Miracle-dz/UniFAR-FTUs huggingface dataset:
Balancing Profit, Risk, and Sustainability for Portfolio Management

Stock portfolio optimization is the process of continuous reallocation of funds to a selection of stocks. This is a particularly well-suited problem for reinforcement learning, as daily rewards are compounding and objective functions may include more than just profit, e.g., risk and sustainability. We developed a novel utility function with the Sharpe ratio representing risk and the environmental, social, and governance score (ESG) representing sustainability. We show that a state- of-the-art policy gradient method – multi-agent deep deterministic policy gradients (MADDPG) – fails to find the optimum policy due to flat policy gradients and we therefore replaced gradient descent with a genetic algorithm for parameter optimization. We show that our system outperforms MADDPG while improving on deep Q-learning approaches by allowing for continuous action spaces. Crucially, by incorporating risk and sustainability criteria in the utility function, we improve on the state-of-the-art in reinforcement learning for portfolio optimization; risk and sustainability are essential in any modern trading strategy, and we propose a system that does not merely report these metrics, but that actively optimizes the portfolio to improve on them.
Highly deformable printed organic trapezoidal micro-beams for vibration energy harvesting

In this work, we are interested in the design of polymer MEMS for future integration of electroactive materials for mechanical energy harvesting from environmental vibrations. Organic micro-beams are designed by both analytical modeling and finite element analysis to exhibit a low resonant frequency and a high global strain on top surface under vibration. An original method of fabrication combining photolithography and screen-printing is used for the fabrication of these micro-structures. The patterned micro-beams are based on SU8 epoxy photoresist, while the printed seismic mass material is a silver loaded epoxy composite. Trapezoidal shaped micro-beams give the best results with a resonant frequency of ∼350 Hz and a uniform strain overall the structure, well adapted for a further piezoelectric or electrostrictive transduction. On the other hand, a multimode harvester is realized with a set of micro-beam-mass systems to cover a frequency range larger than 300 Hz, within the standard ambient vibration frequency domain.
Synthsonic: Fast, Probabilistic modeling and Synthesis of Tabular Data

The creation of realistic, synthetic datasets has several purposes with growing demand in recent times, e.g. privacy protection and other cases where real data cannot be easily shared. A multitude of primarily neural networks (NNs), e.g. Generative Adversarial Networks (GANs) and Variational Autoen-coders (VAEs), or Bayesian Network (BN) approaches have been created to tackle this problem, however these require extensive compute resources, lack interpretability, and in some instances lack replication ﬁdelity as well. We propose a hybrid, probabilistic approach for synthesizing pairwise independent tabular data, called Synthsonic. A sequence of well-understood, invertible statistical transformations removes ﬁrst-order correlations, then a Bayesian Network jointly models continuous and categorical variables, and a calibrated discriminative learner captures the remaining dependencies. Replication studies on MIT’s SDGym benchmark show marginally or sig-niﬁcantly better performance than all prior BN-based approaches, while being competitive with NN-based approaches (ﬁrst place in 10 out of 13 benchmark datasets). The computational time required to learn the data distribution is at least one order of magnitude lower than the NN methods. Furthermore, in-specting intermediate results during the synthetic data generation allows easy diagnostics and tailored corrections. We believe the combination of out-of-the-box performance,
A View of Exact Inference in Graphs from the Degree-4 Sum-of-Squares Hierarchy

Performing inference in graphs is a common task within several machine learning problems, e.g., image segmentation, community detection, among others. For a given undirected connected graph, we tackle the statistical problem of exactly recovering an unknown ground-truth binary labeling of the nodes from a single corrupted observation of each edge. Such problem can be formulated as a quadratic combinatorial optimization problem over the boolean hypercube, where it has been shown before that one can (with high probability and in polynomial time) exactly recover the ground-truth labeling of graphs that have an isoperimetric number that grows with respect to the number of nodes (e.g., complete graphs, regular expanders). In this work, we apply a powerful hierarchy of relaxations, known as the sum-of-squares (SoS) hierarchy, to the combinatorial problem. Motivated by empirical evidence on the improvement in exact recoverability, we center our attention on the degree-4 SoS relaxation and set out to understand the origin of such improvement from a graph theoretical perspective. We show that the solution of the dual of the relaxed problem is related to ﬁnding edge weights of the Johnson and Kneser graphs, where the weights fulﬁll the SoS constraints and intuitively allow the input graph to increase its algebraic connectivity. Finally, as byproduct of our analysis, we derive a novel Cheeger-type lower bound for the algebraic connectivity of graphs with signed edge weights.
Warping Layer: Representation Learning for Label Structures in Weakly Supervised Learning

