Miracle-dz/UniFAR-FTUs huggingface dataset:
CLIP4MC: An RL-Friendly Vision-Language Model for Minecraft

One of the essential missions in the AI research community is to build an autonomous embodied agent that can attain high-level performance across a wide spectrum of tasks. However, acquiring reward/penalty in all open-ended tasks is unrealistic, making the Reinforcement Learning (RL) training procedure impossible. In this paper, we propose a novel cross-modal contrastive learning framework architecture, CLIP4MC, aiming to learn an RL-friendly vision-language model that serves as a reward function for open-ended tasks. Therefore, no further task-specific reward design is needed. Intuitively, it is more reasonable for the model to address the similarity between the video snippet and the language prompt at both the action and entity levels. To this end, a motion encoder is proposed to capture the motion embeddings across different intervals. The correlation scores are then used to construct the auxiliary reward signal for RL agents. Moreover, we construct a neat YouTube dataset based on the large-scale YouTube database provided by MineDojo. Specifically, two rounds of filtering operations guarantee that the dataset covers enough essential information and that the video-text pair is highly correlated. Empirically, we show that the proposed method achieves better performance on RL tasks compared with baselines.
Supervision Interpolation via LossMix: Generalizing Mixup for Object Detection and Beyond

The success of data mixing augmentations in image classification tasks has been well-received. However, these techniques cannot be readily applied to object detection due to challenges such as spatial misalignment, foreground/background distinction, and plurality of instances. To tackle these issues, we first introduce a novel conceptual framework called Supervision Interpolation (SI), which offers a fresh perspective on interpolation-based augmentations by relaxing and generalizing Mixup. Based on SI, we propose LossMix, a simple yet versatile and effective regularization that enhances the performance and robustness of object detectors and more. Our key insight is that we can effectively regularize the training on mixed data by interpolating their loss errors instead of ground truth labels. Empirical results on the PASCAL VOC and MS COCO datasets demonstrate that LossMix can consistently outperform state-of-the-art methods widely adopted for detection. Furthermore, by jointly leveraging LossMix with unsupervised domain adaptation, we successfully improve existing approaches and set a new state of the art for cross-domain object detection.
Unsupervised Learning for Solving the Travelling Salesman Problem

We propose UTSP, an unsupervised learning (UL) framework for solving the Travelling Salesman Problem (TSP). We train a Graph Neural Network (GNN) using a surrogate loss. The GNN outputs a heat map representing the probability for each edge to be part of the optimal path. We then apply local search to generate our final prediction based on the heat map. Our loss function consists of two parts: one pushes the model to find the shortest path and the other serves as a surrogate for the constraint that the route should form a Hamiltonian Cycle. Experimental results show that UTSP outperforms the existing data-driven TSP heuristics. Our approach is parameter efficient as well as data efficient: the model takes $\sim$ 10\% of the number of parameters and $\sim$ 0.2\% of training samples compared with reinforcement learning or supervised learning methods.
Distributed exact quantum algorithms for Bernstein-Vazirani and search problems

Distributed quantum computation has gained extensive attention since small-qubit quantum computers seem to be built more practically in the noisy intermediate-scale quantum (NISQ) era. In this paper, we give a distributed Bernstein-Vazirani algorithm (DBVA) with $t$ computing nodes, and a distributed exact Grover's algorithm (DEGA) that solve the search problem with only one target item in the unordered databases. Though the designing techniques are simple in the light of BV algorithm, Grover's algorithm, the improved Grover's algorithm by Long, and the distributed Grover's algorithm by Qiu et al, in comparison to BV algorithm, the circuit depth of DBVA is not greater than $2^{\text{max}(n_0, n_1, \cdots, n_{t-1})}+3$ instead of $2^{n}+3$, and the circuit depth of DEGA is $8(n~\text{mod}~2)+9$, which is less than the circuit depth of Grover's algorithm, $1 + 8\left\lfloor \frac{\pi}{4}\sqrt{2^n} \right\rfloor$. In particular, we provide situations of our DBVA and DEGA on MindQuantum (a quantum software) to validate the correctness and practicability of our methods. By simulating the algorithms running in the depolarized channel, it further illustrates that distributed quantum algorithm has superiority of resisting noise.
Six-Degree-of-Freedom Localization Under Multiple Permanent Magnets Actuation

