Device-independent Certification of One-shot Distillable Entanglement

Entanglement sources that produce many entangled states act as a main component in applications exploiting quantum physics such as quantum communication and cryptography. Realistic sources are inherently noisy, cannot run for an infinitely long time, and do not necessarily behave in an independent and identically distributed manner. An important question then arises -- how can one test, or certify, that a realistic source produces high amounts of entanglement? Crucially, a meaningful and operational solution should allow us to certify the entanglement which is available for further applications after performing the test itself (in contrast to assuming the availability of an additional source which can produce more entangled states, identical to those which were tested). To answer the above question and lower bound the amount of entanglement produced by an uncharacterised source, we present a protocol that can be run by interacting classically with uncharacterised (but not entangled to one another) measurement devices used to measure the states produced by the source. A successful run of the protocol implies that the remaining quantum state has high amounts of one-shot distillable entanglement. That is, one can distill many maximally entangled states out of the single remaining state. Importantly, our protocol can tolerate noise and, thus, certify entanglement produced by realistic sources. With the above properties, the protocol acts as the first "operational device-independent entanglement certification protocol" and allows one to test and benchmark uncharacterised entanglement sources which may be otherwise incomparable

Finding the SWEET Spot: Analysis and Improvement of Adaptive Inference in Low Resource Settings

Adaptive inference is a simple method for reducing inference costs. The method works by maintaining multiple classifiers of different capacities, and allocating resources to each test instance according to its difficulty. In this work, we compare the two main approaches for adaptive inference, Early-Exit and Multi-Model, when training data is limited. First, we observe that for models with the same architecture and size, individual Multi-Model classifiers outperform their Early-Exit counterparts by an average of 2.3%. We show that this gap is caused by Early-Exit classifiers sharing model parameters during training, resulting in conflicting gradient updates of model weights. We find that despite this gap, Early-Exit still provides a better speed-accuracy trade-off due to the overhead of the Multi-Model approach. To address these issues, we propose SWEET (Separating Weights in Early Exit Transformers), an Early-Exit fine-tuning method that assigns each classifier its own set of unique model weights, not updated by other classifiers. We compare SWEET's speed-accuracy curve to standard Early-Exit and Multi-Model baselines and find that it outperforms both methods at fast speeds while maintaining comparable scores to Early-Exit at slow speeds. Moreover, SWEET individual classifiers outperform Early-Exit ones by 1.1% on average. SWEET enjoys the benefits of both methods, paving the way for further reduction of inference costs in NLP.Comment: Proceedings of ACL 202

A sudden presentation of abdominal compartment syndrome

Dear Editor, Abdominal compartment syndrome (ACS) is defined as sustained intra-abdominal pressure (IAP) exceeding 20 mm Hg, which causes end-organ damage due to impaired tissue perfusion, as with other compartment syndromes [1, 2]. This dysfunction can extend beyond the abdomen to other organs like the heart and lungs. ACS is most commonly caused by trauma or surgery to the abdomen. It is characterised by interstitial oedema, which can be exacerbated by large fluid shifts during massive transfusion of blood products and other fluid resuscitation [3]. Normally, IAP is nearly equal to or slightly above ambient pressure. Intra-abdominal hypertension is typically defined as abdominal pressure greater than or equal to 12 mm Hg [4]. Initially, the abdomen is able to distend to accommodate the increase in pressure caused by oedema; however, IAP becomes highly sensitive to any additional volume once maximum distension is reached. This is a function of abdominal compliance, which plays a key role in the development and progression of intra-abdominal hypertension [5]. Surgical decompression is required in severe cases of organ dysfunction – usually when IAPs are refractory to other treatment options [6]. Excessive abdominal pressure leads to systemic pathophysiological consequences that may warrant admission to a critical care unit. These include hypoventilation secondary to restriction of the deflection of the diaphragm, which results in reduced chest wall compliance. This is accompanied by hypoxaemia, which is exacerbated by a decrease in venous return. Combined, these consequences lead to decreased cardiac output, a V/Q mismatch, and compromised perfusion to intra-abdominal organs, most notably the kidneys [7]. Kidney damage can be prerenal due to renal vein or artery compression, or intrarenal due to glomerular compression [8] – both share decreased urine output as a manifestation. Elevated bladder pressure is also seen from compression due to increased abdominal pressure, and its measurement, via a Foley catheter, is a diagnostic hallmark. Sustained intra-bladder pressures beyond 20 mm Hg with organ dysfunction are indicative of ACS requiring inter­vention [2, 8]. ACS is an important aetiology to consider in the differential diagnosis for signs of organ dysfunction – especially in the perioperative setting – as highlighted in the case below

Behavioral reconsolidation interference with episodic memory within-subjects is elusive.

In studies of behavioral reconsolidation interference, reactivation of a consolidated memory using some form of reminder is followed by the presentation of new information that can cause interference with that memory. Under these conditions, the interference not only impairs retrieval by indirect processes such as cue interference, but supposedly disrupts the original memory trace directly. Almost all studies of behavioral reconsolidation interference in episodic memory in humans have employed between-subjects paradigms, and deduced reminder effects from intrusion errors. Such studies might introduce confounds arising, for example, from differences in retrieval strategies engendered by the pre-test treatments. We therefore set out to examine whether behavioral reconsolidation interference in episodic memory might be demonstrated within-subjects and by direct memory strength rather than intrusion errors. In three separate experiments, we attempted to disrupt reconsolidation of episodic object-picture memory using a reminder + retroactive interference manipulation. We applied the manipulation over three consecutive days, using a forced-choice recognition test without intrusions from interfering learning, keeping all other study and test parameters constant. No effects of reminder-potentiated interference were observed for measures of accuracy, response times, subjective expressions of recollection, or levels of confidence, as substantiated by Bayesian analyses. These results highlight the difficulty of observing clear behavioral reconsolidation interference effects within-subjects in human episodic memory, and provide some indications of what might be boundary conditions for its demonstration

