Miracle-dz/UniFAR-FTUs huggingface dataset:
Trauma-focused cognitive-behavioral therapy for posttraumatic stress disorder in three-through six year-old children: a randomized clinical trial.

BACKGROUND The evidence base for trauma-focused cognitive behavioral therapy (TF-CBT) to treat posttraumatic stress disorder (PTSD) in youth is compelling, but the number of controlled trials in very young children is few and limited to sexual abuse victims. These considerations plus theoretical limitations have led to doubts about the feasibility of TF-CBT techniques in very young children. This study examined the efficacy and feasibility of TF-CBT for treating PTSD in three- through six-year-old children exposed to heterogeneous types of traumas. METHODS Procedures and feasibilities of the protocol were refined in Phase 1 with 11 children. Then 64 children were randomly assigned in Phase 2 to either 12-session manualized TF-CBT or 12-weeks wait list. RESULTS In the randomized design the intervention group improved significantly more on symptoms of PTSD, but not on depression, separation anxiety, oppositional defiant, or attention deficit/hyperactivity disorders. After the waiting period, all participants were offered treatment. Effect sizes were large for PTSD, depression, separation anxiety, and oppositional defiant disorders, but not attention-deficit/hyperactivity disorder. At six-month follow-up, the effect size increased for PTSD, while remaining fairly constant for the comorbid disorders. The frequencies with which children were able to understand and complete specific techniques documented the feasibility of TF-CBT across this age span. The majority were minority race (Black/African-American) and without a biological father in the home, in contrast to most prior efficacy studies. CONCLUSIONS These preliminary findings suggest that TF-CBT is feasible and more effective than a wait list condition for PTSD symptoms, and the effect appears lasting. There may also be benefits for reducing symptoms of several comorbid disorders. Multiple factors may explain the unusually high attrition, and future studies ought to oversample on these demographics to better understand this understudied population.
Detection of Human Influence on a New, Validated 1500-Year Temperature Reconstruction

Abstract Climate records over the last millennium place the twentieth-century warming in a longer historical context. Reconstructions of millennial temperatures show a wide range of variability, raising questions about the reliability of currently available reconstruction techniques and the uniqueness of late-twentieth-century warming. A calibration method is suggested that avoids the loss of low-frequency variance. A new reconstruction using this method shows substantial variability over the last 1500 yr. This record is consistent with independent temperature change estimates from borehole geothermal records, compared over the same spatial and temporal domain. The record is also broadly consistent with other recent reconstructions that attempt to fully recover low-frequency climate variability in their central estimate. High variability in reconstructions does not hamper the detection of greenhouse gas–induced climate change, since a substantial fraction of the variance in these reconstructions from the .
The Euglenoid Flagellates

BY THEODORE LOUIS JAHN Zoological Laboratories, State University of Iowa P ROBABLY no other order of free-living Protista has received such widespread attention as the Euglenida. This is largely the result of the unique taxonomic position of the order. The obvious plant-like characteristics of some genera (e.g. Euglena) and the obvious animal-like characteristics of others (e.g., Peranema) require that the euglenoid flagellates be considered by both botanists and zoologists. The absence of any clear line of demarcation between green and colorless forms makes it seem inadvisable to assort the organisms among the plant and animal kingdoms and thereby separate, on the basis of obviously arbitrary criteria, the members of a closely related although superficially heterogeneous group. The purpose of the present paper is to discuss the general biology of the euglenoids and to cite enough references to the widely scattered literature so that available detailed information may easily be found. The most useful general treatments of the group are those of Smith (1933), Dangeard (1933), Fritsch (1935), and Kudo (1946).
The classification of complete improper affine spheres with singularities of low total curvature and new examples

We classify complete improper affine spheres with singularities (say improper affine fronts) in unimodular affine three-space $\boldsymbol{R}^3$ whose total curvature is greater than or equal to $-8\pi$. We also study the asymptotic behavior of complete embedded ends of improper affine fronts. Moreover, we give new examples for this class of surfaces, including one which satisfies the equality condition of an Osserman-type inequality and is of positive genus.
Constant yield displacement approach for seismic design of structures

