AIOps for a Cloud Object Storage Service

With the growing reliance on the ubiquitous availability of IT systems and services, these systems become more global, scaled, and complex to operate. To maintain business viability, IT service providers must put in place reliable and cost efficient operations support. Artificial Intelligence for IT Operations (AIOps) is a promising technology for alleviating operational complexity of IT systems and services. AIOps platforms utilize big data, machine learning and other advanced analytics technologies to enhance IT operations with proactive actionable dynamic insight. In this paper we share our experience applying the AIOps approach to a production cloud object storage service to get actionable insights into system's behavior and health. We describe a real-life production cloud scale service and its operational data, present the AIOps platform we have created, and show how it has helped us resolving operational pain points.Comment: 5 page

Improving neural network representations using human similarity judgments

Deep neural networks have reached human-level performance on many computer vision tasks. However, the objectives used to train these networks enforce only that similar images are embedded at similar locations in the representation space, and do not directly constrain the global structure of the resulting space. Here, we explore the impact of supervising this global structure by linearly aligning it with human similarity judgments. We find that a naive approach leads to large changes in local representational structure that harm downstream performance. Thus, we propose a novel method that aligns the global structure of representations while preserving their local structure. This global-local transform considerably improves accuracy across a variety of few-shot learning and anomaly detection tasks. Our results indicate that human visual representations are globally organized in a way that facilitates learning from few examples, and incorporating this global structure into neural network representations improves performance on downstream tasks.Comment: Published as a conference paper at NeurIPS 202

Couple Resilience to Economic Pressure Over Time and Across Generations

Research suggests that economic stress disrupts perceived romantic relationship quality; yet less is known regarding the direct influence of economic stress on negative behavioral exchanges between partners over time. Another intriguing question concerns the degree to which effective problem-solving might protect against this hypothesized association. To address these issues, the authors studied two generations of couples who were assessed approximately 13 years apart (Generation 1: N = 367, Generation 2: N = 311). On average and for both generations, economic pressure predicted relative increases in couples’ hostile, contemptuous, and angry behaviors; however, couples who were highly effective problem solvers experienced no increases in these behaviors in response to economic pressure. Less effective problem solvers experienced the steepest increases in hostile behaviors in response to economic pressure. Because these predictive pathways were replicated in both generations of couples it appears that these stress and resilience processes unfold over time and across generations

Human cochlear nucleus on 7 tesla diffusion tensor imaging: insights into micro-anatomy and function for auditory brainstem implant surgery

OBJECTIVE: The cochlear nucleus (CN) is the target of the auditory brainstem implant (ABI). Most ABI candidates have Neurofibromatosis Type 2 (NF2) and distorted brainstem anatomy from bilateral vestibular schwannomas. The CN is difficult to characterize as routine structural MRI does not resolve detailed anatomy. We hypothesize that diffusion tensor imaging (DTI) enables both in vivo localization and quantitative measurements of CN morphology. STUDY DESIGN: We analyzed 7 Tesla (T) DTI images of 100 subjects (200 CN) and relevant anatomic structures using an MRI brainstem atlas with submillimetric (50 μm) resolution. SETTING: Tertiary referral center. PATIENTS: Young healthy normal hearing adults. INTERVENTION: Diagnostic. MAIN OUTCOME MEASURES: Diffusion scalar measures such as fractional anisotropy (FA), mean diffusivity (MD), mode of anisotropy (Mode), principal eigenvectors of the CN, and the adjacent inferior cerebellar peduncle (ICP). RESULTS: The CN had a lamellar structure and ventral-dorsal fiber orientation and could be localized lateral to the inferior cerebellar peduncle (ICP). This fiber orientation was orthogonal to tracts of the adjacent ICP where the fibers run mainly caudal-rostrally. The CN had lower FA compared to the medial aspect of the ICP (0.44 ± 0.09 vs. 0.64 ± 0.08, p < 0.001). CONCLUSIONS: 7T DTI enables characterization of human CN morphology and neuronal substructure. An ABI array insertion vector directed more caudally would better correspond to the main fiber axis of CN. State-of-the-art DTI has implications for ABI preoperative planning and future image guidance-assisted placement of the electrode array

The Heavy Metal Survey: Star Formation Constraints and Dynamical Masses of 21 Massive Quiescent Galaxies at z~1.4-2.2

