Miracle-dz/UniFAR-FTUs huggingface dataset:
Prefrontal hemodynamics during forward and backward walking, with and without a cognitive task, in people with MS: An fNIRS study

Objective. To investigate the prefrontal cortex (PFC) hemodynamics during forward and as well as backward walking, with and without a cognitive task, in people with MS (pwMS) and healthy controls. Methods. The observational functional near-infrared spectroscopy (fNIRS) study comprised 18 pwMS and 17 healthy controls. Each subject completed four walking trials: Single task (ST) forward walking, dual task (DT) forward walking, ST backward walking, DT backward walking. PFC activity for all trials was recorded using functional near-infrared spectroscopy (fNIRS). The PFC was subdivided in the frontal eye field (FEF), frontopolar cortex (FPC) and the dorsolateral PFC (DLPFC). Results. The relative oxygenated hemoglobin (HbO) concentration was higher during the DT forward walking in all PFC subareas compared with the ST forward walking for both groups. The relative HbO concentration was higher during ST backward walking compared with ST forward walking in pwMS (DLPFC, FEF) and the healthy controls (FEF, FPC), specifically during the initial part of the trial. There was no distinct difference in the relative HbO concentration between ST backward walking with DT backward walking in pwMS. In contrast, the relative HbO concentration in the FEF and the FPC increased in healthy controls during DT backward walking compared with ST backward walking, specifically during the second half of the trial. Conclusions. ST backward walking and DT forward walking impact the hemodynamics at the PFC, although, the difference between pwMS and healthy adults requires further clarification. Future RCT's are encouraged to examine the impact of an intervention program based on DT forward and backward walking on PFC activity in pwMS.
REBench: Microbenchmarking Framework for Relation Extraction Systems

In recent years, several relation extractions (RE) models have been developed to extract knowledge from natural language texts. Accordingly, several benchmark datasets have been proposed to evaluate these models. These RE datasets consisted of natural language sentences with a ﬁxed number of relations from a particular domain. Albeit useful for general-purpose RE benchmarking, they do not allow the generation of customized microbenchmarks according to user-speciﬁed criteria for a speciﬁc use case. Microbenchmarks are key to testing the individual functionalities of a system and hence pinpoint component-based insights. This article proposes REBench, a framework for microbench-marking RE systems, which can select customized relation samples from existing RE datasets from diverse domains. The framework is ﬂexible enough to choose relation samples of diﬀerent sizes and according to the user-deﬁned criteria on essential features to be considered for RE benchmarking. We used various clustering algorithms to generate microbench-marks. We evaluated the state-of-the-art RE systems using diﬀerent RE benchmarking samples. The evaluation results show that specialized microbenchmarking is crucial for identifying the limitations of various RE models and their components.
A Photometric Survey for Lyα-He II Dual Emitters: Searching for Population III Stars in High-Redshift Galaxies

We present a new photometric search for high-z galaxies hosting Population III (Pop III) stars based on deep intermediate-band imaging observations obtained in the Subaru Deep Field (SDF), using Suprime-Cam on the Subaru Telescope. By combining our new data with the existing broadband and narrowband data, we searched for galaxies that emit strongly in both Lyα and He II λ1640 (dual emitters) and are promising candidates for Pop III-hosting galaxies at 3.93≲ z≲ 4.01 and 4.57≲ z≲ 4.65. Although we found 10 dual emitters, most of them turn out to be [O II]-[O III] dual emitters or Hβ-(Hα+[N II]) dual emitters at z < 1, as inferred from their broadband colors and from the ratio of the equivalent widths. No convincing candidate Lyα-He II dual emitter of SFRPopIII≳ 2 M☉ yr−1 was found by our photometric search in 4.03 × 105 Mpc3 in the SDF. This result disfavors low-feedback models for Pop III star clusters and implies an upper limit on the Pop III SFR density of SFRDPopIII < 5 × 10−6 M☉ yr−1 Mpc−3. This new selection method to search for Pop III-hosting galaxies should be useful in future narrowband surveys to achieve the first observational detection of Pop III-hosting galaxies at high redshifts.
High flux symmetry of the spherical hohlraum with octahedral 6LEHs at the hohlraum-to-capsule radius ratio of 5.14

We propose a spherical hohlraum with octahedral six laser entrance holes at a specific hohlraum-to-capsule radius ratio of 5.14 for inertial fusion study, which has robust high symmetry during the capsule implosion and low backscatter without supplementary technology. To produce an ignition radiation pulse of 300 eV, it needs 1.5 MJ absorbed laser energy in such a golden octahedral hohlraum, about 30% more than a traditional cylinder. Nevertheless, it is worth for a high symmetry and low backscatter. The proposed octahedral hohlraum is also flexible and can be applicable to diverse inertial fusion drive approaches.
NozMod: Nozzle Modification for Efficient FDM 3D Printing

