We study a novel problem of continuously predicting a number of user-subscribed continuous analytics targets (CATs) in dynamic networks. Our architecture includes any dynamic graph neural network model as the back end applied over the network data, and per CAT front end models that return results with their confidence to users. We devise a data filtering algorithm that feeds a provably optimal subset of data in the embedding space from back end model to front end models. Secondly, to ensure fairness in terms of query result accuracy for different CATs and users, we propose a fairness metric and a fairness-aware training scheduling algorithm, along with accuracy guarantees on fairness estimation. Our experiments over five real-world datasets show that our proposed solution is effective, efficient, fair, extensible, and adaptive.

This paper presents a dual-band (2.4G/5.8G) BLE/BT transceiver in 55nm CMOS employing polar modulation to optimize performance while minimizing cost. A single-ended digitally modulated PA is implemented to modulate the RF envelope. A novel RX/TX co-matching method using bondwires is presented to optimize RX/TX performance. The measured sensitivity and max output power in 2.4G BLE mode are -97.5dBm and +12dBm while consuming only 10.8mW and 55.6mW. The 28.5% overall system efficiency in TX mode is a leader in state-of-the-art designs. In 5.8G BLE mode, the sensitivity is -95.8dBm and max output power is +3dBm. In EDR3 mode, the sensitivity and max output power are -88.5/-86.8dBm and +10/0dBm at 2.4G/5.8GHz. The DEVM is 8.5% with ±45°/0° phase rotating data due to limited PLL deviation frequency. The 110/99dB link budget in BLE/EDR mode with low power consumption greatly extends the IoT coverage range and battery life.

This paper presents a 5.8 GHz dedicated short range communication (DSRC) CMOS RF-SoC transceiver with digitally controlled RF architecture for China electronic toll collection (ETC) system. The operation of key RF blocks, such as ASK modulator, power amplifier, LNA, and mixer, are directly controlled by digital baseband. Compared with state-of-the-art designs in literature, this work demonstrates remarkable advantages in design simplicity, Tx output peak power, adjacent channel power ratio (ACPR), dynamic range, occupied bandwidth (OBW), bit error rate (BER), and so on.

The confidentiality of tenant's data is confronted with high risk when facing hardware attacks and privileged malicious software. Hardware-based memory encryption is one of the promising means to provide strong guarantees of data security. Recently AMD has proposed its new memory encryption hardware called SME and SEV, which can selectively encrypt memory regions in a fine-grained manner, e.g., by setting the C-bits in the page table entries. More importantly, SEV further supports encrypted virtual machines. This, intuitively, has provided a new opportunity to protect data confidentiality in guest VMs against an untrusted hypervisor in the cloud environment. In this paper, we first provide a security analysis on the (in)security of SEV and uncover a set of security issues of using SEV as a means to defend against an untrusted hypervisor. Based on the study, we then propose a software-based extension to the SEV feature, namely Fidelius, to address those issues while retaining performance efficiency. Fidelius separates the management of critical resources from service provisioning and revokes the permissions of accessing specific resources from the un-trusted hypervisor. By adopting a sibling-based protection mechanism with non-bypassable memory isolation, Fidelius embraces both security and efficiency, as it introduces no new layer of abstraction. Meanwhile, Fidelius reuses the SEV API to provide a full VM life-cycle protection, including two sets of para-virtualized I/O interfaces to encode the I/O data, which is not considered in the SEV hardware design. A detailed and quantitative security analysis shows its effectiveness in protecting tenant's data from a variety of attack surfaces, and the performance evaluation confirms the performance efficiency of Fidelius.

We study the problem of optimal location querying for location-based services in road networks, which aims to find locations for new servers or facilities. The existing optimal solutions on this problem consider only the cases with one new server. When two or more new servers are to be set up, the problem with minmax cost criteria, MinMax, becomes NP-hard. In this work we identify some useful properties about the potential locations for the new servers, from which we derive a novel algorithm for MinMax, and show that it is efficient when the number of new servers is small. When the number of new servers is large, we propose an efficient 3-approximate algorithm. We verify with experiments on real road networks that our solutions are effective and attain significantly better result quality compared to the existing greedy algorithms.

Subgraph querying in a large data graph is interesting for different applications. A recent study shows that top-k diversified results are useful since the number of matching subgraphs can be very large. In this work, we study the problem of top-k diversified subgraph querying that asks for a set of up to k subgraphs isomorphic to a given query graph, and that covers the largest number of vertices. We propose a novel level-based algorithm for this problem which supports early termination and has a theoretical approximation guarantee. From experiments, most of our results on real datasets used in previous works are near optimal with a query time within 10ms on a commodity machine.

The computation of Minimum Spanning Trees (MSTs) is a fundamental graph problem with important applications. However, there has been little study of MSTs for temporal graphs, which is becoming common as time information is collected for many existing networks. We define two types of MSTs for temporal graphs, MSTa and MSTw, based on the optimization of time and cost, respectively. We propose efficient linear time algorithms for computing MSTa. We show that computing MSTw is much harder. We design efficient approximation algorithms based on a transformation to the Directed Steiner Tree problem (DST). Our solution also solves the classical DST problem with a better time complexity and the same approximation factor compared to the state-of-the-art algorithm. Our experiments on real temporal networks further verify the effectiveness of our algorithms. For MSTw, our solution is capable of shortening the runtime from 10 hours to 3 seconds.

BACKGROUND: Despite increased investment in pharmaceutical research and development, fewer and fewer new drugs are entering the marketplace. This has prompted studies in repurposing existing drugs for use against diseases with unmet medical needs. A popular approach is to develop a classification model based on drugs with and without a desired therapeutic effect. For this approach to be statistically sound, it requires a large number of drugs in both classes. However, given few or no approved drugs for the diseases of highest medical urgency and interest, different strategies need to be investigated. RESULTS: We developed a computational method termed "drug-protein interaction-based repurposing" (DPIR) that is potentially applicable to diseases with very few approved drugs. The method, based on genome-wide drug-protein interaction information and Bayesian statistics, first identifies drug-protein interactions associated with a desired therapeutic effect. Then, it uses key drug-protein interactions to score other drugs for their potential to have the same therapeutic effect. CONCLUSIONS: Detailed cross-validation studies using United States Food and Drug Administration-approved drugs for hypertension, human immunodeficiency virus, and malaria indicated that DPIR provides robust predictions. It achieves high levels of enrichment of drugs approved for a disease even with models developed based on a single drug known to treat the disease. Analysis of our model predictions also indicated that the method is potentially useful for understanding molecular mechanisms of drug action and for identifying protein targets that may potentiate the desired therapeutic effects of other drugs (combination therapies).