Miracle-dz/UniFAR-FTUs huggingface dataset:
The Impact of Judgment Variability on the Consistency of Offline Effectiveness Measures

Measurement of the effectiveness of search engines is often based on use of relevance judgments. It is well known that judgments can be inconsistent between judges, leading to discrepancies that potentially affect not only scores but also system relativities and confidence in the experimental outcomes. We take the perspective that the relevance judgments are an amalgam of perfect relevance assessments plus errors; making use of a model of systematic errors in binary relevance judgments that can be tuned to reflect the kind of judge that is being used, we explore the behavior of measures of effectiveness as error is introduced. Using a novel methodology in which we examine the distribution of “true” effectiveness measurements that could be underlying measurements based on sets of judgments that include error, we find that even moderate amounts of error can lead to conclusions such as orderings of systems that statistical tests report as significant but are nonetheless incorrect. Further, in these results the widely used recall-based measures AP and NDCG are notably more fragile in the presence of judgment error than is the utility-based measure RBP, but all the measures failed under even moderate error rates. We conclude that knowledge of likely error rates in judgments is critical to interpretation of experimental outcomes.
Specific attributes of the VL domain influence both the structure and structural variability of CDR-H3 through steric effects

Antibodies, through their ability to target virtually any epitope, play a key role in driving the adaptive immune response in jawed vertebrates. The binding domains of standard antibodies are their variable light (VL) and heavy (VH) domains, both of which present analogous complementarity-determining region (CDR) loops. It has long been known that the VH CDRs contribute more heavily to the antigen-binding surface (paratope), with the CDR-H3 loop providing a major modality for the generation of diverse paratopes. Here, we provide evidence for an additional role of the VL domain as a modulator of CDR-H3 structure, using a diverse set of antibody crystal structures and a large set of molecular dynamics simulations. We show that specific attributes of the VL domain such as CDR canonical forms and genes can influence the structural diversity of the CDR-H3 loop, and provide a physical model for how this effect occurs through inter-loop contacts and packing of CDRs against each other. Our study provides insights into the interdependent nature of CDR conformations, an understanding of which is important for the rational antibody design process.
Faces capture spatial attention only when we want them to: an inattentional blindness EEG study

Previous research on emotional face processing has shown that emotional faces such as fearful faces may be processed without visual awareness. However, evidence for nonconscious attention capture by fearful faces is limited. In fact, studies using sensory manipulation of awareness (e.g., backward masking paradigms) have shown that fearful faces do not attract attention during subliminal viewings nor when they were task-irrelevant. Here, we used a three-phase inattentional blindness paradigm and electroencephalography to examine whether faces (fearful and neutral) capture attention under different conditions of awareness and task-relevancy. We found that the electrophysiological marker for attention capture, the N2-posterior-contralateral (N2pc), was elicited by face stimuli only when participants were aware of the faces and when they were task-relevant (phase 3). When participants were unaware of the presence of faces (phase 1) or when the faces were irrelevant to the task (phase 2), no N2pc was observed. Together with our previous work, we concluded that fearful faces, or faces in general, do not attract attention unless we want them to.
Stem Cell Repor ts

SUMMARY Whilestudiedextensively inmodel systems,human gastrulation remains obscure. The scarcity of fetalbiological material as well as ethical considerationslimitourunderstandingofthisprocess. Invitro attachmentofnaturalblastocystsshedlightonaspectsofthesecondweekof human development in the absence of the morphological manifestation of gastrulation. Stem cell-derived blastocyst models, blastoids, provide the opportunity to reconstitute pre-to post-implantation development in vitro . Here we show that upon in vitro attachment, human blastoids self-organize a BRA + population and undergo gastrulation. Single-cell RNA sequencing of these models replicates the transcriptomic signature of the human gastrula. Analysis of developmental timing reveals that in both blastoid models and natural human embryos, the onset of gastrulation as deﬁned by molecular markers, can be traced to timescales equivalent to 12 days post fertilization. In all, natural human embryos and blastoid models self-organize primitive streak and mesoderm derivatives upon in vitro attachment.
High-Density Exploration of Activity States in a Multi-Area Brain Model

