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Understanding rebalancing
In portfolio management contexts, rebalancing is often believed to add value. But what is rebalancing? Rebalancing is best understood by contrasting buy-and-hold portfolios and constant proportion portfolios. Buy-and-hold portfolios naturally concentrate over time into the best performing assets. They don't trade. Constant proportion portfolios trade to counter the concentration tendency. They sell the winners and buy the losers.
Under what condition does rebalancing pay off? Rebalancing will pay off as seen from a terminal date if, on average, the price at which a quantity has been bought (respectively sold) is lower (resp. higher) than either the price at which it is subsequently sold (resp. bought) if that happens or the terminal price. This condition is meant to hold across all trades triggered by rebalancing.
What is the likelihood that this condition holds? It depends. First, all that matters for the relative performance of a constant proportion portfolio versus its buy-and-hold counterpart is relative prices. Clearly, if relative prices keep oscillating around an average value, rebalancing will in the end pay off. Oscillations create occasions to buy low and sell high. Rebalancing has great potential in such contexts. But relative prices can typically diverge. In a stock context, Apple has beaten many other competitors over a long period of time.
In a multi-asset context the most important quantity is the relative price of the equity index versus the price of the bond index. The graph below shows that this quantity has been relatively stable over the last thirty years, indicating that this period was favourable to rebalancing. One should however caution that this relative price should in principle trend. After all, bonds and equities don't have the same volatility. Even if they had similar Sharpe ratios, their risk premia should be different. It could be that the historical stationarity of the relative price should not be expected to persist.
Rebalancing is best understood starting with buy-and-hold. Buy-and-hold consists in keeping the number of contracts held in a portfolio constant. If \(n=(n_{i})_{i=1,\ldots,N}\) denotes the constant number of shares (say) held in the portfolio, the change in value of the portfolio will reflect the change in value of the contracts over the given interval \([t,t+1]\), i.e. \(p_{i,t+1}-p_{i,t}\), times the quantities held. This change in value is thus:
\[V_{bh,t+1}-V_{bh,t}=\sum_{i=1}^{N}n_{i}(p_{i,t+1}-p_{i,t}).\]
To understand rebalancing, it is useful to introduce the proportion of the value invested on a given contract:
\[\pi_{i,t}=\frac{n_{i}p_{i,t}}{\sum_{i=1}^{N}n_{i}p_{i,t}}\]
As a reminder, using proportions, the change of value of a portfolio is accounted by:
\[\frac{V_{\pi,t+1}}{V_{\pi,t}}=\sum_{i=1}^{N}\pi_{i}\frac{p_{i,t+1}}{p_{i,t}}.\]
In a buy-and-hold portfolio, the proportions invested on the most performing assets rise while the proportions invested on the least performing assets fall, as can be easily checked. Rebalancing can be conceived as leaning against this natural tendency, i.e. as trading in such a way that the asset proportions are less distorted by relative performance than implied by the buy-and-hold policy. This principle is best illustrated by constant proportion portfolios. These are portfolios that trade so as to keep the proportions \(\pi_{i,t}\) constant. The trading operation carried out to prevent the change in proportions is the rebalancing operation. This amounts to selling the winners and buying the losers.
When does rebalancing work? The value of a constant proportion portfolio evolves according to:
\[V_{\pi,t+1}-V_{\pi,t}=\sum_{i=1}^{N}n_{i,\pi,t}(p_{i,t+1}-p_{i,t}),\]
where we have accounted for the fact that the quantities held \(n_{i,\pi,t}\) change with time. Assume the proportions and quantities have been chosen such that the buy-and-hold portfolio and the constant proportion portfolios are identical at date \(0\). We can subsequently track the difference in quantities induced by rebalancing. Let's call
\[\Delta n_{i,t}=n_{i,\pi,t}-n_{i},\]
the difference in quantities held in the constant proportion portfolio and in the buy-and-hold portfolio. Note that we made sure that \(\Delta n_{i,0}=0.\). Then assuming both portfolios are initialized with the same amount of money, the different in the value of the two portfolios at terminal date \(T\) is just:
\[V_{\pi,T}-V_{bh,T}=\sum_{t=0}^{T-1}\sum_{i=1}^{N}\Delta n_{i,t}(p_{i,t+1}-p_{i,t}).\]
If we designed a convention to keep track of individual trades (for example using last-in first-out accounting), we could split this pay-off into trades, initiated then either terminated or carried up to date \(T\). The cumulated performance \(V_{\pi,T}-V_{bh,T}\) would then depend on the sum of the P&Ls of these individuals trades.