Many learning tasks only receive weak supervision, such as semi-supervised learning and few-shot learning. With limited labeled data, prior structures become especially important, and prominent examples include hierarchies and mutual exclusions in the class space. However, most existing approaches only learn the representations separately in the feature space and the label space, and do not explicitly enforce the logical relationships. In this paper, we propose a novel warping layer that jointly learns representations in both spaces, and thanks to the modularity and diﬀerentiability, it can be directly embedded into generative models to leverage the prior hierarchical structure and unlabeled data. The eﬀectiveness of the warping layer is demonstrated on both few-shot and semi-supervised learning, outperforming the state of the art in practice.
Data-Driven Controller Synthesis of Unknown Nonlinear Polynomial Systems via Control Barrier Certificates

In this work, we propose a data-driven approach to synthesize safety controllers for continuous-time nonlinear polynomial-type systems with unknown dynamics. The proposed framework is based on notions of so-called control barrier certificates , constructed from data while providing a guaranteed confidence of 1 on the safety of unknown systems. Under a certain rank condition, we synthesize polynomial state-feedback controllers to ensure the safety of the unknown system only via a single trajectory collected from it. We demonstrate the effectiveness of our proposed results by applying them to a nonlinear polynomial-type system with unknown dynamics
Deep Generative Learning via Euler Particle Transport

We propose an Euler particle transport (EPT) approach to generative learning. EPT is motivated by the problem of constructing an optimal transport map from a reference distribution to a target distribution characterized by the Monge-Amp`ere equation. Interpreting the inﬁnitesimal linearization of the Monge-Amp`ere equation from the perspective of gradient ﬂows in measure spaces leads to a stochastic McKean-Vlasov equation. We use the forward Euler method to solve this equation. The resulting forward Euler map pushes forward a reference distribution to the target. This map is the composition of a sequence of simple residual maps, which are computationally stable and easy to train. The key task in training is the estimation of the density ratios or differences that determine the residual maps. We estimate the density ratios based on the Bregman divergence with a gradient penalty using deep density-ratio ﬁtting. We show that the proposed density-ratio estimators do not suffer from the “ curse of dimensionality ” if data is supported on a lower-dimensional manifold. Numerical experiments with multi-mode synthetic datasets and comparisons with the existing methods on real benchmark datasets support our theoretical results and demonstrate the effectiveness of the proposed method.
Comparison of Machine Learners on an ABA Experiment Format of the Cart-Pole Task

Current approaches to online learning focus primarily on reinforcement learning (RL) – algorithms that learn through feedback from experience. While most current RL algorithms have shown good results in learning to perform tasks for which they were speciﬁcally designed, most of them lack a level of generalization needed to use existing knowledge to handle novel situations – a property referred to as autonomous transfer learning. Situations encountered by such systems which were not present during the training phase can lead to critical failure. In the present research we analyzed the autonomous transfer learning capabilities of ﬁve diﬀerent machine learning approaches – i.e. an Actor-Critic , a Q-Learner , a Policy Gradient Learner , a Double-Deep Q-Learner , and OpenNARS for Applications . Following a classic ABA experimental format, the learners were all trained on the well-known cart-pole task in phase A-1 , before strategic changes to the task were introduced in phase B , consisting of inverting the direction of control of the cart (move-left command moved the cart to the right and vice versa), as well as the introduction of noise. All analyzed learners show an extreme performance drop when the action command is inverted in phase B , resulting in long (re-)training periods trying to reach A1 performance. Most learners do not reach initial A1 performance levels in phase B , some falling very far from them. Furthermore, previously learned knowledge is not retained during the re-training, resulting in an even larger performance drop when the task is changed back to the original settings in phase A2 . Only one learner (NARS) reached comparable performance in A1 and A2 , demonstrating retention of, and return to, priorly-acquired knowledge.
Inferring Neural Activity Before Plasticity: A Foundation for Learning Beyond Backpropagation

