first on implementing key factors affecting the establishment of a semiconductor brand. This study attempts to fill this gap.Keywords. Crucial influential factors, Establishing semiconductor brand, AHP.JEL. C44, M21, M31, D81, L29
SMOOTH BRIGHTNESS TRANSITION FOR COMPUTING DEVICES
A brightness transition module is described that enables a computing device (e.g., a mobile phone, a camera, a tablet computer, etc.) to smoothly adjust an amount of light output by a display of the computing device when the computing device transitions between a high brightness mode and a normal mode (e.g., transitioning based on ambient lighting conditions). By smoothly adjusting the amount of light emitted by the display when transitioning between modes, the brightness transition module may prevent a sudden or abrupt change in the brightness (e.g., prevent a sudden flash when transitioning from the normal mode to the high brightness mode), thereby improving the user experience
Speeding up Resnet Architecture with Layers Targeted Low Rank Decomposition
Compression of a neural network can help in speeding up both the training and
the inference of the network. In this research, we study applying compression
using low rank decomposition on network layers. Our research demonstrates that
to acquire a speed up, the compression methodology should be aware of the
underlying hardware as analysis should be done to choose which layers to
compress. The advantage of our approach is demonstrated via a case study of
compressing ResNet50 and training on full ImageNet-ILSVRC2012. We tested on two
different hardware systems Nvidia V100 and Huawei Ascend910. With hardware
targeted compression, results on Ascend910 showed 5.36% training speedup and
15.79% inference speed on Ascend310 with only 1% drop in accuracy compared to
the original uncompressed mode
The Convergence of Glutamate and GABA Dysregulation in Schizophrenia
Schizophrenia (SCZ) is a heterogeneous neurodevelopmental disorder that afflicts about 1% of the world population, imposing a huge financial and social burden on the community. Schizophrenia is characterized by three core features, positive (e.g., hallucinations, delusions) and negative symptoms (e.g., emotional blunting, reduced motivation), as well as cognitive impairments (i.e., working memory and attention deficits). Current antipsychotic treatments, which primarily target dopamine receptors, are effective at alleviating positive symptoms. However, dopamineâspecific therapies are insufficient to relieve negative symptoms and cognitive impairments, indicating other neuronal systems are involved in SCZ. Evidence for hypofunctioning glutamate and gammaâaminobutyric acid (GABA) transmission in forebrain tissue has continued to culminate as major contributors to the onset of SCZ. Furthermore, recent genetic studies reveal disrupted mutations in neurodevelopmental proteins at glutamatergic and GABAergic synapses that are potentially responsible for the synaptic abnormalities seen in the disorder. Therefore, schizophrenia symptomatology is influenced by interactions of several neurotransmitter systems. In this chapter, we focus on how glutamatergic and GABAergic hypofunctioning contribute to the variety of symptoms presented in SCZ and its etiology. We also review the current treatment options with respect to their mechanism of action, side effects, and limitations and provide perspective of where research should be directed to move forward with treating this debilitating disease
Causes and Consequences of State Violence against Civilians: The Rohingya of Myanmar
While the United Nations describes Myanmarâs oppression of the Rohingya as âa textbook example of ethnic cleansingâ (UN, 2017), the state maintains that the violence was idiosyncratic and not motivated by anti-Rohingya animus. We assemble existing and original large-sample data to evaluate these claims. First, we document systematic economic motives: violence against minority civilians increased in places suitable for rice cultivation when rice prices were high. Correspondingly, in an original representative survey of Rohingya refugees in Bangladesh we find substantial losses of agricultural land, inputs, and inventories. Next, using a vector auto-regression approach, we find that state violence was consistent with Rohingya-specific animus. The state attacked substantially more than the Rohingya militia, targeted civilians disproportionately relative to other ethnic conflicts in Myanmar, and leveraged nationalist religious ideology. Finally, we document high rates of trauma exposure and depression among Rohingya refugees. Together, these results strongly rebut the governmentâs narrative and illustrate how quantitative tools can shed light on episodes of ethnic cleansing
Magnetic record of deglaciation using FORC-PCA, sortable-silt grain size, and magnetic excursion at 26 ka, from the Rockall Trough (NE Atlantic)
Core MD04-2822 from the Rockall Trough has apparent sedimentation rates of âź 1 m/kyr during the last deglaciation (Termination I). Component magnetization directions indicate a magnetic excursion at 16.3 m depth in the core, corresponding to an age of 26.5 ka, implying an excursion duration of âź350 years. Across Termination I, the mean grain size of sortable silt implies reduced bottom-current velocity in the Younger Dryas and Heinrich Stadial (HS)â1A, and increased velocities during the Bølling-Allerød warm period. Standard bulk magnetic parameters imply fining of magnetic grain size from the mid-Younger Dryas (âź12 ka) until âź 8 ka. First-order reversal curves (FORCs) were analyzed using ridge extraction to differentiate single domain (SD) from background (detrital) components. Principal component analysis (FORC-PCA) was then used to discriminate three end members corresponding to SD, pseudo-single domain (PSD), and multidomain (MD) magnetite. The fining of bulk magnetic grain size from 12 to 8 ka is due to reduction in concentration of detrital (PSD + MD) magnetite, superimposed on a relatively uniform concentration of SD magnetite produced by magnetotactic bacteria. The decrease in PSD+MD magnetite concentration from 12 to 8 ka is synchronized with increase in benthic δ13C, and with major (âź70 m) regional sea-level rise, and may therefore be related to detrital sources on the shelf that had reduced influence as sea level rose, and to bottom-water reorganization as Northern Source Water (NSW) replaced Southern Source Water (SSW).Research supported by US NSF grants 0850413 and 1014506, and the European R12esearch Council under the European Union's Seventh Framework Programme (FP/2007-2013)/ERC Grant Agreement No. 320750. The UK NERC and BGS funded the recovery of Core MD04-2822
