In an inference service system, model selection and scheduling (MS&S) schemes map inference queries to trained machine learning (ML) models, hosted on a finite set of workers, to solicit accurate predictions within strict latency targets. MS&S is challenged by both varying query load and stochastic query inter-arrival patterns; however, state-of-the-art MS&S approaches conservatively account for load exclusively.

The availability of vast video collections and the accuracy of ML models has generated significant interest in video analytics systems. Since naively processing all frames using expensive models is impractical, researchers have proposed optimizations such as selectively using faster but less accurate models to replace or filter frames for expensive models. However, these optimizations are difficult to apply on queries with multiple predicates and models, as users must manually explore a large optimization space. Without significant systems expertise or time investment, an analyst may manually create an execution plan that is unnecessarily expensive and/or terribly inaccurate. We propose Relational Hints , a declarative interface that allows users to suggest ML model relationships based on domain knowledge. Users can express two key relationships: when a model can replace another (CAN REPLACE) and when a model can be used to filter frames for another (CAN FILTER). We aim to design an interface to express model relationships informed by domain specific knowledge and define the constraints by which these relationships hold. We then present the VIVA video analytics system that uses relational hints to optimize SQL queries on video datasets. VIVA automatically selects and validates the hints applicable to the query, generates possible query plans using a formal set of transformations, and finds the best performance plan that meets a user's accuracy requirements. VIVA relieves users from rewriting and manually optimizing video queries as new models become available and execution environments evolve. We evaluate VIVA implemented on top of Spark and show that hints improve performance up to 16.6X without sacrificing accuracy.

Function-as-a-Service (FaaS) has become an increasingly popular way for users to deploy their applications without the burden of managing the underlying infrastructure. However, existing FaaS platforms rely on remote storage to maintain state, limiting the set of applications that can be run efficiently. Recent caching work for FaaS platforms has tried to address this problem, but has fallen short: it disregards the widely different characteristics of FaaS applications, does not scale the cache based on data access patterns, or requires changes to applications. To address these limitations, we present Faa$T, a transparent auto-scaling distributed cache for serverless applications. Each application gets its own cache. After a function executes and the application becomes inactive, the cache is unloaded from memory with the application. Upon reloading for the next invocation, Faa$T pre-warms the cache with objects likely to be accessed. In addition to traditional compute-based scaling, Faa$T scales based on working set and object sizes to manage cache space and I/O bandwidth. We motivate our design with a comprehensive study of data access patterns on Azure Functions. We implement Faa$T for Azure Functions, and show that Faa$T can improve performance by up to 92% (57% on average) for challenging applications, and reduce cost for most users compared to state-of-the-art caching systems, i.e. the cost of having to stand up additional serverful resources.

The proliferation of camera-enabled devices and large video repositories has led to a diverse set of video analytics applications. These applications rely on video pipelines, represented as DAGs of operations, to transform videos, process extracted metadata, and answer questions like, "Is this intersection congested?" The latency and resource efficiency of pipelines can be optimized using configurable knobs for each operation (e.g., sampling rate, batch size, or type of hardware used). However, determining efficient configurations is challenging because (a) the configuration search space is exponentially large, and (b) the optimal configuration depends on users' desired latency and cost targets, (c) input video contents may exercise different paths in the DAG and produce a variable amount intermediate results. Existing video analytics and processing systems leave it to the users to manually configure operations and select hardware resources.

The number of applications relying on inference from machine learning models, especially neural networks, is already large and expected to keep growing. For instance, Facebook applications issue tens-of-trillions of inference queries per day with varying performance, accuracy, and cost constraints. Unfortunately, today's inference serving systems are neither easy to use nor cost effective. Developers must manually match the performance, accuracy, and cost constraints of their applications to a large design space that includes decisions such as selecting the right model and model optimizations, selecting the right hardware architecture, selecting the right scale-out factor, and avoiding cold-start effects. These interacting decisions are difficult to make, especially when the application load varies over time, applications evolve over time, and the available resources vary over time.