Localization of magnetically actuated medical robots is essential for accurate actuation, closed loop control and delivery of functionality. Despite extensive progress in the use of magnetic field and inertial measurements for pose estimation, these have been either under single external permanent magnet actuation or coil systems. With the advent of new magnetic actuation systems comprised of multiple external permanent magnets for increased control and manipulability, new localization techniques are necessary to account for and leverage the additional magnetic field sources. In this letter, we introduce a novel magnetic localization technique in the Special Euclidean Group SE(3) for multiple external permanent magnetic field actuation and control systems. The method relies on a milli-meter scale three-dimensional accelerometer and a three-dimensional magnetic field sensor and is able to estimate the full 6 <inline-formula><tex-math notation="LaTeX">$^{\circ }$</tex-math></inline-formula>-of-freedom pose without any prior pose information. We demonstrated the localization system with two external permanent magnets and achieved localization errors of 8.5 <inline-formula><tex-math notation="LaTeX">$\pm$</tex-math></inline-formula> 2.4 mm in position norm and 3.7 <inline-formula><tex-math notation="LaTeX">$\pm$</tex-math></inline-formula> 3.6<inline-formula><tex-math notation="LaTeX">$^{\circ }$</tex-math></inline-formula> in orientation, across a cubic workspace with <bold>20 cm</bold> length.
Efficiently Counting Substructures by Subgraph GNNs without Running GNN on Subgraphs

Using graph neural networks (GNNs) to approximate specific functions such as counting graph substructures is a recent trend in graph learning. Among these works, a popular way is to use subgraph GNNs, which decompose the input graph into a collection of subgraphs and enhance the representation of the graph by applying GNN to individual subgraphs. Although subgraph GNNs are able to count complicated substructures, they suffer from high computational and memory costs. In this paper, we address a non-trivial question: can we count substructures efficiently with GNNs? To answer the question, we first theoretically show that the distance to the rooted nodes within subgraphs is key to boosting the counting power of subgraph GNNs. We then encode such information into structural embeddings, and precompute the embeddings to avoid extracting information over all subgraphs via GNNs repeatedly. Experiments on various benchmarks show that the proposed model can preserve the counting power of subgraph GNNs while running orders of magnitude faster.
A simulation study of the thermosphere mass density response to substorms using GITM

The temporal and spatial variations of the thermospheric mass density during a series of idealized substorms were investigated using the Global Ionosphere Thermosphere Model (GITM). The maximum mass density perturbation of an idealized substorm with a peak variation of hemispheric power (HP) of 50 GW and interplanetary magnetic field (IMF) Bz of −2 nT was ~14% about 50 min after the substorm onset in the nightside sector of the auroral zone. The mass density response to different types of energy input has a strong local time dependence, with the mass density perturbation due to only an IMF Bz variation peaking in the dusk sector and the density perturbation due to only HP variation peaks in the nightside sector. Simulations with IMF Bz changes only and HP changes only showed that the system behaves slightly nonlinearly when both IMF and HP variations are included (a maximum of 6% of the nonlinearity) and that the nonlinearity grows with energy input. The neutral gas heating rate due to Joule heating was of same magnitude as the heating rate due to precipitation, but the majority of the temperature enhancement due to the heating due to precipitation occurs at lower altitude as compared to the auroral heating. About 110 min after onset, a negative mass density perturbation (~−5%) occurred in the night sector, which was consistent with the mass density measurement of the CHAMP satellite.
Exploring Partial Knowledge Base Inference in Biomedical Entity Linking

