text:
Difference between revisions of "Sand Box"
(Tag: 2017 source edit)
== Glossary of terms ==
Glossary of cerebral palsy
* '''[[akinesia]]''' -decreased body movements.
* '''[[acquired cerebral palsy]]''' — cerebral palsy that occurs as a result of injury to the brain after birth or during early childhood.
* '''[[at risk]]''' -a description of a person whose mother or father has HD or has inherited the HD gene and who therefore has a 50]]''' -50 chance of inheriting the disorder.'''
* '''[[Apgar score]]''' — a numbered scoring system doctors use to assess a baby's physical state at the time of birth.
* '''[[autosomal dominant disorder]]''' -a non sex linked disorder that can be inherited even if only one parent passes on the defective gene.
* '''[[asphyxia]]''' — a lack of oxygen due to trouble with breathing or poor oxygen supply in the air.
* '''[[basal ganglia]]''' -a region located at the base of the brain composed of four clusters of neurons, or nerve cells. This area is responsible for body movement and coordination. The neuron groups most prominently and consistently affected by HD—the pallidum and striatum are located here. See neuron, pallidum, striatum.
* '''[[ataxia]]''' — the loss of muscle control.
* '''[[caudate nuclei]]''' -part of the striatum in the basal ganglia. See basal ganglia, striatum.
* '''[[athetoid]]''' — making slow, sinuous, involuntary, writhing movements, especially with the hands.
* '''[[chorea]]''' -uncontrolled body movements. Chorea is derived from the Greek word for dance.
* '''[[bilirubin]]''' — a bile pigment produced by the liver of the human body as a byproduct of digestion.
* '''[[chromosomes]]''' -the structures in cells that contain genes. They are composed of deoxyribonucleic acid (DNA) and proteins and, under a microscope, appear as rod''' -like structures. See deoxyribonucleic acid (DNA), gene.'''
* '''[[bisphosphonates]]''' — a family of drugs that strengthen bones and reduce the risk of bone fracture in elderly adults.
* '''[[computed tomography (CT)]]''' - a technique used for diagnosing brain disorders. CT uses a computer to produce a high]]''' -quality image of brain structures. These images are called CT scans.'''
* '''[[botulinum toxin]]''' — a drug commonly used to relax spastic muscles; it blocks the release of acetylcholine, a neurotransmitter that energizes muscle tissue.
* '''[[cortex]]''' -part of the brain responsible for thought, perception, and memory. HD affects the basal ganglia and cortex. See basal ganglia.
* '''[[cerebral]]''' — relating to the two hemispheres of the human brain.
* '''[[deoxyribonucleic acid (DNA)]]''' - the substance of heredity containing the genetic information necessary for cells to divide and produce proteins. DNA carries the code for every inherited characteristic of an organism. See gene.
* '''[[cerebral dysgenesis]]''' — defective brain development.
* '''[[dominant]]''' -a trait that is apparent even when the gene for that disorder is inherited from only one parent. See autosomal dominant disorder, recessive, gene.
* '''[[choreoathetoid]]''' — a condition characterized by aimless muscle movements and involuntary motions.
* '''[[gene]]''' -the basic unit of heredity, composed of a segment of DNA containing the code for a specific trait. See deoxyribonucleic acid (DNA).
* '''[[congenital cerebral palsy]]''' — cerebral palsy that is present at birth from causes that have occurred during fetal development.
* '''[[huntingtin]]''' -the protein encoded by the gene that carries the HD defect. The repeated CAG sequence in the gene causes an abnormal form of huntingtin to be formed. The function of the normal form of huntingtin is not yet known.
* '''[[contracture]]''' — a condition in which muscles become fixed in a rigid, abnormal position, which causes distortion or deformity.
* '''[[kindred]]''' -a group of related persons, such as a family or clan.
* '''[[developmental delay]]''' — behind schedule in reaching the milestones of early childhood development.
* '''[[magnetic resonance imaging (MRI)]]''' -an imaging technique that uses radiowaves, magnetic fields, and computer analysis to create a picture of body tissues and structures.
* '''[[dyskinetic]]''' — the impairment of the ability to perform voluntary movements, which results in awkward or incomplete movements.
* '''[[marker]]''' -a piece of DNA that lies on the chromosome so close to a gene that the two are inherited together. Like a signpost, markers are used during genetic testing and research to locate the nearby presence of a gene. See chromosome, deoxyribonucleic acid (DNA).
* '''[[dystonia]]''' (dystonic) a condition of abnormal muscle tone.
* '''[[mitochondria]]''' -microscopic, energy''' -producing bodies within cells that are the cells' "power plants."'''
* '''[[gait analysis]]''' — a technique that uses cameras, force plates, electromyography, and computer analysis to objectively measure an individual's pattern of walking.
* '''[[mutation]]''' -in genetics, any defect in a gene. See gene.
* '''[[gestation]]''' — the period of fetal development from the time of conception until birth.
* '''[[myoclonus]]''' -a condition in which muscles or portions of muscles contract involuntarily in a jerky fashion.
* '''[[hemiparesis]]''' — paralysis affecting only one side of the body.
* '''[[neuron]]''' -Greek word for a nerve cell, the basic impulse''' -conducting unit of the nervous system. Nerve cells communicate with other cells through an electrochemical process called neurotransmission.'''
* '''[[hypertonia]]''' — increased muscle tone.
* '''[[neurotransmitters]]''' -special chemicals that transmit nerve impulses from one cell to another.
* '''[[hypotonia]]''' — decreased muscle tone.
* '''[[pallidum]]''' -part of the basal ganglia of the brain. The pallidum is composed of the globus pallidus and the ventral pallidum.See basal ganglia.
* '''[[hypoxic-ischemic encephalopathy]]''' — brain damage caused by poor blood flow or insufficient oxygen supply to the brain.
* '''[[positron emission tomography (PET)]]''' - a tool used to diagnose brain functions and disorders. PET produces three'''-dimensional, colored images of chemicals or substances functioning within the body. These images are called PET scans. PET shows brain function, in contrast to CT or MRI, which show brain structure.'''
* '''[[intracranial hemorrhage]]''' — bleeding in the brain.
* '''[[prevalence]]''' -the number of cases of a disease that are present in a particular population at a given time.
* '''[[intrathecal baclofen]]''' — baclofen that is injected into the cerebrospinal fluid of the spinal cord to reduce spasticity.
* '''[[putamen]]''' -an area of the brain that decreases in size as a result of the damage produced by HD.
* '''[[jaundice]]''' — a blood disorder caused by the abnormal buildup of bilirubin in the bloodstream.
* '''[[receptor]]''' -proteins that serve as recognition sites on cells and cause a response in the body when stimulated by chemicals called neurotransmitters. They act as on''' -and''' -off switches for the next nerve cell. See neuron, neurotransmitters.
* '''[[kyphosis]]''' — a humpback-like outward curvature of the upper spine.
* '''[[recessive]]''' -a trait that is apparent only when the gene or genes for it are inherited from both parents. See dominant, gene.
* '''[[lordosis]]''' — an increased inward curvature of the lower spine.
* '''[[senile chorea]]''' -a relatively mild and rare disorder found in elderly adults and characterized by choreic movements. It is believed by some scientists to be caused by a different gene mutation than that causing HD.
* '''[[orthotic devices]]''' — special devices, such as splints or braces, used to treat posture problems involving the muscles, ligaments, or bones.
* '''[[striatum]]''' -part of the basal ganglia of the brain. The striatum is composed of the caudate nucleus, putamen, and ventral striatum. See basal ganglia, caudate nuclei.
* '''[[osteopenia]]''' — reduced density and mass of the bones.
* '''[[trait]]''' -any genetically determined characteristic. See dominant, gene, recessive.
* '''[[palsy]]''' — paralysis, or the lack of control over voluntary movement.
* '''[[transgenic mice]]''' -mice that receive injections of foreign genes during the embryonic stage of development. Their cells then follow the "instructions" of the foreign genes, resulting in the development of a certain trait or characteristic. Transgenic mice can serve as an animal model of a certain disease, telling researchers how genes work in specific cells.
* '''[[-paresis or -plegia]]''' — weakness or paralysis. In cerebral palsy, these terms are typically combined with other phrases that describe the distribution of paralysis and weakness; for example, quadriplegia means paralysis of all four limbs.
* '''[[ventricles]]''' -cavities within the brain that are filled with cerebrospinal fluid. In HD, tissue loss causes enlargement of the ventricles.
* '''[[periventricular leukomalacia (PVL)]]''' — "peri" means near; "ventricular" refers to the ventricles or fluid spaces of the brain; and "leukomalacia" refers to softening of the white matter of the brain. PVL is a condition in which the cells that make up white matter die near the ventricles. Under a microscope, the tissue looks soft and sponge-like.
* '''[[placenta]]''' — an organ that joins a mother with her unborn baby and provides nourishment and sustenance.
* '''[[quadriplegia]]''' — paralysis of both the arms and legs.
* '''[[Rh incompatibility]]''' — a blood condition in which antibodies in a pregnant woman's blood attack fetal blood cells and impair an unborn baby's supply of oxygen and nutrients.
* '''[[rubella]]''' — (also known as German measles) a viral infection that can damage the nervous system of an unborn baby if a mother contracts the disease during pregnancy.
* '''[[scoliosis]]''' — a disease of the spine in which the spinal column tilts or curves to one side of the body.
* '''[[selective dorsal rhizotomy]]''' — a surgical procedure in which selected nerves are severed to reduce spasticity in the legs.
* '''[[spastic]]''' (or spasticity) — describes stiff muscles and awkward movements.
* '''[[spastic diplegia]]''' (or diparesis) — a form of cerebral palsy in which spasticity affects both legs, but the arms are relatively or completely spared.
* '''[[spastic hemiplegia]]''' (or hemiparesis) — a form of cerebral palsy in which spasticity affects an arm and leg on one side of the body.
* '''[[spastic quadriplegia]]''' (or quadriparesis) — a form of cerebral palsy in which all four limbs are paralyzed or weakened equally.
* '''[[tremor]]''' — an involuntary trembling or quivering.
== Glossary of Dementia ==
* '''[[alpha-synuclein]]''' — the major protein present in abnormal clumps called Lewy bodies, which are seen in the brains of people with Parkinson's disease and some dementias. Disorders in which alpha-synuclein accumulates inside nerve cells are called synucleinopathies.
* '''[[Alzheimer's disease]]''' — the most common cause of dementia. Nearly all brain functions, including memory, movement, language, judgment, and behavior, are eventually affected. Alzheimer's disease is defined by observing high levels of amyloid-containing plaques and tau protein-containing neurofibrillary tangles in the brain.
* '''[[amyloid]]''' — a protein with beta-pleated sheets, which aggregate to form the characteristic clumps (called plaques) that appear in the brains of people with Alzheimer's disease. Other proteins that form aggregates are tau proteins also seen in Alzheimer's disease, and alpha-synuclein present in dementia with Lewy Body and in Parkinson's Disease.
* '''[[biomarkers]]''' — measurable biological signs in the living body that may indicate risk or progression of a disease and improve diagnosis
* '''[[corticobasal degeneration]]''' — a progressive disorder characterized by abnormal buildup of the protein tau, nerve cell loss, and atrophy in multiple areas of the brain
* '''[[dementia]]''' — a term for a condition that significantly interferes with daily life because of impaired thinking and memory
* '''[[Dementia with Lewy bodies]]''' — a type of Lewy body dementia that is a form of progressive dementia
* '''[[frontotemporal disorders]]''' — a group of dementias characterized by degeneration of nerve cells, especially those in the frontal and temporal lobes of the brain
* '''[[Lewy body dementia]]''' — a progressive dementia characterized by the presence of abnormal structures called Lewy bodies in the brain
* '''[[mild cognitive impairment]]''' — a stage between normal cognitive changes that may occur with age and the more serious symptoms that indicate dementia
* '''[[mixed dementia]]''' — dementia in which one form of dementia and another condition or dementia cause damage to the brain; for example, Alzheimer's disease and small vessel disease or vascular dementia
* '''[[multi-infarct dementia]]''' — a type of vascular cognitive impairment and dementia caused by numerous small strokes in the brain
* '''[[neurodegeneration]]''' — the progressive loss of nerve cell structure or function
* '''[[neurofibrillary tangles]]''' — bundles of twisted filaments largely made up of a protein called tau found in nerve cells in the brains of people with Alzheimer's disease and other types of dementia such as FTD.
* '''[[Parkinson's disease dementia]]''' — a secondary dementia that sometimes occurs in people with advanced Parkinson's disease. Many people with Parkinson's have the amyloid plaques and neurofibrillary tangles found in Alzheimer's disease, but it is not clear if the diseases are linked.
* '''[[plaques]]''' — abnormal clumps of amyloid protein that are found in large numbers in the brains of persons with Alzheimer's disease
* '''[[tau]]''' — a protein that helps the functioning of microtubules, which are part of the cell's structural support and help deliver substances throughout the cell. In several dementia disorders, tau twists into filaments that become tangles. Disorders associated with an accumulation of tau, such as frontotemporal dementia, are called tauopathies.
* '''[[vascular dementia]]''' — a type of dementia caused by brain damage from cerebrovascular or cardiovascular problems, usually strokes
== Glossary of traumatic brain injury ==
* '''[[aneurysm]]''' - a blood-filled sac formed by disease related stretching of an artery or blood vessel.
* '''[[anoxia]]''' - an absence of oxygen supply to an organ's tissues leading to cell death.
* '''[[aphasia]]''' - difficulty understanding and/or producing spoken and written language. ( See also non-fluent aphasia.)
* '''[[apoptosis]]''' - cell death that occurs naturally as part of normal development, maintenance, and renewal of tissues within an organism.
* '''[[arachnoid membrane]]''' - one of the three membranes that cover the brain; it is between the pia mater and the dura. Collectively, these three membranes form the meninges.
* '''[[brain death]]''' - an irreversible cessation of measurable brain function.
* '''[[Broca's aphasia]]''' - see non-fluent aphasia.
* '''[[cerebrospinal fluid (CSF)]]''' - the fluid that bathes and protects the brain and spinal cord.
* '''[[closed head injury]]''' - an injury that occurs when the head suddenly and violently hits an object but the object does not break through the skull.
* '''[[coma]]''' - a state of profound unconsciousness caused by disease, injury, or poison.
* '''[[compressive cranial neuropathies]]''' - degeneration of nerves in the brain caused by pressure on those nerves.
* '''[[computed tomography (CT)]]''' - a scan that creates a series of cross-sectional X-rays of the head and brain; also called computerized axial tomography or CAT scan.
* '''[[concussion]]''' - injury to the brain caused by a hard blow or violent shaking, causing a sudden and temporary impairment of brain function, such as a short loss of consciousness or disturbance of vision and equilibrium.
* '''[[contrecoup]]''' - a contusion caused by the shaking of the brain back and forth within the confines of the skull.
* '''[[contusion]]''' - distinct area of swollen brain tissue mixed with blood released from broken blood vessels.
* '''[[CSF fistula]]''' - a tear between two of the three membranes - the dura and arachnoid membranes - that encase the brain.
* '''[[deep vein thrombosis]]''' - formation of a blood clot deep within a vein.
* '''[[dementia pugilistica]]''' - brain damage caused by cumulative and repetitive head trauma; common in career boxers.
* '''[[depressed skull fracture]]''' - a fracture occurring when pieces of broken skull press into the tissues of the brain.
* '''[[diffuse axonal injury]]''' - see shearing.
* '''[[dysarthria]]''' - inability or difficulty articulating words due to emotional stress, brain injury, paralysis, or spasticity of the muscles needed for speech.
* '''[[dura]]''' - a tough, fibrous membrane lining the brain; the outermost of the three membranes collectively called the meninges.
* '''[[early seizures]]''' - seizures that occur within 1 week after a traumatic brain injury.
* '''[[epidural hematoma]]''' - bleeding into the area between the skull and the dura.
* '''[[erosive gastritis]]''' - inflammation and degeneration of the tissues of the stomach.
* '''[[fluent aphasia]]''' - a condition in which patients display little meaning in their speech even though they speak in complete sentences. Also called Wernicke's or motor aphasia.
* '''[[Glasgow Coma Scale]]''' - a clinical tool used to assess the degree of consciousness and neurological functioning - and therefore severity of brain injury - by testing motor responsiveness, verbal acuity, and eye opening.
* '''[[global aphasia]]''' - a condition in which patients suffer severe communication disabilities as a result of extensive damage to portions of the brain responsible for language.
* '''[[hematoma]]''' - heavy bleeding into or around the brain caused by damage to a major blood vessel in the head.
* '''[[hemorrhagic stroke]]''' - stroke caused by bleeding out of one of the major arteries leading to the brain.
* '''[[hypermetabolism]]''' - a condition in which the body produces too much heat energy.
* '''[[hypothyroidism]]''' - decreased production of thyroid hormone leading to low metabolic rate, weight gain, chronic drowsiness, dry skin and hair, and/or fluid accumulation and retention in connective tissues.
* '''[[hypoxia]]''' - decreased oxygen levels in an organ, such as the brain; less severe than anoxia.
* '''[[immediate seizures]]''' - seizures that occur within 24 hours of a traumatic brain injury.
* '''[[intracerebral hematoma]]''' - bleeding within the brain caused by damage to a major blood vessel.
* '''[[intracranial pressure]]''' - buildup of pressure in the brain as a result of injury.
* '''[[ischemic stroke]]''' - stroke caused by the formation of a clot that blocks blood flow through an artery to the brain.
* '''[[locked-in syndrome]]''' - a condition in which a patient is aware and awake, but cannot move or communicate due to complete paralysis of the body.
* '''[[magnetic resonance imaging (MRI)]]''' - a noninvasive diagnostic technique that uses magnetic fields to detect subtle changes in brain tissue.
* '''[[meningitis]]''' - inflammation of the three membranes that envelop the brain and spinal cord, collectively known as the meninges; the meninges include the dura, pia mater, and arachnoid.
* '''[[motor aphasia]]''' - see non-fluent aphasia.
* '''[[neural stem cells]]''' - cells found only in adult neural tissue that can develop into several different cell types in the central nervous system.
* '''[[neuroexcitation]]''' - the electrical activation of cells in the brain; neuroexcitation is part of the normal functioning of the brain or can also be the result of abnormal activity related to an injury.
* '''[[neuron]]''' - a nerve cell that is one of the main functional cells of the brain and nervous system.
* '''[[neurotransmitters]]''' -chemicals that transmit nerve signals from one neuron to another.
* '''[[non-fluent aphasia]]''' - a condition in which patients have trouble recalling words and speaking in complete sentences. Also called Broca's or motor aphasia.
* '''[[oligodendrocytes]]''' - a type of support cell in the brain that produces myelin, the fatty sheath that surrounds and insulates axons.
* '''[[penetrating head injury]]''' - a brain injury in which an object pierces the skull and enters the brain tissue.