For both humans and machines, the essence of learning is to pinpoint which components in its information processing pipeline are responsible for an error in its output — a challenge that is known as credit assignment1. How the brain solves credit assignment is a key question in neuroscience, and also of significant importance for artificial intelligence. Many recent studies1–12 presuppose that it is solved by backpropagation13–16, which is also the foundation of modern machine learning17–22. However, it has been questioned whether it is possible for the brain to implement backpropagation23, 24, and learning in the brain may actually be more efficient and effective than backpropagation25. Here, we set out a fundamentally different principle on credit assignment, called prospective configuration. In prospective configuration, the network first infers the pattern of neural activity that should result from learning, and then the synaptic weights are modified to consolidate the change in neural activity. We demonstrate that this distinct mechanism, in contrast to backpropagation, (1) underlies learning in a well-established family of models of cortical circuits, (2) enables learning that is more efficient and effective in many contexts faced by biological organisms, and (3) reproduces surprising patterns of neural activity and behaviour observed in diverse human and animal learning experiments. Our findings establish a new foundation for learning beyond backpropagation, for both understanding biological learning and building artificial intelligence.
Tracking and perceiving diverse motion signals: Directional biases in human smooth pursuit and perception

Human smooth pursuit eye movements and motion perception behave similarly when observers track and judge the motion of simple objects, such as dots. But moving objects in our natural environment are complex and contain internal motion. We ask how pursuit and perception integrate the motion of objects with motion that is internal to the object. Observers (n = 20) tracked a moving random-dot kinematogram with their eyes and reported the object’s perceived direction. Objects moved horizontally with vertical shifts of 0, ±3, ±6, or ±9° and contained internal dots that were static or moved ±90° up/down. Results show that whereas pursuit direction was consistently biased in the direction of the internal dot motion, perceptual biases differed between observers. Interestingly, the perceptual bias was related to the magnitude of the pursuit bias (r = 0.75): perceptual and pursuit biases were directionally aligned in observers that showed a large pursuit bias, but went in opposite directions in observers with a smaller pursuit bias. Dissociations between perception and pursuit might reflect different functional demands of the two systems. Pursuit integrates all available motion signals in order to maximize the ability to monitor and collect information from the whole scene. Perception needs to recognize and classify visual information, thus segregating the target from its context. Ambiguity in whether internal motion is part of the scene or contributes to object motion might have resulted in individual differences in perception. The perception-pursuit correlation suggests shared early-stage motion processing or perception-pursuit interactions.
Post-transcriptional control of SRSF9 promotes the epithelial-to-mesenchymal transition (EMT) in colorectal cancer cells

In human colorectal cancer (CRC) cells the Raf/MEK/ERK scaffold Kinase Suppressor of Ras 1 (KSR1)-dependent signaling is required for the epithelial-to-mesenchymal transition (EMT)-like phenotype. Here we show that KSR1 promotes the association of differentially spliced mRNA bearing recognition sites for the Serine/Arginine-Rich (SR) splicing factor SRSF9. CRISPR/Cas9 disruption of KSR1 destabilizes SRSF9 protein, which interacts preferentially with mRNA encoding Epithelial Stromal Interaction 1 (EPSTI1). EPSTI1 protein mediates Ras and KSR1-dependent induction of EMT. Analysis of EPSTI1 splice variants reveals that inclusion of exon 8 is critical to the ability of EPSTI1 to promote the E-to N-cadherin switch and CRC cell motile and invasive behavior. These data reveal a mechanism in CRC cells in which Ras-induced and KSR1-dependent signaling affects pre-mRNA splicing to control behaviors critical to cancer cell dissemination and metastasis.
Comparative genomics of five Valsa species gives insights on their pathogenicity evolution