Biomedical entity linking (EL) consists of named entity recognition (NER) and named entity disambiguation (NED). EL models are trained on corpora labeled by a predefined KB. However, it is a common scenario that only entities within a subset of the KB are precious to stakeholders. We name this scenario partial knowledge base inference; training an EL model with one KB and inferring on the part of it without further training. In this work, we give a detailed definition and evaluation procedures for this practically valuable but significantly understudied scenario and evaluate methods from three representative EL paradigms. We construct partial KB inference benchmarks and witness a catastrophic degradation in EL performance due to dramatically precision drop.Our findings reveal these EL paradigms can not correctly handle unlinkable mentions (NIL), so they are not robust to partial KB inference. We also propose two simple-and-effective redemption methods to combat the NIL issue with little computational overhead.
Optimal path Soft-DTW Regularisation and constraints Soft-DTW DecDTW Expected path Loss Train Inference DTW ( Soft-DTW ) Train Inference

This paper addresses learning end-to-end models for time series data that include a temporal alignment step via dynamic time warping (DTW). Existing approaches to differentiable DTW either differentiate through a fixed warping path or apply a differentiable relaxation to the min operator found in the recursive steps used to solve the DTW problem. We instead propose a DTW layer based around bilevel optimisation and deep declarative networks, which we name DecDTW. By formulating DTW as a continuous, inequality constrained optimisation problem, we can compute gradients for the solution of the optimal alignment (with respect to the underlying time series) using implicit differentiation. An interesting byproduct of this formulation is that DecDTW outputs the optimal warping path between two time series as opposed to a soft approximation, recoverable from Soft-DTW. We show that this property is particularly useful for applications where downstream loss functions are defined on the optimal alignment path itself. This naturally occurs, for instance, when learning to improve the accuracy of predicted alignments against ground truth alignments. We evaluate DecDTW on two such applications, namely the audio-to-score alignment task in music information retrieval and the visual place recognition task in robotics, demonstrating state-of-the-art results in both.
A Brief History of Hemostasis and Thrombosis at the Mayo Clinic

Abstract Coagulation is a crucial biological mechanism in human bodies to prevent blood loss. Abnormal coagulation can cause bleeding diathesis or thrombosis, common pathologic conditions in our clinical practice. Many individuals and organizations have dedicated their efforts in the past decades to understanding the biological and pathological mechanisms of coagulation and developing laboratory testing tools and treatment options to help patients with bleeding or thrombotic conditions. Since 1926, the Mayo Clinic coagulation group has made significant contributions to the clinical and laboratory practice, basic and translational research on various hemostatic and thrombotic disorders, and the education and collaboration to share and advance our knowledge in coagulation through a highly integrated team and practice model. We would like to use this review to share our history and inspire medical professionals and trainees to join the efforts to advance our understanding of coagulation pathophysiology and improve our care for patients with coagulation disorders.
UTILITY OF URINE ANTIGEN TESTING FOR DIAGNOSIS OF BLASTOMYCOSIS IN NONDOMESTIC FELIDS

Abstract: Blastomycosis caused by the fungus Blastomyces dermatitidis has been reported to cause disease in numerous species of nondomestic felids. Diagnosis of blastomycosis in domestic species often relies on the combination of clinical signs, radiographic findings, and commercial urinary antigen testing. In this report, the sensitivity, specificity, and positive and negative predictive values for urine blastomyces antigen testing for use in nondomestic felids were examined and compared with findings on postmortem examination. The study showed a sensitivity of 100%, specificity of 91.86%, positive predictive value of 50%, and negative predictive value of 100% for urine antigen testing. Furthermore, radiographic and hematologic findings were compared with those of animals diagnosed with blastomycosis. Radiographic evidence consistent with blastomycosis was found in those animals diagnosed via urine antigen testing, but no significant differences in plasma biochemistry parameters between diseased and nondiseased animals were found. This study provides evidence that a positive blastomycosis antigenuria test result should be combined with other diagnostic methods to confirm the presence of infection with B. dermatitidis, whereas a negative antigenuria test result is 100% effective in predicting the absence of disease.
INVESTIGATING BLOOD LACTATE CONCENTRATION AS A PROGNOSTIC INDICATOR FOR BIRDS PRESENTING WITH BREVETOXICOSIS: 2020–2021