* '''[[penetrating skull fracture]]''' - a brain injury in which an object pierces the skull and injures brain tissue.
* '''[[persistent vegetative state]]''' - an ongoing state of severely impaired consciousness, in which the patient is incapable of voluntary motion.
* '''[[plasticity]]''' - ability of the brain to adapt to deficits and injury.
* '''[[pneumocephalus]]''' - a condition in which air or gas is trapped within the intracranial cavity.
* '''[[post-concussion syndrome (PCS)]]''' - a complex, poorly understood problem that may cause headache after head injury; in most cases, patients cannot remember the event that caused the concussion and a variable period of time prior to the injury.
* '''[[post-traumatic amnesia (PTA)]]''' - a state of acute confusion due to a traumatic brain injury, marked by difficulty with perception, thinking, remembering, and concentration; during this acute stage, patients often cannot form new memories.
* '''[[post-traumatic dementia]]''' - a condition marked by mental deterioration and emotional apathy following trauma.
* '''[[post-traumatic epilepsy]]''' - recurrent seizures occurring more than 1 week after a traumatic brain injury.
* '''[[prosodic dysfunction]]''' - problems with speech intonation or inflection.
* '''[[pruning]]''' - process whereby an injury destroys an important neural network in children, and another less useful neural network that would have eventually died takes over the responsibilities of the damaged network.
* '''[[seizures]]''' - abnormal activity of nerve cells in the brain causing strange sensations, emotions, and behavior, or sometimes convulsions, muscle spasms, and loss of consciousness.
* '''[[sensory aphasia]]''' - see fluent aphasia.
* '''[[shaken baby syndrome]]''' - a severe form of head injury that occurs when an infant or small child is shaken forcibly enough to cause the brain to bounce against the skull; the degree of brain damage depends on the extent and duration of the shaking. Minor symptoms include irritability, lethargy, tremors, or vomiting; major symptoms include seizures, coma, stupor, or death.
* '''[[shearing]]''' (or diffuse axonal injury) - damage to individual neurons resulting in disruption of neural networks and the breakdown of overall communication among neurons in the brain.
* '''[[stupor]]''' - a state of impaired consciousness in which the patient is unresponsive but can be aroused briefly by a strong stimulus.
* '''[[subdural hematoma]]''' - bleeding confined to the area between the dura and the arachnoid membranes.
* '''[[subdural hygroma]]''' - a buildup of protein rich fluid in the area between the dura and the arachnoid membranes, usually caused by a tear in the arachnoid membrane.
* '''[[syndrome of inappropriate secretion of antidiuretic hormone (SIADH)]]''' - a condition in which excessive secretion of antidiuretic hormone leads to a sodium deficiency in the blood and abnormally concentrated urine; symptoms include weakness, lethargy, confusion, coma, seizures, or death if left untreated.
* '''[[thrombosis or thrombus]]''' - the formation of a blood clot at the site of an injury.
* '''[[vasospasm]]''' - exaggerated, persistent contraction of the walls of a blood vessel.
* '''[[vegetative state]]''' - a condition in which patients are unconscious and unaware of their surroundings, but continue to have a sleep/wake cycle and can have periods of alertness.
* '''[[ventriculostomy]]''' - a surgical procedure that drains cerebrospinal fluid from the brain by creating an opening in one of the small cavities called ventricles.
* '''[[Wernicke's aphasia]]''' - see [[fluent aphasia]].
acquired cerebral palsy — cerebral palsy that occurs as a result of injury to the brain after birth or during early childhood.
Apgar score — a numbered scoring system doctors use to assess a baby's physical state at the time of birth.
asphyxia — a lack of oxygen due to trouble with breathing or poor oxygen supply in the air.
ataxia — the loss of muscle control.
athetoid — making slow, sinuous, involuntary, writhing movements, especially with the hands.
bilirubin — a bile pigment produced by the liver of the human body as a byproduct of digestion.
bisphosphonates — a family of drugs that strengthen bones and reduce the risk of bone fracture in elderly adults.
botulinum toxin — a drug commonly used to relax spastic muscles; it blocks the release of acetylcholine, a neurotransmitter that energizes muscle tissue.
cerebral — relating to the two hemispheres of the human brain.
cerebral dysgenesis — defective brain development.
choreoathetoid — a condition characterized by aimless muscle movements and involuntary motions.
congenital cerebral palsy — cerebral palsy that is present at birth from causes that have occurred during fetal development.
contracture — a condition in which muscles become fixed in a rigid, abnormal position, which causes distortion or deformity.
developmental delay — behind schedule in reaching the milestones of early childhood development.
dyskinetic — the impairment of the ability to perform voluntary movements, which results in awkward or incomplete movements.
dystonia (dystonic) a condition of abnormal muscle tone.
gait analysis — a technique that uses cameras, force plates, electromyography, and computer analysis to objectively measure an individual's pattern of walking.
gestation — the period of fetal development from the time of conception until birth.
hemiparesis — paralysis affecting only one side of the body.
hypertonia — increased muscle tone.
hypotonia — decreased muscle tone.
hypoxic-ischemic encephalopathy — brain damage caused by poor blood flow or insufficient oxygen supply to the brain.
intracranial hemorrhage — bleeding in the brain.
intrathecal baclofen — baclofen that is injected into the cerebrospinal fluid of the spinal cord to reduce spasticity.
jaundice — a blood disorder caused by the abnormal buildup of bilirubin in the bloodstream.
kyphosis — a humpback-like outward curvature of the upper spine.
lordosis — an increased inward curvature of the lower spine.
orthotic devices — special devices, such as splints or braces, used to treat posture problems involving the muscles, ligaments, or bones.
osteopenia — reduced density and mass of the bones.
palsy — paralysis, or the lack of control over voluntary movement.
-paresis or -plegia — weakness or paralysis. In cerebral palsy, these terms are typically combined with other phrases that describe the distribution of paralysis and weakness; for example, quadriplegia means paralysis of all four limbs.
periventricular leukomalacia (PVL) — "peri" means near; "ventricular" refers to the ventricles or fluid spaces of the brain; and "leukomalacia" refers to softening of the white matter of the brain. PVL is a condition in which the cells that make up white matter die near the ventricles. Under a microscope, the tissue looks soft and sponge-like.
placenta — an organ that joins a mother with her unborn baby and provides nourishment and sustenance.
quadriplegia — paralysis of both the arms and legs.
Rh incompatibility — a blood condition in which antibodies in a pregnant woman's blood attack fetal blood cells and impair an unborn baby's supply of oxygen and nutrients.
rubella — (also known as German measles) a viral infection that can damage the nervous system of an unborn baby if a mother contracts the disease during pregnancy.
scoliosis — a disease of the spine in which the spinal column tilts or curves to one side of the body.
selective dorsal rhizotomy — a surgical procedure in which selected nerves are severed to reduce spasticity in the legs.
spastic (or spasticity) — describes stiff muscles and awkward movements.
spastic diplegia (or diparesis) — a form of cerebral palsy in which spasticity affects both legs, but the arms are relatively or completely spared.
spastic hemiplegia (or hemiparesis) — a form of cerebral palsy in which spasticity affects an arm and leg on one side of the body.
spastic quadriplegia (or quadriparesis) — a form of cerebral palsy in which all four limbs are paralyzed or weakened equally.
tremor — an involuntary trembling or quivering.
Glossary of Dementia
alpha-synuclein — the major protein present in abnormal clumps called Lewy bodies, which are seen in the brains of people with Parkinson's disease and some dementias. Disorders in which alpha-synuclein accumulates inside nerve cells are called synucleinopathies.
Alzheimer's disease — the most common cause of dementia. Nearly all brain functions, including memory, movement, language, judgment, and behavior, are eventually affected. Alzheimer's disease is defined by observing high levels of amyloid-containing plaques and tau protein-containing neurofibrillary tangles in the brain.
amyloid — a protein with beta-pleated sheets, which aggregate to form the characteristic clumps (called plaques) that appear in the brains of people with Alzheimer's disease. Other proteins that form aggregates are tau proteins also seen in Alzheimer's disease, and alpha-synuclein present in dementia with Lewy Body and in Parkinson's Disease.
biomarkers — measurable biological signs in the living body that may indicate risk or progression of a disease and improve diagnosis
corticobasal degeneration — a progressive disorder characterized by abnormal buildup of the protein tau, nerve cell loss, and atrophy in multiple areas of the brain
dementia — a term for a condition that significantly interferes with daily life because of impaired thinking and memory
Dementia with Lewy bodies — a type of Lewy body dementia that is a form of progressive dementia
frontotemporal disorders — a group of dementias characterized by degeneration of nerve cells, especially those in the frontal and temporal lobes of the brain
Lewy body dementia — a progressive dementia characterized by the presence of abnormal structures called Lewy bodies in the brain
mild cognitive impairment — a stage between normal cognitive changes that may occur with age and the more serious symptoms that indicate dementia
mixed dementia — dementia in which one form of dementia and another condition or dementia cause damage to the brain; for example, Alzheimer's disease and small vessel disease or vascular dementia
multi-infarct dementia — a type of vascular cognitive impairment and dementia caused by numerous small strokes in the brain
neurodegeneration — the progressive loss of nerve cell structure or function
neurofibrillary tangles — bundles of twisted filaments largely made up of a protein called tau found in nerve cells in the brains of people with Alzheimer's disease and other types of dementia such as FTD.
Parkinson's disease dementia — a secondary dementia that sometimes occurs in people with advanced Parkinson's disease. Many people with Parkinson's have the amyloid plaques and neurofibrillary tangles found in Alzheimer's disease, but it is not clear if the diseases are linked.
plaques — abnormal clumps of amyloid protein that are found in large numbers in the brains of persons with Alzheimer's disease
tau — a protein that helps the functioning of microtubules, which are part of the cell's structural support and help deliver substances throughout the cell. In several dementia disorders, tau twists into filaments that become tangles. Disorders associated with an accumulation of tau, such as frontotemporal dementia, are called tauopathies.
vascular dementia — a type of dementia caused by brain damage from cerebrovascular or cardiovascular problems, usually strokes
Glossary of traumatic brain injury
aneurysm - a blood-filled sac formed by disease related stretching of an artery or blood vessel.
anoxia - an absence of oxygen supply to an organ's tissues leading to cell death.
aphasia - difficulty understanding and/or producing spoken and written language. ( See also non-fluent aphasia.)
apoptosis - cell death that occurs naturally as part of normal development, maintenance, and renewal of tissues within an organism.
arachnoid membrane - one of the three membranes that cover the brain; it is between the pia mater and the dura. Collectively, these three membranes form the meninges.
brain death - an irreversible cessation of measurable brain function.
Broca's aphasia - see non-fluent aphasia.
cerebrospinal fluid (CSF) - the fluid that bathes and protects the brain and spinal cord.
closed head injury - an injury that occurs when the head suddenly and violently hits an object but the object does not break through the skull.
coma - a state of profound unconsciousness caused by disease, injury, or poison.
compressive cranial neuropathies - degeneration of nerves in the brain caused by pressure on those nerves.
computed tomography (CT) - a scan that creates a series of cross-sectional X-rays of the head and brain; also called computerized axial tomography or CAT scan.
concussion - injury to the brain caused by a hard blow or violent shaking, causing a sudden and temporary impairment of brain function, such as a short loss of consciousness or disturbance of vision and equilibrium.
contrecoup - a contusion caused by the shaking of the brain back and forth within the confines of the skull.
contusion - distinct area of swollen brain tissue mixed with blood released from broken blood vessels.
CSF fistula - a tear between two of the three membranes - the dura and arachnoid membranes - that encase the brain.
deep vein thrombosis - formation of a blood clot deep within a vein.
dementia pugilistica - brain damage caused by cumulative and repetitive head trauma; common in career boxers.
depressed skull fracture - a fracture occurring when pieces of broken skull press into the tissues of the brain.
diffuse axonal injury - see shearing.
dysarthria - inability or difficulty articulating words due to emotional stress, brain injury, paralysis, or spasticity of the muscles needed for speech.
dura - a tough, fibrous membrane lining the brain; the outermost of the three membranes collectively called the meninges.
early seizures - seizures that occur within 1 week after a traumatic brain injury.
epidural hematoma - bleeding into the area between the skull and the dura.
erosive gastritis - inflammation and degeneration of the tissues of the stomach.
fluent aphasia - a condition in which patients display little meaning in their speech even though they speak in complete sentences. Also called Wernicke's or motor aphasia.
Glasgow Coma Scale - a clinical tool used to assess the degree of consciousness and neurological functioning - and therefore severity of brain injury - by testing motor responsiveness, verbal acuity, and eye opening.
global aphasia - a condition in which patients suffer severe communication disabilities as a result of extensive damage to portions of the brain responsible for language.
hematoma - heavy bleeding into or around the brain caused by damage to a major blood vessel in the head.
hemorrhagic stroke - stroke caused by bleeding out of one of the major arteries leading to the brain.
hypermetabolism - a condition in which the body produces too much heat energy.
hypothyroidism - decreased production of thyroid hormone leading to low metabolic rate, weight gain, chronic drowsiness, dry skin and hair, and/or fluid accumulation and retention in connective tissues.
hypoxia - decreased oxygen levels in an organ, such as the brain; less severe than anoxia.
immediate seizures - seizures that occur within 24 hours of a traumatic brain injury.
intracerebral hematoma - bleeding within the brain caused by damage to a major blood vessel.
intracranial pressure - buildup of pressure in the brain as a result of injury.
ischemic stroke - stroke caused by the formation of a clot that blocks blood flow through an artery to the brain.
locked-in syndrome - a condition in which a patient is aware and awake, but cannot move or communicate due to complete paralysis of the body.
magnetic resonance imaging (MRI) - a noninvasive diagnostic technique that uses magnetic fields to detect subtle changes in brain tissue.
meningitis - inflammation of the three membranes that envelop the brain and spinal cord, collectively known as the meninges; the meninges include the dura, pia mater, and arachnoid.
motor aphasia - see non-fluent aphasia.
neural stem cells - cells found only in adult neural tissue that can develop into several different cell types in the central nervous system.
neuroexcitation - the electrical activation of cells in the brain; neuroexcitation is part of the normal functioning of the brain or can also be the result of abnormal activity related to an injury.
neuron - a nerve cell that is one of the main functional cells of the brain and nervous system.
neurotransmitters -chemicals that transmit nerve signals from one neuron to another.
non-fluent aphasia - a condition in which patients have trouble recalling words and speaking in complete sentences. Also called Broca's or motor aphasia.
oligodendrocytes - a type of support cell in the brain that produces myelin, the fatty sheath that surrounds and insulates axons.
penetrating head injury - a brain injury in which an object pierces the skull and enters the brain tissue.
penetrating skull fracture - a brain injury in which an object pierces the skull and injures brain tissue.
persistent vegetative state - an ongoing state of severely impaired consciousness, in which the patient is incapable of voluntary motion.
plasticity - ability of the brain to adapt to deficits and injury.
pneumocephalus - a condition in which air or gas is trapped within the intracranial cavity.
post-concussion syndrome (PCS) - a complex, poorly understood problem that may cause headache after head injury; in most cases, patients cannot remember the event that caused the concussion and a variable period of time prior to the injury.
post-traumatic amnesia (PTA) - a state of acute confusion due to a traumatic brain injury, marked by difficulty with perception, thinking, remembering, and concentration; during this acute stage, patients often cannot form new memories.
post-traumatic dementia - a condition marked by mental deterioration and emotional apathy following trauma.
post-traumatic epilepsy - recurrent seizures occurring more than 1 week after a traumatic brain injury.
prosodic dysfunction - problems with speech intonation or inflection.
pruning - process whereby an injury destroys an important neural network in children, and another less useful neural network that would have eventually died takes over the responsibilities of the damaged network.
seizures - abnormal activity of nerve cells in the brain causing strange sensations, emotions, and behavior, or sometimes convulsions, muscle spasms, and loss of consciousness.
sensory aphasia - see fluent aphasia.
shaken baby syndrome - a severe form of head injury that occurs when an infant or small child is shaken forcibly enough to cause the brain to bounce against the skull; the degree of brain damage depends on the extent and duration of the shaking. Minor symptoms include irritability, lethargy, tremors, or vomiting; major symptoms include seizures, coma, stupor, or death.
shearing (or diffuse axonal injury) - damage to individual neurons resulting in disruption of neural networks and the breakdown of overall communication among neurons in the brain.
stupor - a state of impaired consciousness in which the patient is unresponsive but can be aroused briefly by a strong stimulus.
subdural hematoma - bleeding confined to the area between the dura and the arachnoid membranes.
subdural hygroma - a buildup of protein rich fluid in the area between the dura and the arachnoid membranes, usually caused by a tear in the arachnoid membrane.
syndrome of inappropriate secretion of antidiuretic hormone (SIADH) - a condition in which excessive secretion of antidiuretic hormone leads to a sodium deficiency in the blood and abnormally concentrated urine; symptoms include weakness, lethargy, confusion, coma, seizures, or death if left untreated.
thrombosis or thrombus - the formation of a blood clot at the site of an injury.
vasospasm - exaggerated, persistent contraction of the walls of a blood vessel.
vegetative state - a condition in which patients are unconscious and unaware of their surroundings, but continue to have a sleep/wake cycle and can have periods of alertness.
ventriculostomy - a surgical procedure that drains cerebrospinal fluid from the brain by creating an opening in one of the small cavities called ventricles.
Wernicke's aphasia - see fluent aphasia.
Retrieved from "http://www.wikimd.org/wiki/index.php?title=Sand_Box&oldid=125497"


