Depression is one of the most common mental health issues in the United States, affecting the lives of millions of people suffering from it as well as those close to them. Recent advances in research ...on mobile sensing technologies and machine learning have suggested that a person's depression can be passively measured by observing patterns in people's mobility behaviors. However, the majority of work in this area has relied on highly homogeneous samples, most frequently college students. In this study, we analyse over 57 million GPS data points to show that the same procedure that leads to high prediction accuracy in a homogeneous student sample (N = 57; AUC = 0.82), leads to accuracies only slightly higher than chance in a U.S.-wide sample that is heterogeneous in its socio-demographic composition as well as mobility patterns (N = 5,262; AUC = 0.57). This pattern holds across three different modelling approaches which consider both linear and non-linear relationships. Further analyses suggest that the prediction accuracy is low across different socio-demographic groups, and that training the models on more homogeneous subsamples does not substantially improve prediction accuracy. Overall, the findings highlight the challenge of applying mobility-based predictions of depression at scale.
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Evaluating exposure to radio frequencies (RF) at population-scale is important for conducting sound epidemiological studies about possible health impact of RF radiations. Numerous ...studies reported population exposure to RF radiations used in wireless telecommunication technologies, but used very small population samples. In this context, the real exposure of the population at scale remains poorly understood. Here, to the best of our knowledge, we report the largest crowd-based measurement of population exposure to RF produced by cellular antennas, Wi-Fi access points, and Bluetooth devices for 254,410 unique users in 13 countries from January 2017 to December 2020. First, we present methods to assess the population exposure to RF radiations using smartphone measurements obtained using the ElectroSmart Android app. Then, we use these methods to evaluate and characterize the evolution of RF exposure. We show that total exposure has been multiplied by 2.3 in the four-year period considered, with Wi-Fi as the largest contributor. The cellular exposure levels are orders of magnitude lower than regulation limits and are not correlated to national regulation policies. The population tends to be more exposed at home; for half of the study subjects, personal Wi-Fi routers and Bluetooth devices contributed to more than 50% of their total exposure. In this work, we showcase how crowdsource-based data allow large-scale and long-term assessment of population exposure to RF radiations.
We study a group-formation game on an undirected complete graph
G
with all edge-weights in a set
W
⊆
ℝ
∪
{
−
∞
}
. This work is motivated by a recent information-sharing model for social networks ...(Kleinberg and Ligett, Games Econ. Behav.
82
, 702–716
2013
). Specifically, we consider partitions of the vertex-set of
G
into groups. The individual utility of any vertex
v
is the sum of the weights on the edges
uv
between
v
and the other vertices
u
in her group. – Informally,
u
and
v
represent social users, and the weight of
uv
quantifies the extent to which
u
and
v
(dis)agree on some fixed topic. – For a fixed integer
k
≥ 1, a
k
-
stable partition
is a partition in which no coalition of at most
k
vertices would increase their respective utilities by leaving their groups to join or create another
common
group. Before our work, it was known that such a partition always exists if
k
= 1 (Burani and Zwicker, Math. Soc. Sci.
45
(1), 27–52
2003
). We focus on the regime
k
≥ 2.
Our first result is that when all the social users are either friends, enemies or indifferent to each other (i.e.,
W
=
{
−
∞
,
0
,
1
}
), a partition as above always exists if
k
≤ 2,
but it may not exist if
k
≥ 3. This is in sharp contrast with (Kleinberg and Ligett, Games Econ. Behav.
82
, 702–716
2013
) who proved that
k
-stable partitions always exist, for any
k
, if
W
=
{
−
∞
,
1
}
.
We further study the intriguing relationship between the existence of
k
-stable partitions and the allowed set of edge-weights
W
. Specifically, we give sufficient conditions for the existence or the non existence of such partitions based on tools from Graph Theory. Doing so, we obtain for most sets
W
the largest
k
(
W
)
such that
all
graphs with edge-weights in
W
admit a
k
(
W
)
-stable partition.