In practice, the outcome depends on relative prices. Indeed, one can quote prices against any convenient numeraire. Whether \(V_{\pi,T}-V_{bh,T}\) is positive or negative does not depend on the chosen numeraire. We can thus choose the first asset as the numeraire. This amounts to assuming \(p_{1,t}=1\) at all dates. Other prices are then prices quoted relatively to that of the first asset. Then, quite intuitively, the best situation is one where all relative prices oscillate without ever diverging. One can show (this is beyond this simple note) that rebalancing almost surely beats buy-and-hold if one is sufficiently patient. In contrast, if relative prices have different trends, buy-and-hold should outperform. In practice, both behaviors will be observed in a sample and the outcome will depend on the mix between cycles and trends.
I illustrate this in a multi-asset context where the most important relative price is that of stocks versus bonds. The graph below shows that this quantity has been quite stable over the last thirty years, indicating that this period was favourable to rebalancing. One should however caution that this relative price should actually trend. After all, bonds and equities don't have the same volatility. Even if they have similar Sharpe ratios, their risk premia should be different. The historical stationarity of the relative price should not be expected to persist.
Mindfulness as a treatment for Bipolar disorder
Mindfulness has become an increasingly popular in recent years. Research has found mindfulness based therapy to be successful as part of a treatment for depression \citep{hofmann_effect_2010}. This is of particular interest for the treatment for bipolar disorder, where treating depressive symptoms has to be carefully managed to avoid an increase in manic symptoms. This article will outline the main points relating to bipolar disorder, mindfulness, mindfulness based cognitive therapy, and six studies which have looked specifically at MBCT and its effectiveness as a treatment for bipolar disorder.
SpaceCraft Instrumentation Europa Life Finder Mission
and 15 collaborators
NOTE: Default Authoria LATEX class is article. Once finished writing, we change it to book and include Chapters. BOLDED SECTIONS included below are Chapter Titles
Untitled Document
Rate adaptation for a single-user MISO networks can be implemented as follows:
Offline-designed quantizers for the transmitter’s rate and transmit beamforming vectors are known at the transmitter’s and receiver’s sides, denoted by 𝒬_{R} and 𝒬_{b}.
The receiver obtains the channel vector ${\pmb h}$, picks the appropriate rate and transmit beamforming vector determined by 𝒬_{R} and 𝒬_{b}, and sends their indices to the transmitter.
Let 𝒞_{R} = {R_{0}=0,R_{1},R_{2},…,R_{2B1 − 1}} be the codebook for the fixed-length quantizer 𝒬_{R}. The selected rate for the transmitter will be R_{s} such that $R_{s} < \left|\left|\pmb h\right|\right|^2 \leq R_{s+1}$, where 0 ≤ s ≤ 2^{B1} − 1. Let 𝒬_{b} be the variable-length quantizer in our previous work. Then, for ${\pmb h}$ and the selected rate R_{s}, we can find an appropriate beamforming vector such that $\left|{\pmb h}^{+}{\pmb w}\right|^2 \geq R_s$, and the average feedback rate is finite.