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A Basic Emergency Medical Technician is an individual who has been trained to perform basic emergency medical care.The delivery of basic first aid, CPR, AED, administration of oxygen, the delivery of certain medications as defined by the Department of Transportation National Highway Traffic Safety Administration (NHTSA) National Standard Curriculum, and other basic equipment used by emergency response personnel are included at this level.An SNHD licensed EMT may provide basic emergency medical care only when functioning on duty as an Ambulance Attendant, Aeromedical Ambulance Attendant, or Firefighter Attendant.Tuition Includes: Didactic & Clinical experience, National Registry Skills testing, American Heart Association CPR for the Health Care Provider & Class Shirt.
A SNHD licensed Advanced EMT (AEMT) can perform as an EMT with additional training in advanced emergency medical care. An AEMT may provide advanced emergency medical care only when functioning on duty as an Ambulance Attendant, Aeromedical Ambulance Attendant, or Firefighter Attendant.Use of intravenous therapy, supraglottic airway devices, and limited advanced medications administration are included at this level defined by the Department of Transportation National Highway Traffic Safety Administration (NHTSA) and the National Standard Curriculum.


Ohio Public Radio's Andy Chow reports.
A group of state legislators want to freeze Ohio's green energy standards indefinitely. These standards require utilities to cut energy use by 22% and increase the use of renewable energy sources by 12.5% by 2025.
The call for an indefinite freeze comes from the new report just released by the Energy Mandates Study Committee that spent the last few months looking into the cost and effectiveness of the standards.
The committee co-chair, Republican Representative Kristina Roegner of Hudson, says the standards had already raised the average electric bill by $40 a year.
"By the way we're only just at the beginning of the march up what we're calling mandate mountain. So those riders, if those mandates were to increase, you could extrapolate that those extra costs in your utility bills, each and every one of you would go up as well," said Roegner.
But several advanced energy and environmental advocacy groups slammed the report soon after its release. Samantha Williams with the Natural Resources Defense Council disputes Roegner's claims that the standards jack up energy bills.
"The amount that a consumer pays every year on their bill for efficiency and renewables is outweighed by far by the benefits that renewables bring. Lowering electric bills across the system, the opportunities to cut your own electric bills through efficiency and of course all the benefits that come with investments in cleaner energy," Williams said.
The standards were frozen by the General Assembly last year after the House and Senate spent years debating the policies. Some members wanted an overhaul while others, such as Republican Senator Bill Seitz of Cincinnati wanted an all-out repeal.
Instead the lawmakers settled on a two-year freeze that would lift at the end of 2016. The bill that froze the standards also created the study committee.
The indefinite freeze would be a significant setback to renewable energy advocates who say the state's standards were a big incentive for solar and wind energy development in Ohio. Industry experts have said that companies would not want to invest in a state with uncertain policies.
Democratic leaders were not happy with the report either. Representative Michael Stinziano of Columbus sat on the study committee and said the report essentially kicks the can down the road.
"A freeze was supposed to give the opportunity to have additional discussion and look at what the right balance of current resources, future resources could be and to hopefully position the state better an indefinite freeze doesn't do that. I know it's going to stymie job growth, health issues and doesn't help consumers in any shape way or form," said Stinziano.
Roegner defends the report which also called for a shift away from the standards and towards laws that would encourage green energy use, such as a pending bill that would dole out tax incentives.
"If you want to do a project let's say you want to put up a solar panel project up. You can get that assessed on your property taxes so you can get the money and pay it back on your taxes. So it's sort of an incentive to do something like that rather than a mandate," said Roegner.
The standards will automatically start back up again at the beginning of 2017. Anything else, such as the report's suggestion of an indefinite freeze would take a whole new bill with the governor's signature.
Though there's some controversy in Ohio around energy standards and proposals, a national group calling for attention to climate change says the state has the power to shift federal policy and demand clean energy development.
A research group says a new law freezing the state's green energy standards has led to significant declines in Ohio's wind and solar industries.