Valsa is a genus of ascomycetes fungi within the family Valsaceae that includes many wood destructive pathogens. The species such as Valsa mali and Valsa pyri that colonize fruit trees are threatening the global fruit production. Rapid host adaptation and fungicide resistance emergence are the main characteristics that make them devastating and hard to control. Efficient disease management can be achieved from early infection diagnosis and fungicide application, but lack of understandings of their genetic diversity and genomic features that underpin their pathogenicity evolution and drug resistance is essentially impeding the progress of effective and sustainable disease control. Here, we report genome assemblies of Valsa malicola, Valsa persoonii and Valsa sordida which represents close relatives of the two well known Valsa mali and Valsa pyri that cause canker disease with different host preferences. Comparative genomics analysis revealed that segmental rearrangements, inversions and translocations frequently occurred among Valsa spp. genomes. Genes identified in highly active regions exhibited high sequence differentiation and are enriched in membrane transporter proteins involved in anti-drug and nutrient transportation activities. Consistently, we also found membrane transporter gene families have been undergoing significant expansions in Valsa clade. Furthermore, unique genes that possessed or retained by each of the five Valsa species are more likely part of the secondary metabolic (SM) gene clusters which suggests SM one of the critical components that diverge along with the evolution of Valsa species. Repeat sequence content contributes significantly to genome size variation across the five species. The wide spread AT-rich regions resulted from repeat induced point C to T mutation (RIP) exhibited a specific proximity to secondary metabolic gene clusters and this positional proximity is correlated with the diversification of SM clusters suggesting a potential companion evolution between repeat sequence and secondary metabolism cluster. Lastly, we show that LaeA, the global regulator of secondary metabolic gene cluster, exhibiting diverged manner of regulation on the expression of clusters in vegetative and invasive mycelia of the devastating V. mali indicating the complexity of secondary metabolism in fungal species.
Rabphilin 3A binds the N-peptide of SNAP-25 to promote SNARE complex assembly in exocytosis

Exocytosis of secretory vesicles requires the soluble N-ethylmaleimide-sensitive factor attachment protein receptor (SNARE) proteins and small GTPase Rabs. As a Rab3/Rab27 effector protein on secretory vesicles, Rabphilin 3A was implicated to interact with SNAP-25 to regulate vesicle exocytosis in neurons and neuroendocrine cells, yet the underlying mechanism remains unclear. In this study, we have characterized the physiologically relevant binding sites between Rabphilin 3A and SNAP-25. We found that an intramolecular interplay between the N-terminal Rab-binding domain and C-terminal C2AB domain enables Rabphilin 3A to strongly bind the SNAP-25 N-peptide region via its C2B bottom α-helix. Disruption of this interaction significantly impaired docking and fusion of vesicles with the plasma membrane in PC12 cells. In addition, we found that this interaction allows Rabphilin 3A to accelerate SNARE complex assembly. Furthermore, we revealed that this interaction accelerates SNARE complex assembly via inducing a conformational switch from random coils to α-helical structure in the SNAP-25 SNARE motif. Altogether, our data suggest that promotion of SNARE complex assembly by binding of the C2B bottom α-helix of Rabphilin 3A to the N-peptide of SNAP-25 underlies a pre-fusion function of Rabphilin 3A in vesicle exocytosis.
Evidence for Correlated Evolution of Sexual Selection and Pseudogenization in Primate Fertilization