The remaining problem is to design the codebook 𝒞_{b}. The average achieved rate of the MISO network is \begin{align} \sum_{i=0}^{2^{B_1}-2}\text{Prob}\left\{R_i < \left|\left|\pmb h\right|\right|^2 \leq R_{i+1}\right\} \times R_i + \text{Prob}\left\{ \left|\left|\pmb h\right|\right|^2 > R_{2^{B_1}-1}\right\} \times R_{2^{B_1}-1}.\nonumber \end{align} The optimal values for R_{1}, …, R_{2B1 − 1} can be found by maximizing the average achieved rate above.
Rate adaptation schemes for other single-user networks (such as MIMO networks and amplify-and-forward relay networks) can be designed in a similar way.
Take the decode-and-forward relay network (one source ${\sf S}$, N decode-and-forward relays ${\sf R}_1, \ldots, {\sf R}_N$ and one destination $\sf D$) for example. The challenges here include:
To find optimal transmit rates and power allocations for the source and the relays to achieve the maximum achieved data rate at $\sf D$ for each channel state without outage.
To design efficient quantizers for the optimal transmit rates and power allocations.
In the future 5G communication systems, a promising downlink multiple access scheme is the non-orthogonal multiple access (NOMA) which achieves high spectral efficiencies by combining superposition coding at the transmitter with successive interference cancellation (SIC) at the receivers \cite{NOMA}.
We consider the system model with one base station ${\sf B}$ and N downlink users ${\sf U}_1, \ldots, {\sf U}_N$; all terminals are equipped with a single antenna. In the traditional orthogonal multiple access methodology, $\sf B$ serves only one users at any time slot. In NOMA, $\sf B$ simultaneously serve all users by using the entire bandwidth to transmit data via a superposition coding technique at the transmitter side and SIC techniques at the users. More specifically, the transmit signal of $\sf B$ is $\sqrt{P}\sum_{n = 1}^N \sqrt{\alpha_n} x_n$, where P is the transmit power, α_{n} is the power allocation coefficient and x_{n} is the message for ${\sf U}_n$. The received signal at ${\sf U}_n$ is \begin{align} y_n = h_n \sqrt{P}\sum_{m = 1}^N \sqrt{\alpha_m} x_m + v_n. \nonumber \end{align} When the channels are ordered as |h_{1}|^{2} ≤ |h_{2}|^{2} ≤ ⋯|h_{N}|^{2}, SIC will be performed at the users. Therefore, ${\sf U}_n$ will detect ${\sf U}_i$’s message when i < n, and then remove the detected message from its received signal y_{n}, in a successive way. The messages for i > n are treated as noises. As a result,
Quantum entanglement of quarks
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How to make true connections: Opinions and freedom of speech
-1cm The First Stars: formation under cosmic ray feedback
and 2 collaborators
stars: formation — stars: Population III — cosmology: theory — early Universe — dark ages, first stars
The Pressure Structure of Molecular Clouds
and 2 collaborators
Abstract. Broadly, we seek to understand the role of pressure in star forming molecular clouds. We examine molecular line data of the Perseus region from the COMPLETE survey alongside radiative transfer-processed ‘observations’ of the turbulent simulations of S. Offner to try to (1) understand to what extent we can actually measure pressure through observations, and (2) study how pressure changes within a cloud’s substructure.
A simplified feature vector obtained by wavelets method for fast and accurate recognition of handwritten characters off-line
and 2 collaborators
The study of character recognition is divided into off-line and on-line methods mainly \cite{simistira2015recognition}. The difference between them lies on how handwriting is done and analyze. For the off-line recognition, the data are taken to be a static representation of text, since it can not be established the order on which they were produced by a machine or handwritten \cite{tapia2007survey}. On the other hand, the on-line recognition, the original data are glyphs and points. They are normally storage on regular intervals of time \cite{tapia2005understanding}. Character recognition is one the most important topics in pattern recognition. Specially, digit, character, symbol recognition as well as mathematical expressions. Classification and recognition of vehicular plates, postal codes and others are also of great interest among pattern recognition researchers \cite{hallale2013twelve}. The handwriting recognition has been an ongoing research for decades. But, just recently handwriting recognition has been of great in other areas \cite{zanibbi2012recognition}.
