Brittany, assisted by Elizabeth Farrell, styled a big batch of Container Store videos this spring. The informational videos live at containerstore.com on their video page.
So how about some packing tips?
Brittany : I now realize that a neat fold and pack means fitting in a lot more, as opposed to my previous method of throwing a bunch of clothes in a bag the night before and playing fashion roulette when I arrive (ha!).
Elizabeth : I pack small stuff inside shoes to save room!
Below is a sampling of the videos.
All of them can be found over at The Container Store.
29 May Filed under Fashion, Sister Brittany Winter, Travel. Tagged Commercial styling, The Container Store, Video.	Bookmark the permalink.
Sister Mari styled Bianca Watson's gorgeous Highland Park home for Bungalow Magazine's Spring issue.
Lots of beautiful, unique and quirky touches.
Steven shot another round of the Texas Ballet Theater for their 2014/15 season.
Click pics below to enlarge as you read along.


Port Manatee and Carver Maritime LLC have entered a long-term marine terminal operating agreement for a 10 acre aggregate offloading facility at the Florida Gulf Coast port.
The agreement, for as many as 20 years including options, was approved Thursday 16 August by the Manatee County Port Authority, and ensures property lease payments totalling US$1.8 million for the initial five year term, in addition to wharfage payments for annual cargo throughputs.
The Port Manatee facility will be the third maritime endeavor for Carver Companies, which was founded in 1989. The first, the Port of Coeymans, a 400 acre privately owned and operated maritime industrial complex, opened in 2007 on the Hudson River, 10 miles south of Albany, New York. The second was established in 2016 on a former naval base property in North Charleston, South Carolina, and now encompasses 40 acres of industrial laydown and warehousing.
Located 'Where Tampa Bay Meets the Gulf of Mexico', Port Manatee is the closest US deepwater seaport to the expanded Panama Canal, with 1040 ft draft berths serving container, bulk, breakbulk, heavylift, project and general cargo customers. The port generates more than US$2.3 billion in annual economic impact for the local community, while supporting more than 24 000 jobs, without levying ad-valorem taxes.


Gaia Deployable Sunshield Assembly (DSA) integrated onto the spacecraft and undergoing deployment testing at Astrium Toulouse. Since the DSA will operate in microgravity, it is not designed to support its own weight in the one-g environment at Earth's surface. During deployment testing, the DSA panels are attached to a system of support cables and counterweights that bears their weight, preventing damage and providing a realistic test environment. The support system is clearly visible in the video.
As the DSA deploys, the flight model thermal tent comes into view and the mechanically representative dummy payload can be seen through the aperture in the tent. Towards the end of the deployment sequence, the flight model service module comes into view.
This test demonstrated correctness of alignment following integration, confirmed the deployment functionality and verified the flatness of the deployed DSA.
The video shows an edited, time-lapse sequence from the deployment of the DSA during a recent test campaign; the entire deployment sequence takes about 20 minutes.


text:
AI Weekly: Dystopian visions of AI are distractions from present problems
Blair Hanley Frank, ISG@belril	 April 27, 2018 4:28 PM
Above: An Uber self-driving car.
Image Credit: Uber
Writing about AI for an appreciable amount of time is, in my experience, enough to make any reasonable person concerned about the future of humanity. But I worry the focus of that concern is too often directed at the relatively distant future, which could lead to unforeseen consequences in the present.
Headlines from the past few months illuminate how bad things can get. Consider the cases of the self-driving Uber that killed Elaine Herzberg in Tempe, Arizona and that of the Apple engineer who was killed when his Tesla, driving on Autopilot, plowed into a traffic barrier on the highway. You're probably also aware of the content suggestion algorithms from Facebook and YouTube, which have been implicated in the spread of fake news and extremist views.
Then there was a story last week about how companies and cities use Palantir's analytics for corporate security and predictive policing, with potentially disastrous results. One man interviewed in the story claimed that he isn't involved with the Eastside 18 gang in Los Angeles but said the LAPD has him in their database as an associate and officers have been stopping him as a result.
None of these cases involved AI that's advanced enough for Elon Musk to call it an existential threat to humanity. But that didn't stop people from getting hurt. If we're not careful, this will happen more frequently in the coming years.
It's easy to worry about a catastrophic future for bank tellers, truck drivers, or some other profession that's being told today their jobs will go away in the future. Thanks to Westworld, Battlestar Galactica, Iain M. Banks' Culture novels, and other media, we can confidently picture how artificial superintelligence could upend our lives.
Don't get me wrong — that's all very concerning. But we can't let our anxiety about the distant future blind us to what's going on right in front of us. When AI systems fail, they can do so in ways we don't expect. Research and anecdotal evidence have shown us that those systems are often biased against minorities. That bad news turns dire when those systems become critical components of our infrastructure.
Consider the story of the Southern State Parkway in New York that Robert Caro laid out in The Power Broker. According to Caro's interviews with Sidney M. Shapiro, urban developer Robert Moses decided to build overpasses for the parkway that were too short for buses to pass under, which would limit the access people of color had to it. Those overpasses still stand.
It's a story that worries me about the future, considering that we're using AI to build far more powerful systems that can make predictive decisions about all manner of things. This isn't an idle concern: Law enforcement agencies are using AWS' Rekognition service to do image recognition today as part of their work. A recent study of competing facial recognition APIs showed they were less accurate identifying the gender of people with darker skin, especially women.
There's good news: Changing an algorithm is far easier than building a new train line or adjusting the height of a bridge. Our codification of bias need not be set in stone. But we're already developing technologies like algorithms that claim to provide predictive policing capabilities. Companies are already testing the use of AI for investing, money lending, and other tasks. We need to be working on this now.
In my view, there are a few things we can do. First, I'd like to see a non-industry body develop a system of auditing algorithmic bias, which would allow governments and businesses to restrict the use of algorithms that don't meet a particular bias metric. With that in place, I'd like to see governments identify situations where the biases of those algorithms must be made available to the public. Law enforcement, consumer finance, and health care seem like good candidates from the get-go.
Second, I believe governments and corporations should be required to disclose when an algorithm influenced the outcome of a decision. For example, if police arrest someone based in part on the evaluation of a predictive policing system, the suspect should have the right to know that.
I don't have high hopes any of that will happen, given the current political climate. But a boy can dream. It's the only way I'll be able to sleep at night, anyhow.
P.S. This is my last AI Weekly column for VentureBeat. Thanks so much for inviting me into your inbox over the past several months. Your choice to take time out of your day to read this newsletter means the world to me. If you want to stay in touch, find me on Twitter: @belril.
P.P.S. Please enjoy this video from Nvidia of an AI system filling in the contents of photos:
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redpajama_set_name: RedPajamaCommonCrawl


text:
Correlated bursts in temporal networks slow down spreading
Takayuki Hiraoka ORCID: orcid.org/0000-0001-7016-85601 &
Hang-Hyun Jo ORCID: orcid.org/0000-0002-4214-92041,2,3
Complex networks
Spreading dynamics has been considered to take place in temporal networks, where temporal interaction patterns between nodes show non-Poissonian bursty nature. The effects of inhomogeneous interevent times (IETs) on the spreading have been extensively studied in recent years, yet little is known about the effects of correlations between IETs on the spreading. In order to investigate those effects, we study two-step deterministic susceptible-infected (SI) and probabilistic SI dynamics when the interaction patterns are modeled by inhomogeneous and correlated IETs, i.e., correlated bursts. By analyzing the transmission time statistics in a single-link setup and by simulating the spreading in Bethe lattices and random graphs, we conclude that the positive correlation between IETs slows down the spreading. We also argue that the shortest transmission time from one infected node to its susceptible neighbors can successfully explain our numerical results.
Characterizing the interaction structure of constituents of complex systems is of utmost importance to understand the dynamical processes in those systems. The interaction structure has been modeled by a network, where nodes and links denote the constituents and their pairwise interactions, respectively1,2. When the interactions are temporal, one can adopt a framework of temporal networks3, where links are considered to exist only at the moment of interaction. Events in the temporal interaction patterns are often known to be non-Poissonian or bursty4,5,6, e.g., as shown in human communication patterns7,8,9,10,11,12,13,14. Here, bursts denote a number of events occurring in short active periods separated by long inactive periods, which can be related to 1/f noise15,16,17. In general, non-Poissonian temporal patterns can be fully understood both by inhomogeneous interevent times (IETs) and by correlations between IETs18. Inhomogeneous and correlated IETs have been called correlated bursts5,19. Then, along with the information on who interacts with whom, one can comprehensively characterize the topological and temporal interaction structure of complex systems20.
Non-Poissonian bursty interactions between constituents of a system have been known to strongly affect the dynamical processes taking place in the system21,22,23,24,25,26,27,28,29,30,31,32; in particular, spreading dynamics in temporal networks has been extensively studied. An important question is what features of temporal networks are most relevant to predict the speed of propagation, e.g., of disease or information. One of the crucial and widely studied features is the inhomogeneity of IETs in the temporal interaction patterns. It was shown that the bursty interaction patterns can slow down the early-stage spreading by comparing the simulated spreading behaviors in some empirical networks and in their randomized versions21,23,29. The opposite tendency was also reported using another empirical network or model networks25,26,30.
In contrast to the IET distributions, yet little is known about the effects of correlations between IETs on the spreading, except for few recent works33,34. This could be partly because many previous works have considered the contagion dynamics, e.g., susceptible-infected (SI) dynamics31, with the assumption of an immediate infection upon the first contact between susceptible and infected nodes, hence without the need to consider correlated IETs. In another work35, probabilistic contagion dynamics, i.e., naturally involving multiple consecutive IETs, was studied by assuming inhomogeneous but uncorrelated IETs. Therefore, the effects of inhomogeneous and correlated IETs on the spreading need to be systematically studied for better understanding the dynamical processes in complex systems.