To simulate whole brain dynamics with only a few equations, biophysical, mesoscopic models of local neuron populations can be connected using empirical tractography data. The development of mesoscopic mean-field models of neural populations, in particular, the Adaptive Exponential (AdEx mean-field model), has successfully summarized neuron-scale phenomena leading to the emergence of global brain dynamics associated with conscious (asynchronous and rapid dynamics) and unconscious (synchronized slow-waves, with Up-and-Down state dynamics) brain states, based on biophysical mechanisms operating at cellular scales (e.g. neuromodulatory regulation of spike-frequency adaptation during sleep-wake cycles or anesthetics). Using the Virtual Brain (TVB) environment to connect mean-field AdEx models, we have previously simulated the general properties of brain states, playing on spike-frequency adaptation, but have not yet performed detailed analyses of other parameters possibly also regulating transitions in brain-scale dynamics between different brain states. We performed a dense grid parameter exploration of the TVB-AdEx model, making use of High Performance Computing. We report a remarkable robustness of the effect of adaptation to induce synchronized slow-wave activity. Moreover, the occurrence of slow waves is often paralleled with a closer relation between functional and structural connectivity. We find that hyperpolarization can also generate unconscious-like synchronized Up and Down states, which may be a mechanism underlying the action of anesthetics. We conclude that the TVB-AdEx model reveals large-scale properties identified experimentally in sleep and anesthesia.
Classifying Subjects with PFC Lesions from Healthy Controls during Working Memory Encoding via Graph Convolutional Networks

This paper describes a group-level classification of 14 patients with prefrontal cortex (pFC) lesions from 20 healthy controls using multi-layer graph convolutional networks (GCN) with features inferred from the scalp EEG recorded from the encoding phase of working memory (WM) trials. We first construct undirected and directed graphs to represent the WM encoding for each trial for each subject using distance correlation- based functional connectivity measures and differential directed information-based effective connectivity measures, respectively. Centrality measures of betweenness centrality, eigenvector centrality, and closeness centrality are inferred for each of the 64 channels from the brain connectivity. Along with the three centrality measures, each graph uses the relative band powers in the five frequency bands - delta, theta, alpha, beta, and gamma- as node features. The summarized graph representation is learned using two layers of GCN followed by mean pooling, and fully connected layers are used for classification. The final class label for a subject is decided using majority voting based on the results from all the subject's trials. The GCN-based model can correctly classify 28 of the 34 subjects (82.35% accuracy) with undirected edges represented by functional connectivity measure of distance correlation and classify all 34 subjects (100% accuracy) with directed edges characterized by effective connectivity measure of differential directed information.
Cognitive-motor performance assessment during in-person and remote practice of action sequences

The development and use of virtual systems to learn and perform remotely complex action sequences has many applications from rehabilitation to military training. The examination of human cognitive-motor control and learning processes in individuals executing a task physically in-person and remotely via a virtual system can help with the design of such systems as well as inform human performance. An important application of such virtual systems is the remote assessment of cognitive-motor performance and learning for the neurorehabilitation of individuals with neural deficits (e.g., Veterans). This study is a first step that examined performance, mental workload, and fatigue in healthy individuals during remote practice of action sequences compared to physical in-person training. Here individuals were either required to practice an action sequence task physically (in the lab) or remotely (outside the lab) via a virtual system. Action sequence performance, mental workload and fatigue were collected for both remote and physical practice. Findings suggest that remote training via a virtual system improved performance and reduced mental workload leading to enhanced cognitive-motor efficiency similarly to physical practice, while limited practice-induced fatigue effects were observed. To extend this study, future work will consider varying interface characteristics and using EEG with various healthy and clinical populations.
On the Relationship Between Fascicle Diameter and Perineurium Thickness in the Ulnar Nerve of Pigs

The anatomical structures of peripheral nerves significantly influence the performance and selectivity of neural interfaces. Among these structures, the perineurium thickness, because of its high resistivity, is important in shaping the electric field distribution within a nerve. However, data on the perineurium thickness of somatic nerves in animals is sparse. As animal models are the first step towards developing novel implantable nerve interfaces, this study characterises the perineurium thickness in the ulnar nerve of pigs. Distal and proximal sections of the ulnar nerve $(\mathbf{n}=\boldsymbol{6})$ were extracted, stained with haematoxylin and eosin, and the perineurium thickness and fascicular diameters were measured. In total, 129 fascicles were quantified, and the average fascicle diameter was ${269\pm 73.3}\ \upmu \mathrm{m}$ and $\boldsymbol{277\pm 81.1}\ \upmu \mathrm{m}$ for the distal and proximal nerves, respectively $(\boldsymbol{p}=\boldsymbol{0.59})$. A linear relationship was observed between fascicle diameter and perineurium thickness $(\mathbf{R}^{2}=\boldsymbol{0.69})$. This relationship was affected by the location of the nerve section, with distal sections having a greater perineurium thickness than proximal segments. Finally, equations were provided to estimate the perineurium thickness based on fascicle diameter. This information can be used to develop realistic peripheral nerve models, which can reduce variability and improve the selectivity of neural interfaces.
Lithium-niobate-based narrow-linewidth frequency agile integrated lasers with petahertz frequency tuning rate

We demonstrate an electro-optically tunable hybrid integrated laser self-injection locked to a mode of a heterogeneously integrated lithium-niobate-on-Damascene-silicon-nitride microresonator. An intrinsic linewidth of 3 kHz and a frequency tuning rate of 12 × 1015 Hz/s were observed. Proof-of-principle coherent LiDAR experiments were performed. © 2022 The Author(s)
Efficient Parameter Tuning for Multi-agent Simulation Using Deep Reinforcement Learning