The study of on-line characteristics has been one of the main interest, for that reason the researchers have combined several already developed methods for the extraction of on-line and off-line characteristics to recognize characters \cite{keshari2007hybrid}, \cite{winkler1996hmm}, \cite{alvaro2014recognition}.
This paper is focused on the off-line recognition of handwritten characters. The study is based on descriptors such as FKI already in use \cite{marti2001using,alvaro2014offline} and descriptors based on discrete wavelets \cite{obaidullah2015numeral}. The dataset to be used in this work have been generated by \cite{de2009character}, the author mainly concern is the recognition of character from advertisement images, warning signs, magazines thus creating a database of digits and characters (0 − 9, A-Z, a-z) aiming at gathering and let available images for each individual element. In order to evaluate the results on the characteristic extraction by using the above database, the descriptors FKI, discrete wavelet, and our simplified wavelet method are compared in accuracy and time terms using the Nearest Neighbour rule 1-NN as classifier.
The paper is organized as follows: Section 2 we present a review of the descriptors of interest: FKI and descriptors wavelet, with which we will compare the result with our simplified wavelet method. In section 3 a set of characteristics is defined by a simplified wavelets methods, which are the base of this work. The section 4 we present the result by comparing the three different methods. Finally, in Section 5 we present the conclusions and future work.
(10,6) (2,2.2)(1,0)6 (2,2.2) (6,2.2)(4,2)[r]
Unsupervised learning: Clustering and density estimation
and 2 collaborators
When it comes to determining and explaining information within a large, complicated, or multi-dimensional dataset, it can often be difficult to see patterns and relationships. In the case of supervised learning, where there is input data and output data, it is possible to artificially construct and optimize a model that can, with time and several iterations, predict and improve performance through its own experience by splitting the input data into training and testing sets. Naturally, supervised learning can yield a lot of information and results; however, in the case of unsupervised learning, or when output data is not available, many other methods exist to try and capture the natural structure of the data and make useful observations. This report will attempt to use unsupervised methods to try and infer further information about the cars dataset that was used in the previous exercise.
Interferometric Array Multi-Objective Visual Analytics
\label{sec:intro} This document presents a parametric model to help design an Interferometric Array. It focuses in the value vs. cost trade-off inherent to many of its architecture definitions. This is a Multiple Objective problem. This document describes design parameters to consider in § [sec:var] and a set of equations for research and cost objectives in § [sec:obj]. A spreadsheet that uses these design parameters and produces a CSV file for analysis of the emerging Pareto Front is introduced in § [sec:spreadsheet]. This output enables the of Multiple Objective Visual Analytics (MOVA) for complex engineered systems as proposed in \cite{mova}.
\label{sec:var} This section presents selected design parameters that influence selected objectives in § [sec:obj]. We will select design parameters that are specification agnostic. As an example of this, the parameters will be relevant to multiple antenna specifications, including offset Gregorian and symmetric Cassegrain.
We will use A in this document as each array element collecting area (thus we could also write π ⋅ D^{2}, with D being the dish diameter).
We will use η_{a} in this document as the antenna efficiency with \begin{equation}\label{eq:antenna_efficiency} \eta_a = \eta_{\text{surface eff.}} \cdot \eta_{\text{aperture blockage}} \cdot \eta_{\text{feed spillover eff.}} \cdot \eta_{\text{illumination taper eff.}} \end{equation} as defined in \cite{antenna}.
We will use N in this document as the number of array elements.
We will use P in this document as the number of pad built for the array. In case re-configuration of the array is envisioned, there might be a bigger number of pads ready for aperture connection to the system.
We will use the geographic latitudes and longitudes to establish pad location in this document. We will calculate the length of the possible baselines using pad positions. We will also calculate length and complexity of the roads, fiber and power networks needed using pad positions. We will use B as the maximum array element separation in any single configuration.