In our paper, we study the effects of inhomogeneous and correlated IETs on the spreading taking place in temporal networks, by incorporating two contagion models, i.e., two-step deterministic SI and probabilistic SI dynamics. For modeling the inhomogeneous IETs, we consider power-law distributions of IETs, denoted by τ, as
$$P(\tau )\sim {\tau }^{-\alpha },$$
with power-law exponent α6. For characterizing the correlations between IETs, we adopt a memory coefficient M36 among others, e.g., bursty train sizes5. The memory coefficient M for a sequence of n IETs has been estimated by
$$M\equiv \frac{1}{n-1}\sum _{i=1}^{n-1}\,\frac{({\tau }_{i}-{\mu }_{1})({\tau }_{i+1}-{\mu }_{2})}{{\sigma }_{1}{\sigma }_{2}},$$
where μ1 (μ2) and σ1 (σ2) are the average and the standard deviation of the first (last) n−1 IETs, respectively. Positive M implies the tendency of large (small) IETs being followed by large (small) IETs. Negative M points towards the opposite, while M = 0 for uncorrelated IETs. In our setup, both P(τ) and M are inputs of the model, requiring us to consider M as a parameter rather than an estimator. Then by controlling the shape of P(τ) and the value of M for interaction patterns between nodes, one can study the effects of correlated bursts on the spreading in temporal networks. By analyzing the contagion dynamics on a single link, and then by simulating the spreading in regular and random temporal networks, we conclude that the positively-correlated inhomogeneous IETs slow down the spreading.
In order to study the spreading dynamics, we consider one of the extensively studied epidemic processes, i.e., susceptible-infected (SI) dynamics31: A state of each node in a network is either susceptible (S) or infected (I), and an infected node can infect a susceptible node by the contact with it. Here we assume that the contact is instantaneous. One can study a probabilistic SI (PSI) dynamics, in which an infected node can infect a susceptible node with probability η (0 < η < 1) per contact, as depicted in Fig. 1(a), i.e.
$$S+I\,\mathop{\longrightarrow }\limits^{\eta }\,2I\mathrm{.}$$
Due to the stochastic nature of infection, multiple IETs can be involved in the contagion, hence the correlations between IETs in the contact patterns can influence the spreading behavior. The case with η = 1 corresponds to the deterministic version of SI dynamics, which we call one-step deterministic SI (1DSI) dynamics: A susceptible node is immediately infected after its first contact with an infected node, see Fig. 1(b). This dynamics can be described by
$$S+I\to 2I.$$
Schematic diagrams for (a) the probabilistic susceptible-infected (SI) dynamics, (b) the one-step deterministic SI dynamics, and (c) the two-step deterministic SI dynamics. For each node, the susceptible or intermediate state is represented by a dashed horizontal line, while the infected state is by a solid horizontal line. In each panel, a node u gets infected in the time denoted by an upper vertical arrow, then it tries to infect its susceptible neighbor v whenever they make contact (vertical lines). The successful infection of v by u is marked by a lower vertical arrow. The time interval between the infection of u and that of v (striped band) defines the transmission time r. For the definitions of r0 and τs, see the text.
Finally, for studying the effect of correlations between IETs on the spreading in a simpler setup, we introduce two-step deterministic SI (2DSI) dynamics as a variation of generalized epidemic processes37,38,39,40, see Fig. 1(c). Here a susceptible node first changes its state to an intermediate state (S′) upon its first contact with an infected node; it then becomes infected after the second contact with the same or another infected node. This can be written as
$$\begin{array}{rcl}S+I & \to & S^{\prime} +I,\\ S^{\prime} +I & \to & 2I\mathrm{.}\end{array}$$
For modeling the interaction structure in a population, we focus on Bethe lattices as networks of infinite size, while regular random graphs and Erdös-Rényi random graphs will be later considered for finite network models. As for the temporal contact patterns, we assume that the contacts between a pair of nodes or on a link connecting these nodes are instantaneous and undirected. Moreover, the contact pattern on each link is assumed to be independent of the states of two end nodes as well as of contact patterns on other links. The contact pattern on each link is modeled by a statistically identical event sequence with inhomogeneous and correlated IETs. For this, the shape of IET distribution P(τ) and the value of memory coefficient M are inputs of our model. Firstly, we consider a power-law IET distribution with a lower bound τmin and an exponential cutoff τc as follows:
$$P(\tau )=(\begin{array}{ll}\frac{{\tau }_{{\rm{c}}}^{\alpha -1}}{{\rm{\Gamma }}\mathrm{(1}-\alpha ,{\tau }_{{\rm{\min }}}/{\tau }_{{\rm{c}}})}{\tau }^{-\alpha }{e}^{-\tau /{\tau }_{{\rm{c}}}} & {\rm{for}}\,\tau \ge {\tau }_{{\rm{\min }}},\\ 0 & {\rm{otherwise}},\end{array}$$
where Γ is the upper incomplete Gamma function. We fix τmin = 1 without loss of generality and set τc = 103 in our work. We note that our conclusions are robust with respect to the choice of τc. Thus, we are left with one relevant parameter, i.e., the power-law exponent α. Based on the empirical findings for α6, we consider the case with 1.5 ≤ α ≤ 3. Secondly, only the positive memory coefficient M is considered, precisely, 0 ≤ M < 0.4, based on the empirical observations36,41,42,43.
Two-step deterministic contagion
Single-link transmission
For understanding the spreading behavior on temporal networks, we first focus on how long it takes for the infection to transmit across a single link, say, from a node u to its neighbor v. If u gets infected from its neighbor other than v in time tu, and later it infects v in time tv, the time interval between tu and tv defines the transmission time r ≡ tv − tu. Here we assume that v is not affected by any other neighbors than u, for the sake of simplicity. In order for the infected u to infect the susceptible v, u must wait at least for the next contact with v. This waiting or residual time is denoted by r0, see Fig. 1. For the one-step deterministic SI dynamics, r = r0. Due to the independence of contact patterns of neighboring links, we can consider the infection of u to occur at random in time. In addition, since only the IETs larger than r contribute to the probability of having the transmission time r, we have the transmission time distribution as
$${Q}_{1{\rm{D}}}(r)=\frac{1}{\mu }{\int }_{r}^{\infty }\,d\tau P(\tau ),$$
with μ denoting the mean IET, see Methods for the detailed derivation. The average transmission time is directly obtained as
$${\langle r\rangle }_{{\rm{1}}D}\equiv {\int }_{0}^{\infty }\,drr{Q}_{1{\rm{D}}}(r)=\frac{1}{2}(\mu +\frac{{\sigma }^{2}}{\mu }),$$
where σ2 denotes the variance of IETs6. Note that a larger variance of IETs results in a larger average transmission time, expected to slow down the spreading.
In general, r can be given as the sum of r0 and subsequent IETs, as depicted in Fig. 1(a,c), for the generalized epidemic processes, including our two-step deterministic SI (2DSI) dynamics. In the case with 2DSI dynamics, the transmission process involves two consecutive IETs. If the infection of u occurs during the IET of τi, then the transmission time is written as
$$r={r}_{0}+{\tau }_{i+1},$$
with τi +1 denoting the IET following τi. Information on the correlations between τi and τi+1 is carried by the joint distribution P(τi, τi+1) or the conditional distribution P(τi +1|τi). Using P(τi +1|τi) with τi +1 = r − r0, Q1D(r) in equation (4) can be extended to obtain the transmission time distribution for the 2DSI case as
$${Q}_{{\rm{2}}D}(r)=\frac{1}{\mu }{\int }_{0}^{r}\,d{r}_{0}{\int }_{{r}_{0}}^{\infty }\,d{\tau }_{i}P({\tau }_{i})P(r-{r}_{0}|{\tau }_{i}),$$
where it is obvious from equation (6) that τi ≥ r0 and 0 ≤ r0 ≤ r, see Methods for the detailed derivation. The average transmission time is calculated as
$${\langle r\rangle }_{2{\rm{D}}}\equiv {\int }_{0}^{\infty }\,drr{Q}_{2{\rm{D}}}(r)={\langle r\rangle }_{1{\rm{D}}}+\frac{1}{\mu }\langle {\tau }_{i}{\tau }_{i+1}\rangle ,$$
$$\langle {\tau }_{i}{\tau }_{i+1}\rangle \equiv {\int }_{0}^{\infty }\,d{\tau }_{i}{\int }_{0}^{\infty }\,d{\tau }_{i+1}{\tau }_{i}{\tau }_{i+1}P({\tau }_{i},{\tau }_{i+1}\mathrm{).}$$
In order to relate this result to the memory coefficient in equation (2), we define a parameter as
$$M\equiv \frac{\langle {\tau }_{i}{\tau }_{i+1}\rangle -{\mu }^{2}}{{\sigma }^{2}}$$
to finally obtain the analytical result of the average transmission time:
$${\langle r\rangle }_{{\rm{2}}D}=\frac{3}{2}\mu +(\frac{1}{2}+M)\frac{{\sigma }^{2}}{\mu }\mathrm{.}$$
In the case with M = 0 for uncorrelated IETs, one gets 〈r〉2D = 〈r〉1D + μ.
We remark that our result in equation (11) is valid for arbitrary functional forms of IET distributions as long as their mean and variance are finite. M is coupled with σ2/μ, implying that the impact of correlations between IETs becomes larger with broader IET distributions. More importantly, we find that a stronger positive correlation between consecutive IETs leads to a larger average transmission time. This can be understood in terms of the role of the variance of IETs in the average transmission time, as shown in the 1DSI case. That is, the variance of the sum of two consecutive IETs is amplified by the positive correlation between those IETs. Based on the result of the single-link analysis, the positive correlation between IETs is expected to slow down the spreading in a population.