text:
\section{Introduction}
\label{sec:intro}

\begin{figure}[t]

  \centering
   \includegraphics[width=1.0 \linewidth]{bar.pdf}
   \vspace{-0.25in}
   \caption{Comparison of our method with the supervised baseline and previous state-of-the-art SSL method \cite{cmpl}. SVFormer significantly outperforms previous methods under the case with very little labeled data.}
   \label{bar}
\end{figure}

With the development of the Internet, videos gradually replace images and texts and grow at at an exponential rate. On video websites such as YouTube, millions of new videos are uploaded every day. Supervised video understanding works \cite{slowfast,x3d,timesformer,videoswin} have achieved great successes. They rely on large-scale mannual annotations, yet labeling so many videos is time-consuming and labor-intensive.  How to make use of unlabeled videos that are readily available for better video understanding is of great importance.

In this spirit, semi-supervised action recognition \cite{videossl,tcl,ltg} explores how to enhance the performance of deep learning models using large-scale unlabeled data. This is generally done with labeled data to pretrain the networks~\cite{ltg,cmpl}, and then leveraging the pretrained models to generate pseudo labels for unlabeled data, a process known as pseudo labeling. The obtained pseudo labels are further used to refine the pretrained models.
In order to improve the quality of pseudo labeling, previous methods \cite{ltg,mvpl} use additional modalities such as optical flow \cite{opticalflow} and temporal gradient \cite{tsn}, or introduce auxiliary networks \cite{cmpl} to supervise unlabeled data.
Though these methods present promising results, they typically require additional training or inference cost, preventing them from scaling up.

Recently, video transformers \cite{videoswin,timesformer,vivit} have shown strong results compared to CNNs \cite{x3d,slowfast,3dresnet}.
Though great success has been achieved, the exploration of transformers on semi-supervised video tasks remains unexplored.
While it sounds appealing to extend vision transformers directly to SSL, a previous study shows that transformers perform significantly worse compared to CNNs in the low-data regime due to the lack of inductive bias~\cite{semiformer}. As a result, directly applying SSL methods, \eg, FixMatch \cite{fixmatch}, to ViT \cite{vit} leads to an inferior performance~\cite{semiformer}.

Surprisingly, in the video domain, we observe that TimeSformer, a popular video Transformer \cite{timesformer}, initialized with weights from ImageNet \cite{imagenet}, demonstrates promising results even when annotations are limited.
This encourages us to explore the great potential of transformers for action recognition in the SSL setting.

Existing SSL methods generally use image augmentations (\eg, Mixup \cite{mixup} and CutMix \cite{cutmix}) to speed up convergence under limited label resources.
However, such pixel-level mixing strategies are not perfectly suitable for transformer architectures, which operate on tokens produced by patch splitting layers.
In addition, strategies like Mixup and CutMix are particularly designed for image tasks, which fail to consider the temporal nature of video data.
Therefore, as will be shown empirically, directly using Mixup or CutMix for semi-supervised action recognition leads to unsatisfactory performance.

In this work, we propose SVFormer, a transformer-based semi-supervised action recognition method.
Concretely, SVFormer adopts a consistency loss that builds two differently augmented views and demands consistent predictions between them.
Most importantly, we propose Tube TokenMix (TTMix), an augmentation method that is naturally suitable for video Transformer.
Unlike Mixup and CutMix, Tube TokenMix combines features at the token-level after tokenization via a mask, where the mask has consistent masked tokens over the temporal axis.
Such a design could better model the temporal correlations between tokens.

Temporal augmentations in literatures (\emph{e.g.} varying frame rates) only consider simple temporal scaling or shifting, neglecting the complex temporal changes of each part in human action.
To help the model learn strong temporal dynamics, we further introduce the Temporal Warping Augmentation (TWAug), which arbitrarily changes the temporal length of each frame in the clip. TWAug can cover the complex temporal variation in videos and is complementary to spatial augmentations \cite{autoaugment}.
When combining TWAug with TTMix, significant improvements are achieved.