3D printing is based on layered manufacturing, where the layers are printed consecutively in increasing height order. In Fused Depositing Modeling (FDM), the printing head may travel without extruding material between separated “islands” of the sliced layers. These travel movements increase the printing time and reduce the quality of the 3D printed part. We present an extended nozzle modification, which can be applied to off-the-shelf FDM printers, and a corresponding toolpath generation algorithm. Together, these dramatically reduce the amount of travel movement, thus improving the printing time and quality of the results. The extended nozzle allows us to print the sliced layers out of order, where part of a lower layer might be printed after a higher layer was already printed. Our toolpath generation algorithm takes advantage of this capability and generates a toolpath which maximizes the number of consecutive layers printed within the same “island” without requiring any travel movement. In addition, the algorithm optimizes the order of the printed islands, and guarantees that the generated toolpath will not cause collisions between the extended nozzle and the model. We demonstrate our approach on a collection of varied models, and show that we considerably reduce the required travel, and improve the printing time and quality compared to standard layer-by-layer printing.
Achieving Mean–Variance Efficiency by Continuous-Time Reinforcement Learning

We conduct an extensive empirical analysis to evaluate the performance of the recently developed reinforcement learning algorithms by Jia and Zhou [11] in asset allocation tasks. We propose an efficient implementation of the algorithms in a dynamic mean-variance portfolio selection setting. We compare it with the conventional plug-in estimator and two state-of-the-art deep reinforcement learning algorithms, deep deterministic policy gradient (DDPG) and proximal policy optimization (PPO), with both simulated and real market data. On both data sets, our algorithm significantly outperforms the others. In particular, using the US stocks data from Jan 2000 to Dec 2019, we demonstrate the effectiveness of our algorithm in reaching the target return and maximizing the Sharpe ratio for various periods under consideration, including the period of the financial crisis in 2007-2008. By contrast, the plug-in estimator performs poorly on real data sets, and PPO performs better than DDPG but still has lower Sharpe ratio than the market. Our algorithm also outperforms two well-diversified portfolios: the market and equally weighted portfolios.
Scaffolding Generation using a 3D Physarum Polycephalum Simulation

In this demo, we present a novel technique for defining topologically optimal scaffoldings for 3D printed objects using a Monte Carlo algorithm based on the foraging behavior of the Physarum polycephalum slime mold. As a case study, we have created a biologically inspired bicycle helmet using this technique that is designed to be effective in resisting impacts. We have created a prototype of this helmet and propose further studies that measure the effectiveness and validity of the design.
hKVS: a framework for designing a high throughput heterogeneous key-value store with SmartNIC and RDMA

In-memory key-value store (KVS) is a crucial component of data center applications. Since DRAM provides high bandwidth and low latency, the major performance bottleneck of common in-memory KVS lies in the network stack. Prior works have attempted to replace the traditional network stack with remote direct memory access (RDMA), which achieve orders of magnitude higher throughput and reduce the response latency. To further increase the throughput of an in-memory KVS, we propose a framework called hKVS, which enables the developers to design high-throughput heterogeneous KVS systems by adding the latest generations of smart network interface cards (SmartNIC), such as the NVIDIA BlueField DPU, to the host machines. The hKVS enables a host server to efficiently exploit the computational resources and utilize the RDMA capability of the SmartNICs to offload the workload for the CPU and increase the network bandwidth. The hKVS allows popular key-value objects to be replicated from the host to SmartNIC to form a high-throughput RDMA KVS jointly. We design the architecture of the hKVS, optimize its software implementation, and conduct a series of experiments to evaluate the resulted performance in realistic applications. By adding a SmartNIC to the host, hKVS achieves up to 1.86X and 1.48X higher throughput in 100% and 95% read workloads, which is cost-effective and scalable compared to building a KVS with multiple hosts, considering the SmartNIC costs much less than a high-performance server and multiple SmartNICs can be added to scale the throughput if needed.
Structure-Preserving Editing of Plates and Volumes for Laser Cutting

We present a 3D editor for laser cutting that extends the range of models that users can manipulate. Our system gives users control over the detailed elements of laser cutting, i.e., individual plates and the associated joints, yet at the same time also allows for efficient editing by means of volumetric tools while preserving the structure of plates in the model. Our system consists of four functional groups: (1) We started with a fabrication-aware 3D editor capable of handling volumetric models (kyub, Baudisch et al., 2019). This subsystem represents 3D models as a single volume. (2) We added a second subsystem that represents laser-cut models as an arrangement of plates in 3D. This allowed us to add tools that allow manipulating individual plates. (3) We unified these two subsystems by adding a demotion mechanism that breaks volumes down into multiple plates, to allow users to apply plate tools to volumes, as well as (4) a promotion mechanism, which infers volumetric substructures from sets of plates, to allow users to apply volume-based tools to plate structures. We validated the resulting system by recreating the 100-model benchmark of assembler3 (Roumen et al., 2021a). Our combined system successfully recreated 87 of the models, compared to 9 with a volume-only baseline system (kyub) and 15 with a plate-only baseline system (flatFitFab, McCrae et al., 2014).
Core Matrix Regression and Prediction with Regularization