The existing relations between the vibration period, the strength reduction factor and the ductility demand of stiff fixed-base structures indicate that these structures should not be allowed to yield (i.e. Ry=1). This design approach is based on the argument that the inelastic ductility demand of these structures, if they were allowed to yield, would be very high. This study shows that these ductility demand values are not realistic: they emerge from the constant-period, forcedbased design methodology, which leads to unrealistically small yield displacement estimates. Further, this study quantifies the relation between the strength and deformability of structures, based on the argument that the yield displacement of a structure in bending depends mainly on the yield strain of the yielding material and the geometry of the structure and that it does not dependent strongly on the bending strength of that structure. This relation is determined through a statistical analysis of the response of a fixed-base single-degree-of-freedom inelastic structure excited by a large number of recorded ground motions. These motions cover a wide range of ground motion types, magnitudes and distances. Based on this relation, a new seismic design approach is proposed, the Constant-Yield-Displacement-Design (CYDD) approach. This approach is based on the independence of the strength and the yield displacement of a structure. Compared to the existing approaches, it offers: 1) a more realistic calculation of the inelastic displacement ductility demand of structures with a predetermined strength; and 2) a more exact determination of the strength that is required to satisfy certain performance objectives expressed in terms of flexural displacement ductility. The vibration period (i.e. stiffness) of the structure does not play a role in the CYDD approach. The four steps of the approach are easy to implement as they are based on existing design practices.
Multiple Object Tracking as ID Prediction

In Multiple Object Tracking (MOT), tracking-by-detection methods have stood the test for a long time, which split the process into two parts according to the definition: object detection and association. They leverage robust single-frame detectors and treat object association as a post-processing step through hand-crafted heuristic algorithms and surrogate tasks. However, the nature of heuristic techniques prevents end-to-end exploitation of training data, leading to increasingly cumbersome and challenging manual modification while facing complicated or novel scenarios. In this paper, we regard this object association task as an End-to-End in-context ID prediction problem and propose a streamlined baseline called MOTIP. Specifically, we form the target embeddings into historical trajectory information while considering the corresponding IDs as in-context prompts, then directly predict the ID labels for the objects in the current frame. Thanks to this end-to-end process, MOTIP can learn tracking capabilities straight from training data, freeing itself from burdensome hand-crafted algorithms. Without bells and whistles, our method achieves impressive state-of-the-art performance in complex scenarios like DanceTrack and SportsMOT, and it performs competitively with other transformer-based methods on MOT17. We believe that MOTIP demonstrates remarkable potential and can serve as a starting point for future research. The code is available at https://github.com/MCG-NJU/MOTIP.
Analysing the behaviour of the textbook fractional reserve banking model as a complex dynamic system

The Fractional Reserve Banking system has been used for several centuries by western banking systems to regulate the supply of loans against their deposits. It appears to be widely misunderstood, both within current economic theory and outside it. Modern economic textbooks base their explanation on a description that appears to have originated from a British parliamentary report authored in large part by John Maynard Keynes in 1931. This correctly describes how the system creates money through the re-deposit of loans made by banks, but predicts an evolution from initial conditions to a stable supply of money and credit for which there is no empirical evidence in the historical record. It notably fails to include the effects of either loan repayment and loan default, and consequently appears to incorrectly predict the long term behaviour of the process.
Understanding Domain-Size Generalization in Markov Logic Networks