As shown in Fig.~\ref{bar}, SVFormer achieves promising results in several benchmarks. (i) We observe that the supervised Transformer baseline is much better than the Conv-based method \cite{3dresnet}, and is even comparable with the 3D-ResNet state-of-the-art method \cite{cmpl} on Kinetics400 when trained with 1\% of labels. (ii) SVFormer-S significantly outperforms previous state-of-the-art models with similar parameters and inference cost, measured by floating point operations (FLOPs). (iii) Our method is also effective for the larger SVFormer-B model.
To sum up, our contributions are as follows:
\begin{itemize}

\item We are the first to explore the transformer model for semi-supervised video recognition. Unlike SSL for image recognition with transformers, we find that using parameters pretrained on ImageNet is of great importance to ensure decent results for action recognition in the low-data regime.
\item We propose a token-level augmentation Tube TokenMix, which is more suitable for video Transformer than pixel-level mixing strategies.
Coupled with Temporal Warping Augmentation, which improves temporal variations between frames, TTMix achieves significant boost compared with  image augmentation.
\item We conduct extensive experiments on three benchmark datasets. The performances of our method in two different sizes (\emph{i.e.}, SVFormer-B and SVFormer-S) outperform state-of-the-art approaches by clear margins. Our method sets a strong baseline for future transformer-based works.
\end{itemize}

\section{Related Works}
\noindent\textbf{Deep Semi-supervised Learning}~
Deep learning relies on large-scale annotated data, however collecting these annotations is labor-intensive.
Semi-supervised learning is a natural solution to reduce the cost of labeling, which leverages a few labeled samples and a large amount of unlabeled samples to train the model.
The research and application of SSL mainly focus on image recognition \cite{fixmatch,semiformer,meanteacher} with a two-step process: data augmentation and consistency regularization. Concretely, different data augmentations \cite{autoaugment}  views are input to the model, and their output consistencies are enforced through a consistency loss. Another line of work generates new data and labels using mixing~\cite{mixmatch,ict,cowmask} to train the network. Among these state-of-the-art methods, FixMatch have been widely used due its effective and its variants have been extended to many other applications, such as object detection \cite{unbiasedteacher,rethinkingsemi}, semantic segmentation \cite{semiseg1,semiseg2}, 3D reconstruction \cite{ssl3d}, etc. Although FixMatch has achieved good performance in many tasks, it may not achieve satisfactory results when directly transferred to video action recognition due to the lack of temporal augmentation. In this paper, we introduce temporal augmentation TWAug with mixing based method TTMix, which is suitable for video transformers at SSL settings.

\vspace{0.1cm}
\noindent\textbf{Semi-supervised Action Recognition}
VideoSSL \cite{videossl} presents a comparative study of applying 2D SSL methods to videos, which verifies the limitations of the direct extension of pseudo labeling method. TCL \cite{tcl} explores the effect of a group contrastive loss and self-supervised tasks. MvPL \cite{mvpl} and LTG \cite{ltg} introduce optical flow or temporal gradient modal to generate high quality pseudo labels for training, respectively. CMPL \cite{cmpl} introduce an auxiliary network, which requires more frames in training, increasing the difficulty of application. Besides, previous methods are all based on 2D \cite{tcl} or 3D convolutional \cite{mvpl,cmpl,ltg} networks, which require more training epochs. Our approach is the first to make the exploration of Video Transformer for SSL action recognition and achieves the best performance with the least training cost.

\noindent\textbf{Video Transformer}
The great success of Vision Transformer \cite{vit} in image recognition leads to the development of exploring the transformer-base architecture for video recognition tasks. VTN \cite{vtn} uses additional temporal attention on the top of the pretrained ViT \cite{vit}. TimeSformer \cite{timesformer} investigates different spatial-temporal attention mechanisms and adopts factored space time attention as a trade-off of speed and accuracy. ViViT \cite{vivit}  explores four different types of attention, and selects the global spatio-temporal attention as the default to achieve promising performance. In addition, MviT \cite{mvit}, Video Swin \cite{videoswin} and Uniformer \cite{uniformer}  incorporate the inductive bias in convolution into  transformers. While these methods focus on fully-supervised setting,  limited effort has been made for  transformers in the semi-supervised setting.

\vspace{0.2cm}
\noindent\textbf{Data Augmentation}~
Data augmentation is an essential step in modern deep networks to improve the training efficiency and performance.
Cutout \cite{devries2017improved} removes random rectangle regions in images.
Mixup \cite{mixup} performs image mixing by linearly interpolating both the raw image and labels.
In CutMix \cite{cutmix}, patches are cut and pasted among image pairs.
AutoAugment \cite{autoaugment} automatically searches for augmentation strategies to improve the results.
PixMix \cite{pixmix} explores the natural structural complexity of images when performing mixing.
TokenMix \cite{liu2022tokenmix} mixes images at the token-level and allows the region to be multiple isolated parts.
Though these methods have achieved good results, they are all specially designed for pure image and most of them are pixel-level augmentations.
In contrast, our TTMix coupled with TWAug is devised for video.

\section{Method}

In this section, we first introduce the preliminaries of SSL in Sec.~\ref{preliminary}.
The pipeline of our proposed SVFormer is described in Sec.~\ref{pipeline}.
Then we detail the proposed Tube TokenMix (TTMix) in Sec.~\ref{tokenmix}, as well as the effective albeit simple temporal warping augmentation.
Finally, we show the training paradigm in Sec.~\ref{train}.

\subsection{Preliminaries of SSL}
\label{preliminary}
Suppose we have $N$ training video samples, including $N_L$ labeled videos $(x_l,y_l) \in \mathcal{D}_L$ and $N_U$ unlabeled videos $x_u \in \mathcal{D}_U$, where $x_l$ is the labeled video sample with a category label $y_l$, and $x_u$ is the unlabeled video sample. In general, $N_U \gg N_L$. The aim of SSL is to utilize both $\mathcal{D}_L$ and $\mathcal{D}_U$ to train the model.

\subsection{Pipeline}
\label{pipeline}

SVFormer follows the popular semi-supervised learning framework FixMatch \cite{fixmatch} that use a consistency loss between two differently augmented views. The training paradigm is divided into two parts.
For the labeled set $\{(x_l, y_l)\}_{l=1}^{N_L}$, the model optimizes the supervised loss ${\cal{L}}_{s}$:
\begin{equation}
\label{losssup}
{\cal{L}}_{s}=\frac{1}{N_L}\sum_{}^{N_L}{\cal{H}}({\cal{F}} ({x_l}),y_l),
\end{equation}
where ${\cal{F}}(\cdot)$ refers to the predictions produced by the model and ${\cal{H}}$ is the standard cross entropy loss.

For unlabeled samples $x_u$, we first use weak augmentations (\emph{e.g.}, random horizontal flipping, random scaling, and random cropping) and strong augmentations (\emph{e.g.}, AutoAugment~\cite{autoaugment} or Dropout~\cite{dropout}) to generate two views separately, $x_w={\cal{A}}_{weak}(x_u)$, $x_s={\cal{A}}_{strong}(x_u)$. Then the pseudo label of the weak view ${\hat{y}_w}=\arg\max({\cal{F}}(x_w))$, which is produced by the model, is utilized to supervise the strong view, with the following unsupervised loss:
\begin{equation}
\label{lossun}
{\cal{L}}_{un}=\frac{1}{N_U}\sum^{N_U}{\mathbb{I}} (\max({\cal{F}}(x_w)) > \delta){\cal{H}}({\cal{F}}({x_s}),\hat{y}_w),
\end{equation}
where $\delta$ is the predefined threshold, and ${\mathbb{I}}$ is the indicator function that equals $1$ when the maximum class probability exceeds $\delta$ otherwise $0$. The confidence indicator is used to filter the noisy pseudo labels.

\begin{figure}[t]
  \centering
   \includegraphics[width=1 \linewidth]{pipeline_shallow.pdf}
      \vspace{-0.25in}
   \caption{ Overview of our Tube TokenMix training framework. The two input unlabeled samples are mixed via a tube mask, where the generated sample is fed into the Student model.
   The two samples are also fed into the EMA-Reacher to obtain their pseudo labels, which are further linearly interpolated via mask ratio $\lambda$ to produce pseudo label for the generated sample.
``SG" means stop gradient.
 The data augmentation is omitted here.}
   \label{overview}
\end{figure}

\paragraph{EMA-Teacher}
In FixMatch, the two augmented inputs share the same model, which tends to cause model collapsing easily \cite{moco,bootstrap}.
Therefore, we adopt the exponential moving average (EMA)-Teacher in our framework, which is an improved version of FixMatch.
The pseudo labels are generated by the EMA-Teacher model, whose parameters are updated by exponential moving average of the student parameters, formulated as:
\begin{equation}
\label{ema}
{\theta}_t \gets m {\theta}_t +(1- m) {\theta}_s,
\end{equation}
where $m$ is a momentum coefficient, $\theta_{t}$ and $\theta_{s}$ are the parameters of teacher and student model, respectively. EMA has achieved success in many  tasks, such as self-supervised learning \cite{moco,bootstrap}, SSL of image classification \cite{meanteacher,cowmask}, and object detection \cite{unbiasedteacher,rethinkingsemi}. Here we are the first to adopt this method in semi-supervised video action recognition.

\subsection{Tube TokenMix}
\label{tokenmix}

One of the core problems in semi-supervised frameworks is how to enrich the dataset with high-quality pseudo labels.
Mixup \cite{mixup} is a widely adopted data augmentation strategy, which performs convex combination between pairs of samples and labels as follows:
\begin{equation}
  \hat{x} = \lambda \cdot x_1 + (1-\lambda) \cdot x_2,
\end{equation}
\begin{equation}
  \hat{y} = \lambda \cdot y_1 + (1-\lambda) \cdot y_2,
\end{equation}
where the ratio $\lambda$ is a scalar that conforms to the beta distribution.
Mixup~\cite{mixup} and its variants (\emph{e.g.} CutMix \cite{cutmix}) have achieved success in many tasks in the low-data regime, such as long-tail classification \cite{remix,videolt}, domain adaptation \cite{damiup} \cite{dualmixup}, few-shot learning \cite{mixupfew}, \etc.
For SSL, Mixup also performs well by mixing the pseudo labels of unlabeled samples in image classification \cite{ict}.

\paragraph{Mixing in Videos}
While directly applying Mixup or CutMix to video scenarios results in clear improvements in Conv-based methods~\cite{actorcutmix}, these methods show unsatisfactory performance in our method.
The reason is that our method adopts the Transformer backbone, where the pixel-level mixing augmentation (Mixup or CutMix) may be not perfectly suitable for such token-level models.
To narrow the gap, we propose 3 token-level mixing augmentation methods for video data, namely, Rand TokenMix, Frame TokenMix, and Tube TokenMix.

\begin{figure}[t]
  \centering
   \includegraphics[width=1.0 \linewidth]{3mask_color.pdf}
      \vspace{-0.25in}
   \caption{Examples of masks in three token mixing strategies. (a) Rand TokenMix. (b) Tube TokenMix. (c) Frame TokenMix.}
   \label{mask3}
\end{figure}

Fig.~\ref{overview} illustrates the pipeline of our method.
Given unlabeled video clips $x_a, x_b\in \mathbb{R}^{H\times W\times T}$, our method employ a token-level mask $\textbf{M}\in\{0,1\}^{H\times W\times T}$ to perform sample mixing.
Note that $H$ and $W$ are the height and width of the frame after patch tokenization, and $T$ is the clip length. To generate a new sample $x_{mix}$, we mix $x_a$ and $x_b$ after strong data augmentations ${\cal{A}}_{strong}$ as follows:
\begin{equation}
    x_{mix} = {\cal{A}}_{strong}(x_a) \odot \textbf{M} + {\cal{A}}_{strong}(x_b) \odot (\textbf{1}-\textbf{M}),
\end{equation}
where $\odot$ is element-wise multiplication, and $\textbf{1}$ is a binary mask with all ones.