We will use R as the number of frequency bands, being R_{i} the different frequency bands. If the array bandwidth is λ_{max} − λ_{min}, it is useful for our analysis to use wavelength λ = λ_{min}.
Notes: high bandwidth ration: up to 7 might be practical, but could compromise Ae/Tsys. High absolute bandwidth is challenging for digitalization. up to 20GHz might be practical.
Notes: directly at RF (no reference), single sideband down conversion (LO and timing reference), double sideband (IQ) down conversion (two LO, two references, LO tunable.
Bits per sample (dynamic range)
We will geographic latitude and longitude to establish correlator location in this document. We will calculate fiber, power and road network aspects based in this information.
We will use η_{c} as correlator efficiency in this document, with \begin{equation}\label{eq:correlator_efficiency} \eta_c(t_{int}) = \frac{\text{correlator sensitivity}}{\text{sesitivity of a perfect analog correlator having the same } t_{int}} \end{equation} as defined in \cite{sensitivity}.
\label{sec:obj} This section aims to include array performance objectives that might be influenced by design variables in § [sec:var].
As derived in \cite{design}, the antenna diameter determines its beam size $\theta_{ant} \approx \frac{\lambda}{D}$. If the plane area $\frac{B}{\lambda}$ is divided in cells of size $\frac{D}{\lambda}$ then \begin{equation}\label{eq:fourier} N_{occ} \leqslant \pi (\frac{B}{D})^2 \end{equation}
An overall measure of performance is the System Equivalent Flux Density, SEFD, defined in \cite{sensitivity} as the flux density of a source that would deliver the same amount of power: \begin{equation}\label{eq:system_equivalent_flux_density} SEFD = {\frac{T_{sys}}{\frac{\eta_a A}{2k_B}}} \end{equation} in units of Janskys where T_{sys} is the system temperature including contributions from receiver noise, feed losses, spillover, atmospheric emission, galactic background and cosmic background, and k_{B} = 1.380 × 10^{−23} Joule K^{−1} is the Boltzmann constant. According to \cite{sensitivity}, if we assume N apertures with the same SEFD, observing the same bandwidth Δν, during the same integration time t_{int}, then weak-source limit in the sensitivity of a synthesis image of a single polarization is \begin{equation}\label{eq:sens} \Delta I_m = {\frac{1}{\eta_s }}{\frac{SEFD}{\sqrt{(N(N-1) \Delta \nu t_{int}}}} \end{equation} in units of Janskys per synthesized beam area, with η_{s} most important factor being correlator efficiency η_{c}.
According to \cite{moran}, a commonly used rule of thumb for the cost of an antenna is that it is proportional to D^{α}, where α ≈ 2.7 for values of D from a few meters to tens of meters. For N antennas of diameter D meters with accuracy $\frac{\lambda}{16}$, where λ is in millimeters we could use \cite{mmadesign} as an upper limit for Antenna construction cost. \begin{equation}\label{eq:antenna_cost} \text{Antenna Cost} = \frac{890N(\frac{D}{10})^{2.7}}{(\lambda^{0.7})} + 500 \end{equation} in K$.
For M frequency bands, each 30% wide, and dual polarization we could use \cite{mmadesign} as an upper limit for Front-End System Cost: \begin{equation}\label{eq:fe_cost} \text{Front-End System Cost} = 45MN + 200M \end{equation} in K$.
We could use \cite{mmadesign} as an upper limit for LO System Cost: \begin{equation}\label{eq:lo_cost} \text{LO System Cost} = 80N+100 \end{equation} in K$.
We could use \cite{mmadesign} as an upper limit for IF Transmission Cost: \begin{equation}\label{eq:IF_Tx_cost} \text{IF Transmission Cost} = 8BN + 30N + 400 \end{equation} in K$.
We could use \cite{mmadesign} Correlator Cost as an upper limit: \begin{equation}\label{eq:correlator} \text{Correlator cost} = 2N^2 + 112N +1360 \end{equation} in K$.