Abstract: Large blooms of the dinoflagellate Karenia brevis cause annual harmful algal bloom events, or “red tides” on Florida's Gulf Coast. Each year, the Clinic for the Rehabilitation of Wildlife (CROW) is presented with hundreds of cases of aquatic birds that exhibit neurologic clinical signs due to brevetoxicosis. Double-crested cormorants (Phalacrocorax auratus) are the most common species seen, and typically present with a combination of ataxia, head tremors, knuckling, and/or lagophthalmos. Blood lactate levels are known to increase in mammals for a variety of reasons, including stress, hypoxia, sepsis, and trauma, but there is limited literature on blood lactate values in avian species. The objective of this study was to determine the prognostic value of blood lactate concentration on successful rehabilitation and release of birds presenting with clinical signs consistent with brevetoxicosis. Blood lactate levels were collected on intake, the morning after presentation and initial therapy, and prior to disposition (release or euthanasia) from 194 birds (including 98 cormorants) representing 17 species during the 2020–2021 red tide season. Overall, mean blood lactate at intake, the morning after intake, and predisposition was 2.9, 2.8, and 3.2 mmol/L, respectively, for released birds across all species (2.9, 2.9, and 3.2 mmol/L for released cormorants); 3.4, 3.4, and 6.5 mmol/L for birds that died (4.0, 3.5, and 7.9 mmol/L for cormorants that died); and 3.1, 3.5, and 4.7 mmol/L for birds that were euthanized (3.5, 4.7, and 4.9 mmol/L for cormorants that were euthanized). On average, birds that died or were euthanized had an elevated lactate at all time points as compared to those that were released, but these results were not statistically significant (P = 0.13). These results indicate that blood lactate levels do not appear to be useful as a prognostic indicator for successful release of birds, including double-crested cormorants, affected by brevetoxicosis.
Beyond Static Parallel Loops: Supporting Dynamic Task Parallelism on Manycore Architectures with Software-Managed Scratchpad Memories

Manycore architectures integrate hundreds of cores on a single chip by using simple cores and simple memory systems usually based on software-managed scratchpad memories (SPMs). However, such architectures are notoriously challenging to program, since the programmers need to manually manage all aspects of data movement and synchronization for both correctness and performance. We argue that this manycore programmability challenge is one of the key barriers to achieving the promise of manycore architectures. At the same time, the dynamic task parallel programming model is enjoying considerable success in addressing the programmability challenge of multi-core processors with tens of complex cores and hardware cache coherence. Conventional wisdom suggests a work-stealing runtime, which forms the core of most dynamic task parallel programming models, is ill-suited for manycore architectures. In this work, we demonstrate that a work-stealing runtime is not just feasible on manycore architectures with SPMs, but such a runtime can also significantly improve the performance of irregular workloads when executing on these architectures. We also explore three optimizations that allow the runtime to leverage unused SPM space for further performance benefit. Our dynamic task parallel programming framework achieves 1.2–28.5× speedup on workloads that benefit from our techniques, and only induces minimal overhead for workloads that do not.
TeraHeap: Reducing Memory Pressure in Managed Big Data Frameworks