The mask $\textbf{M}$ differs in the three augmentation methods, as demonstrated in Fig.~\ref{mask3}.
For Rand TokenMix, the masked tokens are randomly selected from the whole video clip (from $H\times W\times T$ tokens).
For Frame TokenMix, we randomly select frames from the $T$ frames and mask all the tokens in these frames.
For Tube TokenMix, we adopt the tube-style masking strategy, that is, different frames share the same spatial mask matrix. In this case, the mask $\textbf{M}$ has consistent masked tokens over the temporal axis.
While our mask design shares similarity with the recent masked video modeling \cite{feichtenhofer2022masked,tong2022videomae}, our motivation is totally different. They focus on removing certain regions and making the model predict the masked areas for feature learning.
In contrast, we leverage the mask to mix two clips and synthesize a new data sample.

\begin{algorithm}[t]
\caption{Consistency loss for Tube TokenMix}
\begin{algorithmic}
\REQUIRE Unlabeled clip batch $\mathbf{x_u}$
\REQUIRE Tube TokenMask $\mathbf{M}$, mask ratio $\lambda$
\REQUIRE Teacher model ${\cal{F}}_{t}$
\REQUIRE Student model ${\cal{F}}_{s}$
\REQUIRE Confidence threshold $\delta$
\STATE $\mathbf{{x}_{s}} = \text{shuffle}(\mathbf{x_u})$ \COMMENT{shuffle samples in batch}
\STATE $\mathbf{\hat{x}_u} = \text{spatial\_aug}(\mathbf{x_u})$ \COMMENT{spatial aug.}
\STATE $\mathbf{\hat{x}_s} = \text{temporal\_aug}(\mathbf{{x_s}})$ \COMMENT{temporal warping aug.}
\STATE $\mathbf{z}_{u} = \text{stop\_gradient}({\cal{F}}_{t}(\mathbf{{x}}_{u}))$ \COMMENT{teacher pred.}
\STATE $\mathbf{z}_{s} = \text{stop\_gradient}({\cal{F}}_{t}(\mathbf{{x}}_{s}))$
\STATE $c_u = \max_{i}\mathbf{z}_{u}[i]$   \COMMENT{confidence of prediction}
\STATE $c_s = \max_{i}\mathbf{z}_{s}[i]$
\STATE $\mathbf{\hat{x}}_{m} = \mathbf{\hat{x}}_{u} * \mathbf{M} + \mathbf{\hat{x}}_{s} * (1 - \mathbf{M})$ \COMMENT{mix clips}
\STATE $\mathbf{z}_{m} = \mathbf{z}_{u} * \lambda + \mathbf{z}_{s} * (1- \lambda)$ \COMMENT{mix tea. preds.}
\STATE $c_m =c_u * \lambda + c_s *(1-\lambda) $ \COMMENT{mix confidence.}
\STATE $q = \text{mean}(c_m \geq \delta)$   \COMMENT{mean of conf. mask}
\STATE $\mathbf{y}_{m} = {\cal{F}}_{s}(\mathbf{\hat{x}}_{m})$ \COMMENT{stu. pred. on mixed clip}
\STATE $d = q||\mathbf{y}_{m} - \mathbf{z}_{m}||^{2}_{2}$   \COMMENT{cons. loss}
\STATE \textbf{Return} $d$
\end{algorithmic}
\label{alg}
\end{algorithm}

\begin{figure*}[t]

  \centering
   \includegraphics[width=1.0 \linewidth]{augmix_deep.pdf}
      \vspace{-0.25in}
   \caption{One example of utilizing Temporal Warping Augmentation together with spatial augmentation in TTMix. The two clips are first transformed by the two augmentations separately, after which TTMix is performed to generate a new clip.}
   \label{augmix}
\end{figure*}

The mixed sample $x_{mix}$ is then fed to the student model ${\cal{F}}_{s}$, obtaining the model prediction $y_{mix} = {\cal{F}}_{s}(x_{mix})$.
In addition, the pseudo labels $\hat{y}_a,\hat{y}_b$ for $x_a,x_b$ are produced by inputting the weak augmented samples ${\cal{A}}_{weak}(x_a),{\cal{A}}_{weak}(x_b)$ to the teacher model ${\cal{F}}_{t}$:

\begin{equation}
    \hat{y}_a = \arg\max({\cal{F}}_{t}({\cal{A}}_{weak}(x_a))),
\end{equation}
\begin{equation}
    \hat{y}_b = \arg\max({\cal{F}}_{t}({\cal{A}}_{weak}(x_b))).
\end{equation}
Note that if $\small \max({\cal{F}}_{t}({\cal{A}}_{weak}(x)))<\delta$, the pseudo label $\hat{y}$ remains the soft label ${\cal{F}}_{t}({\cal{A}}_{weak}(x))$.
The pseudo label $\hat{y}_{mix}$ for $x_{mix}$ is generated by mixing $\hat{y}_a$ and $\hat{y}_b$ with mask ratio $\lambda$:
\begin{equation}
    \hat{y}_{mix} = \lambda \cdot \hat{y}_a + (1-\lambda) \cdot \hat{y}_b.
\end{equation}
Finally, the student model is optimized by the following consistency loss:
\begin{equation}
\label{lossmix}
    {\cal{L}}_{mix}= \frac{1}{N_{m}} \sum^{N_{m}}(\hat{y}_{mix} - y_{mix})^2,
\end{equation}
where $N_{m}$ is the number of mixed samples.
The algorithm of consistency loss for Tube TokenMix is shown in Algorithm \ref{alg}.

\paragraph{Temporal Warping Augmentation}
\label{time_aug}
Most existing augmentation methods are designed for image tasks, which focus more on the spatial augmentation. They manipulate single or a pair of images to generate new image samples, without considering any temporal changes.
Even the commonly adopted temporal augmentations, including varying temporal locations \cite{feichtenhofer2021large} and frame rates \cite{tcl,yang2020video}, only consider simple temporal shift or scaling, that is, changing the holistic location or play speed.
However, human actions are very complex and can have different temporal variation at every timestamp.
To cover such challenging cases, we propose to distort the temporal duration of each frame, thus introducing higher randomness into the data.

\begin{figure}[h]

  \centering
   \includegraphics[width=1.0 \linewidth]{warping_deep.pdf}
   \vspace{-0.1in}
   \caption{Illustration of Temporal Warping Augmentation. We demonstrate three augment examples of different selected frames.}
   \label{fig_warp}
\end{figure}

Our Temporal Warping Augmentation (TWAug) can stretch one frame to various temporal length.
Given an extracted video clip of $T$ frames (\emph{e.g.}, 8 frames), we randomly determine to keep all the frames, or select a small portion of frames (\emph{e.g.}, 2 or 4 frames) while masking the others.
The masked frames are then padded with random neighbouring visible (unmasked) frames.
Note that after temporal padding, the frame order is still retained.
Fig.~\ref{fig_warp} shows three examples of selecting 2, 4, and 8 frames, respectively.
The proposed TWAug can help the model learn the flexible temporal dynamics during training.

The Temporal Warping Augmentation serves as a strong augmentation in TTMix.
Typically, we combine TWAug with the conventional spatial augmentation \cite{autoaugment,dropout} to perform the mixing.
As shown in Fig.~\ref{augmix}, the two input clips are first transformed by spatial augmentation and TWAug separately, after which the two clips are mixed through TTMix.
We verify the effectiveness of our TWAug in Sec. \ref{augment}.

\subsection{Training Paradigm}
\label{train}
The training of SVFormer consists of three parts: supervised loss formulated by Eq. (\ref{losssup}), unsupervised pseudo-label consistency loss Eq. (\ref{lossun}), and TTMix consistency loss Eq. (\ref{lossmix}). The final loss function is as follows:
\begin{equation}
\label{losstotal}
{\cal{L}}_{all}= {\cal{L}}_{s} + {\gamma}_1  {\cal{L}}_{un} + {\gamma}_2  {\cal{L}}_{mix},
\end{equation}
where ${\gamma}_1$ and ${\gamma}_2$ are the hyperparameters for balancing the loss items.

\input{tab_main}

\section{Experiment}
In this section, we first introduce the experimental settings in Sec.~\ref{experimentsettings}. Following previous work \cite{cmpl,ltg}, we conduct experiments under different labeling rates in Sec \ref{mainresult}. In addition, we also perform ablation experiments and empirical analysis in Section \ref{ablation}. If not emphasized, we only use RGB modal for inference with the official validation set.

\subsection{Experiment Settings}
\label{experimentsettings}
\noindent\textbf{Datasets}
Kinetics-400 \cite{kinetic} is a large-scale human action video dataset, with up to 245k training samples and 20k validation samples, covering 400 different categories. We follow the state-of-the-art methods MvPL \cite{mvpl} and CMPL \cite{cmpl} to sample 6 or 60 labeled training videos per category, \emph{i.e.} at 1\% or 10\% labeling rates. UCF-101 \cite{ucf101} is a dataset with 13,320 video samples, which consists of 101 categories. We also sample 1 or 10 samples in each category as the labeled set following CMPL \cite{cmpl}. As for HMDB-51 \cite{hmdb}, it is a small-scale dataset with only 51 categories composed of 6,766 videos. Following the division of LTG \cite{ltg} and VideoSSL \cite{videossl}, we conduct experiments at three different labeling rates: 40\%, 50\%, and 60\%.

\vspace{0.05in}
\noindent\textbf{Evaluation Metric}
We show the accuracy of Top-1 in main results, and also present the accuracy of Top-5 in some ablation experiments.