This study proposes a reinforcement-learning-based method for efficient parameter tuning in multi-agent simulations (MAS). Usually, MAS has a high computational burden because of the agents involved; thus, it is important to tune its parameters efficiently. Our proposed method is centered around actorcritic-based reinforcement learning methods, such as deep deterministic policy gradient (DDPG) and soft actor-critic (SAC). In addition, this study proposes three additional components: an action converter (only for DDPG), a redundant full neural network actor, and a seed fixer. As an experiment, a parameter-tuning task is employed in an artificial financial market simulation. A Bayesian estimation-based method is used as the baseline. The results show that our model outperforms the baseline in terms of tuning performance, indicating that additional components of the proposed method are essential. Moreover, the critics of our models effectively function as surrogate models, that is, as an approximate function of the simulation, which allows the actor to appropriately tune the parameters.
Analytical Model of Dynamic Wind Loads on Lattice Towers

Based on base balance technique in a boundary layer wind tunnel,the generalized force spectra and RMS coefficients of the first mode of three typical lattice towers in along-wind,across-wind as well as torsional directions were obtained.In light of these results,the analytical models of generalized force spectra of the first mode of the lattice towers were established by applying least-square method.These models are in good agreement with the experimental results obtain by wind tunnel tests,and a 2-D or 3-D calculation method can be developed on the basis of the models to evaluate wind-induced dynamic responses of typical lattice towers in frequency domain.
Synthesizing Speech by Decoding Intracortical Neural Activity from Dorsal Motor Cortex

Losing the ability to speak due to brain injury or neurodegenerative diseases such as ALS can be debilitating. Brain-computer interfaces could potentially provide affected individuals a fast and intuitive way to communicate by decoding speech-related neural activity into a computer-synthesized voice. Current intracortical BCIs for communication using handwriting or point-and-click typing are substantially slower than natural speech and do not capture the full expressive range of speech. Recent studies have identified speech features from ECoG and sEEG recordings; however, intelligible speech synthesis has not yet been demonstrated. Our previous work has shown speech-related patterns in intracortical recordings from dorsal (arm/hand) motor cortex that enabled discrete word/phoneme classification. This motivates exploring an intracortical approach for continuous voice synthesis. Here, we present a neural decoding framework to synthesize speech by directly translating neural activity recorded from human motor cortex using intracortical multielectrode arrays into a low-dimensional speech feature space from which voice is synthesized.
PERIODIC $k$-GRAPH ALGEBRAS REVISITED

Let $P$ be a finitely generated cancellative abelian monoid. A $P$-graph ${\rm\Lambda}$ is a natural generalization of a $k$-graph. A pullback of ${\rm\Lambda}$ is constructed by pulling it back over a given monoid morphism to $P$, while a pushout of ${\rm\Lambda}$ is obtained by modding out its periodicity, which is deduced from a natural equivalence relation on ${\rm\Lambda}$. One of our main results in this paper shows that, for some $k$-graphs ${\rm\Lambda}$, ${\rm\Lambda}$ is isomorphic to the pullback of its pushout via a natural quotient map, and that its graph $\text{C}^{\ast }$-algebra can be embedded into the tensor product of the graph $\text{C}^{\ast }$-algebra of its pushout and $\text{C}^{\ast }(\text{Per}\,{\rm\Lambda})$. As a consequence, in this case, the cycline algebra generated by the standard generators corresponding to equivalent pairs is a maximal abelian subalgebra, and there is a faithful conditional expectation from the graph $\text{C}^{\ast }$-algebra onto it.
Integrability of difference Calogero-Moser systems

A general class of n‐particle difference Calogero–Moser systems with elliptic potentials is introduced. Besides the step size and two periods, the Hamiltonian depends on nine coupling constants. We prove the quantum integrability of the model for n=2 and present partial results for n≥ 3. In degenerate cases (rational, hyperbolic, or trigonometric limit), the integrability follows for arbitrary particle number from previous work connected with the multivariable q‐polynomials of Koornwinder and Macdonald. Liouville integrability of the corresponding classical systems follows as a corollary. Limit transitions lead to various well‐known models such as the nonrelativistic Calogero–Moser systems associated with classical root systems and the relativistic Calogero–Moser system.
Technical Notes and Correspondence Robust Control With Missing Measurements and Time Delays