In this paper, we present the Heavy Metal Survey, which obtained ultra-deep medium-resolution spectra of 21 massive quiescent galaxies at $1.4\lesssim z\lesssim 2.2$ with Keck/LRIS and MOSFIRE. With integration times of up to 16 hrs per band per galaxy, we observe numerous Balmer and metal absorption lines in atmospheric windows. We successfully derive spectroscopic redshifts for all 21 galaxies and for 19 we also measure stellar velocity dispersions ($\sigma_v$), ages, and elemental abundances, as detailed in an accompanying paper. Except for one emission-line AGN, all galaxies are confirmed as quiescent through their faint or absent H$\alpha$ emission and evolved stellar spectra. For most galaxies exhibiting faint H$\alpha$, elevated [NII]/H$\alpha$ suggests a non-star-forming origin. We calculate dynamical masses ($M_{\rm dyn}$) by combining $\sigma_v$ with structural parameters obtained from HST/COSMOS(-DASH), and compare them with stellar masses ($M_*$) derived using spectrophotometric modeling, considering various assumptions. For a fixed initial mass function (IMF), we observe a strong correlation between $M_{\rm dyn}/M_*$ and $\sigma_v$. This correlation may suggest that a varying IMF, with high-$\sigma_v$ galaxies being more bottom-heavy, was already in place at $z\sim2$. When implementing the $\sigma_v$-dependent IMF found in the cores of nearby early-type galaxies and correcting for biases in our stellar mass and size measurements, we find a low scatter in $M_{\rm dyn}/M_*$ of 0.14 dex. However, these assumptions result in unphysical stellar masses, which exceed the dynamical masses by 34%. This tension suggests that distant quiescent galaxies do not simply grow inside-out into today's massive early-type galaxies and the evolution is more complicated.Comment: Submitted to ApJ (25 pages, 11 figures

Intraperitoneal Nanotherapy for Metastatic Ovarian Cancer Based on siRNA-Mediated Suppression of DJ-1 Protein Combined with a Low Dose of Cisplatin

Herein, we report an efficient combinatorial therapy for metastatic ovarian cancer based on siRNA-mediated suppression of DJ-1 protein combined with a low dose of cisplatin. DJ-1 protein modulates, either directly or indirectly, different oncogenic pathways that support and promote survival, growth, and invasion of ovarian cancer cells. To evaluate the potential of this novel therapy, we have engineered a cancer-targeted nanoplatform and validated that DJ-1 siRNA delivered by this nanoplatform after intraperitoneal injection efficiently downregulates the DJ-1 protein in metastatic ovarian cancer tumors and ascites. In vivo experiments revealed that DJ-1 siRNA monotherapy outperformed cisplatin alone by inhibiting tumor growth and increasing survival of mice with metastatic ovarian cancer. Finally, three cycles of siRNA-mediated DJ-1 therapy in combination with a low dose of cisplatin completely eradicated ovarian cancer tumors from the mice, and there was no cancer recurrence detected for the duration of the study, which lasted 35 weeks

The Functional Connectivity Landscape of the Human Brain

Functional brain networks emerge and dissipate over a primarily static anatomical foundation. The dynamic basis of these networks is inter-regional communication involving local and distal regions. It is assumed that inter-regional distances play a pivotal role in modulating network dynamics. Using three different neuroimaging modalities, 6 datasets were evaluated to determine whether experimental manipulations asymmetrically affect functional relationships based on the distance between brain regions in human participants. Contrary to previous assumptions, here we show that short- and long-range connections are equally likely to strengthen or weaken in response to task demands. Additionally, connections between homotopic areas are the most stable and less likely to change compared to any other type of connection. Our results point to a functional connectivity landscape characterized by fluid transitions between local specialization and global integration. This ability to mediate functional properties irrespective of spatial distance may engender a diverse repertoire of cognitive processes when faced with a dynamic environment.</p

Subclinical Hypothyroidism and Risk for Incident Ischemic Stroke Among Postmenopausal Women

Background: Subclinical hypothyroidism (SCH) is postulated to increase stroke risk via atherogenic changes associated with abnormal thyroid function. However, the direct relationship of SCH with subsequent stroke is poorly studied