We study the generalization behavior of Markov Logic Networks (MLNs) across relational structures of different sizes. Multiple works have noticed that MLNs learned on a given domain generalize poorly across domains of different sizes. This behavior emerges from a lack of internal consistency within an MLN when used across different domain sizes. In this paper, we quantify this inconsistency and bound it in terms of the variance of the MLN parameters. The parameter variance also bounds the KL divergence between an MLN's marginal distributions taken from different domain sizes. We use these bounds to show that maximizing the data log-likelihood while simultaneously minimizing the parameter variance corresponds to two natural notions of generalization across domain sizes. Our theoretical results apply to Exponential Random Graphs and other Markov network based relational models. Finally, we observe that solutions known to decrease the variance of the MLN parameters, like regularization and Domain-Size Aware MLNs, increase the internal consistency of the MLNs. We empirically verify our results on four different datasets, with different methods to control parameter variance, showing that controlling parameter variance leads to better generalization.
Kinetics of leaching and binding of fumonisin B1 in soil microcosms

Fumonisin B1 (FB1) is a water-soluble mycotoxin produced by Fusarium verticillioides. Research objectives were to determine the leaching of FB1 through soil and FB1 binding in soil. Leachate and microcosm columns were used to determine the movement of FB1 through soil. FB1-contaminated corn screenings or water extracts containing FB1 were placed on the surface of soil columns. In 100% sand columns FB1 leaching was only slightly retarded, whereas, at 50%, 75% and 100% Cecil sandy loam, approximately 60%, 50% and 20% of the FB1 was recovered in the column leachate, respectively. The FB1 retained on the 100% Cecil sandy loam column was tightly bound. However, approximately 75% of the bound FB1 was released with 5% formic acid and 5% formic acid:acetonitrile (1:1) suggesting that binding was ionic. The results suggest that FB1 is quite stable in soils and, while tightly bound, under certain environmental conditions could be released.
"It is there, and you need it, so why do you not use it?"Achieving better adoption of AI systems by domain experts, in the case study of natural science research

Artificial Intelligence (AI) is becoming ubiquitous in domains such as medicine and natural science research. However, when AI systems are implemented in practice, domain experts often refuse them. Low acceptance hinders effective human-AI collaboration, even when it is essential for progress. In natural science research, scientists' ineffective use of AI-enabled systems can impede them from analysing their data and advancing their research. We conducted an ethnographically informed study of 10 in-depth interviews with AI practitioners and natural scientists at the organisation facing low adoption of algorithmic systems. Results were consolidated into recommendations for better AI adoption: i) actively supporting experts during the initial stages of system use, ii) communicating the capabilities of a system in a user-relevant way, and iii) following predefined collaboration rules. We discuss the broader implications of our findings and expand on how our proposed requirements could support practitioners and experts across domains.
Lap Shear and Impact Testing of Ochre and Beeswax in Experimental Middle Stone Age Compound Adhesives

The production of compound adhesives using disparate ingredients is seen as some of the best evidence of advanced cognition outside of the use of symbolism. Previous field and laboratory testing of adhesives has shown the complexities involved in creating an effective Middle Stone Age glue using Acacia gum. However, it is currently unclear how efficient different adhesive recipes are, how much specific ingredients influence their performance, and how difficult it may have been for those ingredients to be combined to maximum effect. We conducted a series of laboratory-based lap shear and impact tests, following modern adhesion testing standards, to determine the efficacy of compound adhesives, with particular regard to the ingredient ratios. We tested rosin (colophony) and gum adhesives, containing additives of beeswax and ochre in varying ratios. During both lap shear and impact tests compound rosin adhesives performed better than single component rosin adhesives, and pure acacia gum was the strongest. The large difference in performance between each base adhesive and the significant changes in performance that occur due to relatively small changes in ingredient ratios lend further support to the notion that high levels of skill and knowledge were required to consistently produce the most effective adhesives.
Exotic tetraquarks at the HL-LHC with JETHAD: A high-energy viewpoint