\label{sec:spreadsheet} This section presents a spreadsheet that produces data in the right format for performing visual analytics, consistent with variables in § [sec:var] and objectives in § [sec:obj].
Tricky because you can compensate antenna quality with software. So the equations must capture this trade off.
論澎湖西嶼新發現之「皇明洪門楊氏」墓
and 4 collaborators
Oliver Streiter 奧利華，國立高雄大學 Sandy Lin 林莉倫，國立高雄大學 Nai-Yu Chen 陳乃瑜，國立中興大學 James X. Morris，國立政治大學 Yaqing Zhan 詹雅晴，國立臺北教育大學
Microbial Community Structure of Submerged Aquatic Vegetation in the Potomac River
and 1 collaborator
Submerged aquatic vegetation (SAV) are plants that are rooted in sediment and fully submerged most of the time, and have many adaptations for coping with varied salinity and osmotic conditions. We focus here on one aspect of SAV - their microbiome - which was studied in the Potomac River along a salinity gradient as the river empties into the Chesapeake Bay. The goal was to find a link between the microbial communities on different SAV species and the changing salinity across the river.
One of the four successfully sampled sites was very different from the rest in terms of microbial community and water/sediment chemistry, clustering separately from the other sites on PCoA plots. Methylotenera, Planctomyces, Rhodobacter, and Providencia are commonly found amongst most SAV species across all sites, and sulfur oxidizing bacteria were present in high relative abundance in the roots of Potamogeton perfoliatus at one site.
Site location, which had distinct water and sediment chemistries, was a main driver of the microbial community structure. Host species of SAV and sample types (leaves or roots) also have different microbial communities. Due to the small sample size in this study, it is difficult to draw robust conclusions about the impact of salinity on microbial community structure. Therefore, future efforts will sample more thoroughly along the Potomac river, as well as along the length of the James River, which provides a nearby, parallel salinity gradient.
淺談極座標繪圖
本文旨在用最精簡的方式介紹極座標參數式的繪圖方法，所針對的情形為微積分課本中常見的範例，不見得適用於一般的通式。希望大家在閱讀完畢之後，能對極座標繪圖有初步的概念。底下我們針對r = 2cos3θ這個參數式的作圖來說明。
Step 1. 決定θ的範圍。微積分課本中常見的範例，其圖形大多為週期，也就是說，我們只需要考慮有限的θ範圍即可繪出完整的圖形。要決定θ的範圍，首先得將f(θ)=2cos3θ的圖形給描繪出來。 圖1
上圖中，x軸的刻度是以$\dfrac{\pi}{6}$為單位，可以看到的是，我們將前兩個週期的圖形分別給了由(1)到(8)的編號。為什麼我們要這樣編號呢？因為從(1)到(2)的過程中，r經歷了由正轉負，而(3)到(4)則是由負轉正；這邊要小心一個地方：因為x = rcosθ，y = rsinθ，所以θ的範圍也會影響描點時的相對位置。編號(1)到(3)對應到的θ範圍分別為$\left[0, \dfrac{\pi}{6}\right]$、$\left[\dfrac{\pi}{6}, \dfrac{\pi}{3}\right]$、$\left[\dfrac{\pi}{3}, \dfrac{\pi}{2}\right]$，θ位於第一象限，因此x-y的相對位置完全由r的正負號決定。而(4)到(6)對應到的θ範圍分別為$\left[\dfrac{\pi}{2}, \dfrac{2\pi}{3}\right]$、$\left[\dfrac{2\pi}{3}, \dfrac{5\pi}{6}\right]$、$\left[\dfrac{5\pi}{6}, \pi\right]$，此時θ位於第二象限，因此y的座標與r的正負號正好相反。
我們可以觀察到，(7)、(8)兩個區域的θ正好與(1)、(2)兩個區域的θ相差π，而且r的正負號也恰恰相反；換言之，(7)的圖形會重複(1)的圖形，而(8)的圖形會重複(2)的圖形。因此，我們可以得到底下這個結論：極座標參數式r = 2cos3θ的圖形，其θ的範圍為[0, π]，且可細分為六個區域繪圖。
Step 2. 匯出具有代表性的參考點。這些參考點，基本上就是步驟一當中所得到的六個區域的端點，將這七個點在座標平面標示出來後，圖形也就呼之欲出了。
Step 3. 描繪最終圖形。將步驟二的參考點依序連接，即可得到最終我們所要的圖形。 圖2
RvEBV
and 1 collaborator
The community of astronomical strawmen says that $\RV$ should correlate with ISM density – dust grows and/or agglomerates in dense structures. We can look for this correlation in the intersection of the Valencic+ (year) and Jenkins (2009) samples. Valencic+ provides extinction information, including $\RV$, and Jenkins provides ISM density and dust-to-gas ratio information along the same lines of sight. However, while there is a clear correlation between density and the dust-to-gas ratio (Figure [fig:nH_F]), there does not appear to be a correlation between density and $\RV$ (Figure [fig:lognH_RV]).