From the computational point of view, we prove that for any
W
containing
−
∞
, the problem of deciding whether a
k
-stable partition exists is NP-complete for any
k
>
k
(
W
)
.
Our work hints that the emergence of stable communities in a social network requires a trade-off between the level of collusion between social users, and the diversity of their opinions.
Social networks inherit societal biases present across lines of gender, race, socioeconomic status, and other factors. Networks can structurally perpetuate unequal access to information and ...opportunities through homophilous dynamics. While there is substantial knowledge about inequity in the diffusion of opportunities in a network where nodes seek them from their immediate neighbors, much less is known when considering beyond that first hop. In this paper, we leverage recent mathematical analysis of network fairness to prove that enabling simple multi-hop dissemination can reduce inequity towards a minority group in the network as long as homophily is sufficiently weak. Otherwise, our necessary and sufficient condition proves that multi-hop dissemination strategies amplify the bias already found amongst considering direct neighbors. We empirically validate these results on four social network datasets as well as present an example of a key application of our findings with a scenario of individuals who leverage their personal network to seek job referrals. Our results suggest that online platforms designing algorithms to promote opportunities to multi-hop connections must carefully take into account network metrics measuring group size and homophily in order to avoid amplifying bias against marginalized groups on their platforms.
Parallel and distributed processing systems have expanded in size as technology advances in cloud computing and big data analytics. A critical issue concerns throughput scalability: whether ...throughput decreases to zero as the systems scale in size and capabilities. We model parallel and distributed processing systems as fork and join queueing networks with blocking (FJQN/Bs). Such networks can have arbitrary topology, arbitrary initial state, and generally distributed service times. We propose a key topological concept, called the “minimum level,” that determines the throughput scalability of FJQN/Bs. We construct throughput bounds as functions of minimum level, network degree, buffer sizes, and processing speed, and we present necessary and/or sufficient conditions to guarantee throughput scalability of arbitrary size and topology FJQN/Bs.
The e-companion is available at
https://doi.org/10.1287/opre.2018.1748
.
We study the dissemination of dynamic content, such as news or traffic information, over a mobile social network. In this application, mobile users subscribe to a dynamic-content distribution ...service, offered by their service provider. To improve coverage and increase capacity, we assume that users share any content updates they receive with other users they meet. We make two contributions. First, we determine how the service provider can allocate its bandwidth optimally to make the content at users as "fresh" as possible. More precisely, we define a global fairness objective (namely, maximizing the aggregate utility over all users) and prove that the corresponding optimization problem can be solved by gradient descent. Second, we specify a condition under which the system is highly scalable: even if the total bandwidth dedicated by the service provider remains fixed, the expected content age at each user grows slowly (as log(n)) with the number of users n. To the best of our knowledge, our work is the first to address these two aspects (optimality and scalability) of the distribution of dynamic content over a mobile social network.
Pocket switched networks and human mobility in conference environments Hui, Pan; Chaintreau, Augustin; Scott, James ...
Applications, Technologies, Architectures, and Protocols for Computer Communication: Proceeding of the 2005 ACM SIGCOMM workshop on Delay-tolerant networking; 26-26 Aug. 2005,
2005
Conference Proceeding
Odprti dostop
Pocket Switched Networks (PSN) make use of both human mobility and local/global connectivity in order to transfer data between mobile users' devices. This falls under the Delay Tolerant Networking ...(DTN) space, focusing on the use of opportunistic networking. One key problem in PSN is in designing forwarding algorithms which cope with human mobility patterns. We present an experiment measuring forty-one humans' mobility at the Infocom 2005 conference. The results of this experiment are similar to our previous experiments in corporate and academic working environments, in exhibiting a power-law distrbution for the time between node contacts. We then discuss the implications of these results on the design of forwarding algorithms for PSN.