We review the semi-inclusive hadroproduction of a neutral hidden-flavor tetraquark with light and heavy quark flavor at the HL-LHC, accompanied by another heavy hadron or a light-flavored jet. We make use of the novel TQHL1.0 determinations of leading-twist fragmentation functions to describe the formation mechanism of a tetraquark state within the next-to-leading order perturbative QCD. This framework builds on the basis of a spin-physics inspired model, taken as a proxy for the lowest-scale input of the constituent heavy-quark fragmentation channel. Then, all parton-to-tetraquark fragmentation functions are consistently obtained via the above-threshold DGLAP evolution in a variable-flavor number scheme. We provide predictions for a series of differential distributions calculated by the hands of the JETHAD method well-adapted to NLL/NLO+ hybrid-factorization studies, where the resummation of next-to-leading energy logarithms and beyond is included in the collinear picture. We provide corroborating evidence that high-energy observables sensitive to semi-inclusive tetraquark emissions at the HL-LHC exhibit a fair stability under radiative corrections as well as MHOU studies. Our analysis constitutes a prime contact point between QCD resummations and the exotic matter.
3D-EffiViTCaps: 3D Efficient Vision Transformer with Capsule for Medical Image Segmentation

Medical image segmentation (MIS) aims to finely segment various organs. It requires grasping global information from both parts and the entire image for better segmenting, and clinically there are often certain requirements for segmentation efficiency. Convolutional neural networks (CNNs) have made considerable achievements in MIS. However, they are difficult to fully collect global context information and their pooling layer may cause information loss. Capsule networks, which combine the benefits of CNNs while taking into account additional information such as relative location that CNNs do not, have lately demonstrated some advantages in MIS. Vision Transformer (ViT) employs transformers in visual tasks. Transformer based on attention mechanism has excellent global inductive modeling capabilities and is expected to capture longrange information. Moreover, there have been resent studies on making ViT more lightweight to minimize model complexity and increase efficiency. In this paper, we propose a U-shaped 3D encoder-decoder network named 3D-EffiViTCaps, which combines 3D capsule blocks with 3D EfficientViT blocks for MIS. Our encoder uses capsule blocks and EfficientViT blocks to jointly capture local and global semantic information more effectively and efficiently with less information loss, while the decoder employs CNN blocks and EfficientViT blocks to catch ffner details for segmentation. We conduct experiments on various datasets, including iSeg-2017, Hippocampus and Cardiac to verify the performance and efficiency of 3D-EffiViTCaps, which performs better than previous 3D CNN-based, 3D Capsule-based and 3D Transformer-based models. We further implement a series of ablation experiments on the main blocks. Our code is available at: https://github.com/HidNeuron/3D-EffiViTCaps.
Segment Gating for Static Energy Reduction with Introspective Networks-on-Chip

Chip multiprocessors (CMPs) have emerged as a primary vehicle for overcoming the limitations of uniprocessor scaling, with power constraints now representing a key factor of CMP design. Recent studies have shown that the on-chip interconnection network (NOC) can consume as much as 36% of overall chip power. To date, researchers have employed several techniques to reduce power consumption in the network, including the use of on/off links by means of power gating. However, many of these techniques only target dynamic power, and those that consider static power focus exclusively on flit buffers. This thesis aims to reduce static power consumption through a comprehensive approach that targets buffers, switches, arbitration units, and links. I establish an optimal power-down scheme which is used as an upper bound to evaluate several static policies on synthetic traffic patterns. I also evaluate dynamic utilization-aware power-down policies using traces from the PARSEC benchmark suite. Finally, I show that both static and dynamic policies can greatly reduce static energy at low injection rates with only minimal increases in dynamic energy and latency.
Exploring Person Context and Local Scene Context for Object Detection

In this paper we explore two ways of using context for object detection. The first model focusses on people and the objects they commonly interact with, such as fashion and sports accessories. The second model considers more general object detection and uses the spatial relationships between objects and between objects and scenes. Our models are able to capture precise spatial relationships between the context and the object of interest, and make effective use of the appearance of the contextual region. On the newly released COCO dataset, our models provide relative improvements of up to 5% over CNN-based state-of-the-art detectors, with the gains concentrated on hard cases such as small objects (10% relative improvement).
Aberrant expression of c‐met mRNA in human gastric carcinomas