Spreading in Bethe lattices
In order to investigate the effects of correlations between IETs on the spreading in a population, we numerically study spreading dynamics in a Bethe lattice, i.e., a regular tree of infinite size in which every node has exactly k neighbors. One can relate this dynamics to the early-stage dynamics in regular random graphs, in which cycles are rare as long as the network size is sufficiently large. As mentioned, the contact pattern on each link is modeled by an independent and identical point process with the same P(τ) and M. Precisely, for each link, we draw n random values from P(τ) to make an IET sequence T ≡ {τi}i = 1, …,n, for sufficiently large n. Using equation (2), we measure the memory coefficient from T, denoted by \(\tilde{M}\). Two IETs are randomly chosen in T and swapped only when this swapping makes \(\tilde{M}\) closer to M, i.e., the target value. By repeating the swapping, we obtain the IET sequence whose \(\tilde{M}\) is close enough to M, and from this IET sequence we get the sequence of contact timings for each link. Then the temporal network can be fully described by a set of contact timings for all links. Each simulation begins with one node infected at random in time, which we set as t = 0, while all other nodes are susceptible at this moment. For each simulation, we measure the number of infected nodes as a function of time, I(t). The average number of infected nodes 〈I(t)〉 is found to exponentially increase with time, e.g., as shown in Fig. 2(a):
$$\langle I(t)\rangle \sim {e}^{at},$$
where a = a(k, α, M) denotes the exponential growth rate, known as the Malthusian parameter44. a turns out to be a decreasing function of M, indicating the slowdown of spreading due to the positive correlation between IETs, see Fig. 2(b–d). The slowdown can be more clearly presented in terms of the relative growth rate a/a0 with a0 ≡ a(M = 0) for all cases of k and α, as shown in Fig. 2(e–g). We summarize the main observations from the numerical simulations as follows:
a decreases with M.
a increases with α.
a increases with k.
The deviation of a/a0 from 1 tends to be larger for smaller α.
Two-step deterministic SI dynamics in Bethe lattices. (a) Average numbers of infected nodes as a function of time, 〈I(t)〉, in Bethe lattices with k = 4 for the same IET distribution with power-law exponent α = 1.5 in equation (3), but with various values of memory coefficient M. For each value of M, the average (dashed curve) and its standard error (shaded area) were obtained from 103 runs with different initial conditions. (b–g) Estimated exponential growth rates a, defined in equation (12) (top panels) and their relative growth rates a/a0 with a0 ≡ a(M = 0) (bottom panels) are plotted for various values of k, α, and M. The lines are guides to the eye.
The observation (i) is expected from equation (11), so is (ii) as both μ and σ2/μ decrease with α. (iii) is trivial. (iv) implies that the effect of M becomes larger for smaller α, which can be roughly understood by a larger value of σ2/μ coupled to M in equation (11). We remark that equation (11) is the result for a single-link transmission, requiring us to study the transmission time in networks.
In order to comprehensively understand the above observations, in particular, the k-dependence of a, we need to study the effect of time-ordering between infections to different neighbors45. For this, we introduce the shortest transmission time as
$${r}_{{\rm{s}}}\equiv \,{\rm{\min }}\,\{{r}^{\mathrm{(1)}},\,\cdots ,\,{r}^{(k-\mathrm{1)}}\},$$
where r(j) for j = 1, …, k − 1 denotes the transmission time from an infected node to its jth neighbor. Here we focus on the average of rs, denoted by 〈rs〉, which is a function of k, α, and M. In Fig. 3(a), we numerically find that a〈rs〉 is independent of M, implying that the effect of the correlations between IETs on spreading can be fully understood by 〈rs〉. Then we write a as follows:
$$a=\frac{h(k,\alpha )}{\langle {r}_{{\rm{s}}}\rangle }\mathrm{.}$$
Here h(k, α) is generally expected to be a function of k and α, while only its k-dependence is clearly shown in Fig. 3(b), where h(k, α) increases with k. In Fig. 3(c) we observe that as k increases, 〈rs〉 algebraically decays before converging to a constant, enabling us to assume that
$$\langle {r}_{{\rm{s}}}\rangle =f+g{(k-\mathrm{1)}}^{-\delta },$$
where f, g, and δ are non-negative constants independent of k. By fitting the numerical results of 〈rs〉 using equation (15), we find how these constants depend on α and M, as summarized in Fig. 3(d–f).
Two-step deterministic SI dynamics in Bethe lattices: The exponential growth rate a can be explained in terms of the average shortest transmission time 〈rs〉, where rs is defined in equation (13). (a) a〈rs〉 is overall independent of M for all values of k and α. (b) a〈rs〉, averaged over M, is an increasing function of k. The error bars represent standard deviation. (c) For the case of α = 1.5, 〈rs〉 is plotted as a function of k − 1 for various values of M. Each point was averaged over 5 × 105 realizations. In (a–c), the lines are guides to the eye. (d–f) Using a functional form of 〈rs〉 = f + g(k − 1)−δ in equation (15), f, g, and δ are estimated for all values of α and M. In (e), using g = g0 + g1M, we estimate g0 and g1 and plot (g − g0)/g1 against M, compared to the black line of y = x. The estimated g0 and g1 are shown in the inset of (e).
Firstly, we find that f is overall independent of M. In the limit of k → ∞, 〈rs〉 should asymptotically approach the smallest possible transmission time, denoted by rmin, leading to f = rmin. For the 2DSI dynamics and by our setup, f = τmin = 1 is expected, while the estimated values of f show systematic deviations from 1, possibly due to finite-size effects of k. Secondly and most importantly, g turns out to linearly increase with M such that
$$g={g}_{0}+{g}_{1}M,$$
with positive coefficients g0 and g1, eventually leading to the linear dependence of 〈rs〉 in equation (15) on M. Moreover, both g0 and g1 are found to decrease with α, shown in the inset of Fig. 3(e). These findings are comparable to the analytical result of average transmission time in equation (11). Finally, the estimated values of δ appear to slightly increase with M, while we consider δ to be constant of M in our argument. In sum, we rewrite 〈rs〉 in equation (15) as
$$\langle {r}_{{\rm{s}}}\rangle ={r}_{{\rm{\min }}}+({g}_{0}+{g}_{1}M)(k-{\mathrm{1)}}^{-\delta }\mathrm{.}$$
Combining a in equation (14) and 〈rs〉 in equation (17), we obtain the relative growth rate a/a0 as
$$\frac{a}{{a}_{0}}=\frac{{r}_{{\rm{\min }}}+{g}_{0}{(k-\mathrm{1)}}^{-\delta }}{{r}_{{\rm{\min }}}+({g}_{0}+{g}_{1}M)(k-{\mathrm{1)}}^{-\delta }},$$
by which the observation (iv) can be understood: In one limiting case when \({r}_{{\rm{\min }}}\ll {g}_{0}{(k-\mathrm{1)}}^{-\delta }\), the relative growth rate is approximated as
$$\frac{a}{{a}_{0}}\approx \frac{{g}_{0}}{{g}_{0}+{g}_{1}M},$$
which is independent of k but clearly showing the M-dependence. This can explain the numerical findings in the case with small α in Fig. 2(e). In the other limiting case when \({r}_{{\rm{\min }}}\gg ({g}_{0}+{g}_{1}M)(k-{\mathrm{1)}}^{-\delta }\), one gets
$$\frac{a}{{a}_{0}}\approx 1-\frac{{g}_{1}M}{{r}_{{\rm{\min }}}}{(k-\mathrm{1)}}^{-\delta },$$
i.e., a/a0 linearly but slightly decreases with M, showing a good agreement with the numerical results for large α in Fig. 2(g).
Conclusively, it turns out that the statistical properties of the shortest transmission time can account for the numerical results, while more refined approach needs to be taken for better understanding the effect of network structure on spreading, e.g., k-dependence of a in the case of Bethe lattices.