Many finance time-series analyses often track a matrix of variables at each time and study their co-evolution over a long time. The matrix time series is overly sparse, involves complex interactions among latent matrix factors, and demands advanced models to extract dynamic temporal patterns from these interactions. This paper proposes a Core Matrix Regression with Regularization algorithm (CMRR) to capture spatiotemporal relations in sparse matrix-variate time series. The model decomposes each matrix into three factor matrices of row entities, column entities, and interactions between row entities and column entities, respectively. Subsequently, it applies recurrent neural networks on interaction matrices to extract temporal patterns. Given the sparse matrix, we design an element-wise orthogonal matrix factorization that leverages the Stochastic Gradient Descent (SGD) in a deep learning platform to overcome the challenge of the sparsity and large volume of complex data. The experiment confirms that combining orthogonal matrix factorization with recurrent neural networks is highly effective and outperforms existing graph neural networks and tensor-based time series prediction methods. We apply CMRR in three real-world financial applications: firm earning forecast, predicting firm fundamentals, and firm characteristics, and demonstrate its consistent performance superiority: reducing error by 23%-53% over other state-of-the-art high-dimensional time series prediction algorithms.
Scalable data management and processing for genomic computing

The recent emergence ofNextGeneration Sequencing (NGS) technologies, in genomics field, produced vast amounts of genomic data. NGS resulted in dropped the cost of sequencing (”reading” in general terms) genomic material very fast. There exist many methods to extract signals from the genomic data, that associate a region of the genome with some interesting information such as a mutation or a peak of expression. Thus, a new problem is emerging: making sense of these signals, heterogeneous in nature, through new kind of languages that can extract relevant information from various heterogeneous sources, integrate them in a new data management system, and compute interesting results. Biologists say that a huge amount of information is undiscovered within the repositories that have been built in the last decade therefore, the focus of genomic data management for the next decade is querying and analysing heterogeneous genomic data. This thesis is about scalable datamanagement and processing for genomic data. We developed a new systemwhich consists of a new query language calledGenoMetricQuery Language (GMQL), a new data model for heterogeneous data (called Genomic Data Model GDM), and a new processing engine which embeds scalable genomic algorithms implemented on several data flow engines. The name (GMQL) derives from the ability of the language of dealing with region-based operations which take into account the regions topological location to the reference genome. GDMmediates all existing heterogeneous data formats. In combination, GDM andGMQL introduce a paradigm shift, by providing a high-level, declarative query language which supports data-driven computations. GMQL is a collective effort which has involved a group of students and professors from Politecnico di Milano. The work reported in this PhD thesis is focused on the design, implementation and validation of the scalable genomic data management and processing system; The architecture of various prototypes of GMQL, GDM, and the scalable genomic processing algorithms. GMQL is a domain-specific language. In this thesis, we developed several scalable algorithms for genomic processing for serving the needs ofGMQLqueries. By the use of data flowengines as our
ToxinMI: improving peptide toxicity prediction by fusing multimodal information based on mutual information

Accurately identifying peptide toxicity is a crucial step for computer-aided peptide-based drug screening, which could accelerate novel drug discovery and reduce resource consumption. Recently, deep learning has shown promising performance in bioinformatics. However, one challenge in developing a deep learning-based model for peptide toxicity prediction is how to represent peptides effectively. In this study, we propose an end-to-end deep learning model named ToxinMI, to predict peptide toxicity that learns features directly from sequence alone. Precisely, ToxinMI captures the sequential and evolutionary features of the peptide simultaneously and introduces the mutual information principle to learn a discriminative representation by discarding noisy information and retaining related-task information from them as much as possible. The experimental results demonstrate that ToxinMI achieves superior predictive performance against state-of-the-art baselines.1
Synthetic Data Augmentation for Deep Reinforcement Learning in Financial Trading