We examined mRNA expression for c‐met encoding hepatocyte growth factor receptor in 8 gastric carcinoma cell lines and 31 surgically resected gastric carcinoma tissues by Northern‐blot analysis. Two forms of the transcript, sized 7.0 kb and 6.0 kb, were found in gastric carcinomas. Transcripts of both sizes were detected at various levels in all the gastric carcinoma cell lines except the MKN‐74 line, which expressed only the 7.0‐kb form. Of the 31 gastric carcinomas, 15 (48%) over‐expressed the 7.0‐kb transcript when compared with non‐neoplastic mucosa. The 6.0‐kb transcript of the c‐met gene was expressed at considerable levels in 52% of the gastric carcinoma tissues, whereas it was only slightly expressed in non‐neoplastic mucosa of a small number of cases. An important finding was that expression of the 6.0‐kb transcript was closely correlated with tumor staging, lymph‐node metastasis and depth of tumor invasion. These results, overall, suggest that the 6.0‐kb transcript of the c‐met gene might participate in the development and progression of gastric carcinomas. © 1993 Wiley‐Liss, Inc.
Vitamin A compounds and analogs as inhibitors of mixed-function oxidases that metabolize carcinogenic polycyclic hydrocarbons and other compounds.

Summary Fourteen vitamin A compounds and analogs have been evaluated as inhibitors of microsomal mixed-function oxidases from liver and lung tissues of mice and hamsters. With liver microsomes, benzo( a )pyrene (BP) is metabolized, by one pathway, to a product (BP-X) which is tightly bound to reaction components. The K m for BP in this reaction is 10 ± 2 µm. The formation of BP-X is strongly inhibited by retinol and several other vitamin A compounds, some of which have known anticarcinogenic activity, and by butylated hydroxytoluene, also an anticarcinogenic agent. BP-X accumulates in larger amounts in the presence of cyclohexene oxide, an inhibitor of epoxide hydrase. 7,12-Dimethylbenzanthracene and 3-methylcholanthrene are also converted by liver microsomal oxidases to tightly bound metabolites, the formation of which is inhibited by the compounds that block production of BP-X. BP is converted to 3-hydroxy-BP in another metabolic step, a reaction not inhibited by any of the vitamin A compounds. The K m for BP in this reaction is 8.5 µm. 3-Hydroxy-BP is further metabolized by a microsomal oxidase to a product (BP-Y), which, by mass spectral analysis, contains 2 oxygen atoms. The K m for 3-hydroxy-BP is 3 µm. The disappearance of this substrate is inhibited by retinol and some of its derivatives and by butylated hydroxytoluene. Enzymes that catalyze the oxidation of cyclophosphamide and nicotine to hydroxy derivatives are also inhibited by the agents that block production of BP-X and metabolism of 3-hydroxy-BP, but amine oxidases that use nicotine and trimethylamine as substrates are not inhibited. Microsomes from the lungs of mice pretreated with benz( a )anthracene convert BP to 3-hydroxy-BP but show no detectable formation of BP-X or further metabolism of 3-hydroxy-BP.
Efficiently Estimating Mutual Information Between Attributes Across Tables

Relational data augmentation is a powerful technique for enhancing data analytics and improving machine learning models by incorporating columns from external datasets. However, it is challenging to efficiently discover relevant external tables to join with a given input table. Existing approaches rely on data discovery systems to identify joinable tables from external sources, typically based on overlap or containment. However, the sheer number of tables obtained from these systems results in irrelevant joins that need to be performed; this can be computationally expensive or even infeasible in practice. We address this limitation by proposing the use of efficient mutual information (MI) estimation for finding relevant joinable tables. We introduce a new sketching method that enables efficient evaluation of relationship discovery queries by estimating MI without materializing the joins and returning a smaller set of tables that are more likely to be relevant. We also demonstrate the effectiveness of our approach at approximating MI in extensive experiments using synthetic and real-world datasets.