In this technical note, the robust control problem is investigated for a class of stochastic uncertain discrete time-delay systems with missing measurements. The parameter uncertainties enter into the state matrices, and the missing measurements are described by a binary switching sequence satisfying a conditional probability distribution. The purpose of the problem is to design a full-order dynamic feedback controller such that, for all possible missing observations and admis- sible parameter uncertainties, the closed-loop system is asymptotically mean-square stable and satisfies the prescribed performance con- straint. Delay-dependent conditions are derived under which the desired solution exists, and the controller parameters are designed by solving a linear matrix inequality (LMI). A numerical example is provided to illustrate the usefulness of the proposed design method.
Riemannian geometry-based detection of slow cortical potentials during movement preparation

Slow cortical potentials - such as the readiness potential (RP) and the contingent negative variation (CNV) - provide means for controlling a brain-machine interface (BMI). RP and CNV precede self-paced and cued movements, respectively, and thus can be exploited for a variety of purposes such as robotic exoskeleton control or motor rehabilitation via BMI training. Single-trial detection of these patterns, however, is a challenging and therefore not yet fully resolved task, especially for online applications. Here we propose and evaluate a novel decoding algorithm for this cause, utilizing Riemannian geometry, template matching and adaptive re-centering for robust performance. We recruited 12 young, healthy volunteers who performed a center-out reaching task designed to evoke both RP and CNV in a sequential manner, while their neural activity was recorded using electroencephalography. Separate decoders of the same basic architecture were trained to detect RP and CNV on a single-trial basis, and data was evaluated offline on the subject level. RPs could be identified with a group average accuracy of 62.64±4.75% (ranging from 51.12% to 68.31%) with all but one subject surpassing chance levels, while CNVs were detected with an average accuracy of $\boldsymbol{74.01\pm 7.49\%}$ (ranging from 62.24% to 85.00%) with all participants surpassing chance level. Even though evaluation was carried out offline, the proposed pipeline is readily adaptable to an online setting. Therefore, our Riemannian geometry-based approach show potential in single-trial detection of slow cortical potentials.
A 6Σ–6Σ TRANSITION IN CrH

The infrared band system of CrH has been photographed at high dispersion. Rotational analyses of the 0–0 and 0–1 bands are given. The transition is shown to be a 6Σ+–6Σ+. The upper 6Σ-state shows an extended perturbation. Rotational and vibrational constants for the two states are given. The application of theoretical expressions for the fine structure splitting in 6Σ-states is discussed and approximate values of the splitting constants in the observed states have been derived. The corresponding system of the isotope molecule CrD was also photographed and a vibrational analysis has been carried out. Finally the electronic configurations of the molecular states and the nature of the perturbing state are discussed. The ultraviolet systems of CrH are briefly mentioned.
Identification of Neural Biomarkers of Major Depressive Disorder Symptom Severity Using Computerized Linguistic Analysis

Although numerous treatments are available for major depressive disorder (MDD), patients can be refractory to sequential treatment regimens. Experimental studies have demonstrated promising results implementing deep brain stimulation (DBS) as a therapy for treatment resistant MDD. However, optimization of this technique requires repeated assessments of the clinical effects of treatment in each patient and the ability to reliably capture the complexity and dynamics of depression symptoms. In our initial studies evaluating the feasibility and preliminary efficacy of a novel closed-loop DBS (CL-DBS) approach, we have observed that repeated self-rated MDD metrics can be burdensome to complete and may not provide accurate measures of symptom severity fluctuations over time, making the identification of neural biomarkers of MDD a challenge. To address this, we evaluated if text analysis could identify linguistic indicators of depression, including providing insights into symptom severity. Using the Linguistic Inquiry and Word Count software, we analyzed written symptom reports from one patient in clinical trial for CL-DBS. We found significant linguistic predictors of depression symptoms that were associated with the same frequency- and region- specific spectral power correlates found when assessing symptoms captured by self-rated depression metrics. These preliminary findings suggest that the close association between language use and symptom strength could be utilized to detect neural biomarkers of depression and potentially to assess treatment outcome.
Neural Ranking Strategy for Portfolio Construction Using Transformers

In recent years, machine learning methods such as deep learning have made significant progress, and their application in the financial field has been actively pursued. However, financial markets are highly non-stationary, making applying machine learning to single time series data difficult. Therefore, analyzing the market from multiple financial instruments and predicting their relative relationships is important. In this study, we propose a novel method for constructing investment portfolios based on a ranking model using the Transformer architecture. The essential part of the proposed method is a Rank Transformer (RaT) that determines asset allocation by predicting the investment efficiency ranking of each financial instrument using features obtained from historical price data. We also design a loss function that aims to optimize both Normalized Discounted Cumulative Gain (NDCG), a measure of ranking prediction performance, and investment returns. The proposed method is expected to apply deep learning to financial problems such as investment selection. To confirm the effectiveness of the proposed method, computer experiments were conducted using data from the U.S. stock market and the foreign exchange market. The results suggest that the proposed method can achieve better performance than conventional methods on the Sharpe ratio, a measure of risk-adjusted return. We conclude that the proposed method can be expected to improve annualized return rate.