\vspace{0.05in}
\noindent\textbf{Baseline}
We utilize the ViT \cite{vit} extended video TimeSformer \cite{timesformer} as the backbone of our baseline. The hyperparameters are mostly kept the same as the baseline, and we adopt the divided space-time attention as in TimeSformer \cite{timesformer}. Since TimeSformer only have ViT-Base models, we implement SVFormer-Small model from DeiT-S \cite{deit} with the dimension of 384 and 6 heads, in order to have comparable number of parameters with other Conv-based methods \cite{3dresnet,mvpl,cmpl}.
For fair comparisons, we train 30 epochs for TimeSformer as the supervised baseline.

\begin{table}[t]
\caption{\textbf{Comparisons with state-of-the-art methods on HMDB-51.}  We report Top-1 accuracy.  ``Input'' shows the modalities used during training, where ``V'' is the raw RGB video, ``F'' is optical flow and ``G'' is the temporal gradient.
}
\vspace{-0.0in}
\scalebox{0.85}{
\begin{tabular}{@{}cccccc@{}}
\toprule
            & Backbone   &Input  & 40\% & 50\% & 60\% \\ \midrule
VideoSSL \cite{videossl}    & 3D-R18     & V    & 32.7 & 36.2 & 37.0 \\
ActorCutMix\cite{actorcutmix}  & R(2+1)D-34 & V    & 32.9 & 38.2 & 38.9 \\
MvPL \cite{mvpl}        & 3D-R18     & V+F+G    & 30.5 & 33.9 & 35.8 \\
LTG \cite{ltg}        & 3D-R18     & V+G    & 46.5 & 48.4 & 49.7 \\
TACL \cite{tacl}        & 3D-R18     & V    & 38.7 & 40.2 & 41.7 \\
L2A \cite{l2a}        & 3D-R18     & V    &42.1   & 46.3 &47.1 \\
\midrule
SVFormer-S (Ours)        & ViT-S      & V      & \textbf{56.2}  &\textbf{58.2}    & \textbf{59.7}     \\
SVFormer-B (Ours)        & ViT-B      & V      & \textbf{61.6}  &\textbf{64.4}    & \textbf{68.2}     \\
\bottomrule
\end{tabular}
}
\label{table2}
\end{table}

\vspace{0.00in}
\noindent\textbf{Training and Inference}
For training, we follow the setting of TimeSformer \cite{timesformer}. The training uses 8 or 16 GPUs, with a SGD optimizer using a momentum of 0.9 and a weight decay of 0.001.  For each setting, the basic learning rate is set to 0.005, which is divided by 10 at epochs 25, and 28. As for the confidence score threshold, we search for the optimal $\delta$ from \{0.3, 0.5, 0.7, 0.9\}. ${\gamma}_1$ and ${\gamma}_2$ are set to 2.
The masking ratio $\lambda$ is sampled from beta distribution Beta($\alpha, \alpha$), where $\alpha=10$.
In the testing phase, following the inference strategies in MvPL \cite{mvpl} and CMPL \cite{cmpl}, we uniformly sample five clips from the entire video, and make three different crops to get $224\times224$ resolution to cover most of the spatial areas of the clips. The final prediction is the average of the softmax probabilities of these $5\times3$ predictions. We also conduct a comparison of inference setting in the ablation study in Sec. \ref{augment}.

\subsection{Main Results}
\label{mainresult}
The main results of Kinetics-400 \cite{kinetic} and UCF-101 \cite{ucf101} are shown in Table \ref{table1}. Compared with previous methods, our model SVFormer-S achieves the best performance with the fewest training epochs among the methods that only use RGB data. In particular, at the labeling rate of 1\% setting, SVFormer-S improves previous approach~\cite{cmpl} by 6.3\% in UCF-101 and 15.0\% in Kinetics-400.
In addition, when adopting larger models, SVFormer-B significantly outperforms the state-of-the-art methods.

Specifically, in Kinetics-400, SVFormer-B can achieve 69.4\% with only 10\% labeled data, which is comparable to 77.9\% of fully-supervised setting in TimeSformer \cite{timesformer}.
Moreover, as shown in Table \ref{table2}, for the small-scale dataset HMDB-51 \cite{hmdb}, our SVFormer-S and SVFormer-B have also improved by about 10\% and 15\% compared with the previous method \cite{ltg}.

\begin{figure}[t]
  \centering
   \includegraphics[width=1.0 \linewidth]{mixexample.pdf}

   \caption{ Example of the traditional pixel-level mixing methods and our proposed token-level mixing. Note that token-level methods mix two samples after tokenization but the visualization is shown on image-level for clear presentation.}
   \label{fig_mixexample}
\end{figure}

\subsection{Ablation Studies}
\label{ablation}
To understand the effect of each part of the design in our method, we conduct extensive ablation studies on the Kinetics-400 and UCF-101 at the 1\% labeling ratio setting with SVFormer-S.

\vspace{0.05in}
\noindent\textbf{Analysis of SSL framework}
The comparison of FixMatch and EMA-Teacher is shown in Table \ref{table3}.
It is clear that the two methods have significantly improved the baseline approach.
In addition, EMA-Teacher has exhibited considerable gains over FixMatch in both datasets with very few labeled samples, probably because it has improved the stability of training. FixMatch \cite{fixmatch} may lead to model collapse with limited labels as shown in \cite{semivit}.

\begin{table}[t]\small
\centering
\caption{\textbf{SSL framework selection.} We compare the EMA-Teacher framework with FixMatch. The results are reported on Kinetics-400 and UCF-101 with 1\% labeling ratio.}
\vspace{-0.0in}
\begin{tabular}{@{}lcccc@{}}
\toprule
\multirow{2}{*}{Method} & \multicolumn{2}{c}{UCF-1\%} & \multicolumn{2}{c}{Kinetic-1\%} \\ \cmidrule(l){2-5}
                  & Top-1          & Top-5          & Top-1        & Top-5        \\ \midrule
Baseline         & 12.7             & 29.8          & 19.9         & 42.3   \\ \midrule
FixMatch \cite{fixmatch}          &  25.1              & 47.3               &  28.2       & 54.6             \\
 EMA-Teacher       & \textbf{31.4}       & \textbf{56.9}               & \textbf{32.6}    & \textbf{59.0}             \\ \bottomrule

\end{tabular}
\label{table3}
\end{table}

\begin{table}[t]\small
\centering
\caption{\textbf{TokenMix Mask sampling.}  We compare different token masking strategies. The results are reported on Kinetics-400 and UCF-101 with 1\% labeling ratio. }
\vspace{-0.1in}
\begin{tabular}{@{}lcccc@{}}
\toprule
\multirow{2}{*}{Method/Dataset} & \multicolumn{2}{c}{UCF-1\%}
& \multicolumn{2}{c}{Kinetic-1\%}  \\ \cmidrule(l){2-5}
                                & Top-1          & Top-5          & Top-1        & Top-5        \\ \midrule
Baseline   &26.1 &48.9  &23.6 &47.7  \\
CutMix \cite{cutmix}             & 28.7            & 51.3                & 28.6           & 53.7                    \\
Mixup \cite{mixup}                & 29.8            & 53.0            & 29.3           & 55.1                      \\
PixMix \cite{pixmix}              & 29.7            & 52.4             & 29.6           & 55.8                     \\ \midrule
Frame TokenMix            & 29.8            & 54.2        & 26.3           & 50.0                      \\
Rand TokenMix          & 30.3            & 55.3            & 28.8           & 54.2                     \\
 Tube TokenMix            & \textbf{31.4}            & \textbf{56.9}         & \textbf{32.6}           & \textbf{59.0}                      \\ \bottomrule
\end{tabular}
\label{table4}
\end{table}

\vspace{0.05in}
\noindent\textbf{Analysis of different mixing strategies}
We now compare the Tube TokenMix strategy with three pixel-level mixing methods, CutMix \cite{cutmix}, Mixup \cite{mixup}, PixMix \cite{pixmix}, as well as the other two token-level mixing methods, \emph{i.e.}, Frame TokenMix and Rand TokenMix. The examples of different mixing methods are shown in Fig. \ref{fig_mixexample}. The quantitative results are shown in Table \ref{table4}. Compared with these alternative methods, all mixing methods can improve the performance, which proves the effectiveness of mixing-based consistency losses.
In addition, we observe that the token-level methods (Rand TokenMix and Tube TokenMix) perform better than the pixel-level mixing methods.
This is not surprising since transformers operate on tokens, and thus token-level mixing has inherent advantages.

The performance of Frame TokenMix is even worse than that of pixel-level mixing methods, which is also expected.
We hypothesize that replacing entire frames in video clip will scramble up the temporal reasoning, thus leading to poor temporal attention modeling.
In addition, Tube TokenMix achieves the best results.
We suppose the consistent masked tokens over temporal axis can prevent information leaky between adjacent frames in the same spatial locations, especially in such a short-term clip.
Therefore, Tube TokenMix could better model the spatio-temporal correlations in video.

\vspace{0.05in}
\noindent\textbf{Analysis of Augmentations}
\label{augment}
The effects of spatial augmentation and temporal warping augmentation are evaluated in Table \ref{table5}.
The baseline indicates removing both strong spatial augmentation (\emph{e.g.} AutoAugment \cite{autoaugment} and Dropout \cite{dropout}) and TWAug in all branches.
In this case, the experimental performance drops dramatically.
When spatial augmentation or temporal warping augmentation is incorporated into the baseline separately, the performance is improved. The best practice is to perform data augmentations in both spatial and temporal.

\begin{table}[t]
\centering
\caption{\textbf{Effects of Spatial and Temporal Warping Augmentations (TWAug).} The results are reported on Kinetics-400 and UCF-101  with 1\% labeling ratio.}
\vspace{-0.1in}

\scalebox{0.75}{
\begin{tabular}{@{}lcccccc@{}}
\toprule
\multirow{2}{*}{} &
\multirow{2}{*}{Spatial} &
\multirow{2}{*}{Temporal} &

\multicolumn{2}{c}{UCF-1\%} & \multicolumn{2}{c}{Kinetic-1\%} \\ \cmidrule(l){4-7}
                 &  &  & Top-1 & Top-5  & Top-1  &Top-5 \\ \midrule
Baseline         &         &     &29.5 &52.1     & 28.5      & 54.2       \\ \midrule
Spatial-only     & \checkmark        &     & 29.9 & 53.2      & 30.3      & 55.9    \\
Temporal-only    &         &  \checkmark    & 30.2  & 55.8     & 30.8      & 56.4     \\
 Spatial+Temporal & \checkmark         &   \checkmark   & \textbf{31.4} &  \textbf{56.9}       &\textbf{32.6}       & \textbf{59.0}     \\ \bottomrule
\end{tabular}
}
\label{table5}
\end{table}

\begin{table}[t]
\centering
\caption{\textbf{Effects of different inference schemes.} We compare the sparse sampling strategy and dense sampling method with different frames.
The $a\times b$ in `Frames' column means sampling $a$ frames at frame rate $b$.
The results are reported on Kinetics-400 and UCF-101 with 1\% labeling ratio.}
\vspace{-0.1in}
\scalebox{0.9}{
\begin{tabular}{@{}lcccc@{}}
\toprule
Method    & Frames    & Test View &UCF-1\% &Kinetic-1\%  \\ \midrule
MvPL \cite{mvpl}  & $8\times8$        &$10\times3$ & 22.8  & 17.0  \\
CMPL \cite{cmpl} & $8\times8$         & $10\times3$ & 25.1  & 17.6   \\ \midrule
SVFormer-S &~~$8\times32$         & ~~\bm{$1\times3$}  & 29.3 & 31.0    \\
SVFormer-S  & $8\times8$          & ~~$5\times3$  &31.4  &32.6     \\
SVFormer-S & $16\times4$~~          & $10\times3$   & \textbf{31.6}  & \textbf{33.1} \\

\bottomrule
\end{tabular}
}
\label{table6}
\vspace{-0.0cm}
\end{table}

\vspace{0.05in}
\noindent\textbf{Analysis of Inference}
We evaluate the effect of frame rate sampling and different inference schemes, as shown in Table \ref{table6}.
Previous methods, \emph{i.e.} CMPL \cite{cmpl} and MvPL \cite{mvpl}, utilize the clip-based sparse sampling, which samples 8 frames at frame rate 8 as a clip. For each video, 10 clips are sampled, where each clip is cropped 3 times according to different spatial positions. Finally, the predictions of $10\times3$ samples are averaged.
TimeSformer \cite{timesformer} adopts the video-based sparse sampling, that is, 8 frames are sampled at frame rate 32 as the representation of the whole video. Then 3 different cropped views are used, namely $8\times32$.  Applying the video-based sparse sampling scheme as in TimeSformer \cite{timesformer} can reduce the inference cost, but the performance is worse than that of clip-based sampling.
Experiments at sampling schemes of $8\times8$ and $16\times4$ demonstrate better performance.
For the trade-off between efficiency and accuracy, we use $8\times8$ as the default setting following CMPL \cite{cmpl} and MvPL \cite{mvpl}.

