Hardware for Machine Learning: Challenges and Opportunities

Machine learning plays a critical role in extracting meaningful information out of the zetabytes of sensor data collected every day. For some applications, the goal is to analyze and understand the data to identify trends (e.g., surveillance, portable/wearable electronics); in other applications, the goal is to take immediate action based the data (e.g., robotics/drones, self-driving cars, smart Internet of Things). For many of these applications, local embedded processing near the sensor is preferred over the cloud due to privacy or latency concerns, or limitations in the communication bandwidth. However, at the sensor there are often stringent constraints on energy consumption and cost in addition to throughput and accuracy requirements. Furthermore, flexibility is often required such that the processing can be adapted for different applications or environments (e.g., update the weights and model in the classifier). In many applications, machine learning often involves transforming the input data into a higher dimensional space, which, along with programmable weights, increases data movement and consequently energy consumption. In this paper, we will discuss how these challenges can be addressed at various levels of hardware design ranging from architecture, hardware-friendly algorithms, mixed-signal circuits, and advanced technologies (including memories and sensors).United States. Defense Advanced Research Projects Agency (DARPA)Texas Instruments IncorporatedIntel Corporatio

Exploring Mirror Twin Higgs Cosmology with Present and Future Weak Lensing Surveys

We explore the potential of precision cosmological data to study non-minimal dark sectors by updating the cosmological constraint on the mirror twin Higgs model (MTH). The MTH model addresses the Higgs little hierarchy problem by introducing dark sector particles. In this work, we perform a Bayesian global analysis that includes the latest cosmic shear measurement from the DES three-year survey and the Planck CMB and BAO data. In the early Universe, the mirror baryon and mirror radiation behave as dark matter and dark radiation, and their presence modifies the Universe's expansion history. Additionally, the scattering between mirror baryon and photon generates the dark acoustic oscillation process, suppressing the matter power spectrum from the cosmic shear measurement. We demonstrate how current data constrain these corrections to the $\Lambda$CDM cosmology and find that for a viable solution to the little hierarchy problem, the proportion of MTH dark matter cannot exceed about $30\%$ of the total dark matter density, unless the temperature of twin photon is less than $30\%$ of that of the standard model photon. While the MTH model is presently not a superior solution to the observed $H_0$ tension compared to the $\Lambda$CDM+$\Delta N_{\rm eff}$ model, we demonstrate that it has the potential to alleviate both the $H_0$ and $S_8$ tensions, especially if the $S_8$ tension persists in the future and approaches the result reported by the Planck SZ (2013) analysis. In this case, the MTH model can relax the tensions while satisfying the DES power spectrum constraint up to $k \lesssim 10~h\rm {Mpc}^{-1}$. If the MTH model is indeed accountable for the $S_8$ and $H_0$ tensions, we show that the future China Space Station Telescope (CSST) can determine the twin baryon abundance with a $10\%$ level precision.Comment: 32 pages, 12 figures, 4 table

Association between copy number variation of complement component C4 and Graves' disease

<p>Abstract</p> <p>Background</p> <p>Gene copy number of complement component <it>C4</it>, which varies among individuals, may determine the intrinsic strength of the classical complement pathway. Presuming a major role of complement as an effecter in peptide-mediated inflammation and phagocytosis, we hypothesized that <it>C4 </it>genetic diversity may partially explain the development of Graves' disease (GD) and the variation in its outcomes.</p> <p>Methods</p> <p>A case-control study including 624 patients with GD and 160 healthy individuals were enrolled. CNV of <it>C4 </it>isotypes (<it>C4A </it>and <it>C4B</it>) genes were performed by quantitative real-time polymerase chain reaction analysis. Statistical comparison and identification of CNV of total <it>C4, C4 </it>isotypes (<it>C4A </it>and <it>C4B</it>) and <it>C4 </it>polymorphisms were estimated according to the occurrence of GD and its associated clinical features.</p> <p>Results</p> <p>Individuals with 4, 2, and 2 copies of <it>C4</it>, <it>C4A </it>and <it>C4B </it>genes, especially those with A2B2 polymorphism may associate with the development of GD (p = 0.001, OR = 10.994, 95% CI: 6.277-19.255; p = 0.008, OR = 1.732, 95% CI: 1.190-2.520; p = 2.420 × 10-5, OR = 2.621, 95% CI: 1.791-3.835; and <it>p </it>= 1.395 × 10^-4, OR = 2.671, 95% CI: 1.761-4.052, respectively). Although the distribution of copy number for total <it>C4</it>, <it>C4 </it>isotypes as well as <it>C4 </it>polymorphisms did not associate with the occurrence of goiter, nodular hyperplasia, GO and myxedema, <2 copies of <it>C4A </it>may associate with high risk toward vitiligo in patients with GD (<it>p </it>= 0.001, OR = 5.579, 95% CI: 1.659-18.763).</p> <p>Conclusions</p> <p>These results may be further estimated for its clinical application on GD and the vitiligo in patients with GD.</p

