120-GBaud 16-QAM silicon photonics IQ modulator for data center interconnection.

We demonstrated all-silicon IQ modulators (IQMs) operating at 120-GBaud 16-QAM with suitable bandwidth, and output power. We required optical signal-to-noise-ratio (rOSNR) that have promising potential to be used in 800-Gbps small-form-factor pluggable transceivers for data center interconnection. First, we tested an IQM chip using discrete drivers and achieved a per-polarization TX output power of -18.74 dBm and an rOSNR of 23.51 dB over a 100-km standard SMF. Notably, a low BER of 1.4e-3 was obtained using our SiP IQM chip without employing nonlinear compensation, optical equalization, or an ultra-wide-bandwidth, high-ENOB OMA. Furthermore, we investigated the performance of a 3D packaged transmitter by emulating its frequency response using an IQM chip, discrete drivers, and a programmable optical filter. With a laser power of 17 dBm, we achieved a per-polarization output power of -15.64 dBm and an rOSNR of 23.35 dB.

Network Analytics Enabled by Generating a Pool of Network Variants from Noisy Data.

Mapping network nodes and edges to communities and network functions is crucial to gaining a higher level of understanding of the network structure and functions. Such mappings are particularly challenging to design for covert social networks, which intentionally hide their structure and functions to protect important members from attacks or arrests. Here, we focus on correctly inferring the structures and functions of such networks, but our methodology can be broadly applied. Without the ground truth, knowledge about the allocation of nodes to communities and network functions, no single network based on the noisy data can represent all plausible communities and functions of the true underlying network. To address this limitation, we apply a generative model that randomly distorts the original network based on the noisy data, generating a pool of statistically equivalent networks. Each unique generated network is recorded, while each duplicate of the already recorded network just increases the repetition count of that network. We treat each such network as a variant of the ground truth with the probability of arising in the real world approximated by the ratio of the count of this network's duplicates plus one to the total number of all generated networks. Communities of variants with frequently occurring duplicates contain persistent patterns shared by their structures. Using Shannon entropy, we can find a variant that minimizes the uncertainty for operations planned on the network. Repeatedly generating new pools of networks from the best network of the previous step for several steps lowers the entropy of the best new variant. If the entropy is too high, the network operators can identify nodes, the monitoring of which can achieve the most significant reduction in entropy. Finally, we also present a heuristic for constructing a new variant, which is not randomly generated but has the lowest expected cost of operating on the distorted mappings of network nodes to communities and functions caused by noisy data.

Impact of laser flicker noise and linewidth on 64 to 96 Gbaud/DP-nQAM metro coherent optical links.

We experimentally investigate the impact of laser flicker noise and linewidth on 64 Gbaud/DP-64QAM, 96 Gbaud/DP-32QAM and 64 or 96 Gbaud/DP-16QAM links. To give a more practical viewpoint, the examined flicker noise closely follows that of an industry forum (OIF 400ZR). We have found that higher modulation order (e.g., 64QAM) is sensitive to phase noise from the linewidth and flicker noise, even in the back to back case. Significant optical signal to noise ratio (OSNR) and cycle slip rate penalties can also be observed with a transmission distance $ {\gt} {200}\;{\rm km}$>200km for both 64QAM and 32QAM signals, which mainly comes from equalization-enhanced phase noise. Moreover, with the increasing of transmission distances, the effective linewidth of a tunable laser with a higher flicker noise and higher linewidth (210 KHz) increases significantly, while it remains unchanged for an external cavity laser (ECL) with 47-kHz linewidth. The result indicates the importance of more stringent flicker noise and linewidth requirement for future ultrabaud rate transmissions.

Supporting novel biomedical research via multilayer collaboration networks.

The value of research containing novel combinations of molecules can be seen in many innovative and award-winning research programs. Despite calls to use innovative approaches to address common diseases, an increasing majority of research funding goes toward "safe" incremental research. Counteracting this trend by nurturing novel and potentially transformative scientific research is challenging and it must be supported in competition with established research programs. Therefore, we propose a tool that helps to resolve the tension between safe/fundable research vs. high-risk/potentially transformational research. It does this by identifying hidden overlapping interests around novel molecular research topics. Specifically, it identifies paths of molecular interactions that connect research topics and hypotheses that would not typically be associated, as the basis for scientific collaboration. Because these collaborations are related to the scientists' present trajectory, they are low risk and can be initiated rapidly. Unlike most incremental steps, these collaborations have the potential for leaps in understanding, as they reposition research for novel disease applications. We demonstrate the use of this tool to identify scientists who could contribute to understanding the cellular role of genes with novel associations with Alzheimer's disease, which have not been thoroughly characterized, in part due to the funding emphasis on established research.

Identifying robust communities and multi-community nodes by combining top-down and bottom-up approaches to clustering.

Biological functions are carried out by groups of interacting molecules, cells or tissues, known as communities. Membership in these communities may overlap when biological components are involved in multiple functions. However, traditional clustering methods detect non-overlapping communities. These detected communities may also be unstable and difficult to replicate, because traditional methods are sensitive to noise and parameter settings. These aspects of traditional clustering methods limit our ability to detect biological communities, and therefore our ability to understand biological functions. To address these limitations and detect robust overlapping biological communities, we propose an unorthodox clustering method called SpeakEasy which identifies communities using top-down and bottom-up approaches simultaneously. Specifically, nodes join communities based on their local connections, as well as global information about the network structure. This method can quantify the stability of each community, automatically identify the number of communities, and quickly cluster networks with hundreds of thousands of nodes. SpeakEasy shows top performance on synthetic clustering benchmarks and accurately identifies meaningful biological communities in a range of datasets, including: gene microarrays, protein interactions, sorted cell populations, electrophysiology and fMRI brain imaging.