Despite the eye-catching advances in the area, deploying Deep Reinforcement Learning (DRL) in financial markets remains a challenging task. Model-based techniques often fall short due to epistemic uncertainty, whereas model-free approaches require large amount of data that is often unavailable. Motivated by the recent research on the generation of realistic synthetic financial data, we explore the possibility of using augmented synthetic datasets for training DRL agents without direct access to the real financial data. With our novel approach, termed synthetic data augmented reinforcement learning for trading (SDARL4T), we test whether the performance of DRL for financial trading can be enhanced, by attending to both profitability and generalization abilities. We show that DRL agents trained with SDARL4T make a profit which is comparable, and often much larger, than that obtained by the agents trained on real data, while guaranteeing similar robustness. These results support the adoption of our framework in real-world uses of DRL for trading.
Dynamic Calibration of Order Flow Models with Generative Adversarial Networks

Classical models for order flow dynamics based on point processes, such as Poisson or Hawkes processes, have been studied intensively. Often, several days of limit border book (LOB) data is used to calibrate such models, thereby averaging over different dynamics - such as intraday effects or different trading volumes. This work uses generative adversarial networks (GANs) to learn the distribution of calibrations – obtained by many calibrations based on short time frames. The trained GAN can then be used to generate synthetic, realistic calibrations based on external conditions such as time of the day or volatility. Results show that GANs easily reproduce patterns of the order arrival intensities and can fit the distribution well without heavy parameter tuning. The synthetic calibrations can then be used to simulate order streams which contain new dynamics such as temporary drifts, different volatility regimes, but also intra-day patterns such as the commonly observed U-shape that reflects stylized behaviour around open and close of market hours.
Fault-tolerant mosaic encoding in knot-based cryptography

The cryptographic protocol based on topological knot theory,recently proposed by the authors, is improved for what concerns the efficiency of the encoding of knot diagrams and its error robustness. The standard Dowker-Thistlethwaite code, based on the ordered assignment of two numbers to each crossing of a knot diagram and not unique for some classes of knots, is replaced by a system of eight prototiles (knot mosaics) which, once assembled according to a set of combinatorial rules, reproduces unambiguously any unoriented knot diagram. A Reed-Muller scheme is used to encode with redundancy the eight prototiles into blocks and, once the blank tile is added and suitably encoded, the knot diagram is turned into an N X N mosaic, uniquely associated with a string of length 4 N^2 bits. The complexity of the knot, measured topologically by the number of crossings, is in turn polynomially related to the number of tiles of the associated mosaic, and for knot diagrams of higher complexity the mosaic encoding provides a design of the knot-based protocol which is fault-tolerant under random 1-bit flips. It is also argued that the knot mosaic alphabet might be used in other applications which require high-capacity data transmission.
Learning Not to Spoof

As intelligent trading agents based on reinforcement learning (RL) gain prevalence, it becomes more important to ensure that RL agents obey laws, regulations, and human behavioral expectations. There is substantial literature concerning the aversion of obvious catastrophes like crashing a helicopter or bankrupting a trading account, but little around the avoidance of subtle non-normative behavior for which there are examples, but no programmable definition. Such behavior may violate legal or regulatory, rather than physical or monetary, constraints. In this article, I consider a series of experiments in which an intelligent stock trading agent maximizes profit but may also inadvertently learn to spoof the market in which it participates. I first inject a hand-coded spoofing agent to a multi-agent market simulation and learn to recognize spoofing activity sequences. Then I replace the hand-coded spoofing trader with a simple profit-maximizing RL agent and observe that it independently discovers spoofing as the optimal strategy. Finally, I introduce a method to incorporate the recognizer as normative guide, shaping the agent’s perceived rewards and altering its selected actions. The agent remains profitable while avoiding spoofing behaviors that would result in even higher profit. After presenting the empirical results, I conclude with some recommendations. The method should generalize to the reduction of any unwanted behavior for which a recognizer can be learned.
Genuine Lie semigroups and semi-symmetries of PDEs

Any Lie group G acting on a Euclidean nonvoid open subset M can be seen as a subgroup of the smooth diffeomorphisms Diff^\infty(M,M) of M into itself. Thus actions by such Lie groups G correspond to smooth coordinate transforms on M which, in particular, have smooth inverses. In Rosinger [1, chap. 13], the study of Lie semigroups G in the vastly larger semigroup C^\infty(M,M) of smooth maps of M into itself was initiated. Such semigroups were named "genuine Lie semigroups", or in short, GLS, since they are no longer contained in Diff^\infty(M,M), thus they correspond to smooth coordinate transforms which need not have smooth inverses. Genuine Lie semigroups, or GLS, have a major interest since they still can transform solutions of linear or nonlinear PDEs into other solutions of the respective equations, thus leading to the vastly larger class of "semi-symmetries" of such equations. Certain Lie semigroups have earlier been studied in the literature, Hilgert, et.al. However, such semigroups have always been contained in suitable Lie groups, thus they have been contained in Diff^\infty(M,M) as sub-semigroups. In particular, the coordinate transforms defined by them were always invertible, unlike in the case of "genuine Lie semigroups", or GLS, studied here.