Expansion via Prediction of Importance with Contextualization

The identification of relevance with little textual context is a primary challenge in passage retrieval. We address this problem with a representation-based ranking approach that: (1) explicitly models the importance of each term using a contextualized language model; (2) performs passage expansion by propagating the importance to similar terms; and (3) grounds the representations in the lexicon, making them interpretable. Passage representations can be pre-computed at index time to reduce query-time latency. We call our approach EPIC (Expansion via Prediction of Importance with Contextualization). We show that EPIC significantly outperforms prior importance-modeling and document expansion approaches. We also observe that the performance is additive with the current leading first-stage retrieval methods, further narrowing the gap between inexpensive and cost-prohibitive passage ranking approaches. Specifically, EPIC achieves a MRR@10 of 0.304 on the MS-MARCO passage ranking dataset with 78ms average query latency on commodity hardware. We also find that the latency is further reduced to 68ms by pruning document representations, with virtually no difference in effectiveness

Efficient Document Re-Ranking for Transformers by Precomputing Term Representations

Deep pretrained transformer networks are effective at various ranking tasks, such as question answering and ad-hoc document ranking. However, their computational expenses deem them cost-prohibitive in practice. Our proposed approach, called PreTTR (Precomputing Transformer Term Representations), considerably reduces the query-time latency of deep transformer networks (up to a 42x speedup on web document ranking) making these networks more practical to use in a real-time ranking scenario. Specifically, we precompute part of the document term representations at indexing time (without a query), and merge them with the query representation at query time to compute the final ranking score. Due to the large size of the token representations, we also propose an effective approach to reduce the storage requirement by training a compression layer to match attention scores. Our compression technique reduces the storage required up to 95% and it can be applied without a substantial degradation in ranking performance

Training Curricula for Open Domain Answer Re-Ranking

In precision-oriented tasks like answer ranking, it is more important to rank many relevant answers highly than to retrieve all relevant answers. It follows that a good ranking strategy would be to learn how to identify the easiest correct answers first (i.e., assign a high ranking score to answers that have characteristics that usually indicate relevance, and a low ranking score to those with characteristics that do not), before incorporating more complex logic to handle difficult cases (e.g., semantic matching or reasoning). In this work, we apply this idea to the training of neural answer rankers using curriculum learning. We propose several heuristics to estimate the difficulty of a given training sample. We show that the proposed heuristics can be used to build a training curriculum that down-weights difficult samples early in the training process. As the training process progresses, our approach gradually shifts to weighting all samples equally, regardless of difficulty. We present a comprehensive evaluation of our proposed idea on three answer ranking datasets. Results show that our approach leads to superior performance of two leading neural ranking architectures, namely BERT and ConvKNRM, using both pointwise and pairwise losses. When applied to a BERT-based ranker, our method yields up to a 4% improvement in MRR and a 9% improvement in P@1 (compared to the model trained without a curriculum). This results in models that can achieve comparable performance to more expensive state-of-the-art techniques

Efficacy of Sapheno-Femoral A-V Fistula in Cronic Renal Failure Patients Undergoing Hemodialysis

Introduction: To describe the outcome of the sapheno-femoral fistula as an alternative blood access site for maintenance hemodialysis in a prospective cohort of patients with end - stage renal failure. Methods: Twenty-two patients with vascular access failure in the arms were admitted for establishing sapheno-femoral fistula as a puncture site for hemodialysis. The major saphenous vein was exposed at the junction site with femoral vein and its tributary veins were ligated . The saphenous vein was isolated and mobilized throughout the thigh and end to side anastomosis accomplished with the superficial femoral artery by a running 6-0 prolene suture after conducting the vein through a subcutaneous tunnel. Results: Failure rate of saphenofemoral fistula was 22. 7% at two years follow up. Mean ± SE survival of fistula was 16.4+/- 2.75 months. Significant survival difference wasn’t seen between two sexes. Rate of wound infection and chronic pain of surgery site was similar( 9.1%). Conclusions: Two-year survival rate of 77% and morbidity less than 10% leads to suggestion of saphenofemoral fistula as an alternative for upper extremity fistulas in end- stage renal failure patient

Measurement of time delay for a prospectively gated CT simulator

For the management of mobile tumors, respiratory gating is the ideal option, both during imaging and during therapy. The major advantage of respiratory gating during imaging is that it is possible to create a single artifact-free CT data-set during a selected phase of the patient's breathing cycle. The purpose of the present work is to present a simple technique to measure the time delay during acquisition of a prospectively gated CT. The time delay of a Philips Brilliance BigBore™ (Philips Medical Systems, Madison, WI) scanner attached to a Varian Real-Time Position Management™ (RPM) system (Varian Medical Systems, Palo Alto, CA) was measured. Two methods were used to measure the CT time delay: using a motion phantom and using a recorded data file from the RPM system. In the first technique, a rotating wheel phantom was altered by placing two plastic balls on its axis and rim, respectively. For a desired gate, the relative positions of the balls were measured from the acquired CT data and converted into corresponding phases. Phase difference was calculated between the measured phases and the desired phases. Using period of motion, the phase difference was converted into time delay. The Varian RPM system provides an external breathing signal; it also records transistor-transistor logic (TTL) ‘X-Ray ON’ status signal from the CT scanner in a text file. The TTL ‘X-Ray ON’ indicates the start of CT image acquisition. Thus, knowledge of the start time of CT acquisition, combined with the real-time phase and amplitude data from the external respiratory signal, provides time-stamping of all images in an axial CT scan. The TTL signal with time-stamp was used to calculate when (during the breathing cycle) a slice was recorded. Using the two approaches, the time delay between the prospective gating signal and CT simulator has been determined to be 367 ± 40 ms. The delay requires corrections both at image acquisition and while setting gates for the treatment delivery; otherwise the simulation and treatment may not be correlated with the patient's breathing

Incorporating Reliability into the Optimal Design of Multi-hydropower Systems: A Cellular Automata-based Approach

In recent years, Cellular Automata (CA) has emerged as a powerful tool for solving optimization problems in water resources engineering and, in particular, reservoir operation problems. This study utilizes the capabilities of the CA-based method to maximize the firm energy yield of multi-reservoir hydropower systems. Installed capacities (ICs) are selected as decision variables in the design phase and be determined in an iterative procedure. The reservoir storages at the beginning and the end of the periods are used as the decision variables in the operation phase and be calculated by the CA. The process starts with specifying an arbitrary initial installed IC for each reservoir determined based on long-term average annual inflow. Then, the system is optimally operated to maximize the energy yield under user-specified reliability of the system using a hybrid approach, Cellular Automata-Simulating Annealing (CA-SA). The system’s ICs are then increased/decreased depending on whether the system’s energy yield reliability is greater/less than the target reliability. This iterative process lasts till the system’s energy yield reliability is equal to the target reliability. The proposed method is used to optimally design the three-reservoir Khersan hydropower system and also the largest hydropower reservoir system in Iran composed of 16 reservoirs. The results are presented and compared with those of the existing methods in the literature. The results show that the proposed method can be efficiently and effectively used for improving the firm energy yield of real-world multi-reservoir hydropower systems