The mouse sperm protein ZP3R interacts with proteins in the egg coat and mediates sperm–egg adhesion in a species-specific manner. Understanding the function and evolution of such genes has important implications for studies of reproductive success, infertility, and speciation. A recent analysis showed that (1) the human pseudogene C4BPAP1 is the ortholog of Zp3r, (2) ZP3R pseudogenization evolved independently in eight additional primate lineages, and (3) functional ZP3R genes evolve under positive selection among 20 other primate species for which the gene structure is known. The causes of this pseudogenization of ZP3R are unknown. To explore one plausible cause (relaxation of sexual selection on males), we compared sexual size dimorphism and mating system among primate lineages with and without a functional ZP3R. We found that traits associated with more intense sexual selection on males (greater sexual size dimorphism, polygyny) are associated with functional ZP3R expression, and suggest that a relaxation of sexual selection may be associated with ZP3R pseudogenization. This proposed causal relationship implies an evolutionary cost to maintaining redundancy in the suite of primate fertilization genes.
Identification of Three Medically Important Mosquito Species Using Raman Spectroscopy

Accurate identification of disease vector insects is crucial when collecting epidemiological data. Traditionally, mosquitoes that transmit diseases like malaria, yellow fever, chikungunya, and dengue fever have been identified by looking at their external morphological features at different life cycle stages. This process is tedious and labour intensive. In this paper, the potential of Raman spectroscopy in combination with Linear and Quadratic Discriminant Analysis to classify three mosquito species, namely: Aedes aegypti, Anopheles gambiae and Culex quinquefasciatus, was explored. The classification was based on the mosquitoes’ cuticular melanin. The three mosquito species represented two subfamilies of medically important mosquitoes, i.e. the Anophelinae and the Culicinae. The housefly (Musca domestica) was included as a ‘control’ group to assess the discrimination ability of the classifiers. This study is the first to use Raman spectroscopy to classify mosquitoes. Fresh mosquitoes were anaesthetized with chloroform, and a dispersive Raman microscope was used to capture spectra from their legs. Broad melanin peaks centred around 1400 cm-1, 1590 cm-1, and 2060 cm-1 dominated the spectra. Variance Threshold (VT) and Principal Component Analysis (PCA) were used for feature selection and feature extraction respectively from the preprocessed data. The extracted features were then used to train and test Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA) classifiers. The VT/PCA/QDA classification model performed better than VT/PCA/LDA. VT/PCA/QDA achieved an overall accuracy of 94%, sensitivity of 87% and specificity of 96%, whereas VT/PCA/LDA attained an accuracy of 85%, a sensitivity of 69% and a specificity of 90%. The success of these relatively simple classification models on Raman spectroscopy data lays the groundwork for future development of models for discriminating morphologically indistinguishable insect species.
Carbon signaling protein SbtB possesses redox-regulated apyrase activity to facilitate regulation of bicarbonate transporter SbtA

The PII superfamily consists of widespread signal transduction proteins found in all domains of life. In addition to canonical PII proteins involved in C/N sensing, structurally similar PII-like proteins evolved to fulfill diverse, yet poorly understood cellular functions. In cyanobacteria, the bicarbonate transporter SbtA is expressed with the conserved PII-like protein, SbtB, to augment intracellular Ci levels for efficient CO2-fixation. We identified SbtB as a sensor of various adenine nucleotides including the second messenger nucleotides cAMP, known as carbon-status indicator, and c-di-AMP, involved in global cellular homeostasis. Moreover, many SbtB proteins possess a C-terminal extension with a disulfide bridge. We previously implied a redox-regulatory function of this extension, which we now call R-loop. Here, we reveal an unusual ATP/ADP apyrase (diphosphohydrolase) activity of SbtB that is controlled by the R-loop. We followed the sequence of the hydrolysis reactions from ATP over ADP to AMP in crystal-lographic snapshots and reveal the structural mechanism by which changes of the R-loop redox state modulate apyrase activity. We further gathered evidence that this redox state is controlled by thioredoxin TrxA, suggesting that it is generally linked to cellular metabolism. Finally, we present a refined model of how SbtB regulates SbtA activity, in which both the apyrase activity and its redox regulation play a central role. This highlights SbtB as a central switch-point in cyanobacterial cell physiology, integrating not only signals from the energy state (adenyl-nucleotide binding) and the carbon supply via cAMP binding, but also from the day/night status reported by the C-terminal redox-switch.