Probabilistic contagion
In a more realistic scenario than the two-step deterministic contagion dynamics, the infection can be described by a stochastic process, i.e., probabilistic SI (PSI) dynamics: An infected node infects a susceptible node with probability η upon contact. Similarly to the deterministic cases, we begin with the analysis for a single-link transmission. If the infection of u occurs during the IET of τi as depicted in Fig. 1(a), the transmission time for a successful infection of v after l failed attempts for l ≥ 0 is
$$r=(\begin{array}{ll}{r}_{0} & {\rm{for}}\,l=\mathrm{0,}\\ {r}_{0}+\sum _{j=1}^{l}\,{\tau }_{i+j} & {\rm{for}}\,l > 0.\end{array}$$
Information on the correlations between τi, …, τi+l is carried by the joint distribution P(τi, …, τi+l) for l + 1 consecutive IETs. The distribution of r, denoted by QP(r), can be written as the weighted sum of transmission time distributions for multi-step deterministic dynamics, similarly done in the previous work35:
$${Q}_{{\rm{P}}}(r)=\sum _{l\mathrm{=0}}^{\infty }\,\eta {\mathrm{(1}-\eta )}^{l}{Q}_{l}(r),$$
where Ql(r) denotes the distribution of transmission time after l failed attempts. Note that Q0(r) = Q1D(r) in equation (4) and Q1(r) = Q2D(r) in equation (7). Ql(r) for general l ≥ 1 is obtained using the joint distribution P(τi, …, τi +l) as
$${Q}_{l}(r)=\frac{1}{\mu }{\int }_{0}^{r}\,d{r}_{0}\prod _{j\mathrm{=1}}^{l}{\int }_{0}^{\infty }\,d{\tau }_{i+j}{\int }_{{r}_{0}}^{\infty }\,d{\tau }_{i}P({\tau }_{i},\,\cdots ,\,{\tau }_{i+l})\delta (r-{r}_{0}-\sum _{j\mathrm{=1}}^{l}\,{\tau }_{i+j}),$$
where it is obvious from equation (21) that τi ≥ r0 and 0 ≤ r0 ≤ r, and δ denotes a Dirac delta function. Then one gets the average transmission time as follows:
$$\langle {r}_{l}\rangle \equiv {\int }_{0}^{\infty }\,drr{Q}_{l}(r)={\langle r\rangle }_{1{\rm{D}}}+\frac{1}{\mu }\sum _{j\mathrm{=1}}^{l}\,\langle {\tau }_{i}{\tau }_{i+j}\rangle ,$$
$$\langle {\tau }_{i}{\tau }_{i+j}\rangle \equiv \prod _{j^{\prime} =i}^{i+j}\,{\int }_{0}^{\infty }\,d{\tau }_{j^{\prime} }{\tau }_{i}{\tau }_{i+j}P({\tau }_{i},\,\cdots ,\,{\tau }_{i+j}\mathrm{).}$$
For the details of the derivation, see Methods. We define the generalized memory coefficient between two IETs separated by j − 1 IETs36 as
$${M}_{j}\equiv \frac{\langle {\tau }_{i}{\tau }_{i+j}\rangle -{\mu }^{2}}{{\sigma }^{2}},$$
leading to
$$\langle {r}_{l}\rangle ={\langle r\rangle }_{1{\rm{D}}}+l\mu +\frac{{\sigma }^{2}}{\mu }\sum _{j\mathrm{=1}}^{l}\,{M}_{j}\mathrm{.}$$
We then obtain the analytical result of the average transmission time for the PSI dynamics as
$$\begin{array}{rcl}{\langle r\rangle }_{{\rm{P}}} & \equiv & {\int }_{0}^{\infty }\,drr{Q}_{{\rm{P}}}(r)=\sum _{l\mathrm{=0}}^{\infty }\,\eta {\mathrm{(1}-\eta )}^{l}\langle {r}_{l}\rangle \\ & = & {\langle r\rangle }_{1{\rm{D}}}+\frac{1-\eta }{\eta }\mu +\frac{{\sigma }^{2}}{\mu }\sum _{l=0}^{\infty }\,\eta {\mathrm{(1}-\eta )}^{l}\sum _{j=1}^{l}\,{M}_{j}\mathrm{.}\end{array}$$
As we introduce only the correlations between two consecutive IETs in our model, we expect Mj to exponentially decay according to j, where the decaying coefficient is denoted by γ with |γ| < 1: Mj = γMj−1 = … = γj−1M1, where M1 = M in equation (10). Finally, we have
$${\langle r\rangle }_{{\rm{P}}}=(\frac{1}{2}+\frac{1-\eta }{\eta })\mu +[\frac{1}{2}+\frac{M\mathrm{(1}-\eta )}{1-\gamma \mathrm{(1}-\eta )}]\frac{{\sigma }^{2}}{\mu }\mathrm{.}$$
We note that this result is valid for arbitrary functional forms of IET distributions as long as their mean and variance are finite. Similarly to the deterministic case in equation (11), the average transmission time for the PSI case turns out to be a linearly increasing function of the memory coefficient M.
Next, we numerically examine the spreading behavior for the PSI dynamics with η < 1 in Bethe lattices. Similarly to the two-step deterministic case, we observe an exponential growth in the average number of infected nodes as well as the slowdown of spreading when the memory coefficient is positive. For example, the case with η = 0.1 is depicted in Fig. 4. As η approaches 1, the slowdown effect due to the correlated IETs becomes weak, as expected (not shown). Based on the results in Fig. 5, we make overall the same conclusions as in the 2DSI case: a is a decreasing (increasing) function of M (both α and k), and the deviation of a/a0 from 1 tends to be larger for smaller α.
Probabilistic SI dynamics with η = 0.1 in Bethe lattices. All notations and simulation details are the same as in Fig. 2.
The above observations in the PSI case can be understood by the same argument made for the 2DSI case, namely, the functional form of a in equation (14) with 〈rs〉 in equation (17), but with some important differences: Firstly, the shortest possible transmission time is rmin = 0, although the estimated values of f show systematic deviations from 0 in Fig. 5(d). This deviation is denoted by a small positive value ε, leading to f = ε. Secondly, δ appears to be an increasing function of M rather than a constant in Fig. 5(f), which we assume to be δ = δ0 + δ1M with positive coefficients δ0 and δ1. We therefore modify 〈rs〉 in equation (17) as follows:
$$\langle {r}_{{\rm{s}}}\rangle =\varepsilon +({g}_{0}+{g}_{1}M)(k-{\mathrm{1)}}^{-({\delta }_{0}+{\delta }_{1}M)}\mathrm{.}$$
We note that due to the positive δ1, the above 〈rs〉 may decrease with M but only for sufficiently large k and M. However, we find no evidence for the decreasing behavior in the ranges of k and M studied in our paper. Using equation (30), the relative growth rate is obtained as
$$\frac{a}{{a}_{0}}=\frac{\varepsilon +{g}_{0}{(k-\mathrm{1)}}^{-{\delta }_{0}}}{\varepsilon +({g}_{0}+{g}_{1}M)(k-{\mathrm{1)}}^{-({\delta }_{0}+{\delta }_{1}M)}}\mathrm{.}$$
Since ε is a small number, we consider only the case of \(\varepsilon \ll {g}_{0}{(k-\mathrm{1)}}^{-{\delta }_{0}}\) to get the approximated relative growth rate as
$$\frac{a}{{a}_{0}}\approx \frac{{g}_{0}}{{g}_{0}+{g}_{1}M}{(k-\mathrm{1)}}^{{\delta }_{1}M},$$
which can account for the k-dependence of a/a0, including the case with α = 1.5 in Fig. 4(e). In Fig. 4(e–g), we observe that the difference between curves of a/a0 for different ks increases and then decreases as α increases from 1.5 to 3. This non-monotonic behavior can be related to the non-monotonic behavior of δ1 as a function of α, as depicted in Fig. 5(f).
Spreading in finite networks
So far we have focused on the spreading in Bethe lattices, i.e., regular networks of infinite size, which can also approximate the early-stage dynamics of spreading in finite networks as long as the cycles are rare. In addition to the early stage, the late-stage dynamics of spreading in finite networks has also been of interest30,46. For this, we employ two network models of size N: random regular graphs, in which every node has exactly k neighbors, and Erdös-Rényi random graphs, in which every possible pair of nodes is connected with a probability p, hence the average degree is p(N−1). On each of these graphs, both 2DSI and PSI dynamics are tested by the numerical simulations to measure the average numbers of infected nodes as a function of time, 〈I(t)〉. In all cases, we use networks of size N = 103, and the results are averaged over 103 simulation runs with different initial conditions for each parameter set.
In Fig. 6 we find that the positive correlation between IETs lowers the average number of infected nodes for the almost entire range of time, except for the final-stage dynamics. This final-stage dynamics can be quantified by the time it takes to fully infect the population, denoted by Tfull. As depicted in the bottom panels of Fig. 6, the average value of Tfull is clearly increasing with M when simulated in the regular random graphs, consistent with our previous results, while it appears to fluctuate around a constant in the case with ER graphs. Such fluctuating behavior of Tfull in the case with ER graphs can be understood in terms of the variation of degrees of nodes, in contrast to the regular random graphs with a fixed degree for all nodes: At the final stage, it can take a longer time for some lower-degree susceptible nodes to get infected from their infected neighbors. This large timescale for the infection of low-degree nodes and its large fluctuations can eventually dominate over the early- and intermediate-stage dynamics that show clear dependence on M. In another work using the apparently power-law IET distributions without exponential cutoff33, the positive correlation between IETs was reported to reduce Tfull, calling for more systematic approaches.
Top panels show the average numbers of infected nodes as a function of time, 〈I(t)〉, for the two-step deterministic SI dynamics (a,b) and the probabilistic SI dynamics with η = 0.1 (c,d) in two kinds of random networks of size N = 103: regular random graphs with k = 4 (a,c) and Erdös-Rényi (ER) graphs with p = 0.008 (b,d). In all cases, we used α = 1.5. For each value of M, the average (dashed curve) and its standard error (shaded area) were obtained from 103 runs with different initial conditions. Bottom panels show the average and standard deviation of the time it takes to fully infect the population, Tfull.
Spreading dynamics in temporal networks has been extensively studied for tackling the important issue of what features of temporal networks are most relevant to the speed of spreading taking place in such networks. One of the widely studied features is the inhomogeneity of interevent times (IETs), typically represented by heavy-tailed IET distributions, in the temporal interaction patterns between nodes. Although the impact of the inhomogeneous IETs on the spreading has been largely explored, yet little is known about the effects of correlations between IETs on the spreading. It is partly because many previous works have considered the contagion dynamics with the assumption of the immediate infection upon the first contact between susceptible and infected nodes, hence without the need to consider the correlated IETs. However, since temporal correlations in the interaction patterns can be fully understood both by IET distributions and by correlations between IETs18, the effects of inhomogeneous and correlated IETs on the spreading need to be systematically studied for better understanding the dynamical processes in complex systems. For this, we consider two contagion dynamics, i.e., two-step deterministic SI and probabilistic SI dynamics, naturally involving multiple consecutive IETs. For both dynamics, we derive analytical expressions of average transmission times 〈r〉 for a single-link setup, which turn out to linearly increase with the memory coefficient M as shown in equations (11) and (29). Therefore, the positive correlation between IETs is expected to slow down the spreading. By performing numerical simulations of the contagion dynamics in regular networks of infinite size and random graphs of finite size, we conclude that the positive correlation between IETs indeed slows down the spreading, compared to the case of uncorrelated IETs but from the same IET distributions.