We consider the dynamical superresolution problem consisting in the recovery of positions and velocities of moving particles from low-frequency static measurements taken over multiple time steps. The standard approach to this issue is a two-step process: first, at each time step some static reconstruction method is applied to locate the positions of the particles with superresolution, and, second, some tracking technique is applied to obtain the velocities. In this paper we propose a fully dynamical method based on a phase-space lifting of the positions and the velocities of the particles, which are simultaneously reconstructed with superresolution. We provide a rigorous mathematical analysis of the recovery problem, both for the noiseless case and in the presence of noise (in the discrete setting). Several numerical simulations illustrate and validate our method, which shows some advantage over existing techniques. We then discuss the application of this approach to the dynamical superresolution problem in ultrafast ultrasound imaging: blood vessels' locations and blood flow velocities are recovered with superresolution.

Heterogeneity has grown in popularity both at the core and server level as a way to improve both performance and energy efficiency. However, despite these benefits, scheduling applications in heterogeneous machines remains challenging. Additionally, when these heterogeneous resources accommodate multiple applications to increase utilization, resources are prone to contention, destructive interference, and unpredictable performance. Existing solutions examine heterogeneity either across or within a server, leading to missed performance and efficiency opportunities.

We propose a computational methodology for automatically estimating human behavioral patterns using the multiple instance learning (MIL) paradigm. We describe the incremental diverse density algorithm, a particular formulation of multiple instance learning, and discuss its suitability for behavioral coding. We use a rich multi-modal corpus comprised of chronically distressed married couples having problem-solving discussions as a case study to experimentally evaluate our approach. In the multiple instance learning framework, we treat each discussion as a collection of short-term behavioral expressions which are manifested in the acoustic, lexical, and visual channels. We experimentally demonstrate that this approach successfully learns representations that carry relevant information about the behavioral coding task. Furthermore, we employ this methodology to gain novel insights into human behavioral data, such as the local versus global nature of behavioral constructs as well as the level of ambiguity in the expression of behaviors through each respective modality. Finally, we assess the success of each modality for behavioral classification and compare schemes for multimodal fusion within the proposed framework.

Therapist language plays a critical role in influencing the overall quality of psychotherapy. Notably, it is a major contributor to the perceived level of empathy expressed by therapists, a primary measure for judging their efficacy. We explore psycholinguistics inspired features for predicting therapist empathy. These features model language which conveys information about affective and cognitive processes, which is central to the therapist expressing understanding of the patient’s perspective. We describe the dimensional features obtained based on psycholinguisitic norms, and their application to predicting empathy expressed in motivational interviewing sessions for addiction counseling. We compare these to standard lexical features (n-grams) and demonstrate that these features contain complementary information for predicting therapist empathy. The highest empathy prediction results achieved are 75.28% UAR and 0.6112 Spearman’s correlation.

The current Internet architecture does not properly handle multi-homed hosts, since each interface of a multi-connected end-host terminal generally appears as a completely different node. The lack of transparency about the multi-homing degree of end-hosts brings even more problems when those hosts are mobile. On the one hand, in our view the combined use of local hierarchical locators and global flat identifiers for both end hosts and networks is important to reach an efficient control of multi-homing and mobility. On the other hand, when planning to use flat global identifiers for networks we must consider an increased number of access autonomous systems. To target that scenario, this paper presents a novel addressing scheme and a global lookup system based on multi-level flat identifiers, which operate between the Internet domain naming service and its routing system. The proposed mechanism allows Internet access providers to deploy networks that may not be physically adjacent to each other, without having to reveal their topological location. This is done by grouping access networks of the same organization in the same virtual organizational zone. Virtual zones aim to hide the control of multi- homing and mobility from the Internet core. The second major goal of employing virtual zones is to bring closer to the end-host the decision about the most suitable access network and terminal interface to be used. This allows the exploitation of end-hosts interface diversity and local path diversity in a more efficient way.

Some thinning algorithms for 3D objects, or generalizations of existing ones for 2D, have been proposed in recent years. The paper presents a simple and very fast algorithm compared to most of them, and still it has theoretically favorable properties. It provides a connected surface skeleton that allows shapes to be reconstructed with bounded error. In addition, it is also very attractive because it allows discrete skeletons to be obtained directly from volumes in many representations without converting them to a voxel-based representation. Our algorithm is a generalization of the one presented by Cardoner et al. (1997) for 2D objects. It is based on the application of directional erosions, while retaining those voxels that introduce disconnection.