\vspace{0.05in}
\noindent\textbf{Analysis of hyperparameters}
Here we explore the effect of different hyperparameters. We conduct experiments under 1\% setting of Kinetics-400.
We first explore the effect of different threshold values of $\delta$.
As shown in Fig.~\ref{fig7}(a), we can observe that when labeled samples are extremely scarce, best results are achieved by setting a small $\delta$ value ($\delta=0.3$).
We then evaluate how the ratio between labeled samples and unlabeled samples in a mini-batch affect the result.
We fix the labeled sample number $B_l$ to 1, and sample $B_u$ unlabeled samples to form a mini-batch, where $B_u$ is in \{1, 2, 3, 5, 7\}.
The results are shown in Fig.~\ref{fig7}(b). When $B_u=5$, the model produces the highest result.
Finally, we explore the choice of momentum coefficient $m$ of EMA and the loss weights ${\gamma}_1$ and ${\gamma}_2$, as shown in Fig.~\ref{fig7}(c) and Fig.~\ref{fig7}(d). We thus set $m=0.99$ and ${\gamma}_1 = {\gamma}_2 = 2$ as default setting in all the experiments.

\begin{figure}[t]
  \centering
   \includegraphics[width=1.0 \linewidth]{fig7_huge.pdf}
   \caption{\textbf{Effects of hyperparameters.} Results of varying threshold $\delta$, ratio between unlabeled and labeled data in a mini-batch ($B_u/B_l$), momentum coefficient $m$, and loss weights (${\gamma}_1, {\gamma}_2$) are included to comprehensively study
the effects of the hyperparameters. Reported on Kinetics-400 with 1\% labeling ratio.}
   \label{fig7}
   \vspace{-0.0cm}
\end{figure}

\section{Conclusion}

In this paper we present SVFormer, a transformer-based semi-supervised video action recognition method.
We propose Tube TokenMix, a data augmentation method that is specially designed for video transformer models.
Coupled with the temporal warping augmentation, which covers the complex temporal variations by arbitrarily changing the frame length, TTMix achieves significant improvement compared with conventional augmentations.
SVFormer outperforms the state-of-the-art with a large margin on UCF-101, HMDB-51 and Kinetics-400 without increasing overheads. Our work establishes a new benchmark for semi-supervised action recognition and encourages future work to adopt Transformer architecture.

{\small
\bibliographystyle{ieee_fullname}

redpajama_set_name: RedPajamaArXiv


text:
How did we end up here?
The tales of Mr and Mrs Average and their family unit.. (aka the dog)
Hound Solo
Samburu Game Lodge
A change of scenery was in order so Mr Nomad and I, along with a couple of close friends, embarked upon a trip to discover what Samburu had to offer. We had heard wonderful stories of stunning landscapes and an abundance of animal and bird life so we booked into Samburu Game Lodge which can be found within Samburu National Reserve. Samburu National Reserve can be found at the south eastern corner of Samburu District in the Rift Valley Province of Kenya and was only a few hours drive from home. The Game Lodge where we were to stay can be found on the banks of the Ewaso Nyiro river and it is as a result of the lush vegetation along this river that there is a large concentration of wildlife.
What was of particular interest to us was that Samburu is famed for an abundance of rare species including the Grevy's Zebra, Somali Ostrich, Reticulated giraffe, Gerenuk and Beisa Oryx. There was apparently, over 900 elephants to be found and an expansive number of large predators including lions, leopard and cheetah. The area is also known for a large population of Nile crocodiles. Rhinoceros can no longer be found in Samburu having been heavily poached.
The drive into Samburu National Reserve itself was wonderful. The semi-arid landscape was rugged in its beauty and we were lucky enough to see lots of elephants. I lost count at 20 of these enormous creatures which admittedly, were easy to spot as they had clearly been frolicking in the mud and had a red tinge to their hide.
Upon our arrival at Samburu Game Lodge, we were greeted warmly by the staff with a welcome drink and warm face towels drenched with eucalyptus and we were shown to our accommodation. Our accommodation was excellent – a river side cottage with stunning views up and down the river.
The cottage was rustic and charming and frequented by Vervet monkey's and the odd baboon also made an appearance. The monkeys only added to the charm of the place.
There were also many lizards that joined us, including a large monitor lizard. A small gecko inhabited the bathroom which was more than welcome and certainly kept the insects at bay. The restaurant had a seating area outside it known as Crocodile Bar and we were informed by the staff that at approximately 7.00pm each day, the crocodiles would slither in from the river and rest below the veranda and the staff would quietly feed them!
We wasted no time after lunch going out into the reserve for a game drive to see what we could see and it was not disappointing. The statements of 'abundance of wild life' were true and we were lucky enough to see the rare Grevy's Zebra, reticulated giraffe, Oryx, numerous gazelle and impala and even cheetah and lions! A male lion with a lioness, and what appeared to be 3 juvenile lions (we presumed them to be the cubs)! We witnessed 4 cheetahs feasting on a fresh kill, possibly an Oryx.
There were Dik Dik in plentiful numbers, along with the odd warthog.
There was plenty of bird life too, with huge eagles casting a shadow as they soared above us and the smaller but brightly coloured king fishers and starlings, and hornbills to name only a few. It was truly breath taking.
We returned to the Game Lodge for dinner that evening to be greeted by crocodiles. This really was a sight to behold – huge ancient beasts simply resting and lazing around for all to see. They were silent and slow, seemingly lethargic and some times verging on catatonic but they snapped into action when the chef tossed them a cow bone! The effortless crunching of the bones was a stark reminder of how deadly this ultimate killing machine could be. It was fascinating watching them, even when they were not eating and they simply reverted to doing absolutely nothing.
At dinner, we were joined by a couple of Genet cats. These beautiful cats precariously came down a tree next to where the crocodiles were resting and were just as curious as domestic cats. Mr Nomad tried his best to stroke one, but they were timid and shy. They had lightening reactions and were able to steal a bread roll but it was impossible to touch them. I strongly suspected Mr Nomad would get a 'telling off' by them if he attempted to touch one any way.
The second day of our stay was just as epic as the first and our drive into the bush again revealed many wonderful animals and birds. This time, we saw a Gerenuk in addition to all of the wonderful creatures we had already seen.
The Vervet monkeys were everywhere along with the Dik Dik, impala, gazelle, waterbuck, Grevys Zebra and there were many reticulated giraffe.
We failed to spot the lions that day but we did see the rather large paw prints of the male lion.
Our attempts to track the lion proved fruitless but we were still ecstatic that we had seen so many lions only the previous day.
Disappointingly, we had yet to see a leopard although they are notoriously difficult to spot owing to their elusive nature. We again saw the crocodiles although this time, they were basking along the banks of the river nowhere near to the Game Lodge itself.
Following an afternoon by the pool catching some rays, (whilst Mr Nomad enjoyed a rather strenuous deep tissue massage), we were about to embark on another trip into the bush to attempt to find the elusive leopard, when we were stopped in our tracks by the sound of gun fire. We had booked a guide to accompany us but he refused to come with us and recommended we did not leave the game lodge. The staff explained to us that there was a gun fight going on between rival tribes over cattle rustling and there was a very real danger that we could get caught in the cross fire! This was very worrying but surreal! The gun fire was loud and sounding closer to us by the minute so we took the advice on board and stayed in the game lodge, taking shelter in the bar.
The Samburu people are a sub-tribe of the Maasai and are semi-nomadic pastoralists who herd cattle, sheep, goats and camels. In this instance, the dispute was over cattle and from where we were sat, we could see the herds of cattle being driven rapidly over the plains, their cow bells clanking furiously as they hurried along and there was dramatic gun fire all the way. The staff who seemed to be standing guard over us explained that the conflict was with the Turkana people. Turkana people are native to Turkana county and they too, are semi-nomadic pastoralists. The region they come from borders upon Lake Turkana in the east, Pokot, Rendille and Samburu people to the south, with Uganda to the west and South Sudan and Ethiopia to the north. Apparently such conflict is a regular occurrence, and armed conflict had escalated in recent times centred on declining pastures for ever increasing cattle herd sizes. There was also armed conflict centred on the very fact that both sides were literally starving following severe droughts but again, because there is more cattle than the range lands can support. In this instance, we were informed that the Turkana people had stolen 2 cows from the Samburu people, and the Samburu people had retaliated by recovering those 2 cows but by stealing a further 600 cows using violence and force.
Whilst taking in these stories being regaled to us about the rival tribes, many of the fighting warriors were crossing the river only a stone's throw from where we were sat. We watched them wide eyed as they took time out of the battle to drink from the river, using their machine guns to lean on while they took a breath… than they were off again, running down the river banks to catch up with their adversaries, and the gun fire would rain down once more.
Once this furore had calmed, we were preparing to go for dinner and darkness was seeping in and we heard very loud squawking and screeching from the Vervet moneys in a tree across the river. Somebody spotted the outline of a leopard on the other side of the river and the monkeys were the early detection system! The crocodiles which had been slowly snaking their way up the river about turned and swam rapidly to the shore and in spectacular fashion, a crocodile launched itself out of the water to attack the leopard who had been sneaking to the water's edge for a drink! What a moment! It was too dark and too rapid a movement to snap a photograph but we could not quite believe what we had just witnessed! The leopard had the same lightening reactions as the Genet cat and had avoided being the crocodiles dinner and it lived to see another day. We felt truly honoured to witness such events and could only explain our trip to Samburu Game Lodge as magical. Every moment was exhilarating and each day brought a new adventure.
#magical
#neveradullday
#wildernesslodges.co.ke
#samburu.net
Published by windoefild
View all posts by windoefild
When Hound Solo was home alone
Hound Solo's Big Night(s) Out

redpajama_set_name: RedPajamaCommonCrawl


I'm sure you can appreciate with any child, in any school, having basic first aid is a must.
The sad thing is that in Gambia there is very little medical care available, especially at a cost affordable to the needy.
There is no NHS! Therefore having basic first aid and pain killers available in school is something we feel is very important. The children are very prone to small cuts and gnashes as many play outside without shoes on.
If you would like to help us keep our medicine/first aid cupboard well stocked with plasters, bandages, antiseptic ointment, children's paracetamol and pain killers etc, then any amount you could donate would be greatly appreciated.
Please click the button below to find out how to support this area or donate on our home page. Thank you.