Toll-like receptor gene polymorphisms are associated with susceptibility to graves' ophthalmopathy in Taiwan males

<p>Abstract</p> <p>Background</p> <p>Toll-like receptors (TLRs) are a family of pattern-recognition receptors, which plays a role in eliciting innate/adaptive immune responses and developing chronic inflammation. The polymorphisms of TLRs have been associated with the risk of various autoimmune diseases, including systemic lupus erythematosus (SLE), multiple sclerosis and rheumatorid arthritis. The aim of this study was to evaluate whether TLR genes could be used as genetic markers for the development of Graves' ophthalmopathy (GO).</p> <p>Methods</p> <p>6 TLR-4 and 2 TLR-9 gene polymorphisms in 471 GD patients (200 patients with GO and 271 patients without GO) from a Taiwan Chinese population were evaluated.</p> <p>Results</p> <p>No statistically significant difference was observed in the genotypic and allelic frequencies of TLR-4 and TLR-9 gene polymorphisms between the GD patients with and without GO. However, sex-stratified analyses showed that the association between TLR-9 gene polymorphism and GO phenotype was more pronounced in the male patients. The odds ratios (ORs) was 2.11 (95% confidence interval [CI] = 1.14-3.91) for rs187084 AàG polymorphism and 1.97 (95% CI = 1.07-3.62) for rs352140 AàG polymorphism among the male patients. Increasing one G allele of rs287084 and one A allele of rs352140 increased the risk of GO (<it>p </it>values for trend tests were 0.0195 and 0.0345, respectively). Further, in haplotype analyses, the male patients carrying the GA haplotype had a higher risk of GO (odds ratio [OR] = 2.02, 95% confidence interval [CI] = 1.09-3.73) than those not carrying the GA haplotype.</p> <p>Conclusion</p> <p>The present data suggest that TLR-9 gene polymorphisms were significantly associated with increased susceptibility of ophthalmopathy in male GD patients.</p

The impact and profitability of day trading following the relaxation of day trading restrictions in Taiwan

The relaxation of day trading restrictions in Taiwan at the start of 2016 resulted in a significant increase in day trading volume, which piqued our interest in researching the impact and profitability of day trading, expected (unexpected) day trading, and day trading at high (low) levels of VIX using time series models, with the following key findings. First, we show that a high market trading volume triggers a high day trading volume resulting from liquidity markets that day traders prefer, but a high day trading volume does not trigger a high market trading volume resulting from speculative markets that other market participants don't prefer. Second, contrary to our perception, while the VIX index rises, day trading would be more profitable after the relaxation. We infer that a high VIX index may be accompanied by a volatile market, which may generate profits by widening the intraday spread of a day-tradable stock. Third, as compared with unexpected market trading volume, we reveal that unexpected day trading volume may be more unexpected than market trading volume, being more likely to enhance market volatility and stock returns. These impressive and interesting findings may not be disclosed before the relaxation, which may contribute to the existing literature

Do Implicit Phenomena Matter? Evidence from China Stock Index Futures

[[abstract]]The CSI 300 Futures (CSI300F) index rises (falls) implicitly in five consecutive minutes; this rise (fall) is defined as the implicit rising (falling) phenomena in this study. Owing to big data concerns, we explore whether investors would profit when the implicit rising (falling) phenomena occur, which exist in practice but remain unexplored in the literature. In this study, we reveal that implicit rising (falling) phenomena might trigger the rise (fall) of the CSI300F index, which is rather impressive for investors, thereby implying that momentum strategies are appropriate for trading the CSI300F as the implicit phenomena occurs. We suspect that implicit phenomena are likely to be the manipulation trace of investors with market force and even insiders. Thus, we argue for investors to consider the results when trading index futures.[[notice]]補正完

Enhancing Crypto Success via Heatmap Visualization of Big Data Analytics for Numerous Variable Moving Average Strategies

This study employed variable moving average (VMA) trading rules and heatmap visualization because the flexibility advantage of the VMA technique and the presentation of numerous outcomes using the heatmap visualization technique may not have been thoroughly considered in prior financial research. We not only employ multiple VMA trading rules in trading crypto futures but also present our overall results through heatmap visualization, which will aid investors in selecting an appropriate VMA trading rule, thereby likely generating profits after screening the results generated from various VMA trading rules. Unexpectedly, we demonstrate in this study that our results may impress Ethereum futures traders by disclosing a heatmap matrix that displays multiple geometric average returns (GARs) exceeding 40%, in accordance with various VMA trading rules. Thus, we argue that this study extracted the diverse trading performance of various VMA trading rules, utilized a big data analytics technique for knowledge extraction to observe and evaluate numerous results via heatmap visualization, and then employed this knowledge for investments, thereby contributing to the extant literature. Consequently, this study may cast light on the significance of decision making via big data analytics