The numerically obtained spreading speed, e.g., in Bethe lattices of degree k, could be successfully explained by means of the statistics of the shortest transmission time among k − 1 transmission times from one infected node to its k − 1 susceptible neighbors. In the case when IETs in the interaction patterns are largely homogeneous, the average transmission time 〈r〉 will serve as a representative timescale that determines the spreading speed. However, in the other case with inhomogeneous IETs or heavy-tailed IET distributions, the transmission time to each of k − 1 neighbors will be heterogeneously distributed, implying that neighbors infected earlier tend to spread the disease or information more quickly, hence more broadly, than those infected later. In this sense, the majority of the infected nodes can be largely explained by the descendants of early-infected neighbors, and the characteristic timescale of spreading speed can also be set by the average shortest transmission time 〈rs〉, rather than 〈r〉. Unfortunately, as the analysis of 〈rs〉 appears not to be straightforward, more detailed and rigorous understanding of the behavior of 〈rs〉 is left for future works.
So far we have focused on the positive memory coefficient based on the empirical observations, while the effects of the negative memory coefficient on spreading could also be studied. Our analytic results of average transmission times in equations (11) and (29) and numerical results on temporal networks enable us to predict the accelerated spreading due to the negativity of the memory coefficient. We also note that the shape of IET distributions may restrict the range of memory coefficient42, which should be properly considered when designing models.
Finally, we remark that in addition to the memory coefficient, the correlations between IETs have also been identified by other methods, e.g., in terms of bursty trains, which can detect long-range memory effects between IETs5: The number of events in each bursty train, i.e., the burst size, has been described by heavy-tailed distributions. Regarding this, the relation between memory coefficient and burst size distributions was recently studied19. Our approach can be extended by incorporating such heavy-tailed burst size distributions. We also note that more realistic network structures can be adopted for modeling temporal networks, such as networks with heterogeneous degrees47 and community structure48 among other network properties, e.g., stylized facts in social networks49.
Derivation of transmission time distributions
We first derive the transmission time distribution for the single-link transmission in the case with 1DSI. The transmission time r is equivalent to the time interval between a random arrival and the next event or contact. The probability that a random arrival falls within an IET of τ is τP(τ)/μ, where the mean IET μ is for the normalization. Given the arrival within the IET of τ, the probability of the transmission time being r is 1/τ. Then integrating such probability over the possible range of τ ≥ r gives the transmission time distribution as
$${Q}_{1{\rm{D}}}(r)={\int }_{r}^{\infty }\,d\tau \frac{\tau P(\tau )}{\mu }\frac{1}{\tau }=\frac{1}{\mu }{\int }_{r}^{\infty }\,d\tau P(\tau \mathrm{).}$$
The transmission time distribution for the 2DSI case can be derived similarly. Given the random arrival within an IET of τi and the residual time r0 to the next event, the transmission time r is r0 + τi+1. The probability of having τi+1 is given by the conditional distribution P(τi+1|τi), i.e., P(r − r0|τi). Together with the probabilities we already have for the 1DSI case, we obtain the transmission time distribution for the 2DSI case by integrating the probability over τi ≥ r0 and 0 ≤ r0 ≤ r as
$${Q}_{2{\rm{D}}}(r)={\int }_{0}^{r}\,d{r}_{0}{\int }_{{r}_{0}}^{\infty }\,d{\tau }_{i}\frac{{\tau }_{i}P({\tau }_{i})}{\mu }\frac{1}{{\tau }_{i}}P(r-{r}_{0}|{\tau }_{i})=\frac{1}{\mu }{\int }_{0}^{r}\,d{r}_{0}{\int }_{{r}_{0}}^{\infty }\,d{\tau }_{i}P({\tau }_{i})P(r-{r}_{0}|{\tau }_{i}\mathrm{).}$$
The last expression is rewritten by introducing the integration with respect to τi+1:
$${Q}_{2{\rm{D}}}(r)=\frac{1}{\mu }{\int }_{0}^{r}\,d{r}_{0}{\int }_{0}^{\infty }\,d{\tau }_{i+1}{\int }_{{r}_{0}}^{\infty }\,d{\tau }_{i}P({\tau }_{i},{\tau }_{i+1})\delta (r-{r}_{0}-{\tau }_{i+1}),$$
which then can be straightforwardly extended to the more general case for the transmission time after l failed attempts, see equation (23).
Derivation of the average transmission time after l failed attempts
The average of the transmission time after l failed attempts, i.e., 〈rl〉 in equation (24) can be calculated using Ql(r) in equation (23) as follows:
$$\langle {r}_{l}\rangle =\frac{1}{\mu }{\int }_{0}^{\infty }\,drr{\int }_{0}^{r}\,d{r}_{0}\prod _{j=1}^{l}{\int }_{0}^{\infty }\,d{\tau }_{i+j}{\int }_{{r}_{0}}^{\infty }\,d{\tau }_{i}P({\tau }_{i},\,\cdots ,\,{\tau }_{i+l})\delta (r-{r}_{0}-\sum _{j=1}^{l}\,{\tau }_{i+j})\mathrm{.}$$
We interchange the order of integration with respect to r0 and τi, i.e.,
$${\int }_{0}^{r}\,d{r}_{0}{\int }_{{r}_{0}}^{\infty }\,d{\tau }_{i}={\int }_{0}^{\infty }d{\tau }_{i}{\int }_{0}^{{\rm{\min }}\,\{r,{\tau }_{i}\}}\,d{r}_{0}$$
to rewrite equation (36) as
$$\langle {r}_{l}\rangle =\frac{1}{\mu }\prod _{j=0}^{l}{\int }_{0}^{\infty }\,d{\tau }_{i+j}P({\tau }_{i},\,\cdots ,\,{\tau }_{i+l}){\int }_{0}^{\infty }\,drr{\int }_{0}^{{\rm{\min }}\,\{r,{\tau }_{i}\}}\,d{r}_{0}\delta (r-{r}_{0}-\sum _{j\mathrm{=1}}^{l}\,{\tau }_{i+j})\mathrm{.}$$
Integration with respect to r and r0 in the above equation is straightforward:
$${\int }_{\sum _{j\mathrm{=1}}^{l}{\tau }_{i+j}}^{\sum _{j\mathrm{=0}}^{l}{\tau }_{i+j}}drr=\frac{{\tau }_{i}^{2}}{2}+{\tau }_{i}\sum _{j\mathrm{=1}}^{l}\,{\tau }_{i+j},$$
enabling us to finally obtain
$$\langle {r}_{l}\rangle ={\langle r\rangle }_{1{\rm{D}}}+\frac{1}{\mu }\sum _{j=1}^{l}\,\langle {\tau }_{i}{\tau }_{i+j}\rangle \mathrm{.}$$
Note that 〈r〉1D = 〈τ2〉/(2μ) in equation (5), with 〈τ2〉 denoting the second moment of the IET distribution P(τ).
The data generated and analysed in this study are available from the authors on request.
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The authors acknowledge financial support by Basic Science Research Program through the National Research Foundation of Korea (NRF) grant funded by the Ministry of Education (2015R1D1A1A01058958).
Asia Pacific Center for Theoretical Physics, Pohang, 37673, Republic of Korea
Takayuki Hiraoka & Hang-Hyun Jo
Department of Physics, Pohang University of Science and Technology, Pohang, 37673, Republic of Korea
Hang-Hyun Jo
Department of Computer Science, Aalto University, Espoo, FI-00076, Finland
Takayuki Hiraoka
T.H. and H.-H.J. designed the research. T.H. conducted the numerical simulations. T.H. and H.-H.J. analysed the results, wrote and reviewed the manuscript.
Correspondence to Hang-Hyun Jo.
Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Hiraoka, T., Jo, HH. Correlated bursts in temporal networks slow down spreading. Sci Rep 8, 15321 (2018). https://doi.org/10.1038/s41598-018-33700-8
Temporary Network
Infected Nodes
Bethe Lattice
Transmission Time Distribution
Memory Coefficient
Dynamics of cascades on burstiness-controlled temporal networks
Samuel Unicomb
, Gerardo Iñiguez
, James P. Gleeson
& Márton Karsai
Nature Communications (2021)
Burst-tree decomposition of time series reveals the structure of temporal correlations
, Takayuki Hiraoka
& Mikko Kivelä
Scientific Reports (2020)
Topological epidemic model: Theoretical insight into underlying networks
Yoshito Hirata
Chaos: An Interdisciplinary Journal of Nonlinear Science (2020)
Effects of memory on spreading processes in non-Markovian temporal networks
Oliver E Williams
, Fabrizio Lillo
& Vito Latora
New Journal of Physics (2019)
Analytically solvable autocorrelation function for weakly correlated interevent times
Physical Review E (2019)


A couple months ago, a friend asked me if I had any tips regarding Lake Quinault birding. I had no answer for her, so I decided to find someone who did.
My gumshoeing eventually led me to Mary O'Neil, secretary of the Grays Harbor Audubon Society. After graciously agreeing to talk with me about Lake Quinault birding, O'Neil gave me more information than I'd hoped for in my wildest dreams. My intention was to deliver an informative Lake Quinault birding guide, and I believe O'Neil helped me accomplish that.
O'Neil was introduced to birding by her sisters. Her passion for the hobby eventually led her to be an interpretive program worker at the Lake Quinault Lodge, a job she's still doing today.
Though O'Neil vehemently insists that she's no expert, I find it hard to believe there's anyone else in the world who knows more about Lake Quinault birding than she does.
O'Neil also happens to be a good-humored conversationalist and a pleasure to talk to, and I look forward to someday getting a personal Lake Quinault birding tour from her. So, without further ado, here is my conversation with Mary O'Neil.
Northwest Nomad: What are some of the more common birds people can expect to see around the Lake Quinault Lodge area?