The decomposition of the Faroe-Shetland Channel water masses using Parametric Optimum Multi-Parameter analysis
The Faroe-Shetland Channel (FSC) is an important conduit for the poleward flow of Atlantic water towards the Nordic Seas and, as such, it plays an integral part in the Atlantic's thermohaline circulation. Mixing processes in the FSC are thought to result in an exchange of properties between the channel's inflow and outflow, with wider implications for this circulation; the nature of this mixing in the FSC is, however, uncertain. To constrain this uncertainty, we used a novel empirical method known as Parametric Optimum Multi-Parameter (POMP) analysis to objectively quantify the distribution of water masses in the channel in May 2013. This was achieved by using a combination of temperature and salinity measurements, as well as recently available nutrient and δ18O measurements. The outcomes of POMP analysis are in good agreement with established literature and demonstrate the benefits of representing all five water masses in the FSC. In particular, our results show the recirculation of Modified North Atlantic Water in the surface layers, and the pathways of Norwegian Sea Arctic Intermediate Water and Norwegian Sea Deep Water from north to south for the first time. In a final step, we apply the mixing fractions from POMP analysis to decompose the volume transport through the FSC by water mass. Despite a number of caveats, our study suggests that improved estimates of the volume transport of Atlantic inflow towards the Arctic and, thus, the associated poleward fluxes of salt and heat are possible. A new prospect to more accurately monitor the strength of the FSC branch of the thermohaline circulation emerges from this study
GQKVA: Efficient Pre-training of Transformers by Grouping Queries, Keys, and Values
Massive transformer-based models face several challenges, including slow and
computationally intensive pre-training and over-parametrization. This paper
addresses these challenges by proposing a versatile method called GQKVA, which
generalizes query, key, and value grouping techniques. GQKVA is designed to
speed up transformer pre-training while reducing the model size. Our
experiments with various GQKVA variants highlight a clear trade-off between
performance and model size, allowing for customized choices based on resource
and time limitations. Our findings also indicate that the conventional
multi-head attention approach is not always the best choice, as there are
lighter and faster alternatives available. We tested our method on ViT, which
achieved an approximate 0.3% increase in accuracy while reducing the model size
by about 4% in the task of image classification. Additionally, our most
aggressive model reduction experiment resulted in a reduction of approximately
15% in model size, with only around a 1% drop in accuracy
SwiftLearn: A Data-Efficient Training Method of Deep Learning Models using Importance Sampling
In this paper, we present SwiftLearn, a data-efficient approach to accelerate
training of deep learning models using a subset of data samples selected during
the warm-up stages of training. This subset is selected based on an importance
criteria measured over the entire dataset during warm-up stages, aiming to
preserve the model performance with fewer examples during the rest of training.
The importance measure we propose could be updated during training every once
in a while, to make sure that all of the data samples have a chance to return
to the training loop if they show a higher importance. The model architecture
is unchanged but since the number of data samples controls the number of
forward and backward passes during training, we can reduce the training time by
reducing the number of training samples used in each epoch of training.
Experimental results on a variety of CV and NLP models during both pretraining
and finetuning show that the model performance could be preserved while
achieving a significant speed-up during training. More specifically, BERT
finetuning on GLUE benchmark shows that almost 90% of the data can be dropped
achieving an end-to-end average speedup of 3.36x while keeping the average
accuracy drop less than 0.92%
Downregulation of SREBP Inhibits Tumor Growth and Initiation by Altering Cellular Metabolism in Colon Cancer
Sterol regulatory element-binding proteins (SREBPs) belong to a family of transcription factors that regulate the expression of genes required for the synthesis of fatty acids and cholesterol. Three SREBP isoforms, SREBP1a, SREBP1c, and SREBP2, have been identified in mammalian cells. SREBP1a and SREBP1c are derived from a single gene through the use of alternative transcription start sites. Here we investigated the role of SREBP-mediated lipogenesis in regulating tumor growth and initiation in colon cancer. Knockdown of either SREBP1 or SREBP2 decreased levels of fatty acids as a result of decreased expression of SREBP target genes required for lipid biosynthesis in colon cancer cells. Bioenergetic analysis revealed that silencing SREBP1 or SREBP2 expression reduced the mitochondrial respiration, glycolysis, as well as fatty acid oxidation indicating an alteration in cellular metabolism. Consequently, the rate of cell proliferation and the ability of cancer cells to form tumor spheroids in suspension culture were significantly decreased. Similar results were obtained in colon cancer cells in which the proteolytic activation of SREBP was blocked. Importantly, knockdown of either SREBP1 or SREBP2 inhibited xenograft tumor growth in vivo and decreased the expression of genes associated with cancer stem cells. Taken together, our findings establish the molecular basis of SREBP-dependent metabolic regulation and provide a rationale for targeting lipid biosynthesis as a promising approach in colon cancer treatment
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