The Eval4NLP 2023 Shared Task on Prompting Large Language Models as Explainable Metrics

With an increasing number of parameters and pre-training data, generative large language models (LLMs) have shown remarkable capabilities to solve tasks with minimal or no task-related examples. Notably, LLMs have been successfully employed as evaluation metrics in text generation tasks. Within this context, we introduce the Eval4NLP 2023 shared task that asks participants to explore prompting and score extraction for machine translation (MT) and summarization evaluation. Specifically, we propose a novel competition setting in which we select a list of allowed LLMs and disallow fine-tuning to ensure a focus on prompting. We present an overview of participants' approaches and evaluate them on a new reference-free test set spanning three language pairs for MT and a summarization dataset. Notably, despite the task's restrictions, the best-performing systems achieve results on par with or even surpassing recent reference-free metrics developed using larger models, including GEMBA and Comet-Kiwi-XXL. Finally, as a separate track, we perform a small-scale human evaluation of the plausibility of explanations given by the LLMs

AllSight: A Low-Cost and High-Resolution Round Tactile Sensor with Zero-Shot Learning Capability

Tactile sensing is a necessary capability for a robotic hand to perform fine manipulations and interact with the environment. Optical sensors are a promising solution for high-resolution contact estimation. Nevertheless, they are usually not easy to fabricate and require individual calibration in order to acquire sufficient accuracy. In this letter, we propose AllSight, an optical tactile sensor with a round 3D structure potentially designed for robotic in-hand manipulation tasks. AllSight is mostly 3D printed making it low-cost, modular, durable and in the size of a human thumb while with a large contact surface. We show the ability of AllSight to learn and estimate a full contact state, i.e., contact position, forces and torsion. With that, an experimental benchmark between various configurations of illumination and contact elastomers are provided. Furthermore, the robust design of AllSight provides it with a unique zero-shot capability such that a practitioner can fabricate the open-source design and have a ready-to-use state estimation model. A set of experiments demonstrates the accurate state estimation performance of AllSight

How Much Does Attention Actually Attend? Questioning the Importance of Attention in Pretrained Transformers

The attention mechanism is considered the backbone of the widely-used Transformer architecture. It contextualizes the input by computing input-specific attention matrices. We find that this mechanism, while powerful and elegant, is not as important as typically thought for pretrained language models. We introduce PAPA, a new probing method that replaces the input-dependent attention matrices with constant ones -- the average attention weights over multiple inputs. We use PAPA to analyze several established pretrained Transformers on six downstream tasks. We find that without any input-dependent attention, all models achieve competitive performance -- an average relative drop of only 8% from the probing baseline. Further, little or no performance drop is observed when replacing half of the input-dependent attention matrices with constant (input-independent) ones. Interestingly, we show that better-performing models lose more from applying our method than weaker models, suggesting that the utilization of the input-dependent attention mechanism might be a factor in their success. Our results motivate research on simpler alternatives to input-dependent attention, as well as on methods for better utilization of this mechanism in the Transformer architecture.Comment: Findings of EMNLP 202

The Rheology of the Carotid Sinus: A Path Toward Bioinspired Intervention

The association between blood viscosity and pathological conditions involving a number of organ systems is well known. However, how the body measures and maintains appropriate blood viscosity is not well-described. The literature endorsing the function of the carotid sinus as a site of baroreception can be traced back to some of the earliest descriptions of digital pressure on the neck producing a drop in blood delivery to the brain. For the last 30 years, improved computational fluid dynamic (CFD) simulations of blood flow within the carotid sinus have demonstrated a more nuanced understanding of the changes in the region as it relates to changes in conventional metrics of cardiovascular function, including blood pressure. We suggest that the unique flow patterns within the carotid sinus may make it an ideal site to transduce flow data that can, in turn, enable real-time measurement of blood viscosity. The recent characterization of the PIEZO receptor family in the sinus vessel wall may provide a biological basis for this characterization. When coupled with other biomarkers of cardiovascular performance and descriptions of the blood rheology unique to the sinus region, this represents a novel venue for bioinspired design that may enable end-users to manipulate and optimize blood flow

Utility of human life cost in anaesthesiology cost-benefit decisions

The United States (US) aviation industry provides a potentially useful mental model for dealing with certain cost-benefit decisions in aesthesiology. The Federal Aviation Administration (FAA), the national aviation authority of the United States, quantifies a price for the value of a human life based on the U.S. Department of Transportation’s (DOT) value of a statistical life (VSL) unit. The current VSL is around $9.6 million, indexed to grow with consideration given to inflation and wage changes from the 2016 baseline of$9.4 million [1]. To illustrate the concept, if the FAA estimates that 100 people are likely to die in the future given the current practice standards then the monetary cost of this loss will be $940 million. The FAA uses this estimated monetary value as an official reference point in its regulatory decisions, and the agency publishes in detail how it derives the estimated value. When proposing new regulations, the FAA bases its decisions on comparisons of the human life cost associated with the existing regulation versus the alternative cost that the industry stakeholders will incur subsequent to the adoption of the regulation. In this example, if the cost incurred by the industry is more than the$940 million cost then the FAA will not adopt the proposed regulation and hence will not require the industry to undertake this cost