O'Neil: Steller's jays are the dominant bird there. They raid the feeders constantly. We have the song sparrow. At the moment we have this dysfunctional song sparrow who thinks he's competing with a bird in the lobby window.
Then we have our white-crowned sparrows during the summer months, and they're having babies up there just having a wonderful time. In the summer months we also have ospreys that nest around the lake, and if you take the lodge's lake tour you'll quite often see a contest between the osprey and the eagles catching fish. It's really cool.
Anna's hummingbird. Photograph provided by Reed Moss (https://www.instagram.com/yssom/).
They don't have as many Anna's hummingbirds. Well they may have them, but everybody takes their feeders down after summer, not expecting a winter hummingbird.
Summer months, the hummingbirds are just thick. They get so thick people can't keep their feeders full. They do feed them there at the lodge, and everybody in the area has hummingbird feeders in their yards. You can walk in the woods and the hummingbirds will just buzz you. They're everywhere.
Winter months we're looking for the varied thrush. They should be returning quite soon, and if you look out to the water you'll see the common common merganser and occasionally red-necked grebes; not so much, but occasionally. And, of course, the common loon hangs out there on the lake quite a bit.
The most common bird up there, probably, is the Pacific wren. I saw some just yesterday out on the trail. There was one, probably a male, chipping at us. A second bird was quieter in the background, and it was like the louder chipping one was trying to draw attention away from it.
Nomad: What are some of the rarer birds to have out there, or the more difficult ones to see?
O'Neil: Well, I don't know how difficult it is, but we have grouse in the area. We have the ruffed grouse. Occasionally you'll just see them walking on the trails around the lodge. In the summer months, if you drive the 31 mile loop road, they'll be nesting in the dirt road in the upper end of the valley. It's quite common to see them there.
It's a little more difficult to bird among the trees. In the summer months, you can expect to see warblers. You'll definitely hear the Wilson's warbler, orange-crowned warblers, black-throated grays, but to see them is quite difficult, because they're in the thick trees. You can get lucky, but it's very difficult.
Black-capped chickadee. Image provided by @antlischio (https://www.instagram.com/antlischio/).
It seems year-round we've got the black-capped chickadees and the chestnut-backed chickadees. And if you walk the trails carefully you'll run into them.
One of the more rare birds that I found and took great delight in identifying was the warbling vireo. I'd never run into one before, but I found it, isolated it, and identified it. I felt really good then.
It was up in an alder tree, and it was really hard to pull out of the tree. There was one year and then the next there were three in the area year-round. I think they may have reproduced in the area, but I don't know.
Nomad: What are your tours like?
O'Neil: Well, it has been and will probably continue to be on Friday, Saturday, and Sunday. I just do a standard interpretive walk in the woods. But depending on the interests of the people there, I may turn the focus to a bird watching trip. But a lot of the times the people aren't interested as much in the birds, so I talk more about the plants and animals and some silly stories.
Nomad: How about for newcomers to the area who want to bird on their own? There's a lot of ground to cover out there. What would you say is the best place to start?
O'Neil: Definitely check out the lawn at the lodge. That's probably got your best variety. They're out in the open.
In the summer months, we have evening grosbeaks and black-headed grosbeaks. I think they nest in the area. They also hang out at the feeders quite a bit. You can count up to 10, 12 at a time, maybe even as many as 25. They're really cool to see. That's definitely a good place to start, just checking out the feeders there.
The further off the road you get, the more difficult it is to catch the birds, but around an area above the lodge some trees blew down rather seriously in 2007, so quite often if you get yourself in the right position you can look down over the shrubbery and the newly growing trees and then you'll see the trees in the tops. That's particularly where you want to look for the threshes. You can hear them with their upward, spiraling whistle.
Nomad: Do you know if there's any particularly good time to see eagles?
A magnificent osprey on the lookout. Photograph provided by txbirdguy (https://imgur.com/user/txbirdguy; https://www.reddit.com/user/txbirdguy).
O'Neil: I don't see that many, but they nest there in the summer months so that may be a good time, especially if they're particularly bent on stealing from the osprey. I don't see so many in the winter months, but there's great opportunity for them because the fish run in the winter months.
Nomad: What about winter birding? Winter seems to be a time when few people take advantage of Lake Quinault, but it's so great there during that time of year. So quiet.
O'Neil: I think you have quite a bit of opportunity because the lake birds will show up. I think there was a grebe out the other day. You have your western grebes and your red-throated grebes.
Swans winter near the Rain Forest Resort Village at Lake Quinault.
Song sparrow. Photograph provided by Reed Moss (https://www.instagram.com/yssom/).
Along the sides of the roads there's juncos and song sparrows. Then you've got fox sparrows out there. Winter we get the hermit's thrush; summer we get the Swainson's thrush. You may come across one of those.
If it snows, you can expect to see the varied thrush. The varied thrush likes to hover near the snowbanks. You'll hear it singing all summer long, but picking it out of the woods is not so easy. You can hear it, but you can't see it.
A couple years back the snow was real bad and I was doing a different job. I drove in several times early in the morning, after the snow plow had gone through, and the edge of the road would be so full of varied thrush you couldn't go very fast because they'd fly up in clouds in front of you.
Nomad: And you don't get to see them as much in the summer?
O'Neil: No. If you live there you might hear them, because I think they're around all the time, but they do elevation migration. In the summer months, they'll migrate up to the higher elevations where the snow is.
Nomad: Are there any other tips you'd like to share about Lake Quinault birding?
O'Neil: The most extensive way to do it, is to take a morning and do a morning walk around the lodge area. Then get in the car and drive up the south shore road, up to the bridge area. Take it very slowly. Don't rush it. The open fields just east of the lake area quite often will have hawks. That's also where the elk like to hang out.
There are cooper's hawks and sharp-shinned hawks. That's what you can expect to find if you troll slowly up through the valley. Of course you'll find flocks of Canada geese and sometimes ducks in the upper valley. The common merganser's the most common duck in the valley, but occasionally I've run into the harlequin duck.
Nomad: Well, I'm no good at identifying them, but I've seen lots of birds up at Irely Lake.
Nomad: Not yet, but I'm hoping! Do you have anything else to suggest? Anything I've missed?
O'Neil: Only that if you do plan on coming, check out the Lake Quinault lodge website and see what kinds of things they're offering.
Nomad: Great. Well, Mary, thank you so much for taking the time to share your knowledge with Northwest Nomad and my readers.
If you get the chance to see the Olympic Peninsula (my favorite spot in the Pacific Northwest), then you're going to want to make the absolute most of your time. The area's only sin is that there's too much to see in a short time, unless you have a good game plan. Luckily, Amanda Parshall of Rainshadow Escapes is there to help you do that.
I found Rainshadow Escapes on Twitter and was immediately struck with jealously over the fact that I hadn't thought of the business idea first. The feeling was short-lived, though. Parshall would have had a significant leg up on me in competition. She's been in the travel business, leading people all over the world, for a long time.
What's different about her current venture is that this time she's focusing her expertise on the area she calls home, bringing a combination of personal and professional experience that you're not going to find anywhere else.
I asked Parshall if she'd be interested in discussing her new business, the Olympic Peninsula, and Washington state's beaches. She agreed.
Thanks for your time, Amanda, and for the beautiful photographs.
Northwest Nomad: What inspired you to start Rainshadow Escapes?
Amanda Parshall: I find that the best businesses start with a passion, and Rainshadow Escapes is no different.
For over 10 years, I worked as a Travel Coordinator for expedition travel, helping people to reach exotic places all over the world. I loved that work. However, I started realizing that some of my most memorable travel moments happened in my own, impressive backyard: the Olympic Peninsula.
I also recognized that, while the Peninsula is one of the most special places on the planet, there is a real need to get the message out. Rainshadow Escapes is my platform for sharing this amazing region with the rest of the world, and to help everyone traveling here to discover the side of the Olympic Peninsula that will resonate the most for them.
Northwest Nomad: What are your personal favorite attractions along the Peninsula?
Amanda Parshall: For me, the Peninsula has it all: incredible scenery, abundant wildlife, endless opportunities for adventure and, most importantly, a deep history and culture. There is a strong connection to the past here, a past that locals are prideful of, and that echoes through every aspect of daily life in the small towns and enclaves throughout the region.
Of course, a major draw for many people, including myself, is the vast and varied landscapes. From beaches to mountains to rainforest, there is no shortage of places to lose yourself in solitude and pure beauty. I also love that you can truly get away from it all, but not be too far from civilization when the need for modern convenience arises.
It's hard to deny the lure to the popular attractions (Hurricane Ridge, Lake Crescent, the Hoh Rainforest…the list goes on). However, some of my personal favorite spots are a bit more off the beaten track, and show a side of the Peninsula that the main tourist route sidesteps.
Port Gamble, for example, is a captivating port town just minutes from the Hood Canal Bridge. It's a tiny town, but it's big on history and charm, and is home to some great restaurant, shops and a wonderful logging museum.
The Peninsula is home to many little surprises like this, and those surprises are what I get excited to share with people.
Northwest Nomad: How do you sell wintertime travel along the Peninsula?
Amanda Parshall: It's true that visitors dissipate quickly after the summer season is over, and the Peninsula becomes a different scene. This is not just a challenge, but a great opportunity for Rainshadow Escapes to highlight the benefits of traveling to the area in the off season (or what we would affectionately call, the "value season").
Not only is it quieter, less crowded and cheaper to travel outside of the summer months, but every season has something unique to offer. I am personally a shoulder season traveler, and right now we are in the height of one of my favorite times of year on the Peninsula: fall. The autumn leaves are at the peak of color, days are clear and crisp, and area restaurants are serving up the bounty of the local harvests.
Winter is the perfect time to spend a relaxing few days on the coast, cozying up to a fire and watching the storms pound the surf from the comforts of your rental cabin.