By looking at Eddie’s map of $\RV$ over a large area, we can generate the hypothesis that there’s not a clear density-$\RV$ correlation because much of the $\RV$ variation in Eddie’s map happens on much larger spatial scales than the angular size of a dense ISM structure. There could be a density-$\RV$ relationship on top of this large-scale variation, but there isn’t a clear $\EBV$ vs. $\RV$ correlation because the large-scale variation has a higher magnitude.
So, if we want to look for an $\EBV$ vs. $\RV$ correlation, we need to filter out the large scale structure. One way to do this is to look at differences in $\EBV$ vs. differences in $\RV$ between pairs of sightlines as a function of the sightlines’ separations.
Speed Dating Tool- Authorea
and 1 collaborator
\cite{di_ferrante_ehlers-danlos_1975}
Hi, I am Aliza and I will tell you a little bit about my friend Authorea!
Are you writing a thesis?
Are you a frequent LaTeX user? It’s okay |we support markdown, bibtex etc... (let me just show you)
Would you like to learn Latex? Here is link that will help you out!
Are you using any programming language to handle all your quantitative data i.e. javascript or Ipython notebook? Here is a cool link you will love!!
Authorea is a collaborative research tool. It will save you from doing mundane tasks. Authorea provides a platform for:
Authorea provides a platform for collaborative writing and review of your manuscript
It has an easy automated citation mechanism
It is a one-stop repository for all your figures’ data, code, and editing, and even lets you get pre-publication feedback from your peers.
version control helps you keep track of what changes you have made. Have you hear of Git
Produce neat readable work
Invite co-authors and work on the document at the same time
Attach interactive graphs to your article
Work offline using git (need to know a little about git and github)
Add and manage citations
Write mathematical equations
Add comments!
Export files in PDF and other formats.
follow and unfollow to know who made changes via email...
If you like you need help go here
But that all the boring stuff... lets do something cool
Proposal idea for a new experiment
SMMP - Stochastic Methods for Molecular Properties
Possible titles:
Stochastic Methods for Molecular Properties (SMMP)
Stochastic Methods for Chiroptical Properties (SMCP or ChiroStoch)
Deterministic methods need large Hilbert spaces for effective expansions of the many-electron wave function
This is however largely redundant \cite{Ivanic_2001}
Stochastic algorithms are highly parallelizable in the number of walkers.
I will develop my skills in parallel programming techniques by developing this project.
Research questions:
Objectives of the project:
The calculation of molecular properties with high accuracy and for systems of relevant size
Devise the appropriate stochastic approach to the solution of response equations
The creation of the appropriate software toolbox with good scalability.
General background on quantum chemistry:
State of the art:
QMC:
Properties by QMC
Chiroptical properties:
Response theory:
Problems to address:
TODO:
King Chicken Theorem