Big data analytics frameworks, such as Spark and Giraph, need to process and cache massive amounts of data that do not always fit on the managed heap. Therefore, frameworks temporarily move long-lived objects outside the managed heap (off-heap) on a fast storage device. However, this practice results in (1) high serialization/deserialization (S/D) cost and (2) high memory pressure when off-heap objects are moved back to the heap for processing. In this paper, we propose TeraHeap, a system that eliminates S/D overhead and expensive GC scans for a large portion of the objects in big data frameworks. TeraHeap relies on three concepts. (1) It eliminates S/D cost by extending the managed runtime (JVM) to use a second high-capacity heap (H2) over a fast storage device. (2) It offers a simple hint-based interface, allowing big data analytics frameworks to leverage knowledge about objects to populate H2. (3) It reduces GC cost by fencing the garbage collector from scanning H2 objects while maintaining the illusion of a single managed heap. We implement TeraHeap in OpenJDK and evaluate it with 15 widely used applications in two real-world big data frameworks, Spark and Giraph. Our evaluation shows that for the same DRAM size, TeraHeap improves performance by up to 73% and 28% compared to native Spark and Giraph, respectively. Also, it provides better performance by consuming up to 4.6× and 1.2× less DRAM capacity than native Spark and Giraph, respectively. Finally, it outperforms Panthera, a state-of-the-art garbage collector for hybrid memories, by up to 69%.
APEX: A Framework for Automated Processing Element Design Space Exploration using Frequent Subgraph Analysis

The architecture of a coarse-grained reconfigurable array (CGRA) processing element (PE) has a significant effect on the performance and energy-efficiency of an application running on the CGRA. This paper presents APEX, an automated approach for generating specialized PE architectures for an application or an application domain. APEX first analyzes application domain benchmarks using frequent subgraph mining to extract commonly occurring computational subgraphs. APEX then generates specialized PEs by merging subgraphs using a datapath graph merging algorithm. The merged datapath graphs are translated into a PE specification from which we automatically generate the PE hardware description in Verilog along with a compiler that maps applications to the PE. The PE hardware and compiler are inserted into a flexible CGRA generation and compilation toolchain that allows for agile evaluation of CGRAs. We evaluate APEX for two domains, machine learning and image processing. For image processing applications, our automatically generated CGRAs with specialized PEs achieve from 5% to 30% less area and from 22% to 46% less energy compared to a general-purpose CGRA. For machine learning applications, our automatically generated CGRAs consume 16% to 59% less energy and 22% to 39% less area than a general-purpose CGRA. This work paves the way for creation of application domain-driven design-space exploration frameworks that automatically generate efficient programmable accelerators, with a much lower design effort for both hardware and compiler generation.
ABNDP: Co-optimizing Data Access and Load Balance in Near-Data Processing

Near-Data Processing (NDP) has been a promising architectural paradigm to address the memory wall challenge for data-intensive applications. Typical NDP systems based on 3D-stacked memories contain massive parallel processing units, each of which can access its local memory as well as other remote memory regions in the system. In such an architecture, minimizing remote data accesses and achieving computation load balance exhibit a fundamental tradeoff, where existing solutions can only improve one but sacrifice the other. We propose ABNDP, which leverages novel hardware-software co-optimizations to simultaneously alleviate these two issues without making tradeoffs. ABNDP uses a novel and efficient distributed DRAM cache to allow additional data caching locations in the system, where the computation load at the original data hotspots can be distributed and balanced, without significantly increasing remote accesses. ABNDP also adopts a hybrid task scheduling policy that considers both the remote access cost and the load imbalance impact, and exploits the flexibility of the multiple data caching locations to decide the best computation place. Our evaluation shows that ABNDP successfully achieves the two goals of minimizing remote access cost and maintaining load balance, and significantly outperforms the baseline systems in terms of both performance (1.7×) and energy consumption (25%).
What tasks emerge from Knowledge Work?

Over the 21st century, work has changed from physical labour to knowledge work that is mostly cognitive. To date, very few tools have been developed to facilitate its completion. While much has been written about tasks and many categorisations of tasks have been developed, the work is too broad and lacks discriminatory power. While we may know the task is, for example, to write a report, we do not know what goes on inside the task. Without knowing what tasks are performed at a detailed level, it is difficult to design appropriate tools to support task completion. This research will first identify the key tasks within a particular domain, isolate the various elements and then extract the detailed subtasks. In the second part, these subtasks will be examined in other domains. The research will be conducted primarily using semi-structured interviews, and the results will be thematically analysed. The outcome of this work will be a task structure that identifies the key points in task completion that could be better facilitated by tools.