And spring brings stellar wildlife viewing opportunities, allowing the best chance to see elk, bear and birds, among others. One of my goals with Rainshadow Escapes is to encourage people to experience the Olympic Peninsula in new and different ways, and to take advantage of these amazing seasonal opportunities.
Northwest Nomad: In addition to general travel, does Rainshadow Escapes work with specific interests, such as anglers, hunters, etc.?
Amanda Parshall: We don't specialize in any specific type of travel. Our focus is to know all that the Peninsula has to offer, understand individual traveler's interests, and match the two together. As a travel concierge service, we aim to cater to any interests and travel styles.
Northwest Nomad: If you had a client who asked for one Washington beach to see, and one only, which would you pick?
Amanda Parshall: Our travel planning service is designed around the idea that no two people are the same or are looking for the same trip experience, so this is a difficult question to answer. But if someone was looking for the iconic Washington coast beach experience, I might recommend Second Beach.
A personal favorite, Second Beach requires a short but scenic hike in, and offers rewards of sea stacks and tidepools. And if you're lucky, you might catch a glimpse of one of the estimated 800 sea otters that lives on the Quillayute Needles National Wildlife Refuge, which is within eyesight of the beach.
Northwest Nomad: What is something about the Peninsula that you don't feel gets promoted enough?
Amanda Parshall: I believe one of the more unique attractions in the Peninsula that gets less attention than deserved is the Olympic Discovery Trail.
The trail is a 130-mile path spanning across the Peninsula from Port Townsend to the Pacific Ocean. Whether you're interested in a multi-day bike tour or a short stroll, the Olympic Discovery Trail is an exceptional way to take in views of old growth forest, snowy mountains and picturesque seascapes.
Northwest Nomad: What annual Port Angeles events do you consider "must see;" or, at least, "really, really should see"?
Amanda Parshall: The Dungeness Crab & Seafood Festival in October is a huge event in Port Angeles, and is another great reason to visit the Peninsula in the fall. Not only does the 3-day event highlight a local culinary favorite, it showcases local art, music and cultural activities. If you're interested in the local scene and love seafood, this event is a must-do!
Northwest Nomad: Is there anything else you'd like to tell people about Rainshadow Escapes?
Amanda Parshall: Rainshadow Escapes makes planning a trip to the Olympic Peninsula easy, whether it's a quick weekend getaway or a 2-week adventure.
Our Trip Questionnaire makes it simple for you to share your personal interests and travel style, allowing us to customize the itinerary just for you using insider knowledge. The Peninsula is a magical place and we would love the opportunity to share our home with you!
Lake Quinault and the Quinault Rain Forest provide some of the best outdoors recreation in the Pacific Northwest. They're terrific for hiking, fishing, and viewing wildlife.
Black bears, black-tailed deer, bald eagles, and cougar all inhabit the region. In my travels across the landscape (it's my favorite spot in Washington and I go there frequently), I've run into all of those animals at least once, and often at close proximity (the bear a little too close for comfort in fact) in their natural environments.
Perhaps the best wildlife to see in the area is the herd of Roosevelt elk at Quinault. The sizable herd regularly inhabits the area and can be found quite reliably in the woods and fields just off northeast portion of North Shore Road.
This area of Lake Quinault gets little visitation, especially in the off-season autumn/winter months. The road is rough and sometimes washed out, so even on days when Lake Quinault Lodge is full, you can find some solitary remove in this area.
The elk here were about 100 yards off the side of North Shore Road, Lake Quinault, Washington.
The Roosevelt elk of Quinault feel safe and at home there, so they generally won't scatter if you set up shop to observe them (as long as you don't get too close, of course). They'll go about their social business at ease.
I watched them for nearly an hour once, and they just went about their routine as though I wasn't there. Obviously, I can't speak for every encounter with the animals, but in my personal experience they've never bolted when I stopped to watch.
The elk are named after President Theodore Roosevelt, famous wildlife enthusiast and the man who started the national park system in the United States. The elk at Quinault are significantly bigger than the black-tail deer they share space with (the deer are beautiful, as well, though harder to find).
Male elk at Quinault, identified by their antlers, are larger than the females, but all share the characteristic darker-brown heads and lighter-brown bodies. They eat meadow grasses, ferns, shrubs, and lichens.
Stretching that neck out for the tasty upper leaves.
The elk can also be seen regularly in the Hoh Rain Forest, and I'll be covering that area, as well. For the purposes of this article, however, I'm focusing on the elk at Quinault.
The elk move throughout the area, but they frequently congregate along a stretch of North Shore Road. Google Maps won't let me pin to the fields, so I had to pin the Quinault Rain Forest Ranger Station (which has a nice little nature trail) in the interactive map below.
If you go east about 10 miles east of that ranger station, you'll find some open fields. The Roosevelt elk herd often hangs out in those fields, which are pretty close to the road–I'm talking close enough to watch clearly without equipment. With binoculars or a camera, you can see the sky reflected in their eyes.
You can drive to the area either from North Shore Road or from South Shore Road, both of which run into each other to form a loop around Lake Quinault. South Shore Road has merit because it's better maintained and makes the approach from Lake Quinault Lodge, but I'm going to start with North Shore Road because the ranger station is the most easily identifiable landmark close to the elk.
Before going on, let me stress that you WILL lost phone reception for a good portion of this drive. This isn't a great cause for concern, as I've explained below, but just be aware that you may want to write directions down or bring a hard copy of a map.
It's difficult to get lost on the North Shore Road drive because it's the only real road out there, other than some primitive Forest Service roads branching off into the woods. The one spot that may cause confusion is at the end of North Shore Road. There, you'll encounter a spot where the North Shore Road turns left/north to continue as North Shore Road and right/south to go over a bridge connecting North Shore Road to South Shore Road.
If you want to continue the loop and head back to the main road, you need to go over the bridge to the south rather than the road continuance to the north. Even if you miss this exchange, though, you'll just end up driving a dead end road that leads to North Forks Campground, a Park Service ranger station, and the Irely Lake Trail (among other walks). It's a dead end road and will just take you a few miles out of your way and into beautiful country.
You can also get to the elk-viewing spot by driving up Lake Quinault's South Shore Road from Lake Quinault Lodge, which may be the most desirable route if your car isn't well suited to rough driving. North Shore Road gets pretty tough in parts, particularly after heavy storm events. South Shore Road also has its hairy moments sometimes, but in my experience after years of visiting this area regularly, it's generally kinder to vehicles than the North Shore Road.
If you decide to come up South Shore Road, just remember to turn left over the bridge linking South Shore Road to North Shore. If you've got the vehicle for it, though, I recommend driving the whole Lake Quinault North/South Shore Road loop. It's a terrific drive through moss-laden trees and mountain views.
Regardless, remember to write directions down or take a hard copy map, because you WILL lost phone reception for a good portion of this drive. Also, South Shore starts out well paved, but be prepared because it will turn into a dirt road that has lots of big holes and rocks.
As mentioned in the preceding section, close to the area with the elk herd is the North Forks Campground and the Irely Lake trail. I go camping and hiking in that area frequently.
Lady elk enjoying meadow grasses.
Once, in winter, I stumbled upon a black bear in one of the North Forks camping spots (I was the only person staying there). It was a spectacular encounter. The bear was no more than 10 yards from me when we first realized we weren't alone. He watched me curiously as I walked to the outhouse and then lumbered off. I was a bit nervous camping that evening!
If you go to this area during the salmon runs, you've got a good shot of seeing bald eagles picking off the fish. Having grown up in Pennsylvania where bald eagle were nonexistent, I've never stopped being amazed at the sight of these birds.
One time (I swear) I saw a lynx dip into the woods on South Shore Road. A big chunk of road had collapsed from flooding and made the road inaccessible to vehicles. National Forest Service sites say lynx aren't found in this area, but I swear that I caught a fleeting glimpse of one.
Lake Quinault is also an excellent place for birding.
The area is a terrific spot for wildlife enthusiasts. Obviously, you can never know for sure what you'll run into, but the elk are about as safe a bet as I know of.
Go check them out. Maybe you'll see some bear or eagle, too.
If you decide to make a longer trip of it, consider staying at the Quinault River Inn. There are many great choices for lodging in the Lake Quinault area, but the Inn has always been my favorite. It's run by terrific people and is set off on quiet spot by the riverside.
No, I do not have any affiliation with the Inn and receive no compensation from them. I just really like their establishment.
If any of you need more specific directions to the open fields I discussed, shoot me a message and I'll walk you to the spot. Also, I'd love to hear about your experiences.


Indiana University – Bloomington is a residential academic and research campus with more than 15,600,000 square feet of space serving more than 41,000 students and 7,500 faculty and staff. In July of 2010, 8760 Engineering was hired to prepare an Integrated Energy Master Plan for the campus. The Energy Master Plan was integrated with the Campus Master Plan and the Campus Sustainability Report to meet the sustainability goals of the campus.
The main objective is to achieve a 20% reduction in GHG emissions by 2017, a 50% reduction by 2027, and carbon neutrality by 2047. The overall goal of the work was to position the campus as a model of sustainable energy management and the effective use of life-cycle cost economics to ultimately achieve the goals of the Campus Sustainability Report.


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20.10.2015 19:37 Russia`s recession-hit economy slumps 4.3% in third quarter of 2015
Russia`s economy shrank 4.3% in the third quarter this year, the government said yesterday, as a recession caused by low oil prices and Western sanctions over Ukraine continued to take its toll. Deputy economy minister Alexei Vedev told Russian news agencies that preliminary estimates put the year-on-year drop in gross domestic product for the third quarter at 4.3%, after contracting by 3.8% in September compared with 4.6% in August. Officials in Russia are struggling to breathe life into the economy as the rouble has dropped precipitously in value and inflation and poverty have risen sharply. Overall, the economy shrank 3.8% for the first nine months of the year, Vedev said. In a sign of how tough the situation has become, official statistics released yesterday showed that consumer spending in the country is falling at its steepest rate in 15 years. Year-on-year retail sales fell by 10.4% in September, the sharpest drop since 2000 according to RIA Novosti news agency, as Russians have seen their spending power stripped away by the crisis. Statistics showed that real household incomes dropped by 9.7% in September on the back of a 9.8% plunge in August.