We consider a community formation problem in social networks, where the users are either friends or enemies. The users are partitioned into conflict-free groups (i.e., independent sets in the ...conflict graphG−=(V,E) that represents the enmities between users). The dynamics goes on as long as there exists any set of at most k users, k being any fixed parameter, that can change their current groups in the partition simultaneously, in such a way that they all strictly increase their utilities (number of friends i.e., the cardinality of their respective groups minus one). Previously, the best-known upper-bounds on the maximum time of convergence were O(|V|α(G−)) for k≤2 and O(|V|3) for k=3, with α(G−) being the independence number of G−. Our first contribution in this paper consists in reinterpreting the initial problem as the study of a dominance ordering over the vectors of integer partitions. With this approach, we obtain for k≤2 the tight upper-bound O(|V|min{α(G−),|V|}) and, when G− is the empty graph, the exact value of order (2|V|)3∕23. The time of convergence, for any fixed k≥4, was conjectured to be polynomial (Escoffier et al., 2012; Kleinberg and Ligett, 2013). In this paper we disprove this. Specifically, we prove that for any k≥4, the maximum time of convergence is in Ω(|V|Θ(log|V|)).
To improve the efficiency and the quality of a service, a network operator may consider deploying a peer-to-peer architecture among controlled peers, also called here nano data centers, which ...contrast with the churn and resource heterogeneity of peers in uncontrolled environments. In this paper, we consider a prevalent peer-to-peer application: live video streaming. We demonstrate how nano data centers can take advantage of the self-scaling property of a peer-to-peer architecture, while significantly improving the quality of a live video streaming service, allowing smaller delays and fast channel switching. We introduce the branching architecture for nano datacenters (BAND), where a user can “pull” content from a channel of interest, or content could be “pushed” to it for relaying to other interested users. We prove that there exists an optimal trade-off point between minimizing the number of push, or the number of relaying nodes, and maintaining a robust topology as the number of channels and users get large, which allows scalability. We analyze the performance of content dissemination as users switch between channels, creating migration of nodes in the tree, while flow control insures continuity of data transmission. We prove that this p2p architecture guarantees a throughput independently of the size of the group. Analysis and evaluation of the model demonstrate that pushing content to a small number of relay nodes can have significant performance gains in throughput, start-up time, playback lags and channel switching delays.
Sunlight Lecuyer, Mathias; Spahn, Riley; Spiliopolous, Yannis ...
Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security,
10/2015
Conference Proceeding
We present Sunlight, a system that detects the causes of targeting phenomena on the web -- such as personalized advertisements, recommendations, or content -- at large scale and with solid ...statistical confidence. Today's web is growing increasingly complex and impenetrable as myriad of services collect, analyze, use, and exchange users' personal information. No one can tell who has what data, for what purposes they are using it, and how those uses affect the users. The few studies that exist reveal problematic effects -- such as discriminatory pricing and advertising -- but they are either too small-scale to generalize or lack formal assessments of confidence in the results, making them difficult to trust or interpret. Sunlight brings a principled and scalable methodology to personal data measurements by adapting well-established methods from statistics for the specific problem of targeting detection. Our methodology formally separates different operations into four key phases: scalable hypothesis generation, interpretable hypothesis formation, statistical significance testing, and multiple testing correction. Each phase bears instantiations from multiple mechanisms from statistics, each making different assumptions and tradeoffs. Sunlight offers a modular design that allows exploration of this vast design space. We explore a portion of this space, thoroughly evaluating the tradeoffs both analytically and experimentally. Our exploration reveals subtle tensions between scalability and confidence. Sunlight's default functioning strikes a balance to provide the first system that can diagnose targeting at fine granularity, at scale, and with solid statistical justification of its results.
We showcase our system by running two measurement studies of targeting on the web, both the largest of their kind. Our studies -- about ad targeting in Gmail and on the web -- reveal statistically justifiable evidence that contradicts two Google statements regarding the lack of targeting on sensitive and prohibited topics.
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