18.10.2015 11:29 No Good News for Russia`s Economy
Even such exciting foreign policy news as Russia`s intervention in the Syrian civil war does nothing to help the condition of this country`s economy. To the contrary, even prior to 2014, Russia spent an inordinate amount on military expenses and for the country`s "security" — in the broad sense of the word. And now that military action "justifies" otherwise pointless and nonproductive expenses, it has become even more difficult to reduce outlays for weapons production. It was clear 30 years ago that the Soviet economy could not cope with the burden of excessive military spending. However, leaders refused to cut those expenses until an economic disaster hit and those outlays — along with all others — were drastically reduced. And it is just as wrong to blame those who were in power at the time as it would be to blame an emergency room physician for the death of a patient who had led an unhealthy lifestyle for decades. Neither does the economy receive a boost from cultural news. News that prominent Russian-language writer Svetlana Alexievich won the Nobel Prize for literature cheered those who cherish Russian literature, but it only deepened the paranoia of those who believe the award is given for political reasons and that foreign forces have spawned the recent rash of civil wars and revolutions.
16.10.2015 11:33 Why the US market for buying and selling a business is improving
Why the market for buying and selling a business is improving While the post-recession business-for-sale market has generally favored buyers, there are several signs that we`re experiencing a shift towards a more balanced market. Early 2015 data shows an improvement in the overall financial health of small businesses and an uptick in the pace of small business transactions. In fact, data from BizBuySell.com`s most recent Q1 2015 Insight Report showed a 6 percent year-over-year increase in business transactions, which marks two years of consistent financial improvement in the business-for-sale market. Several factors contribute to market conditions that benefit buyers and sellers alike: 1. Improving financial health. 2. Growing supply and demand. 3. A more balanced market.
15.10.2015 17:51 Russian real estate investment market lags stabilization in macro indicators in H1 2015
In H1 2015 Russian real estate investment volumes decreased by 33% compared to the same period of the previous year, with total investment volumes at USD1.1bn, according to analysts` calculations. This number includes Q2 investment volume of USD578m, down 33% YoY. "Following the market dislocation of 2014 and Q1 2015, during Q2 2015 we have seen evidence building that the economy is starting to stabilize, which, in our view, will have a positive impact on the real estate market in the medium to long term," - Tom Mundy, Head of Research, JLL, Russia and CIS, commented. – "First, the market has upgraded its GDP growth forecasts for Russia. For example, The World Bank lifted its forecast for 2015 forecast to a contraction of 2.7%, up from the contraction of 3.8% it predicted in April. In addition, it has revised up its growth forecast for 2016 to 0.7% from the decline of 0.3%. However there is unlikely to be a positive impact on Russian real estate market in short-term since inflationary expectations remain elevated which is leading to continued pressure on domestic demand. Furthermore, though the central bank continues to decrease the policy rate it remains high which is keeping the cost of debt financing beyond the reach of most. As a result we maintain our forecast for Russian real estate investment volumes for this year at USD3.0bn."
13.10.2015 14:02 The Syrian Campaign Nails Myths About the Size of Russia`s Economy
Anyone who reads Western media coverage of Russia`s intervention in Syria will notice that a note of bafflement has crept in. How can Russia, the country "that doesn`t produce anything", whose economy is supposedly smaller than Britain`s or even Spain`s, and which is said to be economically on the ropes, able to conduct an air campaign in Syria? The short - and obvious - answer is that Russia`s economy is much bigger than Western political leaders and commentators imagine. Not only would Britain or Spain be unable to mount the sort of air campaign the Russians are waging in Syria. They cannot run a huge space programme, develop rockets like the Angara rockets, build a space centre like Vostochny, develop the Arctic, construct nuclear powered ice breakers, or feed themselves. Nor can they build a bridge as rapidly as the one the Russians are building to Crimea. Nor can they create alternatives to Google like Yandex, or alternatives to GPS like GLONASS, or alternatives to SWIFT like the Russians have done in just a few months.
10.10.2015 12:36 Good News On Russia Economy As Inflation Seen 50% Lower Next Year
Russian inflation is on the decline and by next year will be cut in half, if central banker Elvira Nabiullina gets lucky and the country`s weak economy cooperates.Barclays Capital thinks she will get lucky. And that means interest rates will start to fall next year. Once that happens, investors will start creeping into Russian stocks once more. Russia inflation declined slightly in September to a whopping 15.7% year over year, reversing direction after two months of increases. The Russian ruble and inflation pass-throughs have been the driving factors behind price increases there, says Daniel Hewitt, a Russian fixed income analyst for Barclays Capital in London. Between May to mid-September the ruble depreciated by nearly 40% against the dollar, tracking the woes of oil futures. But for the past month, the Russian currency has stabilized and even slightly appreciated. Ruble depreciation has led to inflation pass-through, making imports more expensive and pushing consumer costs higher for several months. With the ruble stabilizing, inflation is likely to stall for now.
09.10.2015 12:28 Russian universities rise in Times Higher Education rankings
The Moscow State University (MSU) jumped 35 places to 161st in the latest Times Higher Education (THE) World University Rankings. The total number of Russian universities listed increased from 2 to 13. The Lomonosov Moscow State University (MSU) was ranked 161 out of 800 in the Times Higher Education (THE) World University Rankings 2015-16, which were released on October 1st. 13 Russian academic institutions made the list this year, compared to just 2 in the 2014-15 rankings. MSU was once again ranked highest among the Russian universities. Last year, MSU was ranked 196 out of 400 (the list was expanded to 800 universities this year). The only other Russian university that was in the list last year was the Novosibirsk State University, ranked in the 301-350 position. This year, it fell in the rankings to 401-500. In the 2015-16 rankings, the Russian top 5 also included the Peter the Great St Petersburg Polytechnic University (201-250), the Tomsk Polytechnic University (251-300), the Kazan Federal University (301-350) and the National Research Nuclear University MePhi (301-350). "It`s great that Russia has 13 institutions in this list, with five of its universities sitting within the top 400," says Phil Baty, Editor, Times Higher Education World University Rankings.
07.10.2015 12:04 IMF Downgrades Global Economic Outlook Again
A slowdown in emerging markets is pushing the world economy into its weakest expansion since the financial crisis, the International Monetary Fund said Tuesday as it warned of a rising risk of a global recession and once again downgraded its growth outlook. China`s deceleration and the subsequent plummet in commodity prices revealed a developing world that overinvested, borrowed excessively and exhausted its ability to expand without major economic overhauls. The IMF cut its forecast for emerging markets to 4% this year, down 0.2 percentage point from its last update in July. That marks the fifth consecutive year of declining growth and a level nearly half the rate the IMF recorded six years ago. Modest growth in the U.S. and a meager recovery in the eurozone, meanwhile, haven`t been able to offset falling output in emerging markets. The IMF cut its projections for global growth to 3.1% this year from its previous forecast of 3.3%. Souring emerging-market prospects also are muting next year`s rebound: The IMF reduced its prediction for global growth in 2016 by the same amount to 3.6%.
04.10.2015 12:39 World Bank Downgrades Russia`s Economic Outlook
Russia`s economic outlook has worsened, the World Bank said on Wednesday, but the government is still betting that the oil-dependent economy will return to growth as soon as next year. Russia`s economy is now seen shrinking by 3.8% in 2015 and by a further 0.6% in 2016, according to the World Bank baseline scenario. In June, the World Bank had expected Russia`s economy to contract 2.7% this year before returning to growth and expanding 0.7% next year. Hit by a drop in the price of oil, along with Western sanctions, Russia`s economy has slid into recession for the first time since 2009. Russian authorities have so far done little to kick-start growth, hoping that the economy will grow anyway next year thanks to the base effect. The World Bank is less optimistic. "Adverse external conditions pose a serious challenge to short-term growth prospects," the World Bank said in a report. "Yet, high policy uncertainty prevails and the country`s outlook hinges not only on the evolution of external factors but also on its internal capacity to adapt to an increasingly difficult macro-fiscal context," it said.
01.10.2015 13:03 Five things to know about Russia`s failing economy
Russian President Vladimir Putin will try to reclaim his place in the superpowers club on Monday, addressing the UN General Assembly and meeting President Obama on the sidelines to discuss the crises in Syria and Ukraine. But while Putin takes center stage in New York, millions of Russians are sliding back into poverty. Here are five things to know about the Russian economy: 1. Economic misery. 2. Low oil prices. 3. Sanctions pain. 4. No new friends. 5. It`s not just oil and sanctions.


Excuse the temporary off-topic post but in a continuing theme of "breadth of content is valuable", I thought I'd share with pride an article recently published about my one of the houses my brother designed/built.
I spend a heap of time away from New Zealand and often when I'm travelling I get asked why I don't just move to Silicon Valley/Sydney/Denver/Wherever else I happen to be. My traditional answer to that question is to just point whomever is asking me to a picture of my home – the space, quiet, sheer romance and cleanliness of it all tends to answer things for them.
My brother's places however take all of that to a new level – idyllic retreats literally carved from living trees and sitting naturally in their environments. So without further ado, here's a selection of images of said brother and said house. Enjoy!


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your basic shoe storage features four ROWS OF ANGLED RACKS and a white METAL FRAME .
this sophisticated shoe storage featues THREE DRAWERS THAT FIT FOUR PAIRS OF SHOES IN EACH DRAWER